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Caching content at base stations has proven effective at reducing transmission delays. This paper investigates the caching problem in a network of highly dynamic cache-enabled Unmanned Aerial Vehicles (UAVs), which serve ground users as aerial base stations. In this scenario, UAVs share their caches to minimize total transmission delays for requested content while simultaneously adjusting their locations. To address this challenge, we formulate a non-convex optimization problem that jointly controls UAV mobility, user association, and content caching to minimize transmission delay time. Considering the highly dynamic environment where traditional optimization approaches fall short, we propose a deep reinforcement learning (RL)-based algorithm. Specifically, we employ the actor-critic-based Deep Deterministic Policy Gradient (DDPG) algorithm to solve the optimization problem effectively. We conducted extensive simulations with respect to different cache sizes and the number of associated users with their home UAVs and compared our proposed algorithm with two baselines. Our proposed solution has demonstrated noteworthy enhancements over the two baseline approaches across various scenarios, including diverse cache sizes and varying numbers of users associated with their respective home UAVs.
Caching content at base stations has proven effective at reducing transmission delays. This paper investigates the caching problem in a network of highly dynamic cache-enabled Unmanned Aerial Vehicles (UAVs), which serve ground users as aerial base stations. In this scenario, UAVs share their caches to minimize total transmission delays for requested content while simultaneously adjusting their locations. To address this challenge, we formulate a non-convex optimization problem that jointly controls UAV mobility, user association, and content caching to minimize transmission delay time. Considering the highly dynamic environment where traditional optimization approaches fall short, we propose a deep reinforcement learning (RL)-based algorithm. Specifically, we employ the actor-critic-based Deep Deterministic Policy Gradient (DDPG) algorithm to solve the optimization problem effectively. We conducted extensive simulations with respect to different cache sizes and the number of associated users with their home UAVs and compared our proposed algorithm with two baselines. Our proposed solution has demonstrated noteworthy enhancements over the two baseline approaches across various scenarios, including diverse cache sizes and varying numbers of users associated with their respective home UAVs.
High mobility travelling trains and drones connected via ultra-dense mobile networks may lead to frequent handovers (HOs). As a consequence, this could arise the mobility problems of the serving network such as handover ping-pong (HOPP), radio link failure (RLF), handover probability (HOP), and handover failure (HOF). Mobility robustness optimization (MRO) function can contribute for fixing such related problems. This can be performed by self-optimization process for the handover control parameters (HCPs), that including time-to-trigger (TTT) and handover margin (HOM). Although various proposed solutions available in the literature, the issues have not been addressed efficiently. Thus, this study proposes a fuzzy logic controller (FLC) along with weighted function (WF) to perform efficient HO self-optimization process for the HCPs over the heterogeneous networks (Het-Nets). The proposed algorithm is defined as velocity-aware-fuzzy logic controller-weighted function (VAW-FLC-WF) algorithm. Additionally, a trigger timer is used along with the proposed algorithm for the purpose of reducing the ratio of HOPP. The objective of the integrated algorithms is to minimize the connections issues such as HOPP, RLF, and received signal reference power (RSRP). Besides, this study highlighted the significant of categorizing the speed scenarios in reducing the mobility issues by comparing the results with non-categorized speed scenarios (proposed FLC-WF). The proposed integrated algorithms show a significant enhancements as compared to the algorithms investigated from the literature. The average RLF probability of the proposed (VAW-FLC-WF) was reduced to 0.006 which was the lowest probability compared to the other HO algorithms. Besides, RSRP, HOPP were shown noticeable improvements compared to other HO algorithms.
In 5G networks and beyond, managing handovers (HOs) becomes complex because of frequent user transitions through small coverage areas. The abundance of small cells (SCs) also complicates HO decisions, potentially leading to inefficient resource utilization. To optimize this process, we propose an intelligent algorithm based on a method that utilizes a fuzzy logic controller (FLC), leveraging prior expertise to dynamically adjust the time-to-trigger (TTT), and handover margin (HOM) in a 5G ultra-dense SC heterogeneous network (HetNet). FLC refines TTT based on the user’s velocity to improve the response to movement. Simultaneously, it adapts HOM by considering inputs such as the reference signal received power (RSRP), user equipment (UE) speed, and cell load. The proposed approach enhances HO decisions, thereby improving the overall system performance. Evaluation using metrics such as handover rate (HOR), handover failure (HOF), radio link failure (RLF), and handover ping-pong (HOPP) demonstrate the superiority of the proposed algorithm over existing approaches.
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