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.
Highlights
In order to satisfy the rapidly increasing requirements for high data rate in current wireless communication systems, a new massive Multiple-input multiple-output (MIMO) technology was introduced in [1–3]
On the base of the aforementioned discussion, a low complexity scheme is needed from the practical point of view for joint antenna selection and user scheduling (JASUS) in multi-cell multi-user massive MIMO scenarios to reduce the complexity of function (11) while decreasing the cost of the channel state information (CSI) feedback
6 Conclusion In this paper, we studied the problem of JASUS in a multi-cell multi-user massive MIMO downlink system operating with Time division duplexing (TDD) mode
Summary
In order to satisfy the rapidly increasing requirements for high data rate in current wireless communication systems, a new massive MIMO (multiple-input multiple-output) technology was introduced in [1–3]. Benmimoune et al [14] proposed a two-step JASUS scheme for downlink multi-user massive MIMO systems It successively closed unnecessary antennas and removes undesired users which contribute little to system performance. Due to the high computational complexity, this algorithm can only be employed to the scenarios with a smaller number of candidate antennas and user sets Using this algorithm in practical multi-cell multi-user massive MIMO system scenarios is difficult. Olyaee et al [15] proposed a JASUS method based on zero-forcing (ZF) precoding algorithm for single-cell multi-user massive MIMO downlink systems. 1. A low complexity JASUS method based on AMCMC algorithm is proposed for downlink multi-cell multi-user massive MIMO systems. MIMO downlink systems with the system capacity formulation model with consideration of the multi-cell interference
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