Distributed neighbor discovery with mobility adaptive probing for beyond 5G D2D communications

Abstract

Representing a paradigm shift in cellular communication, Device-to-Device (D2D) Communication is an emerging technology that enables direct communication between two User Equipments(UEs) without going through the Base Station. It enables decentralization of cellular networks, enhances network capacity, increases spectral efficiency, and offloads network traffic. In order to proceed with direct D2D communication, the devices must have knowledge of the proximal devices and should work in all coverage scenarios such as in-coverage, partial-coverage and out-of-coverage. Most of the existing works on Device Discovery are based on 3GPP Model A and Model B. However, these schemes suffer from overhead of discovery message exchanges with the increase in the number of devices in a mobile environment. To address these issues, we propose a distributed and adaptive device discovery mechanism that can discover the proximal UEs in all possible scenarios. To achieve this, we propose a Hybrid Discovery Model ‘H’, which uses the concept of ‘Who are there?’. In Model ‘H’, the discoverer sends a single request message to discover all proximal devices. The responses of the proximal UEs are staggered based on the estimated number of neighbors to avoid collisions. In addition to that, we adapt the discovery mechanism to the relative mobility of the neighborhood using our proposed Adaptive Probing Algorithm named ‘ReMAP’. The relative mobility is determined based on the current and previous neighbor lists of each UE. The key advantage of our proposed mechanism is that we do not require the knowledge of position and mobility pattern of the UEs. We implement and evaluate our proposed work in D2D Module of the NS3 simulator. From the simulation results, we observe that our proposed model shows better performance in terms of the number of UEs discovered and reduced number of discovery message exchanges compared to the state-of-the-art. We also compare the performance of ReMAP with a constant probing mechanism and show the improvement in terms of adaptability to the mobility of the neighborhood.