Low-Complexity Detection/Equalization in Large-Dimension MIMO-ISI Channels Using Graphical Models

In this paper, we deal with low-complexity near-optimal detection/equalization in large-dimension multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels using message passing on graphical models. A key contribution in the paper is the demonstration that near-optimal performance in MIMO-ISI channels with large dimensions can be achieved at low complexities through simple yet effective simplifications/approximations, although the graphical models that represent MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1) use of Markov Random Field (MRF) based graphical model with pairwise interaction, in conjunction with {\em message/belief damping}, and 2) use of Factor Graph (FG) based graphical model with {\em Gaussian approximation of interference} (GAI). The per-symbol complexities are $O(K^2n_t^2)$ and $O(Kn_t)$ for the MRF and the FG with GAI approaches, respectively, where $K$ and $n_t$ denote the number of channel uses per frame, and number of transmit antennas, respectively. These low-complexities are quite attractive for large dimensions, i.e., for large $Kn_t$. From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing $Kn_t$. Also, we show that these message passing algorithms can be used in an iterative manner with local neighborhood search algorithms to improve the reliability/performance of $M$-QAM symbol detection

Improving MIMO detection performance in presence of phase noise using norm difference criterion

International audience— Practical MIMO communication systems suffer performance loss from oscillator phase noise. In particular, traditional maximum likelihood (ML) detection algorithm results in an error floor in symbol error probability, and thus becomes unable to harvest the spatial diversity to be obtained in MIMO systems without phase noise. In this paper, we propose a method to detect the correctness of the traditional ML solution in the presence of strong phase noise. A criteria based on the ML cost differences between the ML solution and the next best solutions is used to determine a set of possible candidate solutions. We also propose a novel algorithm for data detection using phase noise estimation techniques to obtain an modified ML cost for each of the candidate solutions. This approach results in symbol error rate performance improvement by reducing the error floor without incurring much additional complexity due to phase noise estimation. Theoretical arguments as well as simulation studies are presented to support the performance improvement achieved

On Generalized Spatial Modulation

Generalized spatial modulation (GSM) is a relatively new modulation scheme for multi-antenna wireless communications. It is quite attractive because of its ability to work with less number of transmit RF chains compared to traditional spatial multiplexing (V-BLAST system). In this paper, we show that, by using an optimum combination of number of transmit antennas (N-t) and number of transmit RF chains (N-rf), GSM can achieve better throughput and/or bit error rate (BER) than spatial multiplexing. First, we quantify the percentage savings in the number of transmit RF chains as well as the percentage increase in the rate achieved in GSM compared to spatial multiplexing; 18.75% savings in number of RF chains and 9.375% increase in rate are possible with 16 transmit antennas and 4-QAM modulation. A bottleneck, however, is the complexity of maximum-likelihood (ML) detection of GSM signals, particularly in large MIMO systems where the number of antennas is large. We address this detection complexity issue next. Specifically, we propose a Gibbs sampling based algorithm suited to detect GSM signals. The proposed algorithm yields impressive BER performance and complexity results. For the same spectral efficiency and number of transmit RF chains, GSM with the proposed detection algorithm achieves better performance than spatial multiplexing with ML detection

Generalized space and frequency index modulation

Unlike in conventional modulation where information bits are conveyed only through symbols from modulation alphabets defined in the complex plane (e.g., quadrature amplitude modulation (QAM), phase shift keying (PSK)), in index modulation (IM), additional information bits are conveyed through indices of certain transmit entities that get involved in the transmission. Transmit antennas in multi-antenna systems and subcarriers in multi-carrier systems are examples of such transmit entities that can be used to convey additional information bits through indexing. In this paper, we introduce generalized space and frequency index modulation, where the indices of active transmit antennas and subcarriers convey information bits. We first introduce index modulation in the spatial domain, referred to as generalized spatial index modulation (GSIM). For GSIM, where bits are indexed only in the spatial domain, we derive the expression for achievable rate as well as easy-to-compute upper and lower bounds on this rate. We show that the achievable rate in GSIM can be more than that in spatial multiplexing, and analytically establish the condition under which this can happen. It is noted that GSIM achieves this higher rate using fewer transmit radio frequency (RF) chains compared to spatial multiplexing. We also propose a Gibbs sampling based detection algorithm for GSIM and show that GSIM can achieve better bit error rate (BER) performance than spatial multiplexing. For generalized space-frequency index modulation (GSFIM), where bits are encoded through indexing in both active antennas as well as subcarriers, we derive the achievable rate expression. Numerical results show that GSFIM can achieve higher rates compared to conventional MIMO-OFDM. Also, BER results show the potential for GSFIM performing better than MIMO-OFDM

Lattice reduction aided detection in large-MIMO systems

Lattice reduction (LR) aided detection algorithms are known to achieve the same diversity order as that of maximum-likelihood (ML) detection at low complexity. However, they suffer SNR loss compared to ML performance. The SNR loss is mainly due to imperfect orthogonalization and imperfect nearest neighbor quantization. In this paper, we propose an improved LR-aided (ILR) detection algorithm, where we specifically target to reduce the effects of both imperfect orthogonalization and imperfect nearest neighbor quantization. The proposed ILR detection algorithm is shown to achieve near-ML performance in large-MIMO systems and outperform other LR-aided detection algorithms in the literature. Specifically, the SNR loss incurred by the proposed ILR algorithm compared to ML performance is just 0.1 dB for 4-QAM and < 0.5 dB for 16-QAM in 16 x 16 V-BLAST MIMO system. This performance is superior compared to those of other LR-aided detection algorithms, whose SNR losses are in the 2 dB to 9 dB range

Generalized bidirectional multi-pair multi-antennawireless network coding

In this paper, we focus on increasing the throughput and diversity of network coded MIMO transmissions in bidirectional multi-pair wireless relay networks. All nodes have multi-antenna capability. Pairs of nodes want to exchange messages via a relay having multi-antenna and encoding/decoding capability. Nodes transmit their messages to the relay in the first (MAC) phase. The relay decodes all the messages and XORs them and broadcasts the XORed message in the second (BC) phase. We develop a generalized framework for bidirectional multi-pair multi-antenna wireless network coding, which models different MIMO transmission schemes including spatial multiplexing (V-BLAST), orthogonal STBC (OSTBC), and non-orthogonal STBC (NO-STBC) in a unified way. Enhanced throughputs are achieved by allowing all nodes to simultaneously transmit at their full rate. High diversity orders are achieved through the use of NO-STBCs, characterized by full rate and full transmit diversity. We evaluate and compare the performance of VBLAST, OSTBC, and NO-STBC schemes in one-dimensional 1-pair linear network (one pair of nodes and a relay) and two-dimensional 2-pair `cross' network (two pairs of nodes and a relay)

Gaussian sampling based lattice decoding

The problem of searching the closest lattice point in large dimensional lattices finds many applications in single and/or multiple antenna communications. In this paper, we propose a Gaussian sampling based lattice decoding algorithm (GSLD). The algorithm iteratively updates each coordinate by sampling from a continuous Gaussian distribution and then quantizes the sampled value to the nearest alphabet point. The algorithm complexity per iteration is independent of the size of the alphabet, and hence is of high interest in higher order modulation schemes. We show that the algorithm is able to achieve near-optimal performance in polynomial complexity in different wireless communication system models