Since conventional multiple access schemes, such as time division multiple access (TDMA), frequency division multiple access (FDMA), et al., cannot meet the requirements on flexibility, throughput and access delay in large-scale flying ad hoc networks (FANETs), we propose a fair and efficient CSMA/CA-based MAC protocol to facilitate concurrent uplink transmissions from different unmanned aerial vehicles (UAVs) by leveraging multiple-user MIMO (MU-MIMO). In this paper, we first propose the MIMO-based integrated sensing and backscatter communication model to achieve address resolution and also realize channel estimation by leveraging maximum likelihood estimation. Next, we propose an analytical model to characterize the saturation throughput and mean access delay of this CSMA/CA-based MAC protocol operating in an MUMIMO FANET. Moreover, we derive the accurate expressions of saturation throughput and access delay under the proposed model. By means of the developed model, we evaluate the saturation throughput and access delay performance with respect to different network parameters, including the total payload, the number of UAVs and the number of UAV receiver's antennas. Numerical results indicate that our proposed protocol achieves superior throughput and decreased access delay

Multiple unmanned aerial vehicles (UAVs) form a UAV cluster, which relies on wireless communication networks to facilitate information interaction among the UAVs. UAV clusters offer enhanced efficiency and fault tolerance, making reliable computing services possible. Compared to terrestrial networks, UAV computing network presents several desirable features, including mobility, availability, and flexibility, affording the potential for seamless global coverage. However, due to the inherent openness of wireless communication networks, UAV computing networks are susceptible to various security attacks. Existing access control mechanisms for UAV computing networks predominantly rely on base stations or central servers. This reliance elevates communication overhead for UAVs and exposes them to potential attacks from adversaries. Therefore, we propose a blockchain-aided distributed secure access control scheme specifically tailored for UAV computing networks, enabling UAVs to autonomously manage and determine identity, attributes, and access policies. To address the communication complexity and scalability concerns associated with blockchains, we integrate committee elections and clustering optimizations into the scheme. Security analysis and performance evaluation demonstrate that the proposed scheme can withstand common external and internal attacks in UAV clusters while ensuring lightweight energy consumption and scalability of UAV computing networks.

Metaverse is envisioned to merge the actual world with a virtual world to bring users unprecedented immersive feelings. To ensure user experience, federated learning (FL) has been expected as a critical enabler to provide metaverse users with high-quality sensing, communicating, and rendering. However, considering the limitation of wireless communication resources and the stringent requirements of users, collaborating with massive metaverse users to realize FL still has tremendous challenges. Most pioneer works on improving the performance of FL assume that the system states are static, which is unsuitable in the metaverse. Because the FL in the metaverse is always a complicated long-term iteration process, where the fluctuations of channel status and available computing resources of users are inevitable, a changeless strategy may lead to poor results. Therefore, this paper proposes an efficient federated learning scheme relying on dynamic user selection, gradient quantization, and resource allocation. Specifically, we derive the convergence error bound to reveal the impact of user selection, wireless transmission error, and gradient quantization error of each iteration on FL’s convergence. Based on the theoretical analysis, we jointly and dynamically optimize the user selection, gradient quantization, and resource allocation to minimize the error bound with time and energy consumption budgets. Furthermore, to make the formulated sequential decision-making problem tractable, we transform it into a Markov decision process (MDP) and design a soft actor-critic-based solution. Extensive experiments validate that our proposed scheme has superior performance compared to conventional schemes in dynamic-changing network environments.

In this paper, we consider a wireless network enabled by multiple unmanned aerial vehicles (UAVs), which observe physical processes and transmit status updates to a monitor node over an error-prone communication channel. The communication scenarios are classified into two modes based on monitor types: UAV-to-UAV (U2U) and UAV-to-network (U2N) scenarios. Specifically, the U2N scenario is capable of covering a larger area compared to U2U scenario with a high signal-to-noise ratio (SNR). However, the U2U scenario constructs communication links more quickly, resulting in lower latency and higher rates. To evaluate the timeliness of the UAV-aided network, we utilize the age of information (AoI) as a fundamental indicator. AoI measures the time delay between the most recent data generation and the current moment. To compensate for the error-prone channel, this study employs a combination of hybrid automatic repeat request (HARQ) protocols, which include fixed-redundancy HARQ (FR-HARQ) and infinite incremental redundancy HARQ (IIR-HARQ) protocols. Furthermore, the average and peak Age of Information (AAoI and PAoI) of UAV-aided networks are derived for both U2U and U2N scenarios, and the theoretical expressions agree with simulation results. Additionally, FR-HARQ performs a better time performance than IIR-HARQ, and such observation van be verified through simulations.

Unmanned aerial vehicles (UAVs) empowered Internet of things (IoT) networks have emerged as a burgeoning paradigm in the era of 6G. However, due to substantial data volume and privacy concerns, the conventional UAV backhaul to cloud center framework is not applicable to various latency and privacy-sensitive applications. Therefore, we propose a blockchain-based hierarchical federated learning (HFL) framework for UAV-enabled IoT networks. Specifically, we utilize the total data distance-aware device association to mitigate model impairment arising from imbalanced data distribution. Besides, we introduce a lightweight blockchain into federated learning to tackle the trust deficit caused in decentralized global model aggregation. Furthermore, we design an optimization framework that jointly orchestrating device association, wireless resource allocation, and UAV deployment, aiming at a balance between the learning latency and model accuracy. To address the formulated optimization problem, we proposed a two-stage algorithm that integrates both greedy strategy and soft actor–critic algorithm. Extensive experiments show that our proposed scheme outperforms contemporary relative to state-of-the-art alternatives.

With the rapid development of wireless communication networks, UAVs serving as base stations are increasingly being applied in various scenarios which not only include edge computation and task offloading, but also involve emergency communication, vehicular network enhancement, etc. In order to enhance the utility of UAV base stations’ allocation and deployment, a series of algorithms have been proposed, utilizing heuristic methods, learning-based algorithms, or optimization approaches. However, it is intractable for current algorithms to handle the exponential computation increment with UAV base stations increasing, and complicated application scenarios with high dynamic demands. To solve the above issues, we formulate a decision problem with a long sequence to optimize the deployment of multi-UAV base stations for maximizing vehicular networks’ communication coverage ratio, which needs to be subject to co-constraints consisting of moving velocity, energy consumption, and communication coverage radius. To solve this optimization problem, we creatively propose an algorithm named dense multiagent reinforcement learning (DMARL), which is under the dual-layer nested decision-making framework, centralized training with decentralized deployment, and accelerates training by only collecting critical states into the dense sampling buffer. To prove our proposed algorithm’s effectiveness and generalization ability, we conduct experimental simulations in scenarios with different scales. Corresponding results have been provided to verify our algorithm’s superiority in training efficiency and performance metrics, including coverage ratio and energy consumption, compared with other algorithms.