Multiple-input Multiple-output Multiplexing Systems Research Articles

Adaptive Parallel and Iterative QRDM Detection Algorithms for MIMO Multiplexing Systems

QR-decomposition with M-algorithm (QRDM) achieves quasi-ML performance in multiple-input multiple-output (MIMO) multiplexing systems. Nevertheless, QRDM performs avoidable computations because of i...

Error Performance of Prerake Diversity Combining-Based UWB Spatial Multiplexing MIMO Systems over Indoor Wireless Channels

In this letter, we consider a novel ultra-wideband (UWB) spatial multiplexing (SM) multiple input multiple output (MIMO) structure, which consists of prerake diversity combiners in the transmitter and a zero forcing (ZF) detector in the receiver. For a UWB SM MIMO system with N transmit antennas, M receive antennas, and L resolvable multipath components, it is shown that the proposed prerake combining-based MIMO detection scheme has the diversity order of (LN - M + 1) and its BER performance is analytically presented in a log-normal fading channel and also compared with that of a rake combining-based ZF scheme.

Hybrid-ARQ for layered space time MIMO systems with channel state information only at the receiver

The authors investigate hybrid automatic repeat request (H-ARQ) schemes for spatially multiplexed multiple-input multiple-output (MIMO) systems with channel state information available only at the receiver. In particular, the authors compare the multiple H-ARQ scheme and the single H-ARQ scheme with repetition. The authors first propose a system model for symbol detection for the multiple H-ARQ processes, and then discuss joint and separate detection algorithms for both multiple H-ARQ and single H-ARQ. Simulation results show that with linear detection the single H-ARQ outperforms multiple H-ARQ in the high signal-to-noise ratio region. With the vertical Bell Labs space-time (V-BLAST) architecture, multiple H-ARQ always outperforms single H-ARQ. Additionally, joint detection always outperforms separate detection.

A novel analytical method for maximum likelihood detection in MIMO multiplexing systems

This letter addresses the problem of symbol error probability (SEP) analysis for maximum likelihood (ML) detection in multiple-input multiple-output (MIMO) multiplexing systems. A new analytical method is presented based on the total probability theorem. The effects of imperfect channel estimation and power allocation scheme are investigated. The accuracy of the proposed method is demonstrated by Monte-Carlo simulations. It is shown that the analytical results match quite well with the simulation ones irrespective of the signal-to noise- ratio (SNR).

A New Signal Detection Method for Spatially Multiplexed MIMO Systems and Its VLSI Implementation

This brief proposes a new signal detection method called QR ordered successive interference cancellation (OSIC) with candidates (QOC) method for spatially multiplexed multiple-input-multiple-output (MIMO) systems. By using the OSIC algorithm and the maximum-likelihood (ML) metric, the proposed method achieves near-ML performance without requiring a large number of candidates. Although the proposed method can be used for both hard and soft decoding systems, it is particularly useful for soft decoding systems since the log-likelihood-ratio (LLR) values for all the bits can efficiently be computed without using LLR estimation. The proposed method is also suitable for very large scale integration (VLSI) implementation since it leads to a fixed throughput system. A QOC detector for a 4 times4 16-quadrature-amplitude-modulation (16-QAM) MIMO system has been designed and synthesized with a 0.13-mum complimentary metal-oxide-semiconductor (CMOS) technology. The implementation results show that the proposed detector reduces the hardware complexity by 73% relative to a K-best detector.

Depth-First and Breadth-First Search Based Multilevel SGA Algorithms for Near Optimal Symbol Detection in MIMO Systems

The multilevel structure of the N-QAM modulation constellations is exploited to significantly reduce the complexity of the sequential Gaussian approximation (SGA) algorithm for near optimal symbol detection in spatial multiplexing multiple- input multiple-output (MIMO) system. We propose two multilevel SGA algorithms (MSGA) which are based on depth- first search (DFS) and breadth-first search (BFS) respectively. Additionally, an important methodological contribution to this multilevel technique is proposed where the mismatch between the pseudo symbols and the true symbols is taken into consideration for the computation of posterior probabilities of symbol combinations. We justify this from a theoretical perspective as well as with numerical results. Simulation results show that the performance of the two proposed multilevel algorithms can approach that of the optimal a posteriori probability (APP) detector while its total computation cost is at most 81% and 48% of that of the original SGA algorithm for 16QAM and 64QAM modulation MIMO systems with 4 transmit/receive antennas respectively.

High-SNR Analytical Performance of Spatial Multiplexing MIMO Systems With CSI

In this paper, we investigate the average and outage performance of spatial multiplexing multiple-input multiple-output (MIMO) systems with channel state information at both sides of the link. Such systems result, for example, from exploiting the channel eigenmodes in multiantenna systems. Due to the complexity of obtaining the exact expression for the average bit error rate (BER) and the outage probability, we derive approximations in the high signal-to-noise ratio (SNR) regime assuming an uncorrelated Rayleigh flat-fading channel. More exactly, capitalizing on previous work by Wang and Giannakis, the average BER and outage probability versus SNR curves of spatial multiplexing MIMO systems are characterized in terms of two key parameters: the array gain and the diversity gain. Finally, these results are applied to analyze the performance of a variety of linear MIMO transceiver designs available in the literature.

Gaussian Approximation Based Mixture Reduction for Joint Channel Estimation and Detection in MIMO Systems

A novel Gaussian approximation based mixture reduction algorithm is proposed for semi-blind joint channel tracking and symbol detection for spatial multiplexing multiple-input multiple-output (MIMO) systems with frequency-flat time-selective channels. The proposed algorithm is based on a modified sequential Gaussian approximation detector (SGA) which takes into account channel uncertainty, and the first order generalized pseudo-Bayesian (GPB1) channel estimator. Simulation results show that the proposed algorithm performs better than the conventional and computationally expensive decision-directed method with Kalman filter based channel estimation and a posteriori probability (APP) symbol detection.

