On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
Advanced receiver algorithms for MIMO wireless communications
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Efficient detection algorithms for MIMO channels: a geometrical approach to approximate ML detection
IEEE Transactions on Signal Processing
Iterative decoding of binary block and convolutional codes
IEEE Transactions on Information Theory
Bit-interleaved coded modulation
IEEE Transactions on Information Theory
Instrumentable tree encoding of information sources (Corresp.)
IEEE Transactions on Information Theory
On maximum-likelihood detection and the search for the closest lattice point
IEEE Transactions on Information Theory
A unified framework for tree search decoding: rediscovering the sequential decoder
IEEE Transactions on Information Theory
Algorithm and implementation of the K-best sphere decoding for MIMO detection
IEEE Journal on Selected Areas in Communications
Soft-output sphere decoding: algorithms and VLSI implementation
IEEE Journal on Selected Areas in Communications
Novel detector implementations for 3G LTE downlink and uplink
Analog Integrated Circuits and Signal Processing
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A list sphere decoder (LSD) can be used to approximate the optimal maximum a posteriori (MAP) detector for the detection of multiple-input multiple-output (MIMO) signals. In this paper, we consider two LSD algorithms with different search methods and study some algorithm design choices which relate to the performance and computational complexity of the algorithm. We show that by limiting the dynamic range of log-likelihood ratio, the required LSD list size can be lowered, and, thus, the complexity of the LSD algorithm is decreased. We compare the real and the complex-valued signal models and their impact on the complexity of the algorithms. We show that the real-valued signal model is clearly the less complex choice and a better alternative for implementation. We also show the complexity of the sequential search LSD algorithm can be reduced by limiting the maximum number of checked nodes without sacrificing the performance of the system. Finally, we study the complexity and performance of an iterative receiver, analyze the tradeoff choices between complexity and performance, and show that the additional computational cost in LSD is justified to get better soft-output approximation.