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Massive MIMO perspective: improved sea lion for optimal antenna selection

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Abstract

The massive MIMO is an advanced technology in the wideband wireless communication system’s future, which provokes extensive attention in both academia and telecommunication industry. Pilot contamination is considered as a fundamental issue in the system of massive MIMO. The designing of wireless systems has the main concern over the system throughput. Though, the environmental protection and energy-saving have concerned as inevitable trends and global demands. Hence, on considering all these consequences, this paper intends to introduce a new improved sea lion optimization algorithm to select the optimal transmit antennas selection by accounting the multi-objective issue that maximizes both the relative energy efficiency and capacity. In fact, the proposed algorithm is the enhanced version of traditional sea lion optimization algorithm, which optimally tunes the count of transmit antennas and determines which antenna to be selected. Finally, the performance of proposed work is compared and proved over other conventional models regarding capacity analysis, relative efficiency analysis, and optimal antenna selection analysis as well.

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Abbreviations

SMV:

Square maximum-volume

LSAS:

Large-scale antenna systems

MIMO:

Muliple input–multiple output

BS:

Base station

RF:

Radio frequency

BAB:

Branch and bound

AS:

Antenna selection

CBF:

Correlation-based best first

EE:

Energy efficiency

SE:

Spectral efficiency

MOO:

Multi-objective optimization

PSO:

Partcle swarm optimization

WS-PSO:

Weighted sum-PSO

NBI-PSO:

Normal-boundary-intersection PSO

RAS:

Received antenna selection

NOMA:

Non-orthogonal multiple access

MU-MIMO:

Multiuser MIMO

RFC:

Radio frequency chain

RMV:

Rectangular maximum-volume

SMV:

Square maximum-volume

TAS:

Transmit antennas selection

MMU-MIMO:

Massive multiuser MIMO

BF:

Beamforming

MRT:

Maximum ratio transmission

MMSE:

Minimum mean square error

CM:

Capacity maximization

PM:

Probability minimization

CSI:

Channel state information

SLnO:

Sea lion optimization

ZF:

Zero forcing

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Authors and Affiliations

  1. KL (Deemed To Be University), Vaddeswaram, Andhra Pradesh, India

    Inumula Veeraraghava Rao

  2. GITAM (Deemed To Be University, Visakhapatnam, India

    V. Malleswara Rao

Authors
  1. Inumula Veeraraghava Rao
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  2. V. Malleswara Rao
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Correspondence to Inumula Veeraraghava Rao.

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Rao, I.V., Rao, V.M. Massive MIMO perspective: improved sea lion for optimal antenna selection. Evol. Intel. 14, 1831–1845 (2021). https://doi.org/10.1007/s12065-020-00457-x

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  • DOI: https://doi.org/10.1007/s12065-020-00457-x

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