In response to the challenges of spectrum scarcity and the exponential growth of the number of connected devices, this paper addresses the joint optimization problem of user-base station association, channel assignment and power allocation in a multi-band wireless network, where sub-6 GHz, millimeter wave, and terahertz frequency bands coexist. The problem is formulated as a mixed integer non-linear programming, a known NP-hard problem. Each user requests both a minimum data rate and a minimum reliability level defined by a signal-to-noise ratio. Considering the goal of optimizing the number of satisfied users, this paper proposes a multi-agent deep reinforcement learning solution. Simulation results convincingly demonstrate the effectiveness of our proposed algorithm and its ability to learn fast the best resource allocation solution.

To cope with the spectrum scarcity of emerging mobile applications, using higher frequency bands (such as THz) becomes a necessity. Combining simultaneously higher and lower bands is then an important step for future cellular networks. Meanwhile, resource management plays a dominant effect on the system performance especially when different quality of service requirements are considered. In this paper, we formulate and investigate a joint optimization problem of resource allocation in integrated sub-6 GHz, mmWave and THz networks to maximize the system energy efficiency (EE). Therefore, we propose efficient centralized and distributed low-complexity greedy solutions. Also, we propose more efficient multi-agent reinforcement learning (MARL) solutions where users, modeled as agents, collaborate to learn and converge to the optimal user association that maximizes the EE. Simulation results show the EE provided by the proposed solutions and illustrate the superiority of the MARL-based solutions.

In natural disasters and unforeseen incidents, such as floods, earthquakes and hurricanes, the traditional communication infrastructure may become unavailable to support the emergency tele-operations. Under such circumstances, deploying unmanned aerial vehicles (UAVs) as small flying base stations is seen as a promising solution to provide real-time data communication between physicians and remote robots in both up-link and down-link directions with strict transmission requirements. This paper studies the joint optimization problem of resource allocation and UAVs positioning in UAV-assisted wireless networks with the goal of minimizing the number of deployed UAVs. Since the formulated problem is a non-convex mixed-integer non-linear programming problem, efficient heuristic and genetic solutions are proposed. Simulation results show that the proposed heuristic algorithm approaches the genetic one with an important reduction in computational complexity.

Exploiting the advantages of both non-orthogonal multiple access technique and millimeter-wave communications requires joint efficient resource allocation techniques toward satisfying the stringent requirements of future mobile communication systems. This paper focuses on a multi-band (i.e., millimeter-wave band and sub-6 GHz band) wireless network where both orthogonal and non-orthogonal multiple access techniques coexist. A joint optimization of user association, transmit power allocation, sub-channel assignment, and multiple access technique selection is investigated to maximize the down-link sum-rate under a minimum rate requirement per user and power constraints. The problem is formulated as a non-convex mixed-integer optimization problem; then, it is proved to be$\mathcal {NP}$-hard. First, simple greedy and meta-heuristic solutions are proposed. Then, since model-based approaches have generally a high computational complexity, model-free centralized and distributed approaches based on deep reinforcement learning technique are proposed. The latter are based on multiple parallel deep neural networks to generate resource allocation solutions. The proposed approaches are evaluated and compared. Simulation results corroborate the high performance offered by the proposed solutions for stationary and mobile users. They also highlight the benefits of employing hybrid orthogonal and non-orthogonal multiple access scheme in multi-band systems in terms of down-link sum-rate and user fairness.

This paper investigates the use of cell-free unmanned aerial vehicles (UAVs)-assisted wireless networks and optimizes the number of deployed UAVs under quality of service and coverage constraints. The formulated problem tackles the user-UAVs association, UAVs placement, channel assignment and transmit power allocation while considering both access and backhaul networks. Since the problem is a non-convex and non-linear mixed-integer programming, low-complexity efficient greedy-based algorithmic solutions are proposed. The first one finds the UAVs' best positions and allocates resource whereas the second one guarantees the problem feasibility (i.e., all users can be satisfied) by removing the worst users. For comparison purposes, a meta-heuristic solution based on the Particle Swarm Optimization technique is proposed. Simulation results illustrate the efficiency of the proposed algorithms in terms of the number of deployed UAVs in cell-free wireless networks.

Even though combining multi-access techniques (i.e., orthogonal and non-orthogonal multiple access techniques) and multi-band communications (i.e., millimeter-wave and sub-6 GHz communications) is needed to satisfy the stringent requirements of emerging wireless applications, it triggers new resource management challenges. In this paper, we investigate the joint optimization problem of user association, transmit power allocation, and sub-channel assignment in a such network in order to maximize the down-link sum-rate. The problem is formulated mathematically as a mixed-integer non-linear programming problem. Due to the combinatorial and the non-convex characteristics, solving the problem demands further developed complex mathematical tools. To this end, simple but efficient centralized and fully-distributed greedy-based algorithms are proposed, then, genetic algorithms are presented. Simulation results show the efficiency of the proposed solutions and the effectiveness of using heterogeneous multi-access techniques in terms of down-link sum-rate.

Mobile edge computing (MEC) is an important technology for latency-sensitive applications. One of the biggest challenges in MEC is efficiently allocating resources under strict QoS requirements and resource constraints. The purpose of this paper is to study the joint problem of computation offloading and resource allocation in such networks. The problem is formulated as a mixed-integer non-convex optimization problem and is proved to be NP-hard. In order to solve it efficiently, we propose a multi-agent deep reinforcement learning solution based on actor-critic method. To reduce system latency, each agent aims to learn interactively the best offloading policy independently of other agents. The simulation results illustrate the performance and advantages of the proposed solution compared to benchmark solutions.

This paper studies the joint problem of user association and sub-channel assignment in a wireless network, where orthogonal (OMA) and non-orthogonal multiple access (NOMA) techniques co-exist. We also assume the co-existence of millimeter wave and sub-6 GHz communications. Inspired by the fact that traditional resource allocation methods may not be effective in such heterogeneous multiple access networks (HetNets), a joint optimization problem of user association and sub-channel assignment is formulated where the objective is to maximize the number of associated users. The considered problem is proved to be NP-hard, and therefore an efficient heuristic algorithm is proposed to solve it in a polynomial time. Simulation results corroborate the effectiveness of the proposed algorithm and show that combining OMA and NOMA techniques outperforms single OMA or NOMA technique.

As sub-6 GHz spectrum is becoming increasingly scarce, millimeter wave (mmWave) bands are considered as one of the key technologies for future cellular networks. Motivated by the rapid growth of the data rate demands and the number of wirelessly-connected devices, this paper considers a hybrid (sub-6 GHz and mmWave) heterogeneous network with a limited number of time-frequency resource blocks (RBs). To overcome the mmWave propagation problems and the need of frequent update of association due to mobility, a novel mobility-aware user-base station association strategy based on Markov chain is proposed. Simulation results validate the performance of the proposed strategy by reducing the need of frequent handovers between mmWave base stations in the network.

Combining millimeter wave (mmWave) with sub-6 GHz communications is a promising solution for future heterogeneous cellular networks (HetNets) to improve coverage and capacity. This paper studies the user-base station association problem in HetNets with the existence of both sub-6 GHz and mmWave base stations (BSs) where each BS has a limited number of resource blocks (RBs). Motivated by the observation that traditional user-BS association methods may not be effective in such hybrid HetNet, an optimization problem is formulated in order to maximize the number of associated users and to ensure an efficient resource utilization by minimizing simultaneously the number of used RBs. Since the formulated problem is proved to be NP-hard, a heuristic algorithm is proposed. Simulation results show that the proposed algorithm approaches the optimal one with a significant reduction in computational complexity.