Over the past few years, with the rapid increase in the number of natural disasters, the need to provide smart emergency wireless communication services has become crucial. Unmanned aerial Vehicles (UAVs) have gained much attention as promising candidates due to their unprecedented capabilities and broad flexibility. In this paper, we investigate a UAV-based emergency wireless communication network for a post-disaster area. Our optimization problem aims to optimize the UAV’s flight trajectory to maximize the number of visited ground users during the flight period. Then, a dual cost-aware multi-armed bandit algorithm is adopted to tackle this problem under the limited available energy for both the UAV and ground users. Simulation results show that the proposed algorithm could solve the optimization problem and maximize the achievable throughput under these energy constraints.

Modern wireless networks are notorious for being very dense, uncoordinated, and selfish, especially with greedy user needs. This leads to a critical scarcity problem in spectrum resources. The Dynamic Spectrum Access system (DSA) is considered a promising solution for this scarcity problem. With the aid of Unmanned Aerial Vehicles (UAVs), a post-disaster surveillance system is implemented using Cognitive Radio Network (CRN). UAVs are distributed in the disaster area to capture live images of the damaged area and send them to the disaster management center. CRN enables UAVs to utilize a portion of the spectrum of the Electronic Toll Collection (ETC) gates operating in the same area. In this paper, a joint transmission power selection, data-rate maximization, and interference mitigation problem is addressed. Considering all these conflicting parameters, this problem is investigated as a budget-constrained multi-player multi-armed bandit (MAB) problem. The whole process is done in a decentralized manner, where no information is exchanged between UAVs. To achieve this, two power-budget-aware PBA-MAB) algorithms, namely upper confidence bound (PBA-UCB (MAB) algorithm and Thompson sampling (PBA-TS) algorithm, were proposed to realize the selection of the transmission power value efficiently. The proposed PBA-MAB algorithms show outstanding performance over random power value selection in terms of achievable data rate.

In a post-disaster area, Unmanned Aerial Vehicles (UAVs) are considered one of the most effective ways to provide emergency wireless communication services. In this letter, we investigate the UAV-based emergency wireless communication network for a post-disaster area. Our optimization problem aims to optimize the UAV 3D flight trajectory to maximize the number of visited ground users during the flight round. Then, a dual-cost-aware multi-armed bandit algorithm is adopted to tackle this problem under the limited available energy for both the UAV and ground users. Finally, simulation results show that the proposed algorithm could solve the optimization problem and maximize the achievable throughput under these constraints.

Cell-free massive MIMO (CF-mMIMO) is a promising approach for future mobile networks, utilizing centralized MIMO processing for densely distributed access points (APs). In CF-mMIMO, to reduce the computational load for signal processing while meeting throughput demands, user equipment (UEs) are served by a selected number of APs. A significant challenge is AP clustering for each UE, particularly in dynamic environments with moving UEs. One approach for optimizing the AP cluster involves deep reinforcement learning (DRL). However, with numerous UEs and APs, the computational load of DRL increases due to the larger model size and higher inference frequency. To address this, we propose an AP clustering method using distributed DRL. The model focuses on determining the AP cluster for every single UE to prevent model size expansion. The per-user models act as distributed actors, enabling parallel inference. Furthermore, to suppress inference frequency, multiple UEs with low mobility are assigned to the same actor, minimizing the number of parallel actors required without compromising throughput. Numerical simulation shows that our proposed method achieves efficient AP clustering that satisfies throughput requirements with reduced computational load in DRL, even in large-scale environments.

The evolution toward user-centric radio access networks (RANs) necessitates innovative management strategies to handle growing network demands efficiently. This study presents a novel framework for the disaggregated and distributed deployment of near-real-time RAN intelligent controllers (near-RT RICs) tailored for large-scale user-centric networks. By leveraging a mixed-integer nonlinear programming (MINLP) approach, we aim to optimize both control latency and deployment costs, which are critical considerations in next-generation network management. Our proposed divide-and-conquer (D&C) and heuristic-greedy (HG)-based algorithms significantly reduce computational complexity while maintaining near-optimal performance. Simulation results indicated that the distributed framework achieved a 78% reduction in control latency with only 7% increase in deployment cost compared with the traditional centralized approach. Additionally, it achieved 99% reduction in runtime performance with an objective function value that was just 2% lower than the optimal value. These findings confirm the framework’s scalability and effectiveness, validating its suitability for operational telecom environments

In anticipation of the 6G system, it is crucial to address the challenges related to achieving high throughput and low latency over a wide area. This is particularly important for applications such as 4K 360-degree streaming, tactile communication, and autonomous driving. Cell-free massive MIMO (CF-mMIMO) has been proposed as a solution to enhance radio coverage by eliminating cell edges, where a central processing unit (CPU) orchestrates signal processing across distributed access points (APs). This paper explores the deployment of CF-mMIMO within a realistic hierarchical and distributed site architecture to meet the stringent latency requirements of 6G through mobile edge computing (MEC). However, the deployment of CF-mMIMO and utilization of MEC within a distributed and hierarchical architecture necessitates the placement of CPUs at edge sites. This arrangement may lead to radio quality degradation at the edges of distributed sites due to inter-site interference and AP selection limitations. To address this problem, we propose a multi-homing AP connection that allows APs to connect to multiple edge sites, thereby enabling signal processing across sites. Furthermore, we conduct an analysis of the fronthaul (FH) costs associated with multi-homing connections and propose a multi-homing configuration and multi-homing AP selection method that improves radio quality without incurring excessive FH costs. The proposed heuristic algorithm is introduced to optimize the AP connections for multi-homing, giving priority to APs near site edges to strike a balance between cost and performance. Simulation results demonstrate that our proposed method attains near-optimal throughput while preserving cost efficiency.