Sphere-constrained ML detection for frequency-selective channels

The maximum-likelihood (ML) sequence detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the transmitted sequence, but exponential in the channel memory length. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly, and has expected complexity which is a low-degree polynomial (often cubic) in the length of the transmitted sequence over a wide range of signal-to-noise ratios. We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity determined by the VA, but often significantly lower expected complexity

Performance Evaluation of MIMO Spatial Multiplexing Detection Techniques

Multiple Input Multiple Output (MIMO) multiplexing is a promising technology that can greatly increase the channel capacity without additional spectral resources. The challenge is to design detection algorithms that can recover transmitted signals with acceptable complexity and high performance. In this paper, several MIMO Spatial Multiplexing (SM) detection techniques are introduced and evaluated in terms of BER. Different aspects have been considered and discussed in this evaluation such as; signal to noise ratio, number of transmit and receive antennas. The performance comparisons and graphs have been generated using an optimized simulator. This simulator has been developed using MATLAB®