Transceiver design for non-regenerative MIMO relay systems with decision feedback detection

In this paper we consider the design of zero forcing (ZF) and minimum mean square error (MMSE) transceivers for non-regenerative multiple input multiple output (MIMO) relay networks. Our designs utilise linear processors at each stage of the network along with a decision feedback detection device at the receiver. Under the assumption of full channel state information (CSI) across the entire link the processors are jointly optimised to minimise the system arithmetic mean square error (MSE) whilst meeting average power constraints at both the source and the relay terminals. We compare the presented methods to linear designs available in the literature and show the advantages of the proposed transceivers through simulation results

ZF DFE transceiver design for MIMO relay systems with direct source-destination link

In this paper we consider a non-linear transceiver design for non-regenerative multiple-input multiple-output (MIMO) relay networks where a direct link exists between the source and destination. Our system utilises linear processors at the source and relay as well as a zero-forcing (ZF) decision feedback equaliser (DFE) at the receiver. Under the assumption that full channel state information (CSI) is available the precoding and equaliser matrices are designed to minimise the arithmetic mean square error (MSE) whilst meeting transmit power constraints at the source and destination. The source, relay, and destination processors are provided in closed form solution. In the absence of the direct link our design particularises to a previous ZF DFE solution and as such can be viewed as a generalisation of an existing work. We demonstrate the effectiveness of the proposed solution through simulation and show that it outperforms existing techniques in terms of bit error ratio (BER)

Robust Beamforming for Amplify-and-Forward MIMO Relay Systems Based on Quadratic Matrix Programming

In this paper, robust transceiver design based on minimum-mean-square-error (MMSE) criterion for dual-hop amplify-and-forward MIMO relay systems is investigated. The channel estimation errors are modeled as Gaussian random variables, and then the effect are incorporated into the robust transceiver based on the Bayesian framework. An iterative algorithm is proposed to jointly design the precoder at the source, the forward matrix at the relay and the equalizer at the destination, and the joint design problem can be efficiently solved by quadratic matrix programming (QMP).Comment: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'2010), U.S.

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

DMT Optimality of LR-Aided Linear Decoders for a General Class of Channels, Lattice Designs, and System Models

The work identifies the first general, explicit, and non-random MIMO encoder-decoder structures that guarantee optimality with respect to the diversity-multiplexing tradeoff (DMT), without employing a computationally expensive maximum-likelihood (ML) receiver. Specifically, the work establishes the DMT optimality of a class of regularized lattice decoders, and more importantly the DMT optimality of their lattice-reduction (LR)-aided linear counterparts. The results hold for all channel statistics, for all channel dimensions, and most interestingly, irrespective of the particular lattice-code applied. As a special case, it is established that the LLL-based LR-aided linear implementation of the MMSE-GDFE lattice decoder facilitates DMT optimal decoding of any lattice code at a worst-case complexity that grows at most linearly in the data rate. This represents a fundamental reduction in the decoding complexity when compared to ML decoding whose complexity is generally exponential in rate. The results' generality lends them applicable to a plethora of pertinent communication scenarios such as quasi-static MIMO, MIMO-OFDM, ISI, cooperative-relaying, and MIMO-ARQ channels, in all of which the DMT optimality of the LR-aided linear decoder is guaranteed. The adopted approach yields insight, and motivates further study, into joint transceiver designs with an improved SNR gap to ML decoding.Comment: 16 pages, 1 figure (3 subfigures), submitted to the IEEE Transactions on Information Theor