Recent development in pairs of devices communications in the cellular system has necessitated the emergence of various resource allocation strategies for power control with the hope of achieving higher data rate, enhanced system capacity and overall spectral efficiency. Strategies for efficient service delivery in device-to-device (D2D) cellular system have often posed daunting challenges requiring uncompromising techniques. In this work, various techniques for resource allocation strategies for power control in D2D cellular system are investigated. This work is dedicated at the best technique of achieving maximum transmission power of users of cellular and the device pair users with reference to the efficient service delivery coupled with established signal-to-interference-plus-noise ratio (SINR) at the base station and the D2D users across the channel of the uplink because of the ability of the individual transmitter of devices have been able to realign with the power of transmitter relative to the calculated SINR and interference parameters. An expression of SINR, D2D pairs and the distance between device pairs with respect to base station for the optimized network for the various techniques are analyzed. The performance of the resource-based power control technique (RPCT) and the adaptive power control technique (APCT), with respect to the SINR and the device pairs, has shown effectiveness in its ability to address over forty-three pairs of D2D in order to have an improved level of SINR as obtained in this work, it is convenient to say that an increase in the number of cellular users, enables a corresponding increase in the total capacity of D2D users. When an increase is experienced in the number of users of cellular, it automatically adds a value of increase to the reusable channels thereby enabling additional quantity of D2D users in the clusters, such that co-channel interference within the D2D users in the neighboring cluster reduces, thereby enabling an increased capacity of the user of D2D.
Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning