Special Issue on Autonomous Learning-Based Algorithm for Heterogeneous Cellular Networks

摘要截稿:

全文截稿: 2020-03-30

影响因子: 2.613

期刊难度:

CCF分类: C类

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 计算机：信息系统 - 3区

• 小类 : 工程：电子与电气 - 3区

• 小类 : 电信学 - 3区

Overview

The spectrum bands of the multiple base stations comprise the sets of orthogonal wireless channels and spectrum usage scenarios the device to device pairs transmit over the dedicated frequency bands and the device to device pairs operate on the shared cellular channels. The goal of each device pair is to jointly select the wireless channel and power level to maximize its reward, defined as the difference between the achieved throughput and the cost of power consumption, constrained by its minimum tolerable signal-to-interference-plus-noise ratio requirements. We formulate this problem as a stochastic non-cooperative game with multiple players where each player becomes a learning agent whose task is to learn its best strategy and develop a fully autonomous multi-agent Q-learning algorithm converging to a mixed-strategy Nash equilibrium. The learning algorithm shows relatively fast convergence and near-optimal performance after a small number of iterations.

Potential topics included, but not limited

Reinforcement Learning for self organization and power control of two-tier heterogeneous networks

· Optimal new site deployment algorithm for heterogeneous cellular networks· Energy cost minimization in heterogeneous cellular networks with hybrid energy supplies· Configuration algorithm for service scalability in heterogeneous cellular networks· Q-learning based heterogeneous network selection algorithm· Bayesian reinforcement learning-based algorithm for heterogeneous cellular networks·

Machine learning paradigms for next-generation communication networks

· Online distributed user association for heterogeneous radio access network