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  • 1.
    Agrawal, Piyush
    et al.
    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.
    Olofsson, Tomas
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Gidlund, Mikael
    Long Term Channel Characterization for Energy Efficient Transmission in Industrial Environments2014In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 62, no 8, p. 3004-3014Article in journal (Refereed)
    Abstract [en]

    One of the challenges for a successful use of wireless sensor networks in process industries is to design networks with energy efficient transmission, to increase the lifetime of the deployed network while maintaining the required latency and bit-error rate. The design of such transmission schemes depend on the radio channel characteristics of the region. This paper presents an investigation of the statistical properties of the radio channel in a typical process industry, particularly when the network is meant to be deployed for a long time duration, e. g., days, weeks, and even months. Using 17-20-h-long extensive measurement campaigns in a rolling mill and a paper mill, we highlight the non-stationarity in the environment and quantify the ability of various distributions, given in the literature, to describe the variations on the links. Finally, we analyze the design of an optimal received signal-to-noise ratio (SNR) for the deployed nodes and show that improper selection of the distribution for modeling of the variations in the channel can lead to an overuse of energy by a factor of four or even higher.

  • 2.
    Apelfröjd, Rikke
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Zirwas, Wolfgang
    Nokia Bell Labs, D-81541 Munich, Germany.
    Sternad, Mikael
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Low-Overhead Cyclic Reference Signals for Channel Estimation in FDD Massive MIMO2019In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 67, no 5, p. 3279-3291Article in journal (Refereed)
    Abstract [en]

    Massive multiple input multiple output (MIMO) transmission and coordinated multipoint transmission are candidate technologies for increasing data throughput in evolving 5G standards. Frequency division duplex (FDD) is likely to remain predominant in large parts of the spectrum below 6 GHz for future 5G systems. Therefore, it is important to estimate the downlink FDD channels from a very large number of antennas, while avoiding an excessive downlink reference signal overhead. We here propose and investigate a three part solution. First, massive MIMO downlinks use a fixed grid of beams. For each user, only a subset of beams will then be relevant, and require estimation. Second, sets of coded reference signal sequences, with cyclic patterns over time, are used. Third, each terminal estimates its most relevant channels. We here propose and compare a linear mean square estimation and a Kalman estimation. Both utilize frequency and antenna correlation, and the later also utilizes temporal correlation. In extensive simulations, this scheme provides channel estimates that lead to an insignificant beamforming performance degradation as compared to full channel knowledge. The cyclic pattern of coded reference signals is found to be important for reliable channel estimation, without having to adjust the reference signals to specific users.

  • 3.
    Deng, Tao
    et al.
    Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Sichuan, Peoples R China.
    Fan, Pingzhi
    Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Sichuan, Peoples R China.
    Yuan, Di
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Optimizing Retention-Aware Caching in Vehicular Networks2019In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 67, no 9, p. 6139-6152Article in journal (Refereed)
    Abstract [en]

    Caching is an effective way to address the challenges due to explosive data traffic growth and massive device connectivity in fifth-generation (5G) networks. Currently, few works on caching pay attention to the impact of the time duration for which content is stored, called retention time, on caching optimization. The research on retention time is motivated by two practical issues, i.e., flash memory damage and storage rental cost in cloud networks, together giving rise to the storage cost. How to optimize caching contents taking the storage cost into consideration is a challenging problem, especially for the scenarios with cache-enabled mobile nodes. In this paper, a retention-aware caching problem (RACP) in vehicular networks is formulated, considering the impact of the storage cost. The problem's complexity analysis is provided. For symmetric cases, an optimal dynamic programming (DP) algorithm with polynomial time complexity is derived. For general cases, a low complexity and effective retention aware multi-helper caching algorithm (RAMA) is proposed. Numerical results are used to verify the effectiveness of the algorithms.

  • 4.
    Eriksson, Markus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Olofsson, Tomas
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    On Long-Term Statistical Dependences in Channel Gains for Fixed Wireless Links in Factories2016In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 64, no 7, p. 3078-3091Article in journal (Refereed)
    Abstract [en]

    The reliability and throughput in an industrial wireless sensor network can be improved by incorporating the predictions of channel gains when forming routing tables. Necessary conditions for such predictions to be useful are that statistical dependences exist between the channel gains and that those dependences extend over a long enough time to accomplish a rerouting. In this paper, we have studied such long-term dependences in channel gains for fixed wireless links in three factories. Long-term fading properties were modeled using a switched regime model, and Bayesian change point detection was used to split the channel gain measurements into segments. In this way, we translated the study of long-term dependences in channel gains into the study of dependences between fading distribution parameters describing the segments. We measured the strengths of the dependences using mutual information and found that the dependences exist in a majority of the examined links. The strongest dependence appeared between mean received power in adjacent segments, but we also found significant dependences between segment lengths. In addition to the study of statistical dependences, we present the summaries of the distribution of the fading parameters extracted from the segments, as well as the lengths of these segments.

  • 5.
    Eriksson, Markus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Olofsson, Tomas
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    On Long-Term Statistical Dependences in Channel Gains for Fixed Wireless Links in Factories2016In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 64, no 7, p. 3078-3091Article in journal (Refereed)
    Abstract [en]

    The reliability and throughput in an industrial wireless sensor network can be improved by incorporating the predictions of channel gains when forming routing tables. Necessary conditions for such predictions to be useful are that statistical dependences exist between the channel gains and that those dependences extend over a long enough time to accomplish a rerouting. In this paper, we have studied such long-term dependences in channel gains for fixed wireless links in three factories. Long-term fading properties were modeled using a switched regime model, and Bayesian change point detection was used to split the channel gain measurements into segments. In this way, we translated the study of long-term dependences in channel gains into the study of dependences between fading distribution parameters describing the segments. We measured the strengths of the dependences using mutual information and found that the dependences exist in a majority of the examined links. The strongest dependence appeared between mean received power in adjacent segments, but we also found significant dependences between segment lengths. In addition to the study of statistical dependences, we present the summaries of the distribution of the fading parameters extracted from the segments, as well as the lengths of these segments.

