Study on Social-sine Cosine Algorithm-based Cross Layer Resource Allocation in Wireless Network

Cross-layer resource allocation in wireless networks has historically been addressed using communication networks or information theory. A major difficulty in networking is the distribution of limited resources across network users. The resource is allotted at the Medium Access Control (MAC) level in a typical multilayer network, and the network layers use bit pipes to deliver data at a set pace with some random errors. As a result, this research demonstrates how to use the recommended Social-Sine cosine algorithm to allocate cross-layer resources in a wireless network (SSCA). The fundamental purpose of this study topic is to allocate resources between layers using the Social Sine Cosine Algorithm (SSCA). For Cross layer optimization, the MAC and physical layers provide Queue State Information (QSI) and Channel State Interference (CSI), respectively. The cross-layer optimization entity makes the resource allocation choice in order to maximise the network’s sum rate. By changing the channel conditions, the Cross layer entity for optimization adapts the judgement based on new input data.

The recommended SSCA is built by combining the Social Ski Driver (SSD) and the Sine Cosine Algorithm (SSA). In addition, the suggested SSCA analyses max-min, hard-fairness, proportional fairness, mixed-bias, and maximum throughput fitness based on energy and fairness for further improving the resource allocation approach. To minimise the network’s sum rate, the cross-layer optimization entity decides on resource distribution based on energy and fairness. Energy, throughput, and fairness are used to evaluate the proposed model’s resource allocation performance. The created model achieves a maximum energy of 258213, a maximum throughput of 3.703, and a maximum fairness of 0.868.

Author (S) Details

T. Praveena
Department of Computer Science and Engineering, RV College of Engineering, Bengaluru, Karnataka, India.

S. Nagaraja
Department of Computer Science and Engineering, RV College of Engineering, Bengaluru, Karnataka, India.

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