Multi-beam concurrent transmission is one of promising solutions for a millimeter-wave (mmWave) network to provide seamless handover, robustness to blockage, and continuous connectivity. Nevertheless, one of the major obstacles in multi-beam concurrent transmissions is the optimization of beam pair selection, which is essential to improve the mmWave network performance. Therefore, in this paper, we propose a novel heterogeneous multi-beam cloud radio access network (HMBCRAN) architecture which provides seamless mobility and coverage for mmWave networks. We also design a novel acquirement method for candidate beam pair links (BPLs) in HMBCRANs architecture, which reduces user power consumption, signaling overhead, and overall time consumption. Based on HMBCRANs architecture and the resulted candidate BPLs for each user equipment, a beam pair selection optimization problem aiming at maximizing network sum rate is formulated. To find the solution efficiently, the considered problem is reformulated as a non-operative game with local interaction, which only needs local information exchanging among players. A decentralized algorithm based on HMBCRANs architecture and binary log-linear learning is proposed to obtain the optimal pure strategy Nash equilibrium of the proposed game, in which a concurrent multi-player selection scheme and an information exchanging protocol among players are developed to reduce the complexity and signal overheads. The stability, optimality, and complexity of the proposed algorithm are analyzed via theoretical and simulation method. The results prove that the proposed scheme has better convergence speed and sum rate against the state-of-the-art schemes.

This paper investigates the problem of energy-efficient trajectory optimization with wireless charging (ETWC) in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system. A UAV is dispatched to collect computation tasks from specific ground smart devices (GSDs) within its coverage while transmitting energy to the other GSDs. In addition, a high-altitude platform with a laser beam is deployed in the stratosphere to charge the UAV, so as to maintain its flight mission. The ETWC problem is characterized by multi-objective optimization, aiming to maximize both the energy efficiency of the UAV and the number of tasks collected via optimizing the UAV's flight trajectories. The conflict between the two objectives in the problem makes it quite challenging. Recently, some single-objective reinforcement learning (SORL) algorithms have been introduced to address the aforementioned problem. Nevertheless, these SORLs adopt linear scalarization to define the user utility, thus ignoring the conflict between objectives. Furthermore, in dynamic MEC scenarios, the relative importance assigned to each objective may vary over time, posing significant challenges for conventional SORLs. To solve the challenge, we first build a multi-objective Markov decision process that has a vectorial reward mechanism. There is a corresponding relationship between each component of the reward and one of the two objectives. Then, we propose a new trace-based experience replay scheme to modify sample efficiency and reduce replay buffer bias, resulting in a modified multi-objective reinforcement learning algorithm. The experiment results validate that the proposed algorithm can obtain better adaptability to dynamic preferences and a more favorable balance between objectives compared with several algorithms.

Wireless self-backhauling is a promising solution for dense millimeter wave (mmWave) small cell networks, the system efficiency of which, however, depends upon the balance of resources between the backhaul (BH) link and access links of each small cell. In this paper, we address the discrete power control and non-unified transmission duration allocation problem for self-backhauling mmWave cellular networks, in which each small cell is allowed to adopt individual transmission duration allocation ratio according to its own channel and load conditions. We first formulate the considered problem as a noncooperative game G with a common utility function. We prove the feasibility and existence of the pure strategy Nash equilibrium (NE) of game G under some mild conditions. Then, we design a centralized resource allocation algorithm (CRA) based on the best response dynamic and a decentralized resource allocation algorithm (DRA) based on control-plane/user-plane (C-plane/U-plane) split architecture and log-linear learning to obtain a feasible pure strategy NE of game G. For speeding up convergence and reducing signaling overheads, we reformulate the considered problem as a noncooperative game G′ with local interaction, in which only local information exchange is required. Based on DRA, we design a concurrent decentralized resource allocation algorithm (CDRA) to obtain the best feasible pure strategy NE of game G′. Furthermore, we extend the proposed algorithms to the discrete power control and unified transmission duration allocation optimization problem. Extensive simulations are conducted with different system configurations to demonstrate the convergence and effectiveness of the proposed algorithms.

