Interference Minimization in D2D Communication Underlaying Cellular Networks

Interference minimization while maintaining a target system sum rate by sharing radio resources among cellular user equipments (UEs) and device-to-device (D2D) pairs is an important research question in long term evolution (LTE) and beyond (4G and 5G). Total system sum rate of a cellular network can be improved if cellular UEs and D2D pairs share resource blocks. However, some sharing can also decrease the sum rate and increase the system interference. Considering this observation, we address two types of assignments (fair and restricted) in resource allocation for the interference minimization resource allocation problem. We propose a two-phase resource allocation algorithm for both fair and restricted assignments, where our objective is to minimize the system interference and at the same time, maintaining a target system sum rate. In the phase-I of our proposed algorithm, a weighted bipartite matching algorithm is used to minimize the interference and get a feasible initial solution. In some cases, we can decrease the interference introduced in phase-I of our algorithm. Therefore, in the phase-II, local search techniques are used to improve the solution. We compare the fair assignment of our proposed algorithms with a two-phase auction-based fair and interference aware resource allocation algorithm (TAFIRA), which addresses the same research problem. As well as, we compare the restricted assignment of our proposed algorithm with a minimum knapsack-based interference resource allocation algorithm (MIKIRA). We prove that the MIKIRA fails to provide feasible solutions in most of the cases. We also show that the performance ratio of the TAFIRA can be unbounded in the worst case. Moreover, in some cases, TAFIRA cannot provide any solution to the problem though the solutions exist, whereas our proposed algorithms always provide a solution whenever the solution exists. We perform extensive simulations of the algorithms and find that in all the cases, our proposed algorithm outperforms a number of state-of-the-art algorithms.