Distributed Energy Spectral Efficiency Optimization for Partial/Full Interference Alignment in Multi-user Multi-relay Multi-cell MIMO Systems

The energy spectral efficiency maximization (ESEM) problem of a multi-user, multi-relay, multi-cell system is considered, where all the network nodes are equipped with multi-antenna transceivers. To deal with the potentially excessive interference originating from a plethora of geographically distributed transmission sources, a pair of transmission protocols based on interference alignment (IA) are conceived. The first, termed the full-IA, avoids all intra-cell interference (ICI) and other-cell interference by finding the perfect interference-nulling receive beamforming matrices (RxBFMs). The second protocol, termed partial-IA, only attempts to null the ICI. Employing the RxBFMs computed by either of these protocols mathematically decomposes the channel into a multiplicity of non-interfering multiple-input-single-output channels, which we term as spatial multiplexing components (SMCs). The problem of finding the optimal SMCs as well as their power control variables for the ESEM problem considered is formally defined and converted into a convex optimization form with carefully selected variable relaxations and transformations. Thus, the optimal SMCs and power control variables can be distributively computed using both the classic dual decomposition and subgradient methods. Our results indicate that indeed, the ESEM algorithm performs better than the baseline equal power allocation algorithm in terms of its ESE. Furthermore, surprisingly the partial-IA outperforms the full-IA in all cases considered, which is because the partial-IA is less restrictive in terms of the number of available transmit dimensions at the transmitters. Given the typical cell sizes considered in this paper, the path-loss sufficiently attenuates the majority of the interference, and thus the full-IA over-compensates, when trying to avoid all possible sources of interference.

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abstract: System capacity is considered for a group of interfering users employing single-user detection and multiple transmit and receive antennas for flat Rayleigh-fading channels with independent fading coefficients for each path. The focus is on the case where there is no channel state information at the transmitter, but channel state information is assumed at the receiver. It is shown that the optimum signaling is sometimes different from cases where the users do not interfere with each other. In particular, the optimum signaling will sometimes put all power into a single transmitting antenna, rather than divide power equally between independent streams from the different antennas. If the interference is either sufficiently weak or sufficiently strong, we show that either the optimum interference-free approach, which puts equal power into each antenna, or the approach that puts all power into a single antenna is optimum and we show how to find the regions where each approach is best.

title: Near-Capacity Multi-Functional MIMO Systems: Sphere-Packing, Iterative Detection and Cooperation
abstract: About the Authors. OtherWiley IEEE Press Books on Related Topics. Preface. Acknowledgments. 1 Problem Formulation, Objectives and Benefits. 1.1 TheWireless Channel and the Concept of Diversity. 1.2 Diversity and Multiplexing Trade-offs in Multi-functional MIMO Systems.  Historical Perspective and State-of-the-Art Contributions. 1.5 Iterative Detection Schemes and their Convergence Analysis. 1.6 Outline and Novel Aspects of the Monograph. Part I Coherent Versus Differential Turbo Detection of Sphere-packing-aided Single-user MIMO Systems. List of Symbols in Part I. 2 Space-Time Block Code Design using Sphere Packing.   Performance of Turbo-detected Bit-based STBC-SP Schemes. 3.6 Chapter Conclusions.  Turbo Detection of Channel-coded DSTBC-SP Schemes.  4.2 Differential STBC using SP Modulation. 4.3 Bit-based RSC-coded Turbo-detected DSTBC-SP Scheme.   Three-stage Turbo-detected STBC-SP Schemes.  5.4 Maximum Achievable Bandwidth Efficiency. 5.5 Performance of Three-stageTurbo-detected STBC-SP Schemes. 5.6 Chapter Conclusions. 5.7 Chapter Summary. 6 Symbol-based Channel-coded STBC-SP Schemes.  Symbol-based Iterative Decoding. 6.4 Non-binary EXIT Chart Analysis. 6.5 Performance of Bit-based and Symbol-based LDPC-coded STBC-SP Schemes. 6.6 Chapter Conclusions.  List of Symbols in Part II. 7 Linear Dispersion Codes: An EXIT Chart Perspective.  EXIT-chart-based Design of IR-PLDCs.  8 Differential Space-Time Block Codes: A Universal Approach.  Part III Differential Turbo Detection of Multi-functional MIMO-aided Multi-user and Cooperative Systems. List of Symbols in Part III. 10 Differential Space-Time Spreading. 10.1 Introduction. 10.2 DPSK. 10.3 DSTS Designusing Two Transmit Antennas. 10.4 DSTS Design Using Four Transmit Antennas. 10.5 Chapter Conclusions. 10.6 Chapter Summary. 11 Iterative Detection of Channel-coded DSTS Schemes. 11.1 Introduction. 11.2 Iterative Detection of RSC-coded DSTS Schemes. 11.3 Iterative Detection of RSC-coded and Unity-rate Precoded Four-antenna-aided DSTS-SP System. 11.4 Chapter Conclusions.

