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

Communication networks or information theory have traditionally been used to address cross-layer resource allocation in wireless networks. The allocation of limited resources from network users is a major issue in networking. In a typical layered network, the resource is allotted at the Medium Access Control (MAC) level, and the network layers use bit pipes to deliver data at a fixed rate with some random mistakes. As a result, this study shows how to allocate cross-layer resources in a wireless network using the suggested Social-Sine cosine algorithm (SSCA). The primary goal of this research topic is to use Social Sine Cosine Algorithm (SSCA) to allocate resources between layers. Queue State Information (QSI) and Channel State Interference (CSI) are obtained from the MAC and physical layers, respectively, for Cross_layer optimization. The resource allocation decision is made by the cross-layer optimization entity in order to maximise the network’s sum rate. The Cross_layer entity for optimization adjusts the judgement depending on new input data by altering the channel conditions.

By integrating the Social Ski Driver (SSD) and the Sine Cosine Algorithm (SSA), the suggested SSCA is created. Also, for further refining the resource allocation method, the suggested SSCA considers max-min, hard-fairness, proportional fairness, mixed-bias, and maximum throughput fitness based on energy and fairness. The cross-layer optimization entity decides on resource allocation based on energy and fairness to reduce the network's sum rate. The proposed model's resource allocation performance is measured in terms of energy, throughput, and fairness. The developed model achieves the maximal energy of 258213, maximal throughput of 3.703, and the maximal fairness of 0.868, respectively.