Mellin transform-based D2D power optimization in 5G-enabled social IoT network

Mellin transform-based D2D power optimization in 5G-enabled social IoT network

연구 분야: Networking

학회: The Journal of Supercomputing

One of the main sources of information dissemination is social networks. Changes in social and Internet of Things (IoT) relationships can affect the quality of service (QoS) of device-to-device (D2D) communication in fifth-generation (5G) networks. Integration of social networks and IoT networks will allow users to improve their communication abilities with proximity users. The most challenging issues are interference and channel uncertainty due to massive connectivity and random movement of users in an urban dynamic environment. In this paper, D2D-based social Internet of Things (D2D–social IoT) in a 5G network is modeled for urban regions. To address these issues, we formulate a throughput maximization problem under the maximum power and QoS constraints. The problem is divided into multiple subproblems to reduce interference and enhance throughput. First, we leverage the interference channel link for intelligent resource sharing with better QoS by the spatiotemporal method. The second one involves bisection-based power search optimization by utilizing the Mellin transformation method for latency and power minimization. Simulation results demonstrate that the proposed method significantly improves the network performance in terms of throughput, sum rate, and reduction in network latency by up to 19.91%, 26.24%, and 31.57%, respectively. It is expected that an inclusive implementation of the said method can enable resource allocation in 5G-enabled social IoT networks for the development of smart city.