Tanumay Datta

Random-Restart Reactive Tabu Search Algorithm for Detection in Large-MIMO Systems

We present a low-complexity algorithm based on reactive tabu search (RTS) for near maximum likelihood (ML) detection in large-MIMO systems. The conventional RTS algorithm achieves near-ML performance for 4-QAM in large-MIMO systems. But its performance for higher-order QAM is far from ML performance. Here, we propose a random-restart RTS (R3TS) algorithm which achieves significantly better bit error rate (BER) performance compared to that of the conventional RTS algorithm in higher-order QAM. The key idea is to run multiple tabu searches, each search starting with a random initial vector and choosing the best among the resulting solution vectors. A criterion to limit the number of searches is also proposed. Computer simulations show that the R3TS algorithm achieves almost the ML performance in 16 × 16 V-BLAST MIMO system with 16-QAM and 64-QAM at significantly less complexities than the sphere decoder. Also, in a 32 × 32 V-BLAST MIMO system, the R3TS performs close to ML lower bound within 1.6 dB for 16-QAM (128 bps/Hz), and within 2.4 dB for 64-QAM (192 bps/Hz) at 10-3 BER.

Improved large-MIMO detection based on damped belief propagation

In this paper, we consider the application of belief propagation (BP) to achieve near-optimal signal detection in large multiple-input multiple-output (MIMO) systems at low complexities. Large-MIMO architectures based on spatial multiplexing (V-BLAST) as well as non-orthogonal space-time block codes(STBC) from cyclic division algebra (CDA) are considered. We adopt graphical models based on Markov random fields (MRF) and factor graphs (FG). In the MRF based approach, we use pairwise compatibility functions although the graphical models of MIMO systems are fully/densely connected. In the FG approach, we employ a Gaussian approximation (GA) of the multi-antenna interference, which significantly reduces the complexity while achieving very good performance for large dimensions. We show that i) both MRF and FG based BP approaches exhibit large-system behavior, where increasingly closer to optimal performance is achieved with increasing number of dimensions, and ii) damping of messages/beliefs significantly improves the bit error performance.

Low-Complexity Detection 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 message damping, and 2) use of factor graph (FG)-based graphical model with Gaussian approximation of interference (GAI). The per-symbol complexities are O(K2nt2) and O(Knt) for the MRF and the FG with GAI approaches, respectively, where K and nt 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 Knt. From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing Knt. 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.

A Novel Monte-Carlo-Sampling-Based Receiver for Large-Scale Uplink Multiuser MIMO Systems

In this paper, we propose low-complexity algorithms based on Monte Carlo sampling for signal detection and channel estimation on the uplink in large-scale multiuser multiple-input-multiple-output (MIMO) systems with tens to hundreds of antennas at the base station (BS) and a similar number of uplink users. A BS receiver that employs a novel mixed sampling technique (which makes a probabilistic choice between Gibbs sampling and random uniform sampling in each coordinate update) for detection and a Gibbs-sampling-based method for channel estimation is proposed. The algorithm proposed for detection alleviates the stalling problem encountered at high signal-to-noise ratios (SNRs) in conventional Gibbs-sampling-based detection and achieves near-optimal performance in large systems with M-ary quadrature amplitude modulation ( M-QAM). A novel ingredient in the detection algorithm that is responsible for achieving near-optimal performance at low complexity is the joint use of a mixed Gibbs sampling (MGS) strategy coupled with a multiple restart (MR) strategy with an efficient restart criterion. Near-optimal detection performance is demonstrated for a large number of BS antennas and users (e.g., 64 and 128 BS antennas and users). The proposed Gibbs-sampling-based channel estimation algorithm refines an initial estimate of the channel obtained during the pilot phase through iterations with the proposed MGS-based detection during the data phase. In time-division duplex systems where channel reciprocity holds, these channel estimates can be used for multiuser MIMO precoding on the downlink. The proposed receiver is shown to achieve good performance and scale well for large dimensions.

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) and phase shift keying (PSK)], in index modulation (IM), additional information bits are conveyed through indexes of certain transmit entities that get involved in the transmission. Transmit antennas in multiantenna systems and subcarriers in multicarrier 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 IM, where the indexes of active transmit antennas and subcarriers convey information bits. We first introduce IM in the spatial domain, which is referred to as generalized spatial IM (GSIM). For GSIM, where bits are indexed only in the spatial domain, we derive the expression for achievable rate and 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 with 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 IM (GSFIM), where bits are encoded through indexing in both active antennas and subcarriers, we derive the achievable rate expression. Numerical results show that GSFIM can achieve higher rates compared with conventional multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM). Moreover, BER results show the potential for GSFIM performing better than MIMO-OFDM.

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 (Nt) and number of transmit RF chains (Nrf), 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.

A novel MCMC algorithm for near-optimal detection in large-scale uplink mulituser MIMO systems

In this paper, we propose a low-complexity algorithm based on Markov chain Monte Carlo (MCMC) technique for signal detection on the uplink in large scale multiuser multiple input multiple output (MIMO) systems with tens to hundreds of antennas at the base station (BS) and similar number of uplink users. The algorithm employs a randomized sampling method (which makes a probabilistic choice between Gibbs sampling and random sampling in each iteration) for detection. The proposed algorithm alleviates the stalling problem encountered at high SNRs in conventional MCMC algorithm and achieves near-optimal performance in large systems with M-QAM. A novel ingredient in the algorithm that is responsible for achieving near-optimal performance at low complexities is the joint use of a randomized MCMC (R-MCMC) strategy coupled with a multiple restart strategy with an efficient restart criterion. Near-optimal detection performance is demonstrated for large number of BS antennas and users (e.g., 64, 128, 256 BS antennas/users).

Layered Tabu Search Algorithm for Large-MIMO Detection and a Lower Bound on ML Performance

In this paper, we are concerned with low-complexity detection in large multiple-input multiple-output (MIMO) systems with tens of transmit/receive antennas. Our new contributions in this paper are two-fold. First, we propose a low-complexity algorithm for large-MIMO detection based on a {\em layered low-complexity local neighborhood search}. Second, we obtain a lower bound on the maximum-likelihood (ML) bit error performance using the local neighborhood search. The advantages of the proposed ML lower bound are i) it is easily obtained for MIMO systems with large number of antennas because of the inherent low complexity of the search algorithm, ii) it is tight at moderate-to-high SNRs, and iii) it can be tightened at low SNRs by increasing the number of symbols in the neighborhood definition. Interestingly, the proposed detection algorithm based on the layered local search achieves bit error performances which are quite close to this lower bound {\em for large number of antennas and higher-order QAM}. For e.g., in a

Lattice reduction aided detection in large-MIMO systems

32\times 32

A hybrid RTS-BP algorithm for improved detection of large-MIMO M-QAM signals

V-BLAST MIMO system, the proposed detection algorithm performs close to within 1.7 dB of the proposed ML lower bound at