Lattice reduction aided detection in large-MIMO systems

Abstract

Abstract-Lattice reduction (LR) aided detection algorithms are known to achieve the same diversity order as that of maximumlikelihood (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 × 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.

A simplified hard output sphere decoder for large MIMO systems with the use of efficient search center and reduced domain neighborhood study

“…However, the main drawback of MIMO technology is the increased complexity at the receiver side when a non-orthogonal (NO) MIMO scheme with a large number of antennas and/or large constellation size is implemented [4,5]. For the detection process, although the performance of the maximum likelihood (ML) detector is optimal, its computational complexity increases exponentially with the number of transmit antennas and with the constellation size.…”

“…In particular, any SD, including the K-best, may be advantageously applied by considering a betterconditioned channel matrix through the introduction of a Reduced Domain Neighborhood (RDN) study and a judicious search center. In [5], an improved LR technique dealing with the RDN has been proposed in the context of large MIMO systems. It is based on the decomposition of the spanned space of the channel matrix into small subspaces in order to improve orthogonality of the quantization.…”

Channel hardening-exploiting message passing (CHEMP) receiver in large MIMO systems

“…The mean and variance in this approximation are given by (11) (12) Denoting the probability of the symbol as , we have (13) Also, note that by Lemma 1, . Because of the above Gaussian approximation, the a posteriori probability (APP) of the symbol can be written as (14) From (14), the log-likelihood ratio (LLR) of , denoted by , can be written as (15) From (15), the probability of symbol , can be written as (16) Message passing: The system is modeled as a fully-connected graph, where the data symbols in represent the nodes. There are nodes in the graph corresponding to the elements in the vector .…”

“…In the recent years, several low complexity detection algorithms which achieve near-optimal performance in large dimensions using complexities comparable to that of MMSE detection have been proposed [1], [2], [6]- [16]. These algorithms are based on local search (e.g., likelihood ascent search (LAS) algorithm and variants in [1], [2], [6], [7]), meta-heuristics (e.g., reactive tabu search (RTS) and variants in [8], [9]), message passing techniques (e.g., belief propagation (BP) based algorithms in [11], [12]), lattice reduction techniques (e.g., lattice reduction (LR) aided detectors in [13], [14]), and Monte-Carlo sampling techniques (e.g., Markov chain Monte Carlo (MCMC) algorithms in [15]). Issues related to channel estimation and low density parity check codes for large-scale MIMO systems are also being addressed [17], [18].…”

Parallel decoding for lattice reduction-aided MIMO Receiver

“…Recently developed algorithms such as element-based LR (ELR) [10] and improved LR (ILR) [11] improve * Correspondence: karthikeyan2709@pec.edu the efficiency of LR-aided MIMO decoders through complexity reduction compared to conventional techniques. In this paper, we increase the decoding efficiency using modified lattice reduction, which is suitable for parallel decoding of the transmitted symbols.…”