How to Understand LMMSE Transceiver Design for MIMO Systems From Quadratic Matrix Programming

In this paper, a unified linear minimum mean-square-error (LMMSE) transceiver design framework is investigated, which is suitable for a wide range of wireless systems. The unified design is based on an elegant and powerful mathematical programming technology termed as quadratic matrix programming (QMP). Based on QMP it can be observed that for different wireless systems, there are certain common characteristics which can be exploited to design LMMSE transceivers e.g., the quadratic forms. It is also discovered that evolving from a point-to-point MIMO system to various advanced wireless systems such as multi-cell coordinated systems, multi-user MIMO systems, MIMO cognitive radio systems, amplify-and-forward MIMO relaying systems and so on, the quadratic nature is always kept and the LMMSE transceiver designs can always be carried out via iteratively solving a number of QMP problems. A comprehensive framework on how to solve QMP problems is also given. The work presented in this paper is likely to be the first shoot for the transceiver design for the future ever-changing wireless systems.Comment: 31 pages, 4 figures, Accepted by IET Communication

Queue-Aware Distributive Resource Control for Delay-Sensitive Two-Hop MIMO Cooperative Systems

In this paper, we consider a queue-aware distributive resource control algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay buffering is an effective way to reduce the intrinsic half-duplex penalty in cooperative systems. The complex interactions of the queues at the source node and the relays are modeled as an average-cost infinite horizon Markov Decision Process (MDP). The traditional approach solving this MDP problem involves centralized control with huge complexity. To obtain a distributive and low complexity solution, we introduce a linear structure which approximates the value function of the associated Bellman equation by the sum of per-node value functions. We derive a distributive two-stage two-winner auction-based control policy which is a function of the local CSI and local QSI only. Furthermore, to estimate the best fit approximation parameter, we propose a distributive online stochastic learning algorithm using stochastic approximation theory. Finally, we establish technical conditions for almost-sure convergence and show that under heavy traffic, the proposed low complexity distributive control is global optimal.Comment: 30 pages, 7 figure

A virtual MIMO dual-hop architecture based on hybrid spatial modulation

International audienceIn this paper, we propose a novel Virtual Multiple-Input-Multiple-Output (VMIMO) architecture based on the concept of Spatial Modulation (SM). Using a dual-hop and Decode-and-Forward protocol, we form a distributed system, called Dual-Hop Hybrid SM (DH-HSM). DH-HSM conveys information from a Source Node (SN) to a Destination Node (DN) via multiple Relay Nodes (RNs). The spatial position of the RNs is exploited for transferring information in addition to, or even without, a conventional symbol. In order to increase the performance of our architecture, while keeping the complexity of the RNs and DN low, we employ linear precoding using Channel State Information (CSI) at the SN. In this way, we form a Receive-Spatial Modulation (R-SM) pattern from the SN to the RNs, which is able to employ a centralized coordinated or a distributed uncoordinated detection algorithm at the RNs. In addition, we focus on the SN and propose two regularized linear precoding methods that employ realistic Imperfect Channel State Information at the Transmitter. The power of each precoder is analyzed theoretically. Using the Bit Error Rate (BER) metric, we evaluate our architecture against the following benchmark systems: 1) single relay; 2) best relay selection; 3) distributed Space Time Block Coding (STBC) VMIMO scheme; and 4) the direct communication link. We show that DH-HSM is able to achieve significant Signal-to-Noise Ratio (SNR) gains, which can be as high as 10.5 dB for a very large scale system setup. In order to verify our simulation results, we provide an analytical framework for the evaluation of the Average Bit Error Probability (ABEP)

On the Throughput of Large-but-Finite MIMO Networks using Schedulers

This paper studies the sum throughput of the {multi-user} multiple-input-single-output (MISO) networks in the cases with large but finite number of transmit antennas and users. Considering continuous and bursty communication scenarios with different users' data request probabilities, we derive quasi-closed-form expressions for the maximum achievable throughput of the networks using optimal schedulers. The results are obtained in various cases with different levels of interference cancellation. Also, we develop an efficient scheduling scheme using genetic algorithms (GAs), and evaluate the effect of different parameters, such as channel/precoding models, number of antennas/users, scheduling costs and power amplifiers' efficiency, on the system performance. Finally, we use the recent results on the achievable rates of finite block-length codes to analyze the system performance in the cases with short packets. As demonstrated, the proposed GA-based scheduler reaches (almost) the same throughput as in the exhaustive search-based optimal scheduler, with substantially less implementation complexity. Moreover, the power amplifiers' inefficiency and the scheduling delay affect the performance of the scheduling-based systems significantly