In remote or high-demand wireless cellular networks, efficient data collection from ground users (GUs) with fixed infrastructure poses a significant challenge. Autonomous aerial vehicles (AAVs) have emerged as a promising solution due to their flexible deployment and cost-effectiveness. This article focuses on a AAV-aided wireless cellular communication system comprising a AAV and multiple adjacent GUs, where the mission of the AAV is to collect data from these GUs. The objective is to minimize AAV propulsion energy consumption while ensuring fair data uploading among all GUs. Due to the nonconvex and intractable nature of the above problem, we propose a novel real-time waypoint localization method based on the parallel projection method from a geometric perspective. By enhancing the projection process, this approach achieves energy-efficient and fair data collection, along with an efficient trajectory design algorithm. Further, considering the scenario of densely distributed GUs in large-scale areas, a GU-clustering algorithm is proposed based on Mean Shift. Additionally, this article categorizes GUs into homogeneous and heterogeneous scenarios and designs distinct trajectory designing algorithms to accommodate diverse real-world situations. Simulations and comparisons validate the effectiveness and efficiency of the proposed algorithms in tackling the AAV trajectory design challenges.

Uneven terrains and complex obstacles in off-road environments present significant challenges to the stability and safety of vehicle path planning. This paper presents a hierarchical DRL-DWA path planning framework for unmanned ground vehicles (UGVs). At the global level, an energy-aware D* Lite algorithm generates cost-efficient waypoints considering both distance and energy consumption. At the local level, a deep reinforcement learning-enhanced DWA controller adaptively adjusts the weighting factors of evaluation functions in real time to ensure dynamic feasibility on rough terrain. The parameter selection is formulated as a Markov decision process (MDP), where a novel reward function based on elevation maps, vehicle pose, goal, and obstacle information guides the optimization for off-road navigation. Furthermore, the single UGV framework is extended to a multi-UGV system, where formation control is achieved through the leader–follower strategy. To evaluate the performance of our algorithm, we conduct experiments in 3D simulation environments featuring various terrains and obstacles. The results indicate that the proposed approach outperforms existing path planning techniques, showing a higher success rate and a lower average elevation gradient in uneven terrains.

This paper proposes a novel polar coding scheme tailored for indoor visible light communication (VLC) systems. Simulation results demonstrate a significant reduction in bit error rate (BER) compared to uncoded transmission, with a coding gain of at least 5 dB. Furthermore, the reliable communication area of the VLC system is substantially extended. Building on this foundation, this study explores the joint design of polar codes and physical-layer network coding (PNC) for VLC systems. Simulation results illustrate that the BER of our scheme closely approaches that of the conventional VLC relay scheme. Moreover, our approach doubles the throughput, cuts equipment expenses in half, and boosts effective bit rates per unit time-slot twofold. This proposed design noticeably advances the performance of VLC systems and is particularly well-suited for scenarios with low-latency demands.