MIMO transceiver design via majorization theory
Foundations and Trends in Communications and Information Theory
MPOE Based Prefiltering and MRT Beamforming with Matched Filter Receiver for DS-CDMA Systems
Wireless Personal Communications: An International Journal
Feedback quantization for linear precoded spatial multiplexing
EURASIP Journal on Advances in Signal Processing
Minimum BER linear MIMO transceivers with adaptive number of substreams
IEEE Transactions on Signal Processing
Robust MMSE precoding in MIMO channels with pre-fixed receivers
IEEE Transactions on Signal Processing
Power allocation in team jamming games in wireless ad hoc networks
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
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This paper considers the employment of linear transceivers for communication through multiple-input multiple-output (MIMO) channels with channel state information (CSI) at both sides of the link. The design of linear MIMO transceivers has been studied since the 1970s by optimizing simple measures of the quality of the system, such as the trace of the mean-square error matrix, subject to a power constraint. Recent results showed how to solve the problem in an optimal way for the family of Schur-concave and Schur-convex cost functions. In particular, when the constellations used on the different transmit dimensions are equal, the bit-error rate (BER) averaged over these dimensions happens to be a Schur-convex function, and therefore, it can be optimally solved. In a more general case, however, when different constellations are used, the average BER is not a Schur-convex function, and the optimal design in terms of minimum BER is an open problem. This paper solves the minimum BER problem with arbitrary constellations by first reformulating the problem in convex form and then proposing two solutions. One is a heuristic and suboptimal solution, which performs remarkably well in practice. The other one is the optimal solution obtained by decomposing the convex problem into several subproblems controlled by a master problem (a technique borrowed from optimization theory), for which extremely simple algorithms exist. Thus, the minimum BER problem can be optimally solved in practice with very simple algorithms.