title: Dual methods for nonconvex spectrum optimization of multicarrier systems
abstract: The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations

title: Dynamic Resource Allocation in MIMO-OFDMA Systems with Full-Duplex and Hybrid Relaying
abstract: In this paper, we formulate a joint optimization problem for resource allocation and scheduling in full-duplex multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) relaying systems with amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. Our problem formulation takes into account heterogeneous data rate requirements for delay sensitive and non-delay sensitive users. We also consider a theoretically optimal hybrid relaying scheme as a performance benchmark, which allows a dynamic selection between AF relaying and DF relaying protocols with full-duplex and half-duplex relays. We show that under some mild conditions the optimal transmitter precoding and receiver post-processing matrices jointly diagonalize the MIMO-OFDMA relay channels for all considered relaying protocols transforming the resource allocation and scheduling problem into a scalar optimization problem. Dual decomposition is employed to solve this optimization problem and a distributed iterative resource allocation and scheduling algorithm with closed-form power and subcarrier allocation is derived. Simulation results not only illustrate that the proposed distributed algorithm converges to the optimal solution in a small number of iterations, but also demonstrate the potential performance gains achievable with full-duplex relaying protocols.

title: Energy-Efficient Resource Allocation for Secure OFDMA Systems
abstract: In this paper, resource allocation for energy-efficient secure communication in an orthogonal frequency-division multiple-access (OFDMA) downlink network is studied. The considered problem is modeled as a nonconvex optimization problem that takes into account the sum-rate-dependent circuit power consumption, multiple-antenna eavesdropper, artificial noise generation, and different quality-of-service (QoS) requirements, including a minimum required secrecy sum rate and a maximum tolerable secrecy outage probability. The power, secrecy data rate, and subcarrier allocation policies are optimized for maximization of the energy efficiency of secure data transmission (bit/joule securely delivered to the users). The considered nonconvex optimization problem is transformed into a convex optimization problem by exploiting the properties of fractional programming, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm not only converges in a small number of iterations but maximizes the system energy efficiency and guarantees a nonzero secrecy data rate for the desired users as well. In addition, the obtained results unveil a tradeoff between energy efficiency and secure communication.

title: Optimal Resource Allocation for OFDMA Downlink Systems
abstract: This paper proposes efficient rate and power allocation algorithms for OFDMA downlink systems where each tone is taken by at most one user. Weighted sum rate maximization (WSRmax) and weighted sum power minimization (WSPmin) problems are considered. Since these resource allocation problems are non-convex, complexity of finding the optimal solutions is prohibitively high. This paper employs the Lagrange dual decomposition method to efficiently solve both optimization problems. Because of their non-convex nature, there is no guarantee for the solution obtained by the dual decomposition method to be optimal. However, it is shown that with practical number of tones, the duality gap is virtually zero and the optimal solutions can be efficiently obtained