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  • 1.
    Alpcan, Tansu
    et al.
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    An information-theoretic analysis of distributed resource allocation2013Conference paper (Refereed)
  • 2.
    Biason, Alessandro
    et al.
    Univ Padua, Dept Informat Engn, Via Gradenigo 6b, I-35131 Padua, Italy..
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Zorzi, Michele
    Univ Padua, Dept Informat Engn, Via Gradenigo 6b, I-35131 Padua, Italy..
    Decentralized Transmission Policies for Energy Harvesting Devices2017In: 2017 Ieee Wireless Communications And Networking Conference Workshops (WCNCW), IEEE , 2017Conference paper (Refereed)
    Abstract [en]

    The problem of finding decentralized transmission policies in a wireless communication network with energy harvesting constraints is formulated and solved using the decentralized Markov decision process framework. The proposed policy defines the transmission strategies of all devices so as to correctly balance the collision probabilities with the energy constraints. After an initial coordination phase, in which the network parameters are initialized for all devices, every node proceeds in a fully decentralized fashion. We numerically show that, unlike in the case without energy constraints where a fully orthogonal scheme can be shown to be optimal, in the presence of energy harvesting this is no longer the best choice, and the optimal strategy lies between an orthogonal and a completely symmetric system.

  • 3.
    Biswas, Sinchan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Shirazinia, Amirpasha
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Sensing Throughput Optimization in Cognitive Fading Multiple Access Channels With Energy Harvesting Secondary Transmitters2016In: 2016 24Th European Signal Processing Conference (EUSIPCO), 2016, p. 577-581Conference paper (Refereed)
    Abstract [en]

    The paper investigates the problem of maximizing the expected achievable sum rate in a fading multiple access cognitive radio network when secondary user (SU) transmitters have energy harvesting capability, and perform cooperative spectrum sensing. We formulate the problem as maximization of throughput of the cognitive multiple access network over a finite time horizon subject to a time averaged interference constraint at the primary user (PU) and almost sure energy causality constraints at the SUs. The problem is a mixed integer non-linear program with respect to two decision variables, namely, spectrum access decision and spectrum sensing decision, and the continuous variables sensing time and transmission power. In general, this problem is known to be NP hard. For optimization over these two decision variables, we use an exhaustive search policy when the length of the time horizon is small, and a heuristic policy for longer horizons. For given values of the decision variables, the problem simplifies into a joint optimization on SU transmission power and sensing time, which is non-convex in nature. We present an analytic solution for the resulting optimization problem using an alternating convex optimization problem for non-causal channel state information and harvested energy information patterns at the SU base station (SBS) or fusion center (FC) and infinite battery capacity at the SU transmitters. We formulate the problem with causal information and finite battery capacity as a stochastic control problem and solve it using the technique of dynamic programming. Numerical results are presented to illustrate the performance of the various algorithms.

  • 4. Ciuonzo, Domenico
    et al.
    Rossi, Pierluigi Salvo
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Massive MIMO Channel-Aware Decision Fusion2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 3, p. 604-619Article in journal (Refereed)
    Abstract [en]

    In this paper, we provide a study of channel-aware decision fusion (DF) over a "virtual" multiple-input multiple-output (MIMO) channel in the large-array regime at the DF center (DFC). The considered scenario takes into account channel estimation and inhomogeneous large-scale fading between the sensors and the DFC. The aim is the development of (widely) linear fusion rules, as opposed to the unsuitable optimum log-likelihood ratio (LLR). The proposed rules can effectively benefit from performance improvement via a large array, differently from existing suboptimal alternatives. Performance evaluation, along with theoretical achievable performance and complexity analysis, is presented. Simulation results are provided to confirm the findings. Analogies and differences with uplink communication in a multiuser (massive) MIMO scenario are underlined.

  • 5.
    Ciuonzo, Domenico
    et al.
    Department of Industrial and Information Engineering, Second University of Naples, Italien.
    Salvo Rossi, Pierluigi
    Department of Industrial and Information Engineering, Second University of Naples, Italien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Massive MIMO meets decision fusion: Decode-and-fuse vs. decode-then-fuse2014In: 2014 IEEE 8TH Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014, p. 265-268Conference paper (Refereed)
    Abstract [en]

    We study channel-aware decision fusion over a multiple-input multiple-output (MIMO) channel in the large-array regime at the decision-fusion center (DFC). Inhomogeneous large-scale fading between the sensors and the DFC is consider in addition to the small-scale fading, and pilot-based channel estimation is performed at the DFC. Linear processing techniques are analyzed in order to design low-complexity alternatives to the optimum log-likelihood ratio test (LLRT). Performance evaluation based on Monte Carlo simulations are presented.

  • 6.
    Dey, Subhrakanti
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Chiuso, A
    Schenato, L
    Linear Encoder-decoder-controller design over channels with packet loss and quantization noise2015Conference paper (Refereed)
  • 7.
    Dey, Subhrakanti
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Chiuso, A
    Department of Information Engineering, University of Padova, Italien.
    Schenato, L
    Department of Information Engineering, University of Padova, Italien.
    Remote estimation subject to packet loss and quantization noise2013Conference paper (Refereed)
  • 8.
    Dey, Subhrakanti
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Chiuso, Alessandro
    Univ Padua, Dept Informat Engn, I-35131 Padua, Italy..
    Schenato, Luca
    Univ Padua, Dept Informat Engn, I-35131 Padua, Italy..
    Feedback Control Over Lossy SNR-Limited Channels: Linear Encoder-Decoder-Controller Design2017In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, no 6, p. 3054-3061Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider the problem of encoding and decoding codesign for linear feedback control of a scalar, possibly unstable, stochastic linear system when the sensed signal is to be transmitted over a finite capacity communication channel. In particular, we consider a limited capacity channel which transmits quantized data and is subject to packet losses. We first characterize the optimal strategy when perfect channel feedback is available, i.e., the transmitter has perfect knowledge of the packet loss history. This optimal scheme, innovation forwarding hereafter, is reminiscent of differential pulse-code modulation schemes adapted to deal with state space models, and is strictly better than a scheme which simply transmits the measured data, called measurement forwarding (MF) hereafter. Comparison in terms of control cost as well as of critical regimes, i.e., regimes where the cost is not finite, are provided. We also consider and compare two popular suboptimal schemes from the existing literature, based on 1) state estimate forwarding and 2) measurement forwarding, which ignore quantization effects in the associated estimator and controller design. In particular, it is shown that surprisingly the suboptimal MF strategy is always better then the suboptimal state forwarding strategy for small signal-to-quantization-noise-ratios.

