In aerial base station (ABS) placement studies, leveraging information on topographic features for air-to-ground (A2G) channel analysis has been considered a promising approach. Recently, this approach has been studied in several works. However, these studies have predominantly focused on simplistic 3-D cuboid representations of topographic features, which do not adequately capture the complexity of real-world environments, thereby impeding accurate A2G channel analysis. In this article, we propose a more advanced strategy by generalizing the shapes and arrangements of topographic features to enable their realistic and accurate representations. Utilizing these generalized topographic models, we study the problem of finding the ABS location for coverage maximization. To solve this problem, we identify so-called line-of-sight (LoS) and non-LoS (NLoS) zones formed by these generalized topographic features through geometric analysis and obtain straightforward derivations of the coverage area for any given ABS location. Building upon this foundation, we develop a new ABS placement strategy, termed the polygonal feature-aware ABS placement algorithm (FA-poly). Our simulation results demonstrate that the proposed FA-poly significantly outperforms existing baseline methods in terms of the coverage area, as it accurately identifies channel conditions by leveraging information on generalized topographic features and obtains the coverage area based on them.

In this article, we examine cloud–edge–terminal Internet of Things (IoT) networks, where edges undertake a range of typical dynamic scheduling tasks. In these IoT networks, a central policy for each task can be constructed at a cloud server. The central policy can be then used by the edges conducting the task, thereby mitigating the need for them to learn their own policy from scratch. Furthermore, this central policy can be collaboratively learned at the cloud server by aggregating local experiences from the edges, thanks to the hierarchical architecture of the IoT networks. To this end, we propose a novel collaborative policy learning framework for dynamic scheduling tasks using federated reinforcement learning. For effective learning, our framework adaptively selects the tasks for collaborative learning in each round, taking into account the need for fairness among tasks. In addition, as a key enabler of the framework, we propose an edge-agnostic policy structure that enables the aggregation of local policies from different edges. We then provide the convergence analysis of the framework. Through simulations, we demonstrate that our proposed framework significantly outperforms the approaches without collaborative policy learning. Notably, it accelerates the learning speed of the policies and allows newly arrived edges to adapt to their tasks more easily.

In this paper, we study an unmanned aerial vehicle (UAV) relaying orthogonal frequency division multiple access (OFDMA) network with multiple user equipment (UE) pairs, each having explicitly given quality-of-service (QoS) requirements. Our goal is to maximize the system throughput while satisfying the QoS requirements for UE pairs, despite the air-to-ground (A2G) channel randomness. To this end, we develop a hybrid offline-online algorithm that designs the UAV’s 3D trajectory in an offline manner and then allocates sub-channels during the UAV flight in an online manner. Under the proposed algorithm, the UAV’s 3D trajectory is elaborately designed with statistical channel state information (CSI), and sub-channels are opportunistically allocated based on instantaneous CSI. In practice, the QoS requirements might not be guaranteed since perfect CSI cannot be obtained a priori. Nevertheless, the proposed algorithm significantly improves the QoS satisfaction of UE pairs to the fullest extent possible by leveraging available CSI. Through simulation, we validate the performance of the proposed algorithm in enhancing the QoS satisfaction of UE pairs and the system throughput.

Recent advancements in an unmanned aerial vehicle (UAV)-enabled network have demonstrated potential of a directional antenna to enhance network performance by utilizing limited resources more efficiently. In the UAV-enabled network where a directional antenna is utilized, controlling both its beam direction and beamwidth appropriately is an important issue in order to maximize its efficiency. Existing studies on the UAV-enabled network with a directional antenna, however, have primarily concentrated on adjusting antenna’s beamwidth with a fixed beam direction for simplicity. In this paper, we explore joint optimization of both beam direction and beamwidth of a UAV equipped with controllable directional antennas. To this end, we consider a UAV-enabled network where the UAV functions as an aerial base station (ABS), relaying data from a ground base station (GBS) to multiple ground users (GUs), aiming at maximizing the sum rate for all GUs by controlling the location, beam direction, and beamwidth of the UAV and resource allocation. To address this complex problem, we develop an algorithm called Joint optimization of location, beam direction, beamwidth, and resource allocation (Joint-LDWR). Through comprehensive simulations, we show the outstanding performance of Joint-LDWR, focusing on its efficiency for enhancing network performance. The results highlight a significant benefit of simultaneously controlling beam direction and beamwidth of the ABS together with its location in the UAV-enabled network.

