Low-complexity dominance-based Sphere Decoder for MIMO Systems

The sphere decoder (SD) is an attractive low-complexity alternative to maximum likelihood (ML) detection in a variety of communication systems. It is also employed in multiple-input multiple-output (MIMO) systems where the computational complexity of the optimum detector grows exponentially with the number of transmit antennas. We propose an enhanced version of the SD based on an additional cost function derived from conditions on worst case interference, that we call dominance conditions. The proposed detector, the king sphere decoder (KSD), has a computational complexity that results to be not larger than the complexity of the sphere decoder and numerical simulations show that the complexity reduction is usually quite significant

Reduced-complexity maximum-likelihood decoding for 3D MIMO code

The 3D MIMO code is a robust and efficient space-time coding scheme for the distributed MIMO broadcasting. However, it suffers from the high computational complexity if the optimal maximum-likelihood (ML) decoding is used. In this paper we first investigate the unique properties of the 3D MIMO code and consequently propose a simplified decoding algorithm without sacrificing the ML optimality. Analysis shows that the decoding complexity is reduced from O(M^8) to O(M^{4.5}) in quasi-static channels when M-ary square QAM constellation is used. Moreover, we propose an efficient implementation of the simplified ML decoder which achieves a much lower decoding time delay compared to the classical sphere decoder with Schnorr-Euchner enumeration.Comment: IEEE Wireless Communications and Networking Conference (WCNC 2013), Shanghai : China (2013

Achieving Low-Complexity Maximum-Likelihood Detection for the 3D MIMO Code

The 3D MIMO code is a robust and efficient space-time block code (STBC) for the distributed MIMO broadcasting but suffers from high maximum-likelihood (ML) decoding complexity. In this paper, we first analyze some properties of the 3D MIMO code to show that the 3D MIMO code is fast-decodable. It is proved that the ML decoding performance can be achieved with a complexity of O(M^{4.5}) instead of O(M^8) in quasi static channel with M-ary square QAM modulations. Consequently, we propose a simplified ML decoder exploiting the unique properties of 3D MIMO code. Simulation results show that the proposed simplified ML decoder can achieve much lower processing time latency compared to the classical sphere decoder with Schnorr-Euchner enumeration

Iterative Near-Maximum-Likelihood Detection in Rank-Deficient Downlink SDMA Systems

Abstract—In this paper, a precoded and iteratively detected downlink multiuser system is proposed, which is capable of operating in rankdeficient scenarios, when the number of transmitters exceeds the number of receivers. The literature of uplink space division multiple access (SDMA) systems is rich, but at the time of writing there is a paucity of information on the employment of SDMA techniques in the downlink. Hence, we propose a novel precoded downlink SDMA (DL-SDMA) multiuser communication system, which invokes a low-complexity nearmaximum-likelihood sphere decoder and is particularly suitable for the aforementioned rank-deficient scenario. Powerful iterative decoding is carried out by exchanging extrinsic information between the precoder’s decoder and the outer channel decoder. Furthermore, we demonstrate with the aid of extrinsic information transfer charts that our proposed precoded DL-SDMA system has a better convergence behavior than its nonprecoded DL-SDMA counterpart. Quantitatively, the proposed system having a normalized system load of Ls = 1.333, i.e., 1.333 times higher effective throughput facilitated by having 1.333 times more DL-SDMA transmitters than receivers, exhibits a “turbo cliff” at an Eb/N0 of 5 dB and hence results in an infinitesimally low bit error rate (BER). By contrast, at Eb/N0 = 5 dB, the equivalent system dispensing with precoding exhibits a BER in excess of 10%. Index Terms—Iterative decoding, maximum likelihood detection, space division multiple access (SDMA) downlink, sphere decoding

Decoding by Sampling: A Randomized Lattice Algorithm for Bounded Distance Decoding

Despite its reduced complexity, lattice reduction-aided decoding exhibits a widening gap to maximum-likelihood (ML) performance as the dimension increases. To improve its performance, this paper presents randomized lattice decoding based on Klein's sampling technique, which is a randomized version of Babai's nearest plane algorithm (i.e., successive interference cancelation (SIC)). To find the closest lattice point, Klein's algorithm is used to sample some lattice points and the closest among those samples is chosen. Lattice reduction increases the probability of finding the closest lattice point, and only needs to be run once during pre-processing. Further, the sampling can operate very efficiently in parallel. The technical contribution of this paper is two-fold: we analyze and optimize the decoding radius of sampling decoding resulting in better error performance than Klein's original algorithm, and propose a very efficient implementation of random rounding. Of particular interest is that a fixed gain in the decoding radius compared to Babai's decoding can be achieved at polynomial complexity. The proposed decoder is useful for moderate dimensions where sphere decoding becomes computationally intensive, while lattice reduction-aided decoding starts to suffer considerable loss. Simulation results demonstrate near-ML performance is achieved by a moderate number of samples, even if the dimension is as high as 32