Weighted De-Synchronization Based Resource Allocation in Wireless Networks

Abstract

Considering the exponential growth of wireless devices with datastarving applications fused with artificial intelligence, the significance of wireless network scalability using distributed behavior and fairness among users is a crucial feature in guaranteeing reliable service to numerous users in the network environment. The Kuramoto model is described as nonlinear selfsustained phase oscillators spinning at varying intrinsic frequencies connected through the sine of their phase differences and displays a phase transition at a specific coupling strength, in which a mutual behavior is accomplished. In this work, we apply the Kuramoto model to achieve a weighted fair resource allocation in a wireless network, where each user has different quality of service (QoS) requirements. Because the original Kuramoto model is the synchronization model, we propose a new weighting parameter for representing requirement of each node resource and modify the Kuramoto model to achieve weighted fair resource allocation for users with different QoS requirements. The proposed modified Kuramoto model allocates all users the resource based on their weight among contending nodes in a distributed manner. We analyze the convergence condition for the proposed model, and the results reveal that the proposed algorithm achieves a weighted fair resource allocation and with potentially high convergence speed compared to previous algorithm.

A Novel Hybrid Approach for Similarity-Based Link Prediction in Complex Networks

“…The hybrid algorithm is also more accurate than traditional methods for calculating similarity because it uses the Jaccard, Adamic-Adar, and MapSim similarities, which are all well-established measures of similarity. [13,14,15]. The hybrid algorithm is more complex than traditional methods for calculating similarity, but the complexity is justified by the increased efficiency and accuracy of the algorithm.…”