A cell-free massive multiple-input multiple-output (mMIMO) system employs a large number of access points (APs). Since the APs collaboratively serve user equipments (UEs), its energy and spectral efficiency can be much higher than that of a conventional cellular system. Typically, the inter-user interference (IUI) is suppressed appropriately to improve performance, which usually results in high computational complexity. To reduce this computational complexity, we propose a new cell-free mMIMO architecture, called a dedicated AP-based interference cancellation (DAP-IC) architecture with two types of APs: DS-APs that transmit the data signal (DS) to UEs and IUI-APs that transmit the signal to eliminate the IUI. To this end, we first formulate an optimization problem of maximizing the spectral efficiency while ensuring the fairness of UEs by optimizing the precoding vectors of the APs. We then develop DAP-IC algorithm that solves this problem with a low computational complexity. The simulation results show that the DAP-IC algorithm provides good performance with much lower computational complexity, compared with the weighted minimum mean square error (WMMSE) algorithm for the conventional cell-free mMIMO architecture.

In this paper, we consider an orthogonal frequency division multiple access (OFDMA)-based unmanned aerial vehicle (UAV) relaying system with multiple user equipment (UE) pairs, which has not yet been well studied despite its potential for promising use cases (e.g., a UAV relay-enabled standalone private 5G network). We study a joint optimization problem for UAV trajectory and sub-channel allocation to maximize the total average end-to-end throughput while satisfying the quality-of-serivce (QoS) requirements of UE pairs. To address this problem, we develop a block coordinate descent (BCD)-based algorithm that iteratively solves two sub-problems: 1) a sub-channel allocation optimization problem with a given UAV trajectory, and 2) a UAV trajectory optimization problem with a given sub-channel allocation. Through simulation, we demonstrate the effectiveness of the proposed algorithm.

The use of unmanned aerial vehicles as aerial base stations (ABSs) can significantly enhance the capacity and coverage of wireless systems. In this paper, the problem of optimal ABS placement is studied while exploiting high-rise topographic features to maximize wireless coverage. In contrast to prior art that relies on simplified full line-of-sight (LoS) channel models or impractical probabilistic LoS channel models, this paper presents a novel feature-aware channel model that decisively discerns whether an air-to-ground (A2G) link is in LoS or non-LoS (NLoS) based on the topographical environment data for the target area. To resolve the challenges created by the dependence between the channel gain and the topographical environment in this feature-aware channel model, the LoS and NLoS zones in the target area are analyzed from a geometrical point of view. Then, based on the analysis results, the coverage area is derived in a tractable form and then used to develop a feature-aware ABS placement algorithm, called ABS-FA, based on particle swarm optimization (PSO). The effectiveness of the proposed approach is compared with two other baseline algorithms based on the full LoS and probabilistic LoS channel models, called ABS-LoS and ABS-Prob, respectively. Simulation results show that, depending on the topographical environment, ABS-LoS may outperform ABS-Prob, or vice versa, and even both may be very limited in some cases, because the full LoS and probabilistic LoS channel models cannot properly capture whether an A2G link is in LoS or NLoS. The results also show that the proposed ABS-FA scheme always outperforms these baseline algorithms and that, for instance, it can provide approximately 25% and 50% higher coverage performance compared to ABS-Prob and ABS-LoS, respectively. These results verify that considering a feature-aware channel model can be a very effective approach for determining the ABS location.

In this article, we study a joint antenna and Internet of Things device (ID) scheduling problem in the full-duplex (FD) multiple-input–multiple-output (MIMO) wireless-powered communication network (WPCN) over time-varying fading channels. We first formulate an optimization problem to maximize the average sum rate of IDs while satisfying their minimum average data rate requirements by jointly scheduling ID selection for uplink data transmission, antenna switching, and beamforming. To deal with the problem, we propose a scheduling algorithm based on Lagrangian duality and the stochastic optimization theory. The proposed scheduling algorithm necessitates solving per-time-slot problems, each of which aims at maximizing the weighted sum of the selected ID’s uplink data rate and the nonselected IDs’ harvested power from the downlink by jointly optimizing ID selection, antenna switching between uplink and downlink, and beamforming at that time slot. To solve the per-time-slot problem, we develop a joint ID selection, antenna switching, and beamforming (Joint-IAB) algorithm based on the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Through simulation, we demonstrate that our scheduling algorithm with the proposed Joint-IAB algorithm provides better performance than the other scheduling algorithms while well satisfying the given minimum average data rate requirements of IDs.

