MIMO Detection for High-Order QAM Based on a Gaussian Tree Approximation

This paper proposes a new detection algorithm for MIMO communication systems employing high order QAM constellations. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete graph. Hence, a straightforward application of the Belief Propagation (BP) algorithm yields very poor results. Our algorithm is based on an optimal tree approximation of the Gaussian density of the unconstrained linear system. The finite-set constraint is then applied to obtain a loop-free discrete distribution. It is shown that even though the approximation is not directly applied to the exact discrete distribution, applying the BP algorithm to the loop-free factor graph outperforms current methods in terms of both performance and complexity. The improved performance of the proposed algorithm is demonstrated on the problem of MIMO detection

Power-efficient space shift keying transmission via semidefinite programming

Space shift keying (SSK) transmission is a low-complexity complement to spatial modulation (SM) that solely relies on a spatial-constellation diagram for conveying information. The achievable performance of SSK is determined by the channel conditions, which in turn define the minimum Euclidean distance (MED) of the symbols in the received SSK constellation. In this contribution we concentrate on improving the power efficiency of SSK transmission via symbol pre-scaling. Specifically, we pose a pair of related optimization problems for a) enhancing the MED at reception while satisfying a given power constraint at the transmitter, and b) reducing the transmission power required for achieving a given MED. The resultant optimization problems are NP-hard, hence they are subsequently reformulated and solved via semidefinite programming. The results presented demonstrate that the proposed pre-scaling strategies are capable of enhancing the attainable performance of conventional SSK, while simultaneously extending its applicability and reducing the complexity of the existing pre-scaling schemes

Graph-Based Decoding in the Presence of ISI

We propose an approximation of maximum-likelihood detection in ISI channels based on linear programming or message passing. We convert the detection problem into a binary decoding problem, which can be easily combined with LDPC decoding. We show that, for a certain class of channels and in the absence of coding, the proposed technique provides the exact ML solution without an exponential complexity in the size of channel memory, while for some other channels, this method has a non-diminishing probability of failure as SNR increases. Some analysis is provided for the error events of the proposed technique under linear programming.Comment: 25 pages, 8 figures, Submitted to IEEE Transactions on Information Theor