Uncrewed Aerial Vehicle-mounted Base Stations (UAV-BSs) have been envisioned as a promising solution to enable high-quality services in next-generation mobile networks. With inherent flexibility, one key challenge is placing the UAV-BSs adaptively to time-varying network conditions to maintain stable connections. Conventional methods mainly focus on optimizing UAV-BS deployment in static networks where users are static or with limited mobility. This study considers a dynamic network where multiple non-stationary UAV-BSs are deployed to serve mobile users with time-varying heterogeneous traffic. Incomplete downloads of users are backlogged in download queues at the UAV-BSs, and together with the newly requested traffic, the download queue size reflects the user’s instantaneous traffic demand. With constraints on the queue stability, we aim to maximize the user’s long-term mean opinion score (MOS), reflecting how the perceived data rate satisfies their traffic demand. Since the users’ location and traffic demand vary over time, we dynamically group users into clusters using a K-means-based algorithm and adjust the 3D location of UAV-BSs using an actor-critic deep reinforcement learning (DRL) framework. The actor module is encoded using a deep neural network (DNN) that obtains the UAV-BS’s current location and a traffic heatmap of users to predict the optimal movement for UAV-BSs. The critic module utilizes Lyapunov optimization to control the queue stability constraint and evaluate the actor’s decisions. Extensive simulations demonstrate the proposed method’s superior performance over conventional rate-maximization approaches.

Mobile Edge Computing (MEC) is a key technology towards delay-sensitive and computation-intensive applications in future cellular networks. In this paper, we consider a multi-user, multi-server system where the cellular base station is assisted by a UAV, both of which provide additional MEC services to the terrestrial users. Via dual connectivity (DC), each user can simultaneously offload tasks to the macro base station and the UAV-mounted MEC server for parallel computing, while also processing some tasks locally. We aim to propose an online resource management framework that minimizes the average power consumption of the whole system, considering long-term constraints on queue stability and computational delay of the queueing system. Due to the coexistence of two servers, the problem is highly complex and formulated as a multi-stage mixed integer non-linear programming (MINLP) problem. To solve the MINLP with reduced computational complexity, we first adopt Lyapunov optimization to transform the original multi-stage problem into deterministic problems that are manageable in each time slot. Afterward, the transformed problem is solved using an integrated learning-optimization approach, where model-free Deep Reinforcement Learning (DRL) is combined with model-based optimization. Via extensive simulation and theoretical analyses, we show that the proposed framework is guaranteed to converge and can produce nearly the same performance as the optimal solution obtained via an exhaustive search.

This paper investigates the issue of missing-tag event detection in practical radio frequency identification (RFID) systems with the presence of not only unexpected tags but also the detection error. Among all the previous works, the recently proposed protocol “RFID monitoring with UNexpected tags (RUN)” is one of the first studies taking the unexpected tags into account. The protocol is proven to outperform conventional ones in terms of achieving a required reliability. Nevertheless, it completely ignores the effect of the so-called detection error, which is a common phenomenon in the literature of RFID, on tag reading. The phenomenon might result in the false-alarm detection of the event and it is believed that RUN is no longer efficient and reliable. We therefore propose two modified versions of the RUN protocol, namely, mRUN1 and mRUN2, as solutions for the issue. Similarly to RUN, the protocols execute multiple Aloha reading rounds to cope with the unexpected tags. On the other hand, they utilize tracking counters supposedly available at the reader to mitigate the effect of the detection error. While mRUN1 requires many counters to monitor the existence of each expected tag (the tag’s identity is already known), mRUN2 uses only one counter to deal with