The road to 6G: a comprehensive survey of deep learning applications in cell-free massive MIMO communications systems

Abstract

The fifth generation (5G) of telecommunications networks is currently commercially deployed. One of their core enabling technologies is cellular Massive Multiple-Input-Multiple-Output (M-MIMO) systems. However, future wireless networks are expected to serve a very large number of devices and the current MIMO networks are not scalable, highlighting the need for novel solutions. At this moment, Cell-free Massive MIMO (CF M-MIMO) technology seems to be the most promising idea in this direction. Despite their appealing characteristics, CF M-MIMO systems face their own challenges, such as power allocation and channel estimation. Deep Learning (DL) has been successfully employed to a wide range of problems in many different research areas, including wireless communications. In this paper, a review of the state-of-the-art DL methods applied to CF M-MIMO communications systems is provided. In addition, the basic characteristics of Cell-free networks are introduced, along with the presentation of the most commonly used DL models. Finally, future research directions are highlighted.

Optimization Mobility in Sensor Actor Networks: A Comprehensive Framework

“…In summary, this study has demonstrated the value of hardware control and realistic implementations in the context of sensor-actor networks, in addition to offering a fresh approach for mobility optimization. The suggested model is a useful tool for study participants, engineers, and practitioners looking to improve the efficacy and efficiency of sensor-actor networks because it emphasizes practical experimentation and simulation above theoretical frameworks [30] . The future of flexible and receptive networked systems will be greatly influenced by the knowledge gathered from this research, as technology develops and sensor-actor network applications grow.…”

“…The study examines future trends, challenges, and current research in optimizing resource management. Ijemaru, Li-Minn, and Kah [30] look into swarm intelligence methods for traffic engineering and opportunistic data collection in a smart city waste management through the Internet of Vehicles. While not specifically relevant to sensoractor networks, the research offers valuable perspectives on how to optimize data collection and handling traffic through the use of swarm intelligence.…”

“…The studies [15]- [30] that are cited mainly address resource allocation, security, congestion mitigation, and different optimization strategies in wireless sensor networks. Though they make a substantial contribution to the discipline, the proposed model focuses on optimizing mobility inside sensor-actor networks, offering a thorough framework that takes into account energy usage, communication effectiveness, and task distribution in dynamic environments.…”