Traffic profile based clustering for dynamic TDD in dense mobile networks

Due to the wide spread of many heterogeneous applications and services, the traffic profile of future mobile networks is foreseen to become more variable in time and among different base stations (BSs). Dynamic time division duplex (TDD) has been recognized as an important enabler to cope with this traffic variability, in particular for dense networks with many BSs and few user equipments (UEs) served by each BS. In such a context, one of the main issues consists in developing an incisive scheme to manage the BS-to-BS and UE-to-UE interferences that arise in this scenario. In this work, we propose a novel long-term BS clustering scheme that groups BSs that have a similar traffic profile and would be characterized, without clustering, by strong BS-to-BS interference. Due to the complexity of the optimal solution, we propose an heuristic algorithm that efficiently solves the optimization problem. Numerical results in a dense homogeneous pico BS network show that the proposed scheme strongly outperforms even a baseline dynamic TDD without clustering by ensuring a packet delay reduction up to 35%.