A Distributed Algorithm for D2D Communication in 5G using Stochastic Model

Device to device (D2D) communication in 5G is on the spurge to compensate for the exponential increase in mobile users and their data requirements. In D2D communication, proximity users can communicate and control the links among themselves without the need of base station. This brings a real challenge to model the network to incorporate uncertainty which arises due to the mobility of users. We have developed a stochastic integer programming (SIP) based model to optimize the average network delay while keeping the packet loss below a threshold. This SIP model involves a probabilistic constraint which deals with conditional probability of link breakage in the next time instance given it was active at current time instance. By exploiting the SIP model, we have developed a greedy metric termed as connectivity factor (CF) which captures the nodes' mobility and hence takes care of link reliability which in turn controls packet loss and delay per hop. Based on CF we give a pure distributed greedy algorithm for forwarding the packets to an appropriate next hop node. Advantage of our algorithm to find an appropriate next hop node is that it requires only to compute the expectation and variance of path loss, which in turn can be computed from the distributions of mobility related parameters. Here we have used random walk mobility model for simplicity, but it can be extended to other mobility model with known relevant distributions. Through simulation we have shown that our proposed algorithm gives a significant improvement in packet loss due to mobility of nodes over the traditional received signal strength (RSS) based approach and an existing contention based forwarding (CBF) approach.