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.

The sixth generation (6G) mobile communication system is expected to meet the different service needs of modern communication scenarios. Heterogeneous networks (HetNets) have received a lot of attention in recent years due to their potential as a novel structure for evolutionary networks. When compared to homogeneous networks, HetNets provide more potential for spatial spectrum reuse and higher quality of service (QoS). However, effective resource management (RM) solutions are essential to prevent interference and accomplish spectrum sharing due to mutual interference. This paper presents a comprehensive review of resource management in 6G HetNets. The study aims to give crucial background on HetNets to aid in the creation of more effective methods in this field of study. First, a detailed examination of recent work is presented in resource management aspects such as power allocation, user association, mode selection, and spectrum allocation. Second, we identify the most severe challenges associated with the current resource management methods and propose suitable solutions. Finally, several open issues and emerging areas of research are highlighted.

Radio resource management (RRM) for future fifth-generation (5G) heterogeneous networks (HetNets) has emerged as a critical area due to the increased density of small-cell networks and radio access technologies. Recent research has mostly concentrated on resource management, including spectrum utilization and interference mitigation, but the complexities of these resources have been given little attention. This paper provides an overview of the issues arising from future 5G systems and highlights their importance. The different approaches used in recently published surveys categorizing RRM schemes are discussed, and the survey method is presented. We report on a survey of HetNet RRM schemes that have been studied recently, with a focus on the joint optimization of radio resource allocation with other mechanisms. These RRM schemes were subcategorized according to their optimization metrics and qualitatively analyzed and compared. An analysis of the complexity of RRM schemes in terms of implementation and computation is presented. Several potential scopes of research for future RRM in 5G HetNets are also identified.

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.

Millimeter wave technology will be dominating the fifth-generation networks due to the clear advantage of higher frequency bands and hence wider spectrum. In this paper, the indoor radio wave propagation at 28 GHz is studied by developing an efficient three-dimensional ray tracing (ETRT) method. The simulation software based on the ETRT model has been verified by measurement data. The received signal strength indication and path loss have shown significant agreement between simulation and measurement. Compared with the conventional shooting bouncing ray tracing method, the proposed ETRT method has better agreement with measurement data.