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.

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.

It has been widely speculated that the performance of the next generation Internet of Things (IoT) based wireless network should meet a transmission speed on the order of 1000 times more than current wireless networks; energy consumption on the order of 10 times less and access delay of less than 1 ns that will be provided by future 5G systems. To increase the current mobile broadband capacity in future 5G systems, the millimeter wave (mmWave) band will be used with huge amounts of bandwidth available in this band. Hence, to support this wider bandwith at the mmWave band, new radio access technology (RAT) should be provided for 5G systems. The new RAT with symmetry design for downlink and uplink should support different scenarios such as device to device (D2D) and multi-hop communications. This paper presents the path loss models in parking lot environment which represents the multi-end users for future 5G applications. To completely assess the typical performance of 5G wireless network systems across these different frequency bands, it is necessary to develop path loss (PL) models across these wide frequency ranges. The short wavelength of the highest frequency bands provides many scatterings from different objects. Cars and other objects are some examples of scatterings, which represent a critical issue at millimeter-wave bands. This paper presents the large-scale propagation characteristics for millimeter-wave in a parking lot environment. A new physical-based path loss model for parking lots is proposed. The path loss was investigated based on different models. The measurement was conducted at 28 GHz and 38 GHz frequencies for different scenarios. Results showed that the path loss exponent values were approximately identical at 28 GHz and 38 GHz for different scenarios of parking lots. It was found that the proposed compensation factor varied between 10.6 dB and 23.1 dB and between 13.1 and 19.1 in 28 GHz and 38 GHz, respectively. The proposed path loss models showed that more compensation factors are required for more scattering objects, especially at 28 GHz.

This study presents a novel miniature ultra-high frequency (UHF) radio frequency identification (RFID) tag for metallic objects. Its arrangement includes a U-shaped feeder, which is inductively coupled to two E-type connected patches. Size reduction is achieved by means of utilizing the U-shaped feeder, and introducing a defection in the connection between the two E-type patches. The defection in the connection area between the patches modifies the current distribution by increasing the electrical length, and the antenna inductance. Furthermore, increasing the length of the U-shaped structure significantly raises the antenna inductance and, consequently, the flexibility of the structure for the impedance matching purpose. Polytetrafluoroethylene (PTFE) is employed for the fabrication of the designed tag. The tag’s size is $65\,\,mm\times 20\,\,mm\times 1.5\,\,mm$ , and does not require any complex fabrication process such as utilization of metal vias and/or shorting stubs. It is observed that with 4W EIRP, the measured detection distance is $6.9~m$ when it is attached onto metal sheets ( $20\,\,cm\times 20\,\,cm$ ), and $5.54~m$ when it is in free space. The measurement results also are in good agreement with the simulated results.

This article describes the design of an Ultra-High Frequency (UHF) miniature folded dipole Radio Frequency Identification (RFID) tag antenna that can be mountable on metallic objects. The compact tag antenna is formed from symmetric C-shaped resonators connected with additional arms embedded into the outer strip lines for miniaturization purposes. It is loaded with outer strip lines, resulting in a flexible tuning method that is capable of matching the integrated circuit (IC) chip’s impedance. The proposed tag is fabricated on a single layer of Polytetrafluoroethylene (PTFE) substrate. It has simple structure and does not require any metallic vias or shorting plate. The miniature tag antenna with a size of $82.75\times 19.5\times 1.5{\mathrm{mm}}^3$ yields a total realized gain of $-0.53\mathrm{dB}$ at the resonance frequency when attached to a $40\times 40{\mathrm{cm}}^2$ metal plate. The presented design utilizes a European RFID band, and the simulated results of realized gain, read range, and input impedance are verified with measurement results.