Performance evaluation of spatially multiplexed MIMO systems with subset antenna transmission in interference limited environments

Transmit antenna selection in spatially multiplexed multiple-input multiple-output (MIMO) systems is a low complexity low-rate feedback technique, which involves transmission of a reduced number of streams from the maximum possible to improve the error rate performance of linear receivers. It has been shown to be effective in enhancing the performance of single-user interference-free point-to-point MIMO systems. However, performance of transmit antenna selection techniques in interference-limited environments and over frequency selective channels is less well understood. In this paper, we investigate the performance of transmit antenna selection in spatially multiplexed MIMO systems in the presence of co-channel interference. We propose a transmission technique for the downlink of a cellular MIMO system that employs transmit antenna selection to minimize the effect of co-channel interference from surrounding cells. Several transmit antenna selection algorithms are proposed and their performance is evaluated in both frequency flat and frequency selective channels. Various antenna selection algorithms proposed in the literature for single user MIMO links are extended to a cellular scenario, where each user experiences co-channel interference from the other cells (intercell interference) in the system. For frequency selective channels, we consider orthogonal frequency division multiplexing (OFDM) with MIMO. We propose a selection algorithm that maximizes the average output SINR over all subcarriers. A method to quantify selection gain in frequency selective channel is discussed. The effect of delay spread on the selection gain is studied by simulating practical fading environments with different delay spreads. The effect of the variable signal constellation sizes and the number of transmitted streams on the bit error rate (BER) performance of the proposed system is also investigated in conjunction with the transmit antenna selection. Simulation results show that for low to moderate interference power, significant improvement in the system performance is achievable with the use of transmit antenna selection algorithms. Even though the gain due to selection in frequency selective channels is reduced compared to that in flat fading channels due to the inherent frequency diversity, the performance improvement is significant when the system is interference limited. The performance improvement due to reduced number of transmit streams at larger signal constellation sizes is found to be more significant in spatially correlated scenarios, and the gain due to selection is found to be reduced with the increased delay spread. It is found that employing transmit antenna selection algorithms in conjunction with adaptation of the number of transmitted streams and the signal constellation sizes can significantly improve the performance of MIMO systems with co-channel interference.

On the Performance of Link Adaptation Techniques in MIMO Systems

Recent research results on multiple antennas system namely the multiple input multiple output (MIMO) system show that huge potential be it the spectral efficiency or the enhancement in transmission reliability can be achieved. In this paper, with the assumption that the channel is narrowband and the channel coefficients are known at both link ends, we propose and evaluate several link adaptation (LA) algorithms applied to the spatial multiplexing MIMO systems. Depending on the requirements, the LA technique is designed to optimize either the system spectral efficiency or the bit error rate (BER). We also consider the potential use of the maximum eigen beamforming in supporting concurrent users. The results show that efficient use of the channel information could lead to a significant improvement in the system performance.

Coset detection in MIMO systems based on spatial multiplexing

For multiple-input multiple-output (MIMO) spatial multiplexing systems the complexity of the maximum likelihood detector (MLD) can be prohibitively large when the number of transmitting antennas and constellation points is high. To simplify the MLD many linear and nonlinear techniques have been proposed. In this letter the principle of reduced state sequence detection, based on mapping by set partitioning, is applied to perform detection in spatial multiplexing MIMO systems based on non-binary constellations. Simulation results are presented to show the benefits of our proposal. An upper bound to the symbol error probability is also derived.

Gaussian approximation based mixture reduction for near optimum detection in MIMO systems

The optimal soft symbol detection for spatial multiplexing multiple input multiple output (MIMO) system with known channel information requires knowledge of the marginal posterior symbol probabilities for each antenna. The calculation of these quantities requires the evaluation of the likelihood function of the system for all possible symbol combinations, which is prohibitive for large systems. It is however most often the case that most of the transmitted symbol combinations contribute only very little to these marginal posterior probabilities. We propose in this paper a suboptimal procedure which identifies the most significant symbol combinations via a sequential algorithm with Gaussian Approximation (SGA). Simulation results show that our method can approach the optimal a posteriori probability detector (APP) performance while being less complex than comparable suboptimal algorithms, such as the sphere decoder (SD). We further demonstrate that as opposed to the SD the complexity and memory requirements of our algorithm are fixed, therefore easing practical implementation.

Optimizing the Joint Transmit and Receive MMSE Design Using Mode Selection

To approach the potential multiple-input multiple-output (MIMO) capacity while optimizing the system bit-error rate (BER) performance, the joint transmit and receive minimum mean squared error (joint Tx/Rx MMSE) design has been proposed. It is the optimal linear scheme for spatial multiplexing MIMO systems, assuming a fixed number of spatial streams p as well as fixed modulation and coding across these spatial streams. However, the number of spatial streams has been arbitrarily chosen and fixed, which may lead to an inefficient power allocation strategy and a poor BER performance. In this paper, we relax the constraint of fixed number of streams p and optimize this value for the current channel realization, under the constraints of fixed total transmit power P/sub T/ and fixed rate R, what we refer to as mode selection . Based on the observation of the existence of a dominant optimal number of streams value for the considered Rayleigh flat-fading MIMO channel model, we further propose an average mode selection that avoids the per-channel adaptation through using the latter dominant value for all channel realizations. Finally, we exhibit the significant BER improvement provided by our mode selection over the conventional joint Tx/Rx MMSE design. Such significant improvement is due to the better exploitation of the MIMO spatial diversity and the more efficient power allocation enabled by our mode selection.