Efficient coordination of almost blank subframes with coupling macro cells in heterogeneous networks

Abstract

Heterogeneous networking is an inevitable trend in mobile communication systems, where macrocells and small cells coexist to provide the service of wireless access. The technique of enhanced inter-cell interference coordination (eICIC) is popularly used to mitigate signal interference among cells. A number of subframes are picked as almost blank subframes (ABSs), during which a macro-cell base station suspends data transmissions to improve signal quality of small cells that it covers. Since the throughput of macro cells drops to zero in an ABS, how to decide the number of ABSs (ie, the ABS ratio) and their positions (ie, the ABS placement) is critical. Many studies implicitly consider that every small cell resides in only one macro cell and they manage ABSs for each individual macro cell accordingly. In practice, some macro cells may cover the same small cells (called coupling macrocells) which makes the management of their ABSs depend on each other. To address this issue, the paper proposes an efficient coordination of ABS with coupling macro cells (ECO) algorithm. It divides the network into component subnetworks depending on whether macro cells are coupling or not. Then, ECO computes the ABS ratios for the macro cells in each component subnetwork based on multiple factors, including data rate, traffic demand, and quality of service (QoS) support. Moreover, ECO jointly places the ABSs of coupling macro cells to shorten packet latency. Experimental results show that the ECO algorithm not only increases the network throughput but also reduces the loss rate of real-time data. KEYWORDS almost blank subframe (ABS), coupling macro cells, heterogeneous network, interference management Int J Commun Syst. 2020;33:e4256.wileyonlinelibrary.com/journal/dac

Online reinforcement learning for adaptive interference coordination

“…Previous works have addressed the efficient configuration of eICIC parameters. Some works focused solely on the CRE bias, 14‐16 others focused on the ABS ratio, 17‐21,27 while fewer works 22,23,28 aimed at optimizing both parameters simultaneously, like this work.…”

“…Regarding the dynamic nature of the network conditions (eg, traffic intensity, number of users, user positions), most previous works 7,8,14‐17,20,21,29‐34 assume static or stationary conditions. While some works consider nonstationary scenarios, 9,18,19,22,28 their approaches are based on optimization or heuristic algorithms over mathematical models of the network , like in References 6‐8,10,20,21,32,34‐37. For mathematical tractability, even the most complete mathematical network models comprise simplifications and assumptions that may limit their application to real operating networks.…”