  • 9.
    Dey, Subhrakanti
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Chiuso, Alessandro
    Department of Information Engineering, University of Padova, Italien.
    Schenato, Luca
    Department of Information Engineering, University of Padova, Italien.
    Remote estimation with noisy measurements subject to packet loss and quantization noise2014In: IEEE Transactions on Control of Network Systems, Vol. 1, no 3, p. 204-217Article in journal (Refereed)
  • 10.
    Ding, Kemi
    et al.
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Hong Kong, Peoples R China..
    Li, Yuzhe
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Hong Kong, Peoples R China..
    Quevedo, Daniel E.
    Univ Paderborn, Dept Elect Engn, Paderborn, Germany..
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Shi, Ling
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Hong Kong, Peoples R China..
    A multi-channel transmission schedule for remote state estimation under DoS attacks2017In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 78, p. 194-201Article in journal (Refereed)
    Abstract [en]

    This paper considers a cyber-physical system (CPS) under denial-of-service (DoS) attacks. The measurements of a sensor are transmitted to a remote estimator over a multi-channel network, which may be congested by a malicious attacker. Among these multiple communication paths with different characteristics and properties at each time step, the sensor needs to choose a single channel for sending data packets while reducing the probability of being attacked. In the meanwhile, the attacker needs to decide the target channel to jam under an energy budget constraint. To model this interactive decision making process between the two sides, we formulate a two-player zero-sum stochastic game framework. A Nash Q-learning algorithm is proposed to tackle the computation complexity when solving the optimal strategies for both players. Numerical examples are provided to illustrate the obtained results.

  • 11.
    Ding, Kemi
    et al.
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China..
    Quevedo, Daniel E.
    Paderborn Univ, Dept Elect Engn, Paderborn, Germany..
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Shi, Ling
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China..
    A Secure Cross-Layer Design for Remote Estimation under DoS Attack: When Multi-sensor meets Multi-channel2016In: 2016 IEEE 55Th Conference On Decision And Control (CDC), New York: IEEE, 2016, p. 6297-6302Conference paper (Refereed)
    Abstract [en]

    This paper considers security issues of a cyberphysical system (CPS) under denial-of-service (DoS) attacks. The measurements of multiple sensors are transmitted to a remote estimator over a multi-channel network, which may be congested by an intelligent attacker. Aiming at improving the estimation accuracy, we first propose a novel aggregation scheme for the estimator to produce accurate state estimates, from which we obtain a closed-form expression of the expected estimation error covariance. We further develop a sensor attacker game to design the cooperative and defensive channel selection strategy, which avoids the sensors being attacked in an energy-efficient way. Numerical examples are provided to illustrate the developed results.

  • 12.
    Guo, Xiaoxi
    et al.
    Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3010, Australia.
    Leong, Alex S
    Univ Paderborn, Dept Elect Engn, D-33098 Paderborn, Germany.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Distortion outage minimization in distributed estimation with estimation secrecy outage constraints2017In: IEEE Transactions on Signal and Information Processing over Networks, E-ISSN 2373-776X, Vol. 3, no 1, p. 12-28Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate a class of distortion outage minimization problems for a wireless sensor network in the presence of an eavesdropper. The observation signals transmitted from the sensors to the fusion center (FC) are overheard by the eavesdropper. Both the FC and the eavesdropper reconstruct minimum mean squared error estimates of the physical quantity observed. We address the problem of transmit power allocation to minimize the distortion outage at the FC, subject to an expected total transmit power constraint across the sensor(s) and a secrecy outage constraint at the eavesdropper. Applying a rigorous probabilistic power allocation technique, we derive power policies for the full channel state information (CSI) case. Suboptimal power control policies are studied for the partial CSI case in order to reduce the high computational cost associated with large numbers of sensors or receive antennas. Numerical results show that significantly improved performance can be achieved by adding multiple receive antennas at the FC. In the case of multiple transmit antennas, the distortion outage at the FC can be dramatically reduced and in some cases completely eliminated, by transmitting the observations on the null space of the eavesdropper's channel or deploying an artificial noise technique, in the full CSI and partial CSI cases, respectively.

  • 13.
    Guo, Xiaoxi
    et al.
    Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia..
    Leong, Alex S.
    Paderborn Univ, Dept Elect Engn EIM E, D-33098 Paderborn, Germany..
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Estimation in Wireless Sensor Networks With Security Constraints2017In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 53, no 2, p. 544-561Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the performance of distributed estimation schemes in a wireless sensor network in the presence of an eavesdropper. The sensors transmit observations to the fusion center (FC), which at the same time are overheard by the eavesdropper. Both the FC and the eavesdropper reconstruct a minimum mean-squared error estimate of the physical quantity observed. We address the problem of transmit power allocation for system performance optimization subject to a total average power constraint on the sensor(s), and a security/secrecy constraint on the eavesdropper. We mainly focus on two scenarios: 1) a single sensor with multiple transmit antennas and 2) multiple sensors with each sensor having a single transmit antenna. For each scenario, given perfect channel state information (CSI) of the FC and full or partial CSI of the eavesdropper, we derive the transmission policies for short-term and long-term cases. For the long-term power allocation case, when the sensor is equipped with multiple antennas, we can achieve zero information leakage in the full CSI case, and dramatically enhance the system performance by deploying the artificial noise technique for the partial CSI case. Asymptotic expressions are derived for the long-term distortion at the FC as the number of sensors or the number of antennas becomes large. In addition, we also consider multiple-sensor multiple-antenna scenario, and simulations show that given the same total number of transmitting antennas the multiple-antenna sensor network is superior to the performance of the multiple-sensor single-antenna network.

