Multi-input Multi-output Detection Research Articles

Block-Mode Lattice Reduction for Low-Complexity MIMO Detection

We propose a very-low-complexity lattice-reduction (LR) algorithm for multi-input multi-output detection in time-varying channels. The proposed scheme reduces the complexity by performing LR in a block-wise manner. The proposed scheme takes advantage of the temporal correlation of the channel matrices in a block and its impact on the lattice transformation matrices during the LR process. From this, the proposed scheme can skip a number of redundant LR processes for consecutive channel matrices and performs a single LR in a block. As the Doppler frequency decreases, the complexity reduction efficiency becomes more significant.

Loop‐Reduction LLL Algorithm and Architecture for Lattice‐Reduction‐Aided MIMO Detection

We propose a loop‐reduction LLL (LR‐LLL) algorithm for lattice‐reduction‐aided (LRA) multi‐input multioutput (MIMO) detection. The LLL algorithm is an iterative algorithm that contains many check and process operations; however, the traditional LLL algorithm itself possesses a lot of redundant check operations. To solve this problem, we propose a look‐ahead check technique that not only reduces the complexity of the LLL algorithm but also produces the lattice‐reduced matrix which obeys the original LLL criterion. Simulation results show that the proposed LR‐LLL algorithm reduces the average number of loops or computation complexity. Besides, it also shortens the latency of clock cycles about 19.4%, 29.1%, and 46.1% for 4 × 4, 8 × 8, and 12 × 12 MIMO systems, respectively.

A simple soft linear detection for coded multi‐input multi‐output systems

ABSTRACTWe propose a very simple and efficient soft linear multi‐input multi‐output (MIMO) detection scheme. The detection process is divided into two separate problems. The proposed scheme first detects MIMO symbols using conventional linear detection methods and produces soft bit information using a simple soft demapping method. Next, we refine the soft information by accounting for uneven post‐detection noise variance across MIMO layers. From the simulation result investigated in this paper, we first emphasize that powerful channel coding may suppress the differences of diversity gains among various MIMO detection schemes. This implies that the channel decoding operation may not be transparent to performance gain that resulted from MIMO detection process. The proposed scheme concentrates on accurate estimation of soft post‐MIMO detected information in a very simple manner, rather than concentrating on a complex MIMO detection scheme prior to decoding process. In combination with turbo codes, the proposed scheme produces comparable performance to maximum likelihood detection, even with the simplest scheme such as zero forcing detection, with drastically reduced complexity. Copyright © 2011 John Wiley & Sons, Ltd.

부호화된 MIMO 시스템에서 연판정 채널 이득값의 계산

현대 디지털 통신 시스템에서는 여러 가지 열화 조건을 극복하기 위하여 거의 모든 시스템에서 오류정정 부호를 사용하며, 최근에는 반복적인 복호 기법으로 우수한 복호 성능 도출이 가능한 터보부호나 저밀도 패리티 검사부호 등을 사용하는 시스템이 늘고 있는 추세이다. 특히 반복적인 복호 방법으로 성능을 향상 시키는 부호를 다중안테나 시스템에서 사용하기 위해서는 수신단에서 송선 신호를 검출할 때 연판정된 채널 이득값의 계산이 필수적으로 요구된다. 본 논문에서는 반복 복호 기법을 사용하는 다중안테나 시스템에서 효과적으로 연판정 값을 검출하는 방법에 대해 살펴보고, 여러 가지 MIMO 검출 방법에 대하여 그 유도 과정을 제시한다. 또한, 유도된 연판정 검출 방법에 대한 시뮬레이션 결과를 제시하여, 제안된 방식이 적은 복잡도로 우수한 성능을 얻을 수 있음을 보인다. Modem digital communication systems are required to use forward error correction (FEC) codes to combat inevitable channel impairment. Turbo codes or low density parity check (LDPC) codes, using iterative decoding with soft decision detection (SDD) information, are the most common examples. The excellent performance of these codes should be conditioned on accurate estimation of soft decision detection information. In order to use FEC codes with iterative decoding for Multi-Input Multi-Output (MIMO) system, reliable soft decision channel gain should be provided. In this paper, we investigate efficient SDD methods for turbo-coded MIMO system, and derive the corresponding formulas of SDD for various MIMO detection schemes. We present simulation results of the derived SDD schemes for turbo-coded MIMO systems, and show that the presented results almost approximate to maximum likelihood detection performance with much less computational load.