  • 6. Guo, X
    et al.
    He, Y
    Atapattu, S
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Evans, J
    Power allocation for distributed detection systems in wireless sensor networks with limited fusion centre feedback2018In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 66, no 10, p. 4753-4766Article in journal (Refereed)
    Abstract [en]

    We consider a distributed detection system for a wireless sensor network over slow-fading channels. Each sensor only has knowledge of quantized channel state information (CSI) which is received from the fusion center via a limited feedback channel. We then consider transmit power allocation at each sensor in order to maximize a J-divergence based detection metric subject to a total and individual transmit power constraints. Our aim is to jointly design the quantization regions of all sensors CSI and the corresponding power allocations. A locally optimum solution is obtained by applying the generalized Lloyd algorithm (GLA). To overcome the high computational complexity of the GLA, we then propose a low-complexity near-optimal scheme which performs very close to its GLA based counterpart. This enables us to explicitly formulate the problem and to find the unique solution despite the non-convexity of the optimization problem. An asymptotic analysis is also provided when the number of feedback bits becomes large. Numerical results illustrate that only a small amount of feedback is needed to achieve a detection performance close to the full CSI case.

  • 7.
    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)
  • 8.
    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.
    Throughput 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)
  • 9.
    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)
  • 10.
    Lindbom, Lars
    et al.
    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.
    Falkenström, M
    Tracking of time-varying mobile radio channels: Part II: A case study2002In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 50, p. 156-167Article in journal (Refereed)
  • 11.
    Lindbom, Lars
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Sternad, Mikael
    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.
    Tracking of time-varying mobile radio channels: Part I: The Wiener LMS algorithm2001In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 49, p. 2207-2217Article in journal (Refereed)
  • 12.
    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.

  • 13.
    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.

  • 14.
    Razavikia, Saeed
    et al.
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
    Barros da Silva Junior, Jose Mairton
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication.
    Fischione, Carlo
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
    ChannelComp: A General Method for Computation by Communications2024In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 72, no 2, p. 692-706Article in journal (Refereed)
    Abstract [en]

    Over-the-air computation (AirComp) is a well-known technique by which several wireless devices transmit by analog amplitude modulation to achieve a sum of their transmit signals at a common receiver. The underlying physical principle is the superposition property of the radio waves. Since such superposition is analog and in amplitude, it is natural that AirComp uses analog amplitude modulations. Unfortunately, this is impractical because most wireless devices today use digital modulations. It would be highly desirable to use digital communications because of their numerous benefits, such as error correction, synchronization, acquisition of channel state information, and widespread use. However, when we use digital modulations for AirComp, a general belief is that the superposition property of the radio waves returns a meaningless overlapping of the digital signals. In this paper, we break through such beliefs and propose an entirely new digital channel computing method named ChannelComp, which can use digital as well as analog modulations. We propose a feasibility optimization problem that ascertains the optimal modulation for computing arbitrary functions over-the-air. Additionally, we propose pre-coders to adapt existing digital modulation schemes for computing the function over the multiple access channel. The simulation results verify the superior performance of ChannelComp compared to AirComp, particularly for the product functions, with more than 10 dB improvement of the computation error.

  • 15. Wang, Zhe
    et al.
    Alpcan, Tansu
    Evans, Jamie S.
    Dey, Subhrakanti
    Hamilton Institute, National University of Ireland Maynooth, Maynooth, Ireland.
    Truthful Mechanism Design for Wireless Powered Network With Channel Gain Reporting2019In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 67, no 11, p. 7966-7979Article in journal (Refereed)
    Abstract [en]

    Directional wireless power transfer (WPT) technology provides a promising energy solution to remotely recharge the Internet of things sensors using directional antennas. Under a harvest-then-transmit protocol, the access point can adaptively allocate the transmit power among multiple energy directions to maximize the social welfare of the sensors, i.e., downlink sum received energy or uplink sum rate, based on full or quantized channel gains reported from the sensors. However, such power allocation can be challenged if each sensor belongs to a different agent and works in a competitive way. In order to maximize their own utilities, the sensors have the incentives to falsely report their channel gains, which unfortunately reduces the social welfare. To tackle this problem, we design the strategy-proof mechanisms to ensure that each sensor’s dominant strategy is to truthfully reveal its channel gain regardless of other sensors’ strategies. Under the benchmark full channel gain reporting (CGR) scheme, we adopt the Vickrey-Clarke-Groves (VCG) mechanism to derive the price functions for both downlink and uplink, where the truthfulness is guaranteed by asking each sensor to pay the social welfare loss of all other sensors attributable to its presence. For the 1-bit CGR scheme, the problem is more challenging due to the severe information asymmetry, where each sensor has true valuation of full channel gain but may report the false information of quantized channel gain. We prove that the classic VCG mechanism is no longer truthful and then propose two threshold-based price functions for both downlink and uplink, where the truthfulness is ensured by letting each sensor pay its own achievable utility improvement due to its participation. The numerical results validate the truthfulness of the proposed mechanism designs.

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