  • 14.
    Guo, Xiaoxi
    et al.
    Department of Electrical and Electronic Engineering, The University of Melbourne, VIC 3010, Australien.
    Leong, Alex S
    Department of Electrical and Electronic Engineering, The University of Melbourne, VIC 3010, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Power allocation for distortion minimization in distributed estimation with security constraints2014In: 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2014, p. 299-303Conference paper (Refereed)
  • 15.
    Guo, Xiaoxi
    et al.
    Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia..
    Leong, Alex S.
    Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia..
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Power Allocation for Estimation Outage Minimization with Secrecy Outage Constraints2016In: 2016 Australian Communications Theory Workshop (AusCTW), New York: IEEE, 2016, p. 71-76Conference paper (Refereed)
    Abstract [en]

    In this paper, we investigate the distortion outage minimization problem for a wireless sensor network (WSN) in the presence of an eavesdropper. The observation signals transmitted from the sensors to the fusion center (FC) are overheard by the eavesdropper. Both the FC and the eavesdropper reconstruct minimum mean squared error (MMSE) estimates of the physical quantity observed. We address the problem of transmit power allocation to minimize the distortion outage at the FC, subject to a long-term transmit power constraint among the sensors and a secrecy outage constraint at the eavesdropper. Applying a rigorous probabilistic power allocation technique we derive power policies for the full channel state information (CSI) case. Additional suboptimal power control policies are studied for the partial CSI case in order to reduce the high computational cost as the number of sensors or receive antennas grows. Numerical results show better performance can be achieved by adding multiple receive antennas at the FC.

  • 16.
    He, Yuan Yuan
    et al.
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Power allocation for secondary outage minimization in spectrum sharing networks with limited feedback2013In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 61, no 7, p. 2648-2663Article in journal (Refereed)
  • 17. He, Yuan Yuan
    et al.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Sum Rate Maximization for Cognitive MISO Broadcast Channels: Beamforming Design and Large Systems Analysis2014In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 13, no 5, p. 2383-2401Article in journal (Refereed)
    Abstract [en]

    This paper considers the ergodic weighted sum rate maximization (WSRMax) problem for an underlay cognitive radio multiple input single output (MISO) broadcast channel. In this setting, a secondary network, consisting of a base-station with M transmit antennas and K single-antenna secondary users (SUs), is allowed to share the same spectrum with a primary user (PU), under an average total transmit power (ATTP) constraint and an average interference power (AIP) constraint at the PU receiver. We show that the ATTP constraint always remains active, and as the maximum ATTP P-av -> infinity, the ergodic WSR approaches infinity similar to conventional non-CR networks. We propose a novel low-complexity suboptimal beamforming scheme termed "Partially-Projected & Regularized Zero-Forcing Beamforming" (PP-RZFBF) with a close-form beamformer, by combining the regularized zero-forcing (RZF) with the channel projection idea, to achieve a tradeoff between maximizing secondary throughput and suppressing secondary multiuser interference as well as the interference on PU. In order to analyze and optimize the performance of this scheme, we employ the large system analysis technique, in the limit as M and K approach infinity with a fixed ratio r = K/M. This allows us to derive deterministic limiting approximations for the PP-RZFBF problem which enables us to determine asymptotically optimal beamformers for PP-RZFBF. In the large system limit, for the PP-RZFBF scheme, we also find that as P-av -> infinity, the interference on PU caused by the secondary transmission is asymptotically removed. A special suboptimal beamforming scheme called "CZFBF" is also considered, which involves zero forcing all the interference, including the secondary multiuser interference as well as the interference imposed on PU. Various interesting comparisons between PP-RZFBF and CZFBF are provided. Numerical simulations illustrate that the asymptotically optimal beamformers for the PP-RZFBF scheme provide an excellent performance even for finite-sized systems.

  • 18.
    He, Yuan Yuan
    et al.
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Throuoghput maximization in poisson fading channels with limited feedback2013In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 61, no 10, p. 4343-4356Article in journal (Refereed)
  • 19.
    He, Yuan Yuan
    et al.
    Department of Electrical and Electronic Engineering, University of Melbourne, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Weighted sum rate maximization for cognitive MISO broadcast channel: Large system analysis2013Conference paper (Refereed)
  • 20.
    He, YuanYuan
    et al.
    Electrical and Computer Engineering, Monash University, Melbourne, Australien.
    Evans, Jamie
    Electrical and Computer Engineering, Monash University, Melbourne, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Secrecy rate maximization for cooperative overlay cognitive radio networks with artificial noise2014Conference paper (Refereed)
  • 21.
    Knorn, Steffi
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Optimal energy allocation for linear control over a packet-dropping link with energy harvesting constraints2015Conference paper (Refereed)
  • 22.
    Knorn, Steffi
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Optimal energy allocation for linear control with packet loss under energy harvesting constraints2017In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 77, p. 259-267Article in journal (Refereed)
    Abstract [en]