This paper studies the age of information (AoI) and energy tradeoff (AET) problem in an aerial-ground collaborative mobile edge computing system, where a high-altitude platform and an unmanned aerial vehicle (UAV) work together to offer computing services for ground devices (GDs). The AET problem is formulated as a multi-objective optimization problem (MOP) that aims at simultaneously minimizing the total AoI of GDs and total energy consumption of the UAV by optimizing its flight paths and task offloading ratios. Addressing the AET problem poses a significant challenge due to the inherent conflict between the two objectives. The existing methods cannot well address the MOP because they adopt the linear combination to transform an MOP into a single-objective optimization problem using fixed weights (i.e., preferences), ignoring the conflict between objectives. Moreover, user preferences may change over time in dynamic MEC systems. To overcome these challenges, we first build a multi-objective Markov decision process model with a vectorial reward for the AET problem. There are one-to-one relationships between each component of the reward and one of the two objectives. Then, we propose a multi-objective learning algorithm based on proximal policy optimization (PPO), which primarily comprises a training phase and an evolutionary phase. The former adopts multi-objective PPO to iteratively optimize multiple learning individuals, aiming to obtain a nondominated policy set. The latter employs a genetic operator to further improve the quality of each policy in the set. Specifically, the crossover and mutation operators operate at the parameter level of policy networks, avoiding stagnation and premature convergence. The experiment results validate that the proposed approach obtains a set of excellent nondominated policies and a favorable balance between objectives. Moreover, the proposed approach achieves improvements of at least 39.8%, 2.1%, and 15.3% regarding AoI, energy consumption, and cost compared with several algorithms.

In this paper, we investigate the joint unmanned aerial vehicle (UAV) deployment and resource allocation problem to minimize the latency of multi-UAV-assisted computation offloading in air-ground integrated millimeter-wave (mmWave) networks, in which UAVs have both computing and relaying capabilities, thereby providing more opportunities for ground user equipments (UEs) to access the moble edge computing (MEC) servers with rich computing resources. Moreover, the study also takes into account the dynamic interference experienced by UEs due to different uploading completion times during the computation offloading process. To efficiently address the considered non-convex problem, we split it into four subproblems, i.e., UAV deployment, MEC server selection, computation resource and task ratio allocation, and power allocation subproblems, and solve them iteratively. Specifically, the first one is solved by three-dimensional-strategy iterative weekly acyclic game, the second one is addressed by Markov Approximation approach in which the third one is solved by the interior point method at each iteration, and the last one is solved by whale optimization algorithm (WOA). Finally, extensive simulations are provided to demonstrate the effectiveness of the proposed approach, and results have shown the approach can effectively mitigate the effect of blockage on mmWave transmissions and reduce the total latency of all UEs, particularly in scenarios where the communication bandwidth is limited or data volumes of tasks are large.

The intensification of global climate change, combined with increasing human activities, has significantly increased wildfire frequency and severity, posing a major global environmental challenge. As an illustration, Guizhou Province in China encountered a total of 221 wildfires over a span of 12 days. Despite significant advancements in wildfire prediction models, challenges related to data imbalance and model interpretability persist, undermining their overall reliability. In response to these challenges, this study proposes an explainable wildfire risk prediction model (EWXS) leveraging Extreme Gradient Boosting (XGBoost), with a focus on Guizhou Province. The methodology involved converting raster and vector data into structured tabular formats, merging, normalizing, and encoding them using the Weight of Evidence (WOE) technique to enhance feature representation. Subsequently, the cleaned data were balanced to establish a robust foundation for the EWXS model. The performance of the EWXS model was evaluated in comparison to established models, such as CatBoost, using a range of performance metrics. The results indicated that the EWXS model achieved an accuracy of 99.22%, precision of 98.48%, recall of 96.82%, an F1 score of 97.64%, and an AUC of 0.983, thereby demonstrating its strong performance. Moreover, the SHAP framework was employed to enhance model interpretability, unveiling key factors influencing wildfire risk, including proximity to villages, meteorological conditions, air humidity, and variations in vegetation temperature. This analysis provides valuable support for decision-making bodies by offering clear, explanatory insights into the factors contributing to wildfire risk.