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In this technical note, we present a new theoretical result for multi-cell non-orthogonal multiple access (NOMA). For multi-cell scenarios, a so-called load-coupling model has been proposed earlier to characterize the presence of mutual interference for NOMA, and the optimization process relies on the use of fixed-point iterations across cells. One difficulty here is that the order of decoding for successive interference cancellation (SIC) in NOMA is generally not known a priori. This is because the decoding order in one cell depends on interference, which, in turn, is governed by resource usage in other cells, and vice versa. To achieve convergence, previous works have used workarounds that pose restrictions to NOMA, such that the SIC decoding order remains throughout the fixed-point iterations. As a comment to the previous works, we derive and prove the following result: The convergence is guaranteed, even if the order changes over the iterations. The result not only waives the need of previous workarounds, but also implies that a wide class of optimization problems for multi-cell NOMA is tractable, as long as that for single cell is.

In a cloud radio access network (C-RAN), distributed remote radio heads (RRHs) are coordinated by baseband units (BBUs) in the cloud. The centralization of signal processing provides flexibility for coordinated multipoint transmission (CoMP) of RRHs to cooperatively serve user equipments (UEs). We target enhancing UEs' capacity performance, by jointly optimizing the selection of RRHs for serving UEs, i.e., CoMP selection, and resource allocation. We analyze the computational complexity of the problem. Next, we prove that under fixed CoMP selection, the optimal resource allocation amounts to solving a so-called iterated function. Towards user-centric network optimization, we propose an algorithm for the joint optimization problem, aiming at scaling up the capacity maximally for any target UE group of interest. The proposed algorithm enables network-level performance evaluation for quality of experience.

In this paper, we study a class of minimum-energy scheduling problems in non-orthogonal multiple access (NOMA) systems. NOMA is adopted to enable efficient channel utilization and interference mitigation, such that base stations can consume minimal energy to empty their queued data in presence of transmission deadlines, and each user can obtain all the requested data timely. Due to the high computational complexity in resource scheduling and the stringent execution-time constraints in practical systems, providing a time-efficient and high-quality solution to 5G real-time systems is challenging. The conventional iterative optimization approaches may exhibit their limitations in supporting online optimization. We herein explore a viable alternative and develop a learning-assisted optimization framework to improve the computational efficiency while retaining competitive energy-saving performance. The idea is to use deep-learning based predictions to accelerate the optimization process in conventional optimization methods for tackling the NOMA resource scheduling problems. In numerical studies, the proposed optimization framework demonstrates high computational efficiency. Its computational time is insensitive to the input size. The framework is able to provide optimal solutions as long as the learning-based predictions satisfy a derived optimality condition. For the general cases with imperfect predictions, the algorithmic solution is error-tolerable and performance scaleable, leading the energy-saving performance close to the global optimum.

In this paper, we investigate energy optimization in multi-cell and multi-carrier non-orthogonal multiple access (NOMA) networks. We apply a load-coupling model for NOMA networks to capture the coupling relation of mutual interference among cells. With this analytical tool, we formulate an energy minimization problem in a NOMA-based load-coupled system, where optimizing load-rate-power allocation, and determining decoding order and user grouping are the key aspects. Theoretically, we prove that the minimum consumed energy can be achieved by using all the time-frequency resources in each cell to deliver users' demand, and allowing all the users to share resource units. From a practical perspective, we consider three types of NOMA grouping schemes, i.e., all-user grouping, partitioned and non-partitioned grouping. We develop tailored solutions for each grouping scheme to enable efficient load-rate-power optimization. These three algorithmic components are embedded into a power-adjustment framework to provide energy-efficient solutions for NOMA networks. Numerical results demonstrate promising energy-saving gains of NOMA over orthogonal multiple access in large-scale cellular networks, in particular for high-demand and resource-limited scenarios. The results also show fast convergence of the proposed algorithms and demonstrate the effectiveness of the solutions.

