Likelihood Ascent Search Detection for Coded Massive MU-MIMO Systems to Mitigate IAI and MUI
01 May 2020-Radioelectronics and Communications Systems (Pleiades Publishing)-Vol. 63, Iss: 5, pp 223-234
TL;DR: The proposed work presents joint SVD precoding and LAS MUD to mitigate both IAI and MUI and can achieve a near-optimal performance with smaller number of matrix computations.
Abstract: The main aim of massive multiuser multiple-input multiple-output (MU-MIMO) system is to improve the throughput and spectral efficiency in 5G wireless networks. The performance of MU-MIMO system is severely influenced by inter-antenna interference (IAI) and multiuser interference (MUI). The IAI occurs due to space limitations at each user terminal (UT) and the MUI is added when one UT is in the vicinity of another UT in the same cellular network. IAI can be mitigated through a precoding scheme such as singular value decomposition (SVD), and MUI is suppressed by an efficient multiuser detection (MUD) schemes. The maximum likelihood (ML) detector has optimal performance; however, it has a highly complex structure and involves the need of a large number of computations especially in massive structures. Thus, the neighborhood search-based algorithm such as likelihood ascent search (LAS) has been found to be a better alternative for mitigation of MUI as it results in near optimal performance with low complexity. Most of the recent papers are aimed at eliminating either MUI or IAI, whereas the proposed work presents joint SVD precoding and LAS MUD to mitigate both IAI and MUI. The proposed scheme can achieve a near-optimal performance with smaller number of matrix computations.
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4 citations
TL;DR: The lattice reduction (LR) precoding based user level local likelihood ascent search (ULAS) detection scheme is proposed in this paper for efficient signal detection in large scale MU-MIMO system.
Abstract: The large scale multiuser multiple input multiple output (MU-MIMO) is one of the promising communication technology for 5G wireless networks as it offers reliability, high spectral efficiency and high throughput. The lattice reduction (LR) precoding based user level local likelihood ascent search (ULAS) detection scheme is proposed in this paper for efficient signal detection in large scale MU-MIMO system. The initial solution of ULAS algorithm is obtained from the LR precoding assisted zero forcing detector. The LR precoding transforms the non-orthogonal channel matrix into nearly orthogonal channel, which helps to mitigate inter antenna interference (IAI) exists at each user. The remaining multiuser interference (MUI) imposed to each user from undesired users is cancelled by the proposed ULAS multiuser detection scheme. Thus, the proposed LR precoding assisted ULAS mitigates both IAI and MUI unlike the classical detector, those try to moderate either IAI or MUI. By contrast, the proposed ULAS detector provides performance close to optimal maximum likelihood detector with just a fraction of its complexity.
3 citations
16 Jan 2023
DOI•
TL;DR: In this article , a joint pre-coding and MUD, Lenstra-Lovasz (LLL) based Lattice Reduction (LR) assisted likelihood accent search (LAS) was implemented to mitigate IAI and MUI simultaneously, and the LAS algorithm is a neighborhood search-based MUD that cancels BS MUI.
Abstract: Multiple antennas at each user equipment (UE) and/or thousands of antennas at the base station (BS) comprise the extremely spectrum efficient large scale multi‐user multiple input multiple output system (BS). Due to space constraints, the closely spaced numerous antennas at each UE may cause inter antenna interference (IAI). Furthermore, when one UE comes into contact with another UE in the same cellular network, multi‐user interference (MUI) may be introduced to the received signal. To mitigate IAI, efficient precoding pre‐coding is necessary at each UE, and the MUI present at the BS can be canceled by efficient Multi‐user Detection (MUD) techniques. The majority of earlier literature deal with one or more of these interferences. This paper implements a joint pre‐coding and MUD, Lenstra‐Lovasz (LLL) based Lattice Reduction (LR) assisted likelihood accent search (LAS) (LLL‐LR‐LAS), to mitigate IAI and MUI simultaneously LLL‐based LR pre‐coding mitigates IAI at each UE, and the LAS algorithm is a neighborhood search‐based MUD that cancels BS MUI. The proposed approaches' performance was evaluated using Bit Error Rate analysis, and their complexity were determined using multiplication and addition.
References
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TL;DR: This work proposes a likelihood based branching criteria to reduce the number of QPs required to be solved and combines this branching criteria with a node selection strategy to achieve a better error performance than the reported BB approach, at a lower computational complexity.
