A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

Abstract

The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

Joint Antenna and User Scheduling in the Massive MIMO System Over Time-Varying Fading Channels

“…where p k 's are given by the solution to the problem in (26) for currently given K sel and M sel . After finding k * , we update K sel by deleting user k * from it, i.e.,…”

“…More recently, with recognizing the importance of user selection in multi-user massive MIMO systems, joint antenna and user selection (JAUS) has been studied [24]- [26]. The authors in [24] have proposed a heuristic JAUS algorithm for sum rate maximization, in which starting with all antennas activated, the antennas are deactivated one by one in a greedy manner until the number of active antennas equals the number of RF chains, while user selection is conducted using the semi-orthogonal user selection algorithm (SUS) [27] that selects a user subset in such a way that their channel orthogonality becomes maximized.…”

“…In [25], the authors have developed another heuristic JAUS algorithm for maximizing the sum rate per unit energy consumption, in which users with superior channels are selected first, and then antennas with the least contributions to the sum rate are iteratively deleted until the number of active antennas equals the number of RF chains. In [26], the authors have developed a probabilitybased JAUS algorithm that finds the probabilities that each antenna and each user will be activated, respectively, with the aim of maximizing the sum capacity. Although these works [24]- [26] have studied the JAUS problem, there are still several critical limitations.…”

“…In [26], the authors have developed a probabilitybased JAUS algorithm that finds the probabilities that each antenna and each user will be activated, respectively, with the aim of maximizing the sum capacity. Although these works [24]- [26] have studied the JAUS problem, there are still several critical limitations. First, they all have considered the sum rate as the objective function to be maximized.…”

“…In addition, although there are many studies on either antenna selection, user selection, or both, most of the existing studies including [15]- [26] have focused on optimization problems from a snapshot perspective. That is, the antenna and/or user selection under fixed channel conditions has been dealt with, rather than antenna and/or user scheduling with considering time-varying fading channels.…”