    This paper studies a closed loop linear control system over a lossy communication link. A sensor computes a state estimate of the observed discrete-time system and sends it (in the form of packetized transmission) to the controller in the receiver block over a randomly time-varying (fading) packet dropping link. The receiver sends an ACK/NACK packet to the transmitter over an acknowledgement channel which might also be prone to packet loss. It is assumed that the energy used in packet transmission depletes a battery of limited capacity at the sensor, but is also replenished by an energy harvester which has access to a source of everlasting but random harvested energy. Under an assumption of finite-state Markov chain models of the energy harvesting and the fading channel gain processes, the objective is to design an optimal energy allocation policy at the transmitter and an optimal control policy at the receiver so that an average infinite horizon linear quadratic Gaussian (LQG) control cost is minimized. It is shown that in the case of perfect channel feedback a separation principle holds, the optimal LQG controller is linear and the optimal energy allocation policy at the transmitter can be obtained via solving the Bellman dynamic programming equation. A Q-learning algorithm is used to approximate the optimal energy allocation policy in case the system parameters are unknown. Numerical simulation examples show that the dynamic programming based policies outperform various simple heuristic policies, especially at higher battery capacities.

  • 23.
    Knorn, Steffi
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Optimal sensor transmission energy allocation for linear control over a packet dropping link with energy harvesting2015In: 2015 54Th IEEE Conference On Decision And Control (CDC), 2015, p. 1199-1204Conference paper (Refereed)
    Abstract [en]

    This paper studies a closed loop linear control system. The sensor computes a state estimate and sends it to the controller/actuator in the receiver block over a randomly fading packet dropping link. The receiver sends an ACK/NACK packet to the transmitter over a link. It is assumed that the transmission energy per packet at the sensor depletes a battery of limited capacity, replenished by an energy harvester. The objective is to design an optimal energy allocation policy and an optimal control policy so that a finite horizon LQG control cost is minimized. It is shown that in case the receiver to sensor feedback channel is free of errors, a separation principle holds. Hence, the optimal LQG controller is linear, the Kalman filter is optimal and the optimal energy allocation policy is obtained via solving a backward dynamic programming equation. In case the feedback channel is erroneous, the separation principle does not hold. In this case, we propose a suboptimal policy where the controller still uses a linear control, and the transmitter minimizes an expected sum of the trace of an "estimated" receiver state estimation error covariance matrix. Simulations are used to illustrate the relative performance of the proposed algorithms and various heuristic algorithms for both the perfect and imperfect feedback cases. It is seen that the dynamic programming based policies outperform the simple heuristic policies by a margin.

  • 24.
    Knorn, Steffi
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Ahlén, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Quevedo, Daniel E.
    Distortion Minimization in Multi-Sensor Estimation Using Energy Harvesting and Energy Sharing2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 11, p. 2848-2863Article in journal (Refereed)
    Abstract [en]

    This paper investigates an optimal energy allocation problem for multisensor estimation of a random source where sensors communicate their measurements to a remote fusion center (FC) over orthogonal fading wireless channels using uncoded analog transmissions. The FC reconstructs the source using the best linear unbiased estimator (BLUE). The sensors have limited batteries but can harvest energy and also transfer energy to other sensors in the network. A distortion minimization problem over a finite-time horizon with causal and noncausal centralized information is studied and the optimal energy allocation policy for transmission and sharing is derived. Several structural necessary conditions for optimality are presented for the two sensor problem with noncausal information and a horizon of two time steps. A decentralized energy allocation algorithm is also presented where each sensor has causal information of its own channel gain and harvested energy levels and has statistical information about the channel gains and harvested energies of the remaining sensors. Various other suboptimal energy allocation policies are also proposed for reducing the computational complexity of dynamic programming based solutions to the energy allocation problems with causal information patterns. Numerical simulations are included to illustrate the theoretical results. These illustrate that energy sharing can reduce the distortion at the FC when sensors have asymmetric fading channels and asymmetric energy harvesting processes.

  • 25.
    Knorn, Steffi
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Ahlén, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Quevedo, Daniel E
    Univ Newcastle, Sch Elect Engn & Comp Sci, Callaghan, NSW 2308, Australia.
    Multi-sensor estimation using energy harvesting and energy sharing2015Conference paper (Refereed)
    Abstract [en]

    This paper investigates an optimal energy allocation problem for multi sensor estimation of a random source where sensors communicate their measurements to a remote fusion centre (FC) over orthogonal fading wireless channels using uncoded analog transmissions. The FC reconstructs the source using the best linear unbiased estimator (BLUE). The sensors have limited batteries but can harvest energy and also transfer energy to other sensors in the network. A distortion minimization problem over a finite-time horizon with causal and non-causal information is studied and the optimal energy allocation policy for transmission and sharing is derived. Several structural necessary conditions for optimality are presented for the two sensor problem with non-causal information and a horizon of two time steps. Numerical simulations are included to illustrate the theoretical results.

  • 26.
    Kung, Enoch
    et al.
    Hong Kong Univ Sci & Technol, Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China..
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Shi, Ling
    Hong Kong Univ Sci & Technol, Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China..
    Optimal Stealthy Attack under KL Divergence and Countermeasure with Randomized Threshold2017In: IFAC-PapersOnLine, Elsevier, 2017, Vol. 50, no 1, p. 9496-9501Conference paper (Refereed)
    Abstract [en]

    In a cyber-physical system, there are potential sources of malicious attacks that can damage the estimation quality in an underlying network control system. The attacker aims to maximize these damages while the estimator attempts to minimize them. In this paper we define an attack's stealth based on the KL divergence and obtain an optimal attack. Furthermore, we suggest one method in which the estimator may limit the damage to the system while imposing on any attack a probability for it to be non-stealthy. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

  • 27.
    Kung, Enoch
    et al.
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Shi, Ling
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China.
    The performance and limitations of ε-stealthy attacks on higher order systems2016In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, no 2, p. 941-947Article in journal (Refereed)
    Abstract [en]

    In a cyber-physical system, security problems are of vital importance as the failure of such system can have catastrophic effects. Detection methods can be employed to sense the existence of an attack. In a previous study of an attack on the controller while avoiding detection in scalar systems under a certain control assumption, the notion of epsilon-stealthiness was introduced and the strength of epsilon-stealthy attacks was fully characterized. We generalize to the vector system and prove the cases in which we show that the limitations of epsilon-stealthy attack do not extend, in the sense that epsilon-stealthy can inflict damage of arbitrary magnitude to a vector system.

