Realtime dynamic clustering for interference and traffic adaptation in wireless TDD system

Abstract

The dynamic time-division duplex (TDD) system is a recently proposed technology that can accommodate downlink (DL)/uplink (UL) traffic asymmetry and sufficiently utilize the spectrum resource. Its feature of sufficiency and flexibility will also induce a more sophisticated interference environment, which is known as interference mitigation and traffic adaptation (IMTA) problem. Clustering is a new idea which has been widely accepted to solve IMTA problem. However, most previous works just took large-scale path loss or coupling loss as criteria of the clustering schemes, thus the throughput performance would be limited by the varying traffic requirements among different small cells within one cluster. In this paper, a realtime dynamic cluster-based IMTA scheme is proposed and evaluated with dense deployment of small cells (SCs). Firstly, a new clustering criterion named Differentiating Metric (DM) is defined. Based on the defined DM value, a DM matrix is formed and further presented by a clustering graph. In the clustering graph, the dynamic clustering strategy is mapped to a MAX N-CUT problem, which is addressed in polynomial time by a proposed heuristic clustering algorithm. Furthermore, the system level simulation results demonstrate a promising improvement on uplink traffic throughput (UTP) in our proposed scheme compared with traditional clustering schemes.