Maximizing the spectral efficiency of finite dimensional multiuser massive MIMO

Miriestahbanati, Maryam; Saatlou, O.; Soleymani, M.R.

doi:10.1016/j.phycom.2021.101430

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…In 2021, Miriestahbanati et al 23 have examined a single‐cell multi‐user MIMO system by investigating a “closed‐form approximation” for achieving uplink “ergodic rate of the users,” in which a “finite number of separate directions and the base station” was attained that was deployed with a huge count of antenna elements. Further, they have utilized different detectors like “linear minimum mean square error (LMMSE), zero‐forcing (ZF), and maximum‐ratio combining (MRC)” by approximating the spectral efficiency.…”

Section: A Study On Existing Workmentioning

confidence: 99%

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

Byreddy

Логашанмугам

2022

Int J Communication

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Massive multiple-input multiple-output (MIMO) can considerably enhance the "spectral efficiency and energy efficiency" since it is a major technique for future wireless networks. Thus, the performance needs a huge count of base station antennas to serve a smaller number of terminals in conventional MIMO methodology. Large-scale radio frequency (RF) chains represent the large-scale antennas. There is a need of implementing an effective massive MIMO system for maximizing the efficient performance of the system with high "spectral efficiency and energy efficiency" owing to the high cost of RF chains, and the higher power consumption. In this paper, a massive MIMO communication system is implemented to satisfy the requirements regarding "energy efficiency and spectral efficiency." Here, the number of base station antennas, the transmit power, and beam forming vectors are optimized to maximize "energy efficiency and spectral efficiency" when the channel capacity is known to be higher than some threshold values. The novelty of this work is a new hybrid optimization adaptive shark smell-coyote optimization (ASS-CO) algorithm is developed for improving energy efficiency. The optimization is done with the help of the hybrid optimization ASS-CO Algorithm. The proposed ASS-CO algorithm-based massive MIMO communication system is evaluated by experimental analysis. From the result analysis, the maximum resource efficiency is observed by SS-WOA, which is 6.6%, 50%, 6.6%, 6.6%, and 6.6% maximized than rider optimization algorithm (ROA), spotted hyena optimization (SHO), lion algorithm (LA), Shark Smell Optimization (SSO), and Coyote Optimization Algorithm (COA) by taking the count of base stations as 4. The superior performance enhancement regarding "spectral efficiency and energy efficiency" is accomplished over the traditional systems.

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Section: A Study On Existing Workmentioning

confidence: 99%

“…On the other hand, it is also will not perform well while analyzing strongly nonlinear data and complex to interpret. Resource allocation scheme 23 evaluates well even in lower SNR regions and requires less computation time. On the other hand, this framework was not potential for practical deployment.…”

Section: A Study On Existing Workmentioning

confidence: 99%

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

Byreddy

Логашанмугам

2022

Int J Communication

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Design a hybrid meta-heuristic algorithm for optimal multicell-MMSE to maximize the spectral efficiency in massive MIMO systems

Kondaiah,

Padmaja

2024

Journal of Optical Communications

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Due to many capabilities, “massive multiple-input multiple-output (MIMO) systems” are regarded as a crucial enabling innovation. High energy economy, great spectral efficiency (SE), and simultaneous communication to many user equipments (UEs) are some of the sophisticated characteristics of massive MIMO systems. Huge MIMO, which involves installing arrays of antennas with a high amount of active components at the base station (BS) and utilizing coherent baseband processing, is a viable method for boosting the SE of cell phone networks. Massive MIMO’s spatial multiplexing and unparalleled array gain can increase the processing power of cellular networks. Since its origin, it has been assumed that when the number of radios increases infinitely, the coherent interference brought on by pilot emissions leads to a limited capacity limit. To achieve this objective, an optimal multicell MMSE is proposed for SE maximization. It is processed as the precoding or combining technique that is considered the small amount of spatial channel correlation, more capacity and more number of antennas, large-scale fading variations, and pilot contamination. It is noted that several cases for increasing the SE, thus it contain multiple antenna information. The prime novelty of this paper is introducing the hybrid heuristic algorithm, named as Fitness-condition of red deer and rat swarm algorithm (FRDRSA) for providing the best solution. Finally, the work performance that produced the extensive findings is examined. On the other hand, the suggested method produces an impressive result when measuring the system’s overall SE.

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Maximizing the spectral efficiency of finite dimensional multiuser massive MIMO

Cited by 2 publications

References 31 publications

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

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

Design a hybrid meta-heuristic algorithm for optimal multicell-MMSE to maximize the spectral efficiency in massive MIMO systems

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