  • 28.
    Leong, Alex S
    et al.
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Nair, Girish N
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australien.
    A quantized filtering scheme for multi-sensor linear state estimation with non-detectability at the sensors and fusion center feedback2013Conference paper (Refereed)
  • 29. Leong, Alex S.
    et al.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Nair, Girish N.
    Quantized Filtering Schemes for Multi-Sensor Linear State Estimation: Stability and Performance Under High Rate Quantization2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 15, p. 3852-3865Article in journal (Refereed)
    Abstract [en]

    In this paper we consider state estimation of a discrete time linear system using multiple sensors, where the sensors quantize their individual innovations, which are then combined at the fusion center to form a global state estimate. We prove the stability of the estimation scheme under sufficiently high bit rates. We obtain asymptotic approximations for the error covariance matrix that relates the system parameters and quantization levels used by the different sensors. Numerical results show close agreement with the true error covariance for quantization at high rates. An optimal rate allocation problem amongst the different sensors is also considered.

  • 30. Leong, Alex S
    et al.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Quevedo, Daniel E
    On the optimality of threshold policies in event triggered estimation with packet drops2015Conference paper (Refereed)
  • 31.
    Leong, Alex S.
    et al.
    Univ Paderborn, Dept Elect Engn EIM E, Paderborn, Germany..
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Quevedo, Daniel E.
    Univ Paderborn, Dept Elect Engn EIM E, Paderborn, Germany..
    Optimal Transmission Policies for Variance Based Event Triggered Estimation With an Energy Harvesting Sensor2016In: 2016 24Th European Signal Processing Conference (EUSIPCO), 2016, p. 225-229Conference paper (Refereed)
    Abstract [en]

    This paper considers a remote state estimation problem where a sensor observes a dynamical process, and transmits local state estimates over an independent and identically distributed (i.i.d.) packet dropping channel to a remote estimator. The sensor is equipped with energy harvesting capabilities. At every discrete time instant, provided there is enough battery energy, the sensor decides whether it should transmit or not, in order to minimize the expected estimation error covariance at the remote estimator. For transmission schedules dependent only on the estimation error covariance at the remote estimator, the energy available at the sensor, and the harvested energy, we establish structural results on the optimal scheduling which show that for a given battery energy level and a given harvested energy, the optimal policy is a threshold policy on the error covariance, i.e. transmit if and only if the error covariance exceeds a certain threshold. Similarly, for a given error covariance and a given harvested energy, the optimal policy is a threshold policy on the battery level. Numerical studies confirm the qualitative behaviour predicted by our structural results.

  • 32.
    Leong, Alex S
    et al.
    Paderborn Univ, Dept Elect Engn EIM E, D-33098 Paderborn, Germany.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Quevedo, Daniel E
    Paderborn Univ, Dept Elect Engn EIM E, D-33098 Paderborn, Germany.
    Sensor scheduling in variance based event triggered estimation with packet drops2017In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, no 4, p. 1880-1895Article in journal (Refereed)
    Abstract [en]

    This paper considers a remote state estimation problem with multiple sensors observing a dynamical process, where sensors transmit local state estimates over an independent and identically distributed (i.i.d.) packet dropping channel to a remote estimator. At every discrete time instant, the remote estimator decides whether each sensor should transmit or not, with each sensor transmission incurring a fixed energy cost. The channel is shared such that collisions will occur if more than one sensor transmits at a time. Performance is quantified via an optimization problem that minimizes a convex combination of the expected estimation error covariance at the remote estimator and expected energy usage across the sensors. For transmission schedules dependent only on the estimation error covariance at the remote estimator, this work establishes structural results on the optimal scheduling which show that: 1) for unstable systems, if the error covariance is large then a sensor will always be scheduled to transmit and 2) there is a threshold-type behavior in switching from one sensor transmitting to another. Specializing to the single sensor case, these structural results demonstrate that a threshold policy (i.e., transmit if the error covariance exceeds a certain threshold and don't transmit otherwise) is optimal. We also consider the situation where sensors transmit measurements instead of state estimates, and establish structural results including the optimality of threshold policies for the single sensor, scalar case. These results provide a theoretical justification for the use of such threshold policies in variance based event triggered estimation. Numerical studies confirm the qualitative behavior predicted by our structural results.

  • 33.
    Leong, Alex S.
    et al.
    Paderborn Univ, Dept Elect Engn EIM E, Paderborn, Germany..
    Quevedo, Daniel E.
    Paderborn Univ, Dept Elect Engn EIM E, Paderborn, Germany..
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    State Estimation Over Markovian Packet Dropping Links in the Presence of an Eavesdropper2017In: 2017 IEEE 56Th Annual Conference On Decision And Control (Cdc), IEEE, 2017, p. 6616-6621Conference paper (Refereed)
    Abstract [en]

    Remote state estimation problems in the presence of an eavesdropper have recently been studied. In this setup, a sensor transmits over a random packet dropping link to a remote estimator, which at the same time can be randomly overheard by an eavesdropper. For i.i.d. packet dropping links to the remote estimator and to the eavesdropper, it has been shown that with unstable systems one can keep the expected estimation error covariance bounded, while the expected eavesdropper error covariance becomes unbounded in the infinite horizon. In this paper we show that the same behaviour can be achieved when transmission of local state estimates occur over a Markovian packet dropping link, and eavesdropping occurs according to another Markovian packet dropping link.