Abstract: A recently reported result on large/massive multiple-input multiple-output (MIMO) detection shows the utility of the branch and bound (BB)-based tree search approach for this problem. We can consider strong branching for improving upon this approach. However, that will require the solution of a large number of quadratic programs (QPs). We propose a likelihood based branching criteria to reduce the number of QPs required to be solved. We combine this branching criteria with a node selection strategy to achieve a better error performance than the reported BB approach, that too at a lower computational complexity. Simulation results show that the proposed algorithm outperforms the available detection algorithms for large MIMO systems.
11 citations
TL;DR: A multiple-input multiple-output (MIMO) version of the dirty paper channel is studied, where the channel input and the dirt experience the same fading process, and the fading channel state is known at the receiver.
Abstract: A multiple-input multiple-output (MIMO) version of the dirty paper channel is studied, where the channel input and the dirt experience the same fading process and the fading channel state is known at the receiver (CSIR). This represents settings where signal and interference sources are co-located, such as in the broadcast channel. First, a variant of Costa's dirty paper coding (DPC) is presented, whose achievable rates are within a constant gap to capacity for all signal and dirt powers. Additionally, a lattice coding and decoding scheme is proposed, whose decision regions are independent of the channel realizations. Under Rayleigh fading, the gap to capacity of the lattice coding scheme vanishes with the number of receive antennas, even at finite Signal-to-Noise Ratio (SNR). Thus, although the capacity of the fading dirty paper channel remains unknown, this work shows it is not far from its dirt-free counterpart. The insights from the dirty paper channel directly lead to transmission strategies for the two-user MIMO broadcast channel (BC), where the transmitter emits a superposition of desired and undesired (dirt) signals with respect to each receiver. The performance of the lattice coding scheme is analyzed under different fading dynamics for the two users, showing that high-dimensional lattices achieve rates close to capacity.
11 citations
TL;DR: The present work proposes singular value decomposition (SVD) precoding‐assisted user‐level local likelihood ascent search (LLAS) algorithm to mitigate both IAI and MUI in the uplink MU‐MIMO.
9 citations
01 Nov 2016
TL;DR: This paper presents a low complexity detection technique with near Maximum Likelihood (ML) performance for large multiple-input multiple-output (MIMO) systems and shows that the proposed method outperforms linear detection technique named Zero Forcing as well as heuristic based search algorithms named likelihood ascent search (LAS) and Reactive Tabu Search (RTS).
Abstract: This paper presents a low complexity detection technique with near Maximum Likelihood (ML) performance for large multiple-input multiple-output (MIMO) systems. Large MIMO systems have gained popularity very soon because of high spectral efficiency and increased link reliability. ML based detection is known to give optimal result in terms of accuracy but due to extremely high computational complexity involved, detection time increases exponentially as the number of transmitter and receiver antennas increases. We propose an algorithm which gives near optimal performance along with much reduced computational complexity. Our results show that the proposed method outperforms linear detection technique named Zero Forcing (ZF) as well as heuristic based search algorithms named likelihood ascent search (LAS) and Reactive Tabu Search (RTS). Our algorithm finds the best solution restricted to a given Euclidean distance around initial solution. It searches all the neighbors of initial solution falling under dynamically calculated squared Euclidean distance based cost function value. As the number of antennas can vary in the range of tens to few thousands in large MIMO systems, this algorithm could be a substitute for ML based detection algorithm. We have considered Rayleigh fading channel for our simulations and assumed that perfect channel state information at the receiver (CSIR) is available.
9 citations
01 Dec 2018
TL;DR: This paper investigates the design of LAS algorithm with LR assisted ZF solution as an initial vector to improve performance over the classical ZFLAS detector and attains a more achievable trade-off between Bit Error Rate performance and exponential time detection complexity for large extended systems.
Abstract: Massive multiple input multiple output (MIMO) system achieves high spectral and energy efficiency by incorporating a large number of antennas at the transmitter and/or receivers. Multiuser detection is an important task that needs to be done at the receiver of the Massive MIMO system to mitigate multiuser interference. The classical Zero Forcing (ZF) detector suffers from high residual interference. By making channel matrix orthogonal, the Lattice Reduction (LR) techniques can be assisted for the ZF detector to minimize interference. On the other hand, the Likelihood Ascent Search (LAS) is a neighborhood search based low complexity detection algorithm that is used for massive MIMO systems. It takes the Zero Forcing (ZF) solution as initial vector and searches for a near-optimal solution by examining cost values of its neighborhood vectors. The performance of the LAS algorithm is mainly relying on an initial vector. So, this paper investigates the design of LAS algorithm with LR assisted ZF solution as an initial vector to improve performance over the classical ZFLAS detector. The proposed algorithm attains a more achievable trade-off between Bit Error Rate (BER) performance and exponential time detection complexity for large extended systems.
6 citations