Sub-Stream Fairness and Numerical Correctness in MIMO Interference Channels

Signal-to-interference plus noise ratio (SINR) and rate fairness in a system are substantial quality-of-service (QoS) metrics. The acclaimed SINR maximization (max-SINR) algorithm does not achieve fairness between user's streams, i.e., sub-stream fairness is not achieved. To this end, we propose a distributed power control algorithm to render sub-stream fairness in the system. Sub-stream fairness is a less restrictive design metric than stream fairness (i.e., fairness between all streams) thus sum-rate degradation is milder. Algorithmic parameters can significantly differentiate the results of numerical algorithms. A complete picture for comparison of algorithms can only be depicted by varying these parameters. For example, a predetermined iteration number or a negligible increment in the sum-rate can be the stopping criteria of an algorithm. While the distributed interference alignment (DIA) can reasonably achieve sub-stream fairness for the later, the imbalance between sub-streams increases as the preset iteration number decreases. Thus comparison of max-SINR and DIA with a low preset iteration number can only depict a part of the picture. We analyze such important parameters and their effects on SINR and rate metrics to exhibit numerical correctness in executing the benchmarks. Finally, we propose group filtering schemes that jointly design the streams of a user in contrast to max-SINR scheme that designs each stream of a user separately.Comment: To be presented at IEEE ISWTA'1

Joint Source and Relay Precoding Designs for MIMO Two-Way Relaying Based on MSE Criterion

Properly designed precoders can significantly improve the spectral efficiency of multiple-input multiple-output (MIMO) relay systems. In this paper, we investigate joint source and relay precoding design based on the mean-square-error (MSE) criterion in MIMO two-way relay systems, where two multi-antenna source nodes exchange information via a multi-antenna amplify-and-forward relay node. This problem is non-convex and its optimal solution remains unsolved. Aiming to find an efficient way to solve the problem, we first decouple the primal problem into three tractable sub-problems, and then propose an iterative precoding design algorithm based on alternating optimization. The solution to each sub-problem is optimal and unique, thus the convergence of the iterative algorithm is guaranteed. Secondly, we propose a structured precoding design to lower the computational complexity. The proposed precoding structure is able to parallelize the channels in the multiple access (MAC) phase and broadcast (BC) phase. It thus reduces the precoding design to a simple power allocation problem. Lastly, for the special case where only a single data stream is transmitted from each source node, we present a source-antenna-selection (SAS) based precoding design algorithm. This algorithm selects only one antenna for transmission from each source and thus requires lower signalling overhead. Comprehensive simulation is conducted to evaluate the effectiveness of all the proposed precoding designs.Comment: 32 pages, 10 figure

QoS constrained power minimization in the multiple stream MIMO broadcast channel

This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/ licenses/by-nc-nd/4.0/. This version of the article: González-Coma, J. P., Joham, M., Castro, P. M., & Castedo, L. (2018). 'QoS constrained power minimization in the multiple stream MIMO broadcast channel', has been accepted for publication in Signal Processing, 143, 48–55. The Version of Record is available online at: https://doi.org/10.1016/ j.sigpro.2017.08.015.[Abstract]: This work addresses the design of optimal linear transmit filters for the Multiple Input-Multiple Output (MIMO) Broadcast Channel (BC) when several spatial streams are allocated to each user. We further consider that the Channel State Information (CSI) is perfect at the receivers but is only partial at the transmitter. A statistical model for the partial CSI is assumed and exploited for the filter design. The relationship between average rate and average Mean Square Error (MSE) is studied to determine the optimal way to distribute the per-user rates among the streams. Finally, the feasible average sum-MSE (sMSE) region is studied and the impact of the CSI uncertainty over the overall system performance is evaluated.This work was funded by Xunta de Galicia (ED431C 2016-045, ED341D R2016/012, ED431G/01), AEI of Spain (TEC2013-47141-C4-1-R, TEC2015-69648-REDC, TEC2016-75067-C4-1-R), and ERDF funds (AEI/FEDER, EU).Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED341D R2016/012Xunta de Galicia; ED431G/0

Optimal Precoder Designs for Sum-utility Maximization in SWIPT-enabled Multi-user MIMO Cognitive Radio Networks

In this paper, we propose a generalized framework that combines the cognitive radio (CR) techniques for spectrum sharing and the simultaneous wireless information and power transfer (SWIPT) for energy harvesting (EH) in the conventional multi-user MIMO (MuMIMO) channels, which leads to an MuMIMO-CR-SWIPT network. In this system, we have one secondary base-station (S-BS) that supports multiple secondary information decoding (S-ID) and secondary EH (S-EH) users simultaneously under the condition that interference power that affects the primary ID (P-ID) receivers should stay below a certain threshold. The goal of the paper is to develop a generalized precoder design that maximizes the sum-utility cost function under the transmit power constraint at the S-BS, and the EH constraint at each S-EH user, and the interference power constraint at each P-ID user. Therefore, the previous studies for the CR and SWIPT systems are casted as particular solutions of the proposed framework. The problem is inherently non-convex and even the weighted minimum mean squared error (WMMSE) transformation does not resolve the non-convexity of the original problem. To tackle the problem, we find a solution from the dual optimization via sub-gradient ellipsoid method based on the observation that the WMMSE transformation raises zero-duality gap between the primal and the dual problems. We also propose a simplified algorithm for the case of a single S-ID user, which is shown to achieve the global optimum. Finally, we demonstrate the optimality and efficiency of the proposed algorithms through numerical simulation results.Comment: 12pages, 9 figures, submitted to IEEE Systems Journa