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

• Published in: International journal of communication systems Vol. 36; no. 2
• Main Authors: Byreddy, Amarender Reddy, Logashanmugam, E.
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 25.01.2023
• Subjects: adaptive shark smell‐coyote optimization algorithm; Algorithms; Antennas; Beamforming; Chains; Channel capacity; Communications systems; Energy efficiency; hybrid meta‐heuristic learning; massive multiple‐input multiple‐output system; MIMO communication; multi‐objective function; Optimization; Optimization algorithms; Performance enhancement; Power consumption; Radio equipment; Radio frequency; Wireless networks; “spectral efficiency and energy efficiency” enhancement
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.5381

Summary 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. A novel massive multiple‐input multiple‐output (MIMO) communication system is developed by suggesting a new algorithm to maximize the “energy efficiency and spectral efficiency” trade‐off through solving the resource efficiency. The major goal of this framework is to achieve a trade‐off between “spectral efficiency and energy efficiency” in a massive MIMO system by solving the resource efficiency. Here, the number of base station antennas, power allocation for all u‐users (transmit power), and beamforming vectors are optimized using an adaptive shark smell‐coyote optimization (ASS‐CO) algorithm.