  • 34.
    Leong, Alex S.
    et al.
    Paderborn Univ, Dept Elect Engn EIM E, Paderborn, Germany..
    Quevedo, Daniel E.
    Paderborn Univ, Dept Elect Engn EIM E, Paderborn, Germany..
    Dolz, Daniel
    Procter & Gamble, North Rhine Westphalia, Germany..
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    On Remote State Estimation in the Presence of an Eavesdropper2017In: IFAC-PapersOnLine, Elsevier, 2017, Vol. 50, no 1, p. 7339-7344Conference paper (Refereed)
    Abstract [en]

    This paper studies a remote state estimation problem in the presence of an eavesdropper. A sensor transmits local state estimates over a packet dropping link to a remote estimator, which at the same time can be overheard by an eavesdropper with a certain probability. The objective is to determine when the sensor should transmit, in order to minimize the estimation error covariance at the remote estimator, while trying to keep the eavesdropper error covariance above a certain level. This is done by solving an optimization problem that minimizes a linear combination of the expected estimation error covariance and the negative of the expected eavesdropper error covariance. Structural results on the optimal transmission policy are derived, and shown to exhibit thresholding behaviour in the estimation error covariances. In the infinite horizon situation, it is shown that with unstable systems one can keep the expected estimation error covariance bounded while the expected eavesdropper error covariance becomes unbounded. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

  • 35.
    Leong, Alex S
    et al.
    Department of Electical Engineering, Paderborn University, Tyskland.
    Quevedo, Daniel E
    Department of Electrical Engineering, Paderborn University, Tyskland.
    Tanaka, Takashi
    Royal Inst Technol, Sch Elect Engn, Dept Automat Control, Stockholm, Sweden.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Ahlén, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Event-Based Transmission Scheduling and LQG Control Over a Packet Dropping Link2017In: 20th IFAC World Congress / [ed] Denis Dochain, Didier Henrion, Dimitri Peaucelle, Elsevier, 2017, p. 8945-8950Conference paper (Refereed)
    Abstract [en]

    This paper studies a joint transmission scheduling and controller design problem, which minimizes a linear combination of the control cost and expected energy usage of the sensor. Assuming that the sensor transmission decisions are event-based and determined using the random estimation error covariance information available to the controller, we show a separation in the design of the transmission scheduler and controller. The optimal controller is given as the solution to an LQG-type problem, while the optimal transmission policy is a threshold policy on the estimation error covariance at the controller.

  • 36.
    Li, Yuzhe
    et al.
    Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 1H9, Canada.
    Quevedo, Daniel E
    Univ Paderborn, Dept Elect Engn, D-33098 Paderborn, Germany.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Shi, Ling
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China.
    A game-theoretic approach to fake-acknowledgment attack on cyber-physical systems2017In: IEEE Transactions on Signal and Information Processing over Networks, E-ISSN 2373-776X, Vol. 3, no 1Article in journal (Refereed)
    Abstract [en]

    A class of malicious attacks against remote state estimation in cyber-physical systems is considered. A sensor adopts an acknowledgement (ACK)-based online power schedule to improve the remote state estimation performance under limited resources. To launch malicious attacks, the attacker can modify the ACKs from the remote estimator and convey fake information to the sensor, thereby misleading the sensor with subsequent performance degradation. One feasible attack pattern is proposed and the corresponding effect on the estimation performance is derived analytically. Due to the ACKs being unreliable, the sensor needs to decide at each instant, whether to trust the ACK information or not and adapt the transmission schedule accordingly. In the meanwhile, there is also a tradeoff for the attacker between attacking and not attacking when the modification of ACKs is costly. To investigate the optimal strategies for both the sensor and the attacker, a game-theoretic framework is built and the equilibrium for both sides is studied.

  • 37.
    Li, Yuzhe
    et al.
    Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada.
    Quevedo, Daniel E.
    Univ Paderborn, Dept Elect Engn EIM E, Paderborn, Germany.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Shi, Ling
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Hong Kong, Peoples R China.
    Fake-acknowledgment attack on ACK-based sensor power schedule for remote state estimation2015In: 2015 54th IEEE Conference On Decision And Control (CDC), 2015, p. 5795-5800Conference paper (Refereed)
    Abstract [en]

    We consider a class of malicious attacks against remote state estimation. A sensor with limited resources adopts an acknowledgement (ACK)-based online power schedule to improve the remote state estimation performance. A malicious attacker can modify the ACKs from the remote estimator and convey fake information to the sensor. When the capability of the attacker is limited, we propose an attack strategy for the attacker and analyze the corresponding effect on the estimation performance. The possible responses of the sensor are studied and a condition for the sensor to discard ACKs and switch from online schedule to offline schedule is provided.

  • 38.
    Li, Yuzhe
    et al.
    Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China.
    Quevedo, Daniel E
    Paderborn Univ, Paderborn, Germany.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Shi, Ling
    Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China.
    SINR-based DoS attack on remote state estimation: A game-theoretic approach2017In: IEEE Transactions on Control of Network Systems, ISSN 2325-5870, Vol. 4, no 3, p. 632-642Article in journal (Refereed)
    Abstract [en]

    We consider remote state estimation of cyberphysical systems under signal-to-interference-plus-noise ratio-based denial-of-service attacks. A sensor sends its local estimate to a remote estimator through a wireless network that may suffer interference from an attacker. Both the sensor and the attacker have energy constraints. We first study an associated two-player game when multiple power levels are available. Then, we build a Markov game framework to model the interactive decision-making process based on the current state and information collected from previous time steps. To solve the associated optimality (Bellman) equations, a modified Nash Q-learning algorithm is applied to obtain the optimal solutions. Numerical examples and simulations are provided to demonstrate our results.

