Particle Swarm Optimisation Aided MIMO Multiuser Transmission Designs

Wang, Yao; Chen, Sheng; Hanzo, Lajos

doi:10.1166/jctn.2012.2021

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…Equipped with multiple antennas, a base station (BS) becomes capable of serving multiple users with the same frequency-time resource block by exploiting the spatial dimension, and this effectively offers the potentially unlimited communication resources. In the resulting SDMA induced MIMO system, the adaptive beamforming receiver provides the effective means of uplink data detection at the BS [15,16,17,18,19] , while the transmit beamforming or precoding offers the effective way of downlink data transmission [20,21,22,23,24] . Both uplink detection and downlink precoding require the knowledge of the MIMO channel state information (CSI).…”

Section: Introductionmentioning

confidence: 99%

Recursive least squares semi-blind beamforming for MIMO using decision directed adaptation and constant modulus criterion

Hong

Chen

2017

Int. J. Autom. Comput.

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Abstract:A new semi-blind adaptive beamforming scheme is proposed for multi-input multi-output (MIMO) induced and spacedivision multiple-access based wireless systems that employ high order phase shift keying signaling. A minimum number of training symbols, very close to the number of receiver antenna elements, are used to provide a rough initial least squares estimate of the beamformer's weight vector. A novel cost function combining the constant modulus criterion with decision-directed adaptation is adopted to adapt the beamformer weight vector. This cost function can be approximated as a quadratic form with a closed-form solution, based on which we then derive the recursive least squares (RLS) semi-blind adaptive beamforming algorithm. This semi-blind adaptive beamforming scheme is capable of converging fast to the minimum mean-square-error beamforming solution, as demonstrated in our simulation study. Our proposed semi-blind RLS beamforming algorithm therefore provides an efficient detection scheme for the future generation of MIMO aided mobile communication systems.

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Section: Introductionmentioning

confidence: 99%

Recursive least squares semi-blind beamforming for MIMO using decision directed adaptation and constant modulus criterion

Hong

Chen

2017

Int. J. Autom. Comput.

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“…Markov chain Monte Carlo proposed in [18] is employed for MIMO detection. Genetic algorithm decoder presented in [19], ant colony optimization decoder proposed in [20], particle swarm optimization decoder presented in [21] and so forth are meta-heuristic detection algorithms based on swarm intelligence in [22].…”

Section: Introductionmentioning

confidence: 99%

A novel artificial bee colony detection algorithm for massive MIMO system

Meng

2016

Wirel. Commun. Mob. Comput.

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As a hot-spot of 5G, the research on detection algorithms for massive multiple input multiple output (MIMO) system is significant but difficult. The traditional MIMO detection algorithms or their improvements are not appropriate for large scaled antennas. In this paper, we propose artificial bee colony (ABC) detection algorithm for massive MIMO system. As one advanced technology of swarm intelligence, ABC algorithm is most efficient for large scaled constrained numerical combinatorial optimization problem. Therefore, we employ it to search the optimum solution vector in the modulation alphabet with linear detection result as initial. Simulation and data analysis prove the correctness and efficiency. Versus the scale of massive MIMO systems from 64 64 to 1024 1024 with uncoded four-quadrature-amplitude-modulation signals, the proposed ABC detection algorithm obtains bit error rate of 10 5 at low average received signal-to-noise-ratio of 12 dB with rapid convergence rate, which approximates the optimum bit error rate performance of the maximum likelihood and achieves the theoretical optimum spectral efficiency with low required average received signal-to-noise-ratio of 10 dB in similar increasing regularity, over finite time of low polynomial computational complexity of O.N 2 T / per symbol, where N T denotes the transmitting antennas' number. The proposed ABC detection algorithm is efficient for massive MIMO system. KEYWORDS massive multiple input multiple output (MIMO) system; artificial bee colony (ABC); detection algorithm; bit error rate (BER); computational comlexity; spectral efficiency

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Stress-driven nonlocal Timoshenko beam model for buckling analysis of carbon nanotubes constrained by surface van der Waals interactions

et al. 2022

Microsyst Technol

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Particle Swarm Optimisation Aided MIMO Multiuser Transmission Designs

Cited by 5 publications

References 42 publications

Recursive least squares semi-blind beamforming for MIMO using decision directed adaptation and constant modulus criterion

Recursive least squares semi-blind beamforming for MIMO using decision directed adaptation and constant modulus criterion

A novel artificial bee colony detection algorithm for massive MIMO system

Stress-driven nonlocal Timoshenko beam model for buckling analysis of carbon nanotubes constrained by surface van der Waals interactions

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