An Interference-Oriented 5G Radio Resource Allocation Framework for Ultradense Networks

To cope with the explosive growth in demands of wireless network, ultradense network (UDN) technology is widely adopted, which could increase the capacity of wireless network, but also bring severe intercell interference (ICI). However, existing solutions cannot work well in such complex scenarios, due to the limits of their mechanisms. To solve the problem, in this article, an interference-oriented radio resource allocation framework is proposed with multiple usages, including supplying precise, stable, and timely performance feedbacks, near perfect offline training, and high compatibility. As the use of the framework is derived from precise interference identification, a practical regression-based interference modeling algorithm is proposed to support the framework. With in-depth analysis of the mechanism of interference, the proposed algorithm could efficiently and accurately model interference between users using only data collected from operating wireless networks. Compared with the baseline algorithm, the proposed algorithm could reach the same accuracy with training time of two orders of magnitude shorter. To further show the advantages of the framework, a high-performance double-deep-Q-network-based resource allocation algorithm is also proposed. By integrating into the proposed framework, the proposed algorithm could coordinate ICI better, with 40% to 101% higher energy efficiency compared with baseline algorithms.