Lattice Reduction Aided Detector for MIMO Communication Via Ant Colony Optimisation

Marinello, José Carlos; Abrão, Taufik

doi:10.1007/s11277-013-1495-z

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…n log 2 (q) n log 2 (q) situations since the usual constellations allow to obtain the negative symbols by changing the sign. Continuing the previous 16-QAM example, storing H and 3H is enough to compute (19) with only additions since −H and −3H can be obtained by substracting rather than adding the preprocessed vectors. The lower part of the figure illustrates the detection process for a symbol from the received vector and the estimated noise variance.…”

Section: Complexity Reductionmentioning

confidence: 99%

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When Should We Use Geometrical-Based MIMO Detection Instead of Tree-Based Techniques? A Pareto Analysis

et al. 2020

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The soft-output multiple-input multiple-output (MIMO) detection problem has been extensively studied, and a large number of heuristics and metaheuristics have been proposed to solve it. Unlike classical tree-search based detectors, geometrical heuristic algorithms involved two consecutive steps: (i) an exploration step based on the geometry of the channel matrix singular vectors; (ii) a local exploitation step is performed in order to obtain better final solution. In this paper, new enhancements for geometrical heuristics are introduced to significantly reduce the complexity in quadrature phase-shift keying (QPSK) and allow 16 quadrature amplitude modulation (QAM) capability through new exploration techniques. The performance-complexity trade-off between the new detector and two tree-based algorithms is investigated through Pareto efficiency. The Pareto framework also allows us to select the most efficient tuning parameters based on an exhaustive search. The proposed detector can be customized on the fly using only one or two parameters to balance the trade-off between computational complexity and bit error rate performances. Moreover, the Pareto fronts demonstrate that the new geometrical heuristic is especially efficient with QPSK since it provides a significant reduction in regards to the computational complexity while preserving good bit error rate (BER) performance and ensuring high flexibility. INDEX TERMS Geometrical detection, Pareto efficiency, Soft-output MIMO detection.

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Section: Complexity Reductionmentioning

confidence: 99%

“…The emergence of deep networks trained to tackle the detection problem is also studied in recent work [18]. Finally, bio-inspired metaheuristics based on ant colony optimization (ACO) [19] or on the firefly algorithm (FA) [20], [21] have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

When Should We Use Geometrical-Based MIMO Detection Instead of Tree-Based Techniques? A Pareto Analysis

et al. 2020

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“…ACO-based detectors simulate several ants that choose a path randomly to follow with a nonuniform probability function [31]. Each antenna is processed independently.…”

Section: Bioinspired Detectorsmentioning

confidence: 99%

A Review to Massive MIMO Detection Algorithms: Theory and Implementation

Trotobas¹,

Nafkha²,

Louët³

2020

Advanced Radio Frequency Antennas for Modern Communication and Medical Systems

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Multiple-input multiple-output (MIMO) systems entered most major standards in the past decades, including IEEE 802.11n (Wi-Fi) and long-term evolution (LTE). Moreover, MIMO techniques will be used for 5G by increasing the number of antennas at the base station end. MIMO systems enable spatial multiplexing, which has the potential of increasing the capacity of the communication channel linearly with the minimum of the number of antennas installed at both sides without sacrificing any additional bandwidth or power. To handle the spacedivision multiplexing (SDM), receivers have to implement new algorithms to exploit the spatial information in order to distinguish the transmitted data streams. This chapter provides an overview of the most well-known and promising MIMO detectors, as well as some unusual-yet-interesting ones. We focus on the description of the different paradigms to highlight the different approaches that have been studied. For each paradigm, we describe the mathematical framework and give the underlying philosophy. When hardware implementations are available in the literature, we provide the results reported and give the according references.

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“…Other techniques are available in literature based on mixing exploitation with exploration in order to escape from local minima and produce more reliable results. In particular, bio-inspired metaheuristics such as ant colony optimization (ACO) [15] or FA [16] have been proposed. These algorithms are much more complex than tree-based techniques.…”

Section: Introductionmentioning

confidence: 99%

Adding Exploration to Tree-Based MIMO Detectors Using Insights from Bio-Inspired Firefly Algorithm

Trotobas¹,

Akourim

Nafkha³

et al. 2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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The standard multiple-input multiple-output (MIMO) detectors exploit the available information to resolve the detection problem. Alternative algorithms, such as bioinspired or geometrical detectors, mix exploitation with exploration to bypass local minima and enhance the results. This paper examines the benefits of adding exploration to the traditional tree-based detectors. For this purpose, a new interpretation of the bio-inspired detector based on the firefly algorithm (FA) is proposed. It is studied in a tree search paradigm and extended to soft-outputs. The findings suggest that the addition of a stochastic exploration to tree-based detectors significantly improves performance with a small computational overhead.

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Lattice Reduction Aided Detector for MIMO Communication Via Ant Colony Optimisation

Cited by 13 publications

References 24 publications

When Should We Use Geometrical-Based MIMO Detection Instead of Tree-Based Techniques? A Pareto Analysis

When Should We Use Geometrical-Based MIMO Detection Instead of Tree-Based Techniques? A Pareto Analysis

A Review to Massive MIMO Detection Algorithms: Theory and Implementation

Adding Exploration to Tree-Based MIMO Detectors Using Insights from Bio-Inspired Firefly Algorithm

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