Energy and spectral efficiency improvement using improved shark smell‐coyote optimization for massive MIMO system

Byreddy, Amarender Reddy; Логашанмугам, Э.

doi:10.1002/dac.5381

Cited by 8 publications

(2 citation statements)

References 46 publications

(83 reference statements)

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“…Massive MIMO increases the SE of cellular networks through spatial multiplexing of a high number of users (UEs) for all cells [1]. The utility of massive MIMO technology has the capability to enhance the spectral efficiency (SE) and providing better coverage with improved robustness and are highly reliable [3][4][5]. As a result, MIMO achieves one of the key aims of the future technology by minimizing service delay, which includes both propagation and processing delays [6].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Spectral Efficiency in Massive MIMO System Availing Eagle-Crow Optimization-Based Scheduling Algorithm

2023

IJIES

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The core idea of massive multiple-input multiple-output (MIMO) systems is to raise the throughput and spectral efficiency (SE) of the cell system by placing a huge number of antennas at the base station (BS) to serve multiple users. Since the use of pilots is mandatory for channel estimation, this article discusses the problem of pilot contamination (PC) that affects the SE of the massive MIMO system. Due to the short coherence time in communication systems, the length of the corresponding pilot sequence is limited, and the number of orthogonal pilots that can be assigned to each cell is limited, which will inevitably lead to the repeated use of the pilot sequence in nearby cells which leads to appearance the PC problem. The PC in the networks could be eliminated through the proper scheduling of the pilot sequences and will be achieved by the proper pilot scheduling algorithm. Accordingly, this paper proposes a scheduling algorithm named eagle-crow optimization, which eliminates the contamination and promotes the SE of the massive MIMO system, under different practical constraints and requirements such as the number of antennas and users. The proposed algorithm is a combination of two optimization algorithms that are the crow search (CSA) and bald eagle search (BES) algorithms. Many studies have been performed using crow search (CSA) and bald eagle search (BES) algorithms. However, most of them did not consider PC problems in MIMO systems. The motivation behind the use of these two techniques is that the proposed eagle-crow optimization inherits the hunting characteristics of the eagle and the memory features of the crow in order to prevent the repetition of pilot assignments. The achievements of the proposed scheduling are assessed based on the performance of SE, when the number of antennas in the BS increases to 1000, the SE of the proposed method is 44.91 bps/Hz, and the percentage improvement of the proposed method is high when compared to the existing methods, Random pilot allocation, WGC-PD, and SPRS+WGC-PD.

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Section: Introductionmentioning

confidence: 99%

Enhancement of Spectral Efficiency in Massive MIMO System Availing Eagle-Crow Optimization-Based Scheduling Algorithm

2023

IJIES

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“…Massive multiple-input multiple-output (MIMO) is now an established technology for the next generation of wireless communications [1,2]. It historically has been found to bring vast improvements over earlier methods in radio link systems in terms of spectral efficiency, energy efficiency, data rate, user tracking, robustness, and reliability [3][4][5]. However, the fundamental challenge of existing massive MIMO systems is that high computational complexity and complicated spatial structures bring difficulties in exploiting the characteristics of the channel and sparsity of these multi-antennae systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique

Jenkinson,

Abbasi,

Molaei

et al. 2023

Electronics

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This paper provides a simple yet effective approach to improve direction-of-arrival (DOA) estimation performance in extreme signal-to-noise-ratio (SNR) conditions. As an example, a multiple signal classification (MUSIC) algorithm with a deep learning (DL) approach is used. First, brief research into the existing DOA estimation techniques is provided, followed by a demonstration of a simulation environment created on the MATLAB platform to generate and resolve signals from a uniform rectangular array of antenna elements. Following that is an attempt to improve the estimation accuracy of these signals by training various DL approaches, including multi-layer perceptron and one- and two-dimensional convolutional neural networks, using the generated dataset. Key findings include the cases where the developed DL approach can resolve signals and provide accurate DOA estimations that the MUSIC algorithm cannot.

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RF Chain Selection Using Hybrid Optimization with Precoding in mm-Wave Massive MIMO Systems

Srinivas

Borugadda

2023

Wireless Pers Commun

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Energy and spectral efficiency improvement using improved shark smell‐coyote optimization for massive MIMO system

Cited by 8 publications

References 46 publications

Enhancement of Spectral Efficiency in Massive MIMO System Availing Eagle-Crow Optimization-Based Scheduling Algorithm

Enhancement of Spectral Efficiency in Massive MIMO System Availing Eagle-Crow Optimization-Based Scheduling Algorithm

Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique

RF Chain Selection Using Hybrid Optimization with Precoding in mm-Wave Massive MIMO Systems

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