The upcoming 6G networks are sixth-sense next-generation communication networks with an ever-increasing demand for enhanced end-to-end (E2E) connectivity towards a connected, sustainable world. Recent developments in artificial intelligence (AI) have enabled a wide range of novel technologies through the availability of advanced machine learning (ML) models, large datasets, and high computational power. In addition, intelligent resource management is a key feature of 6G networks that enables self-configuration and self-healing by leveraging the parallel computing and autonomous decision-making ability of ML techniques to enhance energy efficiency and computational capacity in 6G networks. Consequently, ML techniques will play a significant role in addressing resource management and mobility management challenges in 6G wireless networks. This article provides a comprehensive review of state-of-the-art ML algorithms applied in 6G wireless networks, categorized into learning types, including supervised and unsupervised machine learning, Deep Learning (DL), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL) and Federated Learning (FL). In particular, we review the ML algorithms applied in the emerging networks paradigm, such as device-to-device (D2D) networks, vehicular networks (Vnet), and Fog-Radio Access Networks (F-RANs). We highlight the ML-based solutions to address technical challenges in terms of resource allocation, task offloading, and handover management. We also provide a detailed review of the ML techniques to improve energy efficiency and reduce latency in 6G wireless networks. To this end, we identify the open research issues and future trends concerning ML-based intelligent resource management applications in 6G networks.

Beyond Fifth Generation (B5G) networks are expected to be the most efficient cellular wireless networks with greater capacity, lower latency, and higher speed than the current networks. Key enabling technologies, such as millimeter-wave (mm-wave), beamforming, Massive Multiple-Input Multiple-Output (M-MIMO), Device-to-Device (D2D), Relay Node (RN), and Heterogeneous Networks (HetNets) are essential to enable the new network to keep growing. In the forthcoming wireless networks with massive random deployment, frequency re-use strategies and multiple low power nodes, severe interference issues will impact the system. Consequently, interference management represents the main challenge for future wireless networks, commonly referred to as B5G. This paper provides an overview of the interference issues relating to the B5G networks from the perspective of HetNets, D2D, Ultra-Dense Networks (UDNs), and Unmanned Aerial Vehicles (UAVs). Furthermore, the existing interference mitigation techniques are discussed by reviewing the latest relevant studies with a focus on their methods, advantages, limitations, and future directions. Moreover, the open issues and future directions to reduce the effects of interference are also presented. The findings of this work can act as a guide to better understand the current and developing methodologies to mitigate the interference issues in B5G networks.

The advent of fifth-generation (5G) systems and their mechanics have introduced an unconventional frequency spectrum of high bandwidth with most falling under the millimeter wave (mmWave) spectrum. The benefit of adopting these bands of the frequency spectrum is two-fold. First, most of these bands appear to be unutilized and they are free, thus suggesting the absence of interference from other technologies. Second, the availability of a larger bandwidth offers higher data rates for all users, as there are higher numbers of users who are connected in a small geographical area, which is also stated as the Internet of Things (IoT). Nevertheless, high-frequency band poses several challenges in terms of coverage area limitations, signal attenuation, path and penetration losses, as well as scattering. Additionally, mmWave signal bands are susceptible to blockage from buildings and other structures, particularly in higher-density urban areas. Identifying the channel performance at a given frequency is indeed necessary to optimize communication efficiency between the transmitter and receiver. Therefore, this paper investigated the potential ability of mmWave path loss models, such as floating intercept (FI) and close-in (CI), based on real measurements gathered from urban microcell outdoor environments at 38 GHz conducted at the Universiti Teknologi Malaysia (UTM), Kuala Lumpur campus. The measurement data were obtained by using a narrow band mmWave channel sounder equipped with a steerable direction horn antenna. It investigated the potential of the network for outdoor scenarios of line-of-sight (LOS) and non-line-of-sight (NLOS) with both schemes of co- (vertical-vertical) and cross (vertical-horizontal) polarization. The parameters were selected to reflect the performance and the variances with other schemes, such as average users cell throughput, throughput of users that are at cell-edges, fairness index, and spectral efficiency. The outcomes were examined for various antenna configurations as well as at different channel bandwidths to prove the enhancement of overall network performance. This work showed that the CI path loss model predicted greater network performance for the LOS condition, and also estimated significant outcomes for the NLOS environment. The outputs proved that the FI path loss model, particularly for V-V antenna polarization, gave system simulation results that were unsuitable for the NLOS scenario.

Device-to-device (D2D)-assisted 6G networks are expected to support the proliferation of ubiquitous mobile applications by enhancing system capacity and overall energy efficiency towards a connected-sustainable world. However, the stringent quality of service (QoS) requirements for ultra-massive connectivity, high mobility, limited network resources, and interference management are the significant challenges to deploying multiple device-to-device pairs (DDPs) without disrupting cellular users. Hence, intelligent resource management and power control are indispensable for alleviating interference among DDPs to optimize overall system performance and global energy efficiency. Considering this, we present a Federated DRL-based method for energy-efficient resource management in a D2D-assisted heterogeneous network (HetNet). We formulate a joint optimization problem of power control and channel allocation to maximize the system’s energy efficiency under QoS constraints for cellular user equipment (CUEs) and DDPs. The proposed scheme employs federated learning for a decentralized training paradigm to address user privacy, and a double-deep Q-network (DDQN) is used for intelligent resource management. The proposed DDQN method uses two separate Q-networks for action selection and target estimation to rationalize the transmit power and dynamic channel selection in which DDPs as agents could reuse the uplink channels of CUEs. Simulation results depict that the proposed method improves the overall system energy efficiency by 41.52% and achieves a better sum rate of 11.65%, 24.78%, and 47.29% than multi-agent actor-critic (MAAC), distributed deep-deterministic policy gradient (D3PG), and deep Q network (DQN) scheduling, respectively. Moreover, the proposed scheme achieves a 5.88%, 15.79%, and 27.27% reduction in cellular outage probability compared to MAAC, D3PG, and DQN scheduling, respectively, highlighting the scalability and robustness of the proposed solution.

The energy consumption and coverage range of unmanned aerial vehicles (UAVs) are major challenges in UAV-based postdisaster communications. To address these challenges, energy harvesting is employed to power communication devices and prolong the lifetime of the wireless communication network during a disaster. In addition, clustering techniques and device-to-device (D2D) communication are needed to increase the overall network coverage and provide sustainable connectivity during the disaster and postdisaster phases. We have proposed a novel emergency communication system (ECS) using the optimal cluster head (CH) technique to improve the energy transfer efficiency for sustainable network connectivity. We have developed a UAV deployment model assisted by the clustering technique and D2D links that is capable of harvesting energy to increase the network lifetime. This new approach is expected to enhance the reliability of the network in disaster situations. The proposed methods have been evaluated by measuring the energy efficiency performance and the network outage probability. The simulation results demonstrate improved performance with the deployment of optimal CHs, while the outage probability has been effectively reduced. Moreover, the proposed approach has been proven to reduce the computational complexity. In conclusion, UAV deployment with the optimal CH algorithm is a suitable network design to recover from natural disasters and potentially save many lives.