A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

Abstract

The deployment of fifth-generation (5G) broadband wireless cellular networks has enabled the support of highly demanding applications, paving the way towards global broadband connectivity. In the same context, related advances in network infrastructure, such as network function virtualization via the decoupling of related functionalities from hardware equipment has leveraged end-to-end service support in highly heterogeneous environments. To this end, uninterrupted provision of service necessitates a holistic network redesign where access points (APs) should be placed and configured dynamically. One of the key supporting technologies of 5G communications are massive multiple input multiple output (m-MIMO) configurations, deployed over various APs, either in a centralized or in a distributed manner. However, such a configuration would pose an additional degree of complexity during network planning, since on one hand accurate channel estimation for all potential links is limited due to the pilot contamination effect, and on the other hand the number of actually deployed radio frequency chains is again limited when compared to the fully digital approach, in an effort to relax hardware and computational burden. Moreover, additional issues should be taken into consideration as well, such as the coexistence of m-MIMO configurations with other novel technologies in the 5G air interface as well as the need for provision of acceptable quality of service to a large number of mobile users. In an effort to deal with the aforementioned issues, machine learning (ML) algorithms have emerged over the last decade as a promising solution that can deal with complex optimization problems. In this context, the main goal of this survey paper is to present the state-of-the-art approaches in the deployment of ML methods in m-MIMO configurations. Unlike other related survey papers, this is the first scientific attempt to cover all aspects of m-MIMO practical implementations (i.e., channel estimation, MIMO precoding and signal detection, hybrid beamforming, distributed configurations, user pairing and power allocation in non-orthogonal multiple access), in the context of ML-aided calculations. To this end, all presented works are categorized according to the studied aspect, while in the same context potential limitations and future challenges are highlighted as well.INDEX TERMS Massive MIMO, machine learning, channel estimation, hybrid beamforming, cell-free orientations, NOMA.

Deep Learning-aided Channel Estimation Combined with Advanced Pilot Assignment Algorithm to Mitigate Pilot Contamination for Cell-Free Networks

“…All APs are connected to CPU via a back-haul network (21) . The channel h mk UE 'k' and AP 'M' is modelled as Rayleigh Fading channel (11) given by;…”

“…Due to this MMSE, CE technique is more computationally complex as compared to LS, CE technique. Considering above drawbacks of CE, a new research area is emerging which is known as Machine Learning (ML) (11,12) , which will address the problem of CE effectively with incredibly fast and robust training models. In the proposed paper, we are using Deep https://www.indjst.org/ Feed Forward (DFF) neural network for CE, which proves significant in mitigating CEE and thus enhancing the system SE performance.…”

Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

“…where ϖ and γ indicate the learning rate and the discount rate, respectively, and Â(t) is the ideal action for time t. In wireless communication systems, Q-learning can be used to determine the transmit precoding in beam tracking, for instance, given channel information of the moving user as the state [89], [90]. Moreover, trainable deep learning models can function as Q-tables, allowing the handling of continuous, complex-valued input variables, such as channel information.…”

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

“…Due to the promising benefits of using ML for future communications, recently, there have been several studies dealing with such implementations [ 28 , 29 , 30 , 31 , 32 ]. In [ 28 ], the authors discussed applying different ML types at each communication layer between devices.…”

“…In addition, several studies discussed the role of ML algorithms for parameter optimization in m-MIMO communication. In [ 30 ], the authors analyzed ML-aided m-MIMO communications for the 5G network. They carried out several issues, including channel estimation, beamforming and precoding, signal detection, distributed and cell-free configurations, and m-MIMO with NOMA.…”