The rapid development from 4G to 5G of mobile communications poses significant challenges in providing high rate and capacity, making it more crucial for efficient utilization of time-frequency resource via optimally configuring the network. Mathematical optimization serves as a powerful tool for addressing this type of problems. However, gauging its potential in large-scale cellular networks is non-trivial due to the inherent coupling relation of interference among cells. To address this issue, the dissertation adopts a so-called load-coupling system that mathe-matically formulates the mutual influence caused by radio resource allocation among cells. The model defines the time-frequency resource consumption in each cell as the cell load. The load of one cell governs the interference that the cell generates to the others, since the cell trans-mits more frequently with higher load. The model enables joint optimization of resource al-location in multiple cells with respect to the dynamics of resource occupancy of cells. Under the load coupling model, the dissertation applies mathematical optimization to resolve resource management problems with respect to a number of evolving technologies, such as coordinated multipoint (CoMP) transmission, wireless relays, cloud radio access networks (C-RAN), and non-orthogonal multiple access (NOMA). Six research papers are included in the dissertation. Paper I addresses the question of how network planning and coordination may increase the ef-ficiency of spectrum usage, by jointly optimizing user association and resource allocation with CoMP. Paper II investigates the potential of relay cooperation for energy saving. As an extension of Paper I, Paper III studies the capacity maximization for a target group of users, while keep-ing the quality-of-service (QoS) of other users being strictly met. Paper IV provides a general framework and a series of theoretical analysis for algorithmically enabling resource optimization in multi-cell NOMA with load coupling, where users are allowed to group together for sharing time-frequency resource by successive interference cancellation (SIC). Under this framework, Paper V explores the potential of NOMA networks. For a restricted setup of NOMA, the paper achieves globally optimal resource usage efficiency, in terms of power allocation, user pair se-lection, and time-frequency resource allocation. Finally, Paper VI, serving as a complementary note, overcomes a key obstacle in analyzing convergence of applying load coupling in NOMA networks.

We consider the performance optimization of multi-cell networks with LTE and Wi-Fi aggregation (LWA) and LTE-unlicensed (LTE-U) with sharing of the unlicensed band. Theoretical results are derived to enable an algorithm to approach the optimum. Numerical results show the algorithm's effectiveness and benefits of joint use of LWA and LTE-U.

We address an energy-efficient scheduling problem for practical multiple-input single-output (MISO) systems with stringent execution-time requirements. Optimal user-group scheduling is adopted to enable timely and energy-efficient data transmission, such that all the users' demand can be delivered within a limited time. The high computational complexity in optimal iterative algorithms limits their applications in real-time network operations. In this paper, we rethink the conventional optimization algorithms, and embed machine-learning based predictions in the optimization process, aiming at improving the computational efficiency and meeting the stringent execution-time limits in practice, while retaining competitive energy-saving performance for the MISO system. Numerical results demonstrate that the proposed method, i.e., optimization with machine-learning predictions (OMLP), is able to provide a time-efficient and high-quality solution for the considered scheduling problem. Towards online scheduling in real-time communications, OMLP is of high computational efficiency compared to conventional optimal iterative algorithms. OMLP guarantees the optimality as long as the machine-learning based predictions are accurate.

Towards energy savings in large-scale non-orthogonal multiple access (NOMA) networks, we investigate power and load optimization for multi-cell and multi-carrier NOMA systems in this paper. To capture the coupling relation of mutual interference among cells, firstly, we extend a load-coupling model from orthogonal multiple access (OMA) to NOMA networks. Next, with this analytical tool, we formulate the considered optimization problem in NOMA-based load-coupled systems, where optimizing load, power, and determining decoding order are the key aspects in the optimization. Theoretically, we prove that the minimum network energy consumption can be achieved by using all the time-frequency resources in each cell to deliver users' demand. To achieve the optimal load and enable efficient power optimization, we develop a power-adjustment algorithm. Numerical results demonstrate promising energy-saving gains of NOMA over OMA in large-scale cellular networks, in particular for the high-demand and resource-limited scenarios.

We explore the potential of optimizing resource allocation with flexible numerology in frequency domain and variable frame structure in time domain, with services of with different types of requirements. We prove the NP-hardness of the problem and propose a scalable optimization algorithm based on linear programming and Lagrangian duality. Numerical results show significant advantages of adopting flexibility in both time and frequency domains for capacity enhancement and meeting the requirements of mission critical services.