Area-Efficient Antenna-Scalable MIMO Detector for K-best Sphere Decoding

K-best sphere decoding is one of the most popular MIMO (Multi-Input Multi-Output) detection algorithms because of its low complexity and close to Maximum Likelihood (ML) Bit Error Rate (BER) performance. Unfortunately, conventional multi-stage sphere decoders suffer from the inability to adapt to varying antenna configurations, requiring implementation redesign for each specific array structure. In this paper, we propose a reconfigurable in-place architecture that is scalable to an arbitrary number of antennas at run-time, while reducing area significantly compared with other sphere decoders. To improve the throughput of the in-place architecture without any degradation in BER performance, we propose partial-sort-bypass and symbol interleaving techniques, and also exploit multi-core design. Implementation results for a 16-QAM MIMO decoder in a 130 nm CMOS technology show a 41% reduction in area compared to the smallest sphere decoder while maintaining antenna reconfigurability, and better throughput. When implemented for the 802.11n standard, our architecture results in 42% reduction in area compared to the multi-stage architecture.

A Simplified Maximum Likelihood Detector with the Aid of Reduced-Size Searching Set for MIMO Systems

In this paper, a simplified maximum likelihood (ML) detector is proposed for multi-input multi-output (MIMO) systems over the Rayleigh flat-fading channels. The main idea of the proposed method is to pre-construct a reduced-size searching set for the optimal ML detection to reduce its computational load without significant performance loss. This can be done by a two-stage searching algorithm combined with the ordered successive interference cancellation scheme. Computer simulations show that with an adequate size of searching set, the proposed simplified ML detection can outperform the conventional MIMO detectors and achieve the same performance as that of the optimal ML detection.

Efficient Ordering Schemes for High-Throughput MIMO Detectors

The order of the channel matrix columns is an important factor that affects the computational complexity, memory requirement, and detection error rate of multi-input multi-output (MIMO) detectors. Novel efficient ordering schemes proposed in our previous work lead to advantages in the computational complexity and memory requirement of various maximum likelihood (ML) MIMO detectors. In this paper, we apply these ordering schemes to the K-Best detector--a near-ML MIMO detector suitable for high-throughput hardware implementations--and show that our novel ordering schemes improve the performance of the K-Best detector, especially when K is small. With a given detection error rate, our ordering schemes either lead to signal-to-noise ratio (SNR) gains, or allow even smaller K. Two of these ordering schemes, which can be easily embedded into the QR decomposition, are implemented in hardware. We adopt Givens based ordering schemes, due to their numerical stability when fixed point representations are used. Our hardware implementation results show that our novel ordering schemes incur negligible overheads and are particularly suitable for high-throughput implementations.

Asymmetrically optimised poly-diagonalisation for low complexity MIMO detection

A low-complexity multi-input multi-output (MIMO) detector, based on asymmetric poly-diagonalisation and trellis decoding, is investigated. The proposed scheme is an improved version of the MIMO detection based on ‘channel shortening’ and trellis decoding. The channel shortening is performed asymmetrically for both forward and backward recursion.

Near optimal MIMO detection with reduced search space

A multi-input multi-output (MIMO) detection scheme that requires considerable low complexity but still achieves the near optimal performance is proposed. The fundamental idea of the proposed MIMO detection scheme consists of two points: 1) the computational complexity is restrained by a complexity limit in low signal-to-noise ratio (SNR) region; 2) while in high SNR region, the complexity is significantly reduced by the proposed search space method. Comparing with existing fixed-complexity techniques of MIMO detection (e.g., K-best sphere detector and reduced-search maximum-likelihood (RS ML) detection), the significant benefit of proposed detection scheme is that less computational power will be spent for the given data rate, or the throughput of detector can be increased for high SNR cases. According to the simulation results, the near optimal performance can be obtained while the detection complexity is kept considerable small.

