Interference Mitigation by Intelligent Channel Selection for DevicetoDevice Communications DevicetoDevice (D2D) communication is an efficient and interesting feature of wireless networks of the next generation. It provides extremely low latency by allowing immediate communication between nearby wireless devices without transmitting data through the network facilities. This will add advanced features to cellular networks in the 5th generation (5 G) and beyond. Empowering D2D in the mobile network creates many technical problems such as device discovery, mode selection, data security, and interference mitigation. Cognitive radio D2D users (DUs) transmission radiates through diverse ways that cause undesirable interference to primary users (PUs) or cellular users (CUs) that share the same spectrum bands, which eventually lead to serious degradation of service quality and efficiency. This article proposes an intelligent channel selection scheme depending on learning algorithms that drive the selection scheme to be intelligent for D2D Cognitive Radio Network (DCRN) aiming at mitigating interference between DUs and PUs. An extensive analysis and comparisons with other algorithms are carried out to investigate its performance. The simulation results demonstrate that the proposed algorithm improves the accuracy of channel selection, maximizes the average throughput, spectrum utilization, packet delivery, and minimizes both the average delay and interference in D2D network under various network densities and a diverse number of channels.

A novel collision aware network assisted device discovery scheme empowering massive D2D communications in 3GPP LTEA networks Abstract   With the proliferation of proximity based servicesapplications, DevicetoDevice (D2D) communication has become an emerging technology in facilitating data transfer among proximal cellular User Equipments (UEs). However one of the most challenging issues in D2D communication is the need for a robust device discovery scheme that effectively discovers UEs without any performance bottlenecks. In a densely populated cellular network, the conventional device discovery schemes proposed in the literature suffer from one or more adversities. High signaling overhead, large number of collisions and inefficient use of resources are some of the major issues that severely limit the performance of a device discovery scheme. Based on the characteristics of the networkassisted and autonomous device discovery schemes, we propose a novel device discovery scheme that discovers proximal UEs without causing any significant performance degradation. The cellular UEs are first divided into clusters based on their social relationship status and common interests. Every cluster will then execute a device discovery algorithm which is composed of two stages. In the first stage, UEs discover their neighbors using the conventional beacon based discovery scheme. The second stage comprises of successful UEs reporting to BS by initiating the LTE random access procedure. In this way, we not only reduce the signaling overhead but also enable substantial number of device discoveries to happen simultaneously at any instant of time. Further, we present a stochastic geometry based framework to analyze the collision probability and link discovery probability of the proposed scheme. Through extensive simulations we find that our proposed device discovery scheme effectively results in higher discovery probability compared to a recently proposed discovery scheme, by reducing the amount of collisions.