Performance tradeoffs in the VLSI implementation of the sphere decoding algorithm

IEEE Trans. Wireless Commun.

2008

Abstract-We present a unified detection framework for spatial multiplexing multiple-input multiple-output (MIMO) systems by generalizing Heller's classical feedback decoding algorithm for convolutional codes. The resulting generalized feedback detector (GFD) is characterized by three parameters: window size, step size and branch factor. Many existing MIMO detectors are turned out to be special cases of the GFD. Moreover, different parameter choices can provide various performance-complexity tradeoffs. The connection between MIMO detectors and tree search algorithms is also established. To reduce redundant computations in the GFD, a shared computation technique is proposed by using a tree data structure. Using a union bound based analysis of the symbol error rates, the diversity order and signal-to-noise ratio (SNR) gain are derived analytically as functions of the three parameters; for example, the diversity order of the GFD varies between 1 and N . The complexity of the GFD varies between those of the maximum-likelihood (ML) detector and the zero-forcing decision feedback detector (ZF-DFD). Extensive computer simulation results are also provided.Index Terms-MIMO, feedback decoding, decision feedback detector.

Section: V N ]mentioning

confidence: 99%

“…The average complexity of the SD derived in [17], [18] is exponential with n but low with a high SNR. The SD has been realized on VLSI circuits in [24], [25]. For VLSI circuits implementation, the throughput is limited by the worst-case complexity.…”

Section: B Computational Complexity Analysismentioning

confidence: 99%

Generalized feedback detection for spatial multiplexing multi-antenna systems

IEEE Trans. Wireless Commun.

2008

“…These requirements are met by the sphere decoder (SD) [2], with basic versions such as the Fincke-Pohst SD (FP-SD) and the more efficient Schnorr-Euchner SD (SE-SD) 1 [4], [5]. Hardware implementation details of the SD can be found in [6]. Although it avoids the exponential complexity of exhaustive search maximum-likelihood (ML) detection [7], the SD's average complexity is exponential in the number of transmit antennas [8]- [22].…”

Section: Introductionmentioning

confidence: 99%

Probability-Distribution-Based Node Pruning for Sphere Decoding

Han

IEEE Trans. Veh. Technol.

2013

Node pruning strategies based on probability distributions are developed for maximum-likelihood (ML) detection for spatial-multiplexing multiple-input-multiple-output (MIMO) systems. Uniform pruning, geometric pruning, threshold pruning, hybrid pruning, and depth-dependent pruning are thus developed in detail. By considering the symbol error probability in the high signal-to-noise ratio (SNR) region, the desirable diversity order of uniform pruning and the threshold level for threshold pruning are derived. Simulation results show that threshold pruning saves complexity compared with popular sphere decoder (SD) algorithms, such as the K-best SD, the fixed-complexity SD (FSD), and the probabilistic tree pruning SD (PTP-SD), particularly for high SNRs and large-antenna MIMO systems. Furthermore, our proposed node pruning strategies may also be applied to other systems, including coded MIMO systems and relay networks. Index Terms-Maximum likelihood (ML), multiple-inputmultiple-output (MIMO), sphere decoder (SD), statistical pruning, wireless communications.Tao Cui (S'04) received the M.Sc. degree from the University of Alberta, Edmonton, AB, Canada, in 2005 and the M.S. and Ph.D. degrees from California Institute of Technology, Pasadena, in 2006 and 2009, respectively. His research interests include the interactions between networking theory, communication theory, and information theory.Shuangshuang Han (S'11) received the B.Eng. degree in communication engineering and the M.Eng. degree in communication and information systems from Shandong University,

“…In [2], sphere decoding (SD), an algorithm for the MLD, is proposed for attaining low complexity in high SNR. Although the VLSI implementation of SD has been reported [3], the variation of its time complexity can be high, leading to undesirable variable detection delays. Alternative detectors with constant time complexity are thus desirable.…”

Section: Introductionmentioning

confidence: 99%

Linear Programming Detection and Decoding for MIMO Systems

2006 IEEE International Symposium on Information Theory

2006

Abstract-1 We develop an efficient linear programming detector (LPD) for multiple-input multiple-output (MIMO) systems. Instead of using the usual l2 norm, our proposed LPD uses the l1 norm as the detection metric, resulting in a mixed-integer linear program (MILP). Two branch-and-bound algorithms are proposed to solve the MILP. The solution of the MILP achieves the same full diversity order as the maximum likelihood detector. The MILP is further relaxed to a linear program (LP), which can be readily solved using the standard simplex method. We show that in some cases the solution of the LP is guaranteed to be that of the MILP. The LPD is also extended to the joint detection and decoding of linear block coded MIMO systems. Our LPD can be immediately implemented using mature circuits design for the simplex algorithm.