In this paper, we investigate dynamic resource scheduling (i.e., joint user, subchannel, and power scheduling) for downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems over time-varying fading channels. Specifically, we address the weighted average sum rate maximization problem with quality-of-service (QoS) constraints. In particular, to facilitate fast resource scheduling, we focus on developing a very low-complexity algorithm. To this end, by leveraging Lagrangian duality and the stochastic optimization theory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby the original problem is decomposed into a series of subproblems, one for each time slot. Accordingly, resource scheduling works in an online manner by solving one subproblem per time slot, making it more applicable to practical systems. Then, we further develop a heuristic joint subchannel assignment and power allocation (Joint-SAPA) algorithm with very low computational complexity, called Joint-SAPA-LCC, that solves each subproblem. Finally, through simulation, we show that our Joint-SAPA-LCC algorithm provides good performance comparable to the existing Joint-SAPA algorithms despite requiring much lower computational complexity. We also demonstrate that our opportunistic MC-NOMA scheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works well while satisfying given QoS requirements.

In this paper, we study radio and energy resource management in renewable energy-powered wireless networks, where base stations (BSs) are powered by both on-grid and renewable energy sources and can share their harvested energy with each other. To efficiently manage those resources, we propose a hierarchical and distributed resource management framework based on deep reinforcement learning. The proposed framework minimizes the on-grid energy consumption while satisfying the data rate requirement of each user. It is composed of three different policies in a distributed and hierarchical way. An intercell interference coordination policy constrains the transmission power at each BS to coordinate the intercell interference among the BSs. Under the power constraints, a distributed radio resource allocation policy of each BS determines its own user scheduling and power control. Lastly, an energy sharing policy manages the energy resources of the BSs by sharing the harvested energy via power lines between them. Through the simulation, we demonstrate that the proposed framework can effectively reduce the on-grid energy consumption while satisfying the data rate requirements.

In this paper, we study the joint user and power scheduling for downlink NOMA over fading channels. Specifically, we focus on a stochastic optimization problem to maximize the weighted average sum rate while ensuring given minimum average data rates of users. To address this problem, we first develop an opportunistic user and power scheduling algorithm (OUPS) based on the duality and stochastic optimization theories. By OUPS, the stochastic problem is transformed into a series of deterministic ones for the instantaneous weighted sum rate maximization for each slot. Thus, we additionally develop a heuristic algorithm with very low computational complexity, called user selection and power allocation algorithm (USPA), for the instantaneous weighted sum rate maximization problem. Via simulation results, we demonstrate that USPA provides near-optimal performance with very low computational complexity, and OUPS well guarantees given minimum average data rates.

In recent years, the emergence of flying ad hoc networks (FANETs) with multiple unmanned aerial vehicles (UAVs) has made it possible to effectively perform not only the far-off missions but also assorted complex missions. In this article, we consider a mission-critical FANET to perform given missions using multiple UAVs, taking into account a dynamic environment with a time-varying network topology. To effectively operate the mission-critical FANET, we study the joint mission assignment and topology management problem aiming at maximizing the weighted sum of mission and network performances, while guaranteeing end-to-end communications between mission-performing UAVs and their corresponding ground control stations, inter-UAV safety distance maintenance, and other mission-related constraints. To address this problem, we first develop three algorithms: one is to construct a mission-critical FANET from scratch, and the others are to manage the network topology and to switch UAV roles between mission performing and data relaying in response to the changes in the network topology. Then, we develop a dynamic mission-critical FANET operation algorithm incorporating the three algorithms with a few rules, by which the mission-critical FANET can be effectively managed and operated with reasonable computational complexity in the dynamic environment. Through simulation results, we show that our proposed algorithm works well in the dynamic environment while satisfying the constraints, and that its performance is not only superior to the existing algorithms but also close to the optimal performance.