Maximizing Channel Capacity of 3D MIMO System via Antenna Downtilt Angle Adaptation Using a Q-Learning Algorithm

Abstract

3D MIMO introduces the vertical dimension of the antenna downtilt angle to make the direction of signal transmission more accurate to improve system capacity. In this paper, we verify the effect of antenna downtilt angle on channel capacity through simulations of four fixed antenna downtilt angles, 90, 96, 99, and 102 degrees under the conditions that the distance between mobile station (MS) and base station (BS) is 250 m, and the heights of antenna in BS and MS are 25 m and 1.5 m, respectively. The simulation results show that the antenna downtilt angle of 96 degrees has a larger channel capacity than the others. In addition, we proposed an adaptive optimization method by applying the Q-learning algorithm to adaptively optimize the antenna downtilt angles to maximize system capacity. The performance of the proposed method is to investigate the Q-learning algorithm with three different discount rates at 0.9, 0.5, and 0.1, and four different propagation distances on 20 × 1 and 60 × 4 MIMO. We demonstrate that there is only a 1% difference between the adaptively optimized antenna downtilt angle and the ideal optimal antenna downtilt angle when the discount rate of Q-learning algorithm is 0.9, and its channel capacity performance can reach more than 99.72% of the ideal optimal one.

A Robust Power Allocation Algorithm for Cognitive Radio Networks Based on Hybrid PSO

“…Previous studies have found that some of the resource allocation algorithms in the underlying CR network are not robust, some lack fairness among cognitive users, or the algorithms are too complex. Therefore, this paper investigates a robust model [24,25] which underlies CR networks with the objective of maximizing channel capacity [26] and constraints of cognitive user power, primary user interference threshold, and transmission rate variance of cognitive users. The research contents on this model are as follows:…”