Dual-Lattice Ordering and Partial Lattice Reduction for SIC-Based MIMO Detection

In this paper, we propose low-complexity lattice detection algorithms for successive interference cancelation (SIC) in multi-input multi-output (MIMO) communications. First, we present a dual-lattice view of the vertical Bell Labs Layered Space-Time (V-BLAST) detection. We show that V-BLAST ordering is equivalent to applying sorted QR decomposition to the dual basis, or equivalently, applying sorted Cholesky decomposition to the associated Gram matrix. This new view results in lower detection complexity and allows simultaneous ordering and detection. Second, we propose a partial reduction algorithm that only performs lattice reduction for the last several, weak substreams, whose implementation is also facilitated by the dual-lattice view. By tuning the block size of the partial reduction (hence the complexity), it can achieve a variable diversity order, hence offering a graceful tradeoff between performance and complexity for SIC-based MIMO detection. Numerical results are presented to compare the computational costs and to verify the achieved diversity order.

Complex Lattice Reduction Algorithm for Low-Complexity Full-Diversity MIMO Detection

Recently, lattice-reduction-aided detectors have been proposed for multiinput multioutput (MIMO) systems to achieve performance with full diversity like the maximum likelihood receiver. However, these lattice-reduction-aided detectors are based on the traditional Lenstra-Lenstra-Lovasz (LLL) reduction algorithm that was originally introduced for reducing real lattice bases, in spite of the fact that the channel matrices are inherently complex-valued. In this paper, we introduce the complex LLL algorithm for direct application to reducing the basis of a complex lattice which is naturally defined by a complex-valued channel matrix. We derive an upper bound on proximity factors, which not only show the full diversity of complex LLL reduction-aided detectors, but also characterize the performance gap relative to the lattice decoder. Our analysis reveals that the complex LLL algorithm can reduce the complexity by nearly 50% compared to the traditional LLL algorithm, and this is confirmed by simulation. Interestingly, our simulation results suggest that the complex LLL algorithm has practically the same bit-error-rate performance as the traditional LLL algorithm, in spite of its lower complexity.

The Equivalence of Semidefinite Relaxation MIMO Detectors for Higher-Order QAM

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In multi-input multi-output (MIMO) detection, semidefinite relaxation (SDR) has been shown to be an efficient high-performance approach. For BPSK and QPSK, it has been found that SDR can provide near-optimal bit error probability performance. This has stimulated a number of recent research endeavors that aim to apply SDR to the high-order QAM cases. These independently developed SDRs are different in concept, structure and complexity, and presently no serious analysis has been given to compare these methods. This paper analyzes the relationship of three such SDR methods, namely the polynomial-inspired SDR (PI-SDR) by Wiesel <etal/>, the bound-constrained SDR (BC-SDR) by Sidiropoulos and Luo, and the virtually-antipodal SDR (VA-SDR) by Mao <etal/> Rather unexpectedly, we prove that the three SDRs are equivalent in the following sense: The three SDRs yield the same optimal objective values, and their optimal solutions have strong correspondences. Specifically, we establish this solution equivalence between BC-SDR and VA-SDR for any <formula formulatype="inline"><tex Notation="TeX">$4^{q}$</tex> </formula>-QAM constellations, and that between BC-SDR and PI-SDR for 16-QAM and 64-QAM. Moreover, the equivalence result holds for any channel, problem size, and signal-to-noise ratio. Our theoretical findings are confirmed by simulations, where the three SDRs offer identical symbol error probabilities. Additional simulation results are also provided to demonstrate the effectiveness of SDR compared to some other MIMO detectors, in terms of complexity and symbol error performance. </para>

Symbol detection in spatial multiplexing system using particle swarm optimization meta‐heuristics

AbstractSymbol detection in multi‐input multi‐output (MIMO) communication systems using different particle swarm optimization (PSO) algorithms is presented. This approach is particularly attractive as particle swarm intelligence is well suited for real‐time applications, where low complexity and fast convergence is of absolute importance. While an optimal maximum likelihood (ML) detection using an exhaustive search method is prohibitively complex, PSO‐assisted MIMO detection algorithms give near‐optimal bit error rate (BER) performance with a significant reduction in ML complexity. The simulation results show that the proposed detectors give an acceptable BER performance and computational complexity trade‐off in comparison with ML detection. These detection techniques show promising results for MIMO systems using high‐order modulation schemes and more transmitting antennas where conventional ML detector becomes computationally non‐practical to use. Hence, the proposed detectors are best suited for high‐speed multi‐antenna wireless communication systems. Copyright © 2008 John Wiley &amp; Sons, Ltd.