A proportional fair scheduling strategy using multiobjective gradient‐based African buffalo optimization algorithm for effective resource allocation and interference minimization

Abstract

Summary The increased usage of Internet of Things (IoT) applications in several areas, like healthcare, agriculture, and business, has aggravated mobile traffic issues to a large extent. The deployment of 5G technology has resulted in increased traffic globally. These coherent devices, on the other hand, use the internet to fine‐tune the quality of service in order to provide scalability, anonymity, and accessibility. Despite its numerous virtues, it is bound to encounter issues with interference management, fairness, throughput, and computational complexities. In this paper, a novel Multi‐Objective Gradient‐based African Buffalo Optimization (MOGABO) algorithm is developed to handle proportional fairness scheduling, improve interference management, increase throughput, and reduce computational complexities in cellular communication systems, particularly device‐to‐device (D2D) communication. The simulation analysis is done against other approaches such as the Hungarian technique, DDDPG technique, heuristic technique, and SC‐FDMA technique demonstrates that this method has improved system fairness over the existing approaches. Our method maximizes throughput, and for 500 UE, the throughput is 10.4 Mbps, with an average queuing delay of 24 ms. As a result, when compared to the existing method, MOGABO method offers a 1.2% increase in throughput. Thus, our method offers better coverage and throughput for D2D cellular communication with the reduction of computational complexities.

Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique

“…Here, the HKH-ABO model enhances the classification accuracy, which is initiated in the classification layer of BPBRW. Also, HKH-ABO is the combined model of Krill Herd Optimization (KHO) [25] and African Buffalo Optimization (ABO) [14]. Moreover, the goal of hybridization is to improve the efficiency of disease classification.…”

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

“…The applications of the Internet of Things (IoT) became essential in various fields which leads to an increase in the traffic in the mobile networks. Therefore, Kesavan et al [40] introduced multi-objective GBO (MOGABO) to improve the processes, decrease the complexity, and thereby increase the productivity. The results proved the efficiency of MOGABO which achieved a 1.2% increase in productivity compared with other methods in the literature [41].…”

QoS-Aware Downlink Traffic Scheduling for Cellular Networks with Dual Connectivity

“…However, the proposed scheme uses static settings to differentiate traffics. In reference [25], the authors propose a downlink scheduling method for the existence of device-to-device (D2D) links. The proposed scheme can help increase network throughput, but QoS requirements are not addressed.…”