  • 39.
    Li, Yuzhe
    et al.
    Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowlooon, Hong Kong.
    Quevedo, Daniel E
    School of Electrical Engineering & Computer Science, The University of Newcastle, NSW 2308, Australien.
    Lau, Vincent
    Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowlooon, Hong Kong.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Shi, Ling
    Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowlooon, Hong Kong.
    Transmission power scheduling for energy harvesting sensor in remote state estimation2014In: Proceedings of the 19th World Congress, 2014, p. 122-127Conference paper (Refereed)
  • 40. Limmanee, A
    et al.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Nekouei, E
    Optimal power policies and throughput scaling analyses in fading cognitive broadcast channels with primary outage probability constraint2014In: EURASIP Journal on Wireless Communications and Networking, ISSN 1687-1472, E-ISSN 1687-1499, p. 35-Article in journal (Refereed)
    Abstract [en]

    This paper focuses on a spectrum-sharing-based fading cognitive radio broadcast channel (BC) with a single-antenna secondary base station (SBS) and M single-antenna secondary receivers (SRs) utilizing the same spectrum band with a delay-sensitive primary user (PU). The service-quality requirement for the primary user is set by an outage probability constraint (POC). We address the optimal power allocation problem for the SBS ergodic sum capacity (ESC) maximization in the secondary BC network subject to POC and a transmit power constraint at SBS specified by either a long-term or a short-term power constraint. The optimality conditions reveal that in each joint channel state, the SBS allocates transmission power to the only one selected SR with the highest value of a certain metric consisting of the ratio of the SR's direct channel power gain and the sum of interference power and noise power at the SR. Then, the secondary network throughput scaling analysis as the number of SRs becomes large, is also investigated, showing that if PU applies a truncated channel inversion (TCI) power policy, the SBS ESC scales like epsilon(p) log(log M) where epsilon(p) is the PU outage probability threshold. To reduce the amount of channel side information (CSI) transferred between the two networks, we propose a suboptimal transmission scheme which requires only 1-bit feedback from the delay-sensitive PR (partial CSI). We show that the new power control policy is asymptotically optimal, i.e. the SBS ESC under this reduced feedback scheme still scales like epsilon(p) log(log M).

  • 41.
    Limmanee, Athipat
    et al.
    Department of Electrical and Electronic Engineering, The University of Melbourne, VIC 3010, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Ahlén, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Distorsion minimization via multiple sensors under energy harvesting constraints2013Conference paper (Refereed)
  • 42.
    Limmanee, Athipat
    et al.
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Evans, Jamie S
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australien.
    Service-outage capacity maximization in cognitive radio for parallel fading channels2013In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 61, no 2, p. 507-520Article in journal (Refereed)
  • 43. Ling, Y
    et al.
    Zhang, F
    Quevedo, Daniel E
    Lau, VK
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Shi, L
    Power control of an energy harvesting sensor for remote state estimation2016In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523Article in journal (Refereed)
  • 44.
    Nekouei, Ehsan
    et al.
    Department of Electrical and Electronic Engineering, The University of Melbourne, VIC 3010, Australien.
    Inaltekin, Hazer
    Department of Electrical and Electronic Engineering, The University of Melbourne, VIC 3010, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Capacity Scaling Limits of Cognitive Multiple Access Networks2013Book (Refereed)
  • 45.
    Nekouei, Ehsan
    et al.
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australien.
    Inaltekin, Hazer
    Department of Electrical and Electronics Engineering, Antalya International University, Turkiet.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Distributed cognitive multiple access networks: Power control, scheduling and multiuser diversity2013Conference paper (Refereed)
  • 46.
    Nekouei, Ehsan
    et al.
    Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3010, Australia.
    Inaltekin, Hazer
    Antalya Int Univ, Dept Elect & Elect Engn, Antalya, Turkey.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Power control and asymptotic throughput analysis for the distributed cognitive uplink2014In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 64, no 1, p. 41-58Article in journal (Refereed)
    Abstract [en]

    This paper studies optimum power control and sum-rate scaling laws for the distributed cognitive uplink. It is first shown that the optimum distributed power control policy is in the form of a threshold based water-filling power control. Each secondary user executes the derived power control policy in a distributed fashion by using local knowledge of its direct and interference channel gains such that the resulting aggregate (average) interference does not disrupt primary's communication. Then, the tight sum-rate scaling laws are derived as a function of the number of secondary users N under the optimum distributed power control policy. The fading models considered to derive sum-rate scaling laws are general enough to include Rayleigh, Rician and Nakagami fading models as special cases. When transmissions of secondary users are limited by both transmission and interference power constraints, it is shown that the secondary network sum-rate scales according to 1/en(h) log log (N), where n(h) is a parameter obtained from the distribution of direct channel power gains. For the case of transmissions limited only by interference constraints, on the other hand, the secondary network sum-rate scales according to 1/e gamma(g) log (N), where gamma(g) is a parameter obtained from the distribution of interference channel power gains. These results indicate that the distributed cognitive uplink is able to achieve throughput scaling behavior similar to that of the centralized cognitive uplink up to a pre-log multiplier 1/e, whilst primary's quality-of-service requirements are met. The factor 1/e can be interpreted as the cost of distributed implementation of the cognitive uplink.

