Performance Of Spatial Multiplexing Systems Research Articles

Soft Log Likelihood Ratio Replacement for Low-Complexity Maximum-Likelihood Detection

For multiple-input multiple-output systems, spatial multiplexing techniques can provide significant throughput gain. However, this promised gain can be attained by using maximum-likelihood detection (MLD) which requires huge computational complexity, and thus several low-complexity schemes are proposed to reduce the search space of the MLD. In this letter, we consider the log likelihood ratio (LLR) computation in low complexity MLD for soft channel decoding. We design a weighted combining method for a reliable LLR computation, compensating for the performance loss incurred by insufficient candidates of low-complexity MLD. The proposed LLR generation algorithm effectively reduces the computational complexity while maintaining the system performance of spatial multiplexing systems.

Antenna Selection for MIMO–OFDM WLAN Systems

This paper discusses the packet error rate (PER) performance of multiple-input multiple-output (MIMO) wireless systems. We focus our discussion on communication systems based on the IEEE 802.11a/g standard. In particular, we study the performance of spatial multiplexing systems with joint encoding at the transmitter and linear detection at the receiver. We show that spatial multiplexing systems based on minimum mean square error (MMSE) or zero forcing (ZF) demultiplexing benefit greatly from antenna subset selection. These results agree with recent analytical results showing the equivalence in diversity order between a full system (all receive antennas) and a system with antenna selection.

Multimode antenna selection for spatial multiplexing systems with linear receivers

Spatial multiplexing is a simple transmission technique for multiple-input multiple-output (MIMO) wireless communication links in which data is multiplexed across the transmit antennas. In Rayleigh fading matrix channels, however, spatial multiplexing with low-complexity linear receivers suffers due to a lack of diversity advantage. This paper proposes multimode antenna selection, which uses a low-rate feedback channel to improve the error rate performance of spatial multiplexing systems with linear receivers. In the proposed technique, both the number of substreams and the mapping of substreams to antennas are dynamically adjusted, for a fixed total data rate, to the channel based on limited feedback from the receiver. Dual-mode selection, where spatial multiplexing or selection diversity is adaptively chosen, dramatically improves the diversity gain achieved. Multimode selection (i.e., allowing any number of substreams to be dynamically selected) provides additional array gain. Various criteria for selecting the number of substreams and the optimal mapping of substreams to transmit antennas are derived. Relationships are made between the selection criteria and the eigenmodes of the channel. A probabilistic analysis of the selection criteria are provided for Rayleigh fading channels. Applications to nonlinear receivers are mentioned. Monte Carlo simulations demonstrate significant performance improvements in independent and identically distributed (i.i.d.) flat-fading Rayleigh matrix channels with minimal feedback.