On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
On the sphere-decoding algorithm I. Expected complexity
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing - Part I
Iterative decoding for MIMO channels via modified sphere decoding
IEEE Transactions on Wireless Communications
On Some Near Optimal Low Complexity Detectors for MIMO Fading Channels
IEEE Transactions on Wireless Communications
IEEE Transactions on Information Theory
A universal lattice code decoder for fading channels
IEEE Transactions on Information Theory
Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels
IEEE Transactions on Information Theory
Algorithm and implementation of the K-best sphere decoding for MIMO detection
IEEE Journal on Selected Areas in Communications
A tree-search algorithm for ML decoding in underdetermined MIMO systems
ISWCS'09 Proceedings of the 6th international conference on Symposium on Wireless Communication Systems
Prevoting cancellation-based detection for underdetermined MIMO systems
EURASIP Journal on Wireless Communications and Networking
Low-complexity dominance-based sphere decoder for MIMO systems
Signal Processing
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Maximum-likelihood (ML) detection is guaranteed to yield minimum probability of erroneous detection and is thus of great importance for both multiuser detection and space-time decoding. For multiple-input multiple-output (MIMO) antenna systems where the number of receive antennas is at least the number of signals multiplexed in the spatial domain, ML detection can be done efficiently using sphere decoding. Suboptimal detectors are also well known to have reasonable performance at low complexity. It is, nevertheless, much less understood for obtaining good detection at affordable complexity if there are less receive antennas than transmitted signals (i.e., underdetermined MIMO systems). In this paper, our aim is to develop an effcient detection strategy that can achieve near ML performance for underdetermined MIMO systems. Our method is based on the geometrical understanding that the ML point happens to be a point that is "close" to the decoding hyperplane in all directions. The fact that such proximity-close points are much less is used to devise a decoding method that promises to greatly reduce the decoding complexity while achieving near ML performance. An average-case complexity analysis based on Gaussian approximation is also given.