  • 47.
    Nekouei, Ehsan
    et al.
    Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia..
    Inaltekin, Hazer
    Antalya Int Univ, Dept Elect & Elect Engn, TR-07190 Antalya, Turkey..
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Throughput Analysis for the Cognitive Uplink Under Limited Primary Cooperation2016In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 64, no 7, p. 2780-2796Article in journal (Refereed)
    Abstract [en]

    This paper studies the achievable throughput performance of the cognitive uplink under a limited primary cooperation scenario wherein the primary base station cannot feed back all interference channel gains to the secondary base station. To cope with the limited primary cooperation, we propose a feedback protocol called K-out-of-N feedback protocol, in which the primary base station feeds back only the K-N smallest interference channel gains, out of N of them, to the secondary base station. We characterize the throughput performance under the K-out-of-N feedback protocol by analyzing the achievable multiuser diversity gains (MDGs) in cognitive uplinks for three different network types. Our results show that the proposed feedback mechanism is asymptotically optimum for interference-limited (IL) and individual-power-and-interference-limited (IPIL) networks for a fixed positive K-N. It is further shown that the secondary network throughput in the IL and IPIL networks (under both the full and limited cooperation scenarios) logarithmically scales with the number of users in the network. In total-power-and-interference-limited (TPIL) networks, on the other hand, the K-out-of-N feedback protocol is asymptotically optimum for K-N = N-delta, where delta is an element of (0, 1). We also show that, in TPIL networks, the secondary network throughput under both the limited and full cooperation scales logarithmically double with the number of users in the network. These results indicate that the cognitive uplink can achieve the optimum MDG even with limited cooperation from the primary network. They also establish the dependence of pre-log throughput scaling factors on the distribution of fading channel gains for different network types.

  • 48. Nourian, M
    et al.
    Leong, Alex S
    Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3052, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Optimal energy allocation for Kalman filtering over packet dropping links with imperfect acknowledgements and energy harvesting constraints2014In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 59, no 8, p. 2128-2143Article in journal (Refereed)
    Abstract [en]

    This paper presents a design methodology for optimal transmission energy allocation at a sensor equipped with energy harvesting technology for remote state estimation of linear stochastic dynamical systems. In this framework, the sensor measurements as noisy versions of the system states are sent to the receiver over a packet dropping communication channel. The packet dropout probabilities of the channel depend on both the sensor's transmission energies and time varying wireless fading channel gains. The sensor has access to an energy harvesting source which is an everlasting but unreliable energy source compared to conventional batteries with fixed energy storages. The receiver performs optimal state estimation with random packet dropouts to minimize the estimation error covariances based on received measurements. The receiver also sends packet receipt acknowledgments to the sensor via an erroneous feedback communication channel which is itself packet dropping. The objective is to design optimal transmission energy allocation at the energy harvesting sensor to minimize either a finite-time horizon sum or a long term average (infinite-time horizon) of the trace of the expected estimation error covariance of the receiver's Kalman filter. These problems are formulated as Markov decision processes with imperfect state information. The optimal transmission energy allocation policies are obtained by the use of dynamic programming techniques. Using the concept of submodularity, the structure of the optimal transmission energy policies are studied. Suboptimal solutions are also discussed which are far less computationally intensive than optimal solutions. Numerical simulation results are presented illustrating the performance of the energy allocation algorithms.

  • 49. Nourian, Mojtaba
    et al.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Ahlén, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Distortion Minimization in Multi-Sensor Estimation With Energy Harvesting2015In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 33, no 3, p. 524-539Article in journal (Refereed)
    Abstract [en]

    This paper presents a design methodology for optimal energy allocation to estimate a random source using multiple wireless sensors equipped with energy harvesting technology. In this framework, multiple sensors observe a random process and then transmit an amplified uncoded analog version of the observed signal through Markovian fading wireless channels to a remote station. The sensors have access to an energy harvesting source, which is an everlasting but unreliable random energy source compared to conventional batteries with fixed energy storage. The remote station or so-called fusion centre estimates the realization of the random process by using a best linear unbiased estimator. The objective is to design optimal energy allocation policies at the sensor transmitters for minimizing total distortion over a finite-time horizon or a long term average distortion over an infinite-time horizon subject to energy harvesting constraints. This problem is formulated as a Markov decision process (MDP) based stochastic control problem and the optimal energy allocation policies are obtained by the use of dynamic programming techniques. Using the concept of submodularity, the structure of the optimal energy allocation policies is studied, which leads to an optimal threshold policy for binary energy allocation levels. Motivated by the excessive communication burden for the optimal control solutions where each sensor needs to know the channel gains and harvested energies of all other sensors, suboptimal decentralized strategies are developed where only statistical information about all other sensors' channel gains and harvested energies is required. Numerical simulation results are presented illustrating the performance of the optimal and suboptimal algorithms.

  • 50.
    Nourian, Mojtaba
    et al.
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australia.
    Leong, Alex S
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Optimal energy allocation for Kalman filtering over packet dropping links with energy harvesting constraints2013In: 4th IFAC Workshop on Distributed Estimation and Control in Networked Systems (2013) / [ed] Vey, Daniel, Koblenz, Germany: International Federation of Automatic Control , 2013, p. 261-268Conference paper (Refereed)
    Abstract [en]

    This paper studies the problem of optimal transmission energy allocation for error covariance minimization in Kalman filtering with random packet losses for sensors with energy harvesting capabilities. The energy harvesters provide an everlasting but unreliable energy source compared to conventional batteries with fixed energy storages. The packet loss probabilities of the Kalman filtering depend on both the sensor transmit energy and time varying wireless fading channel gains. We minimize either a finite horizon sum or the long term average (infinite horizon) of the trace of the expected error covariance of the Kalman filter subject to energy harvesting constraints. The resulting Markov decision process problems with constraints are approached using the dynamic programming principle for both causal and non-causal system information. Using the concept of submodularity, the structure of the optimal transmission energy policy is studied. Numerical simulation results are presented illustrating the performance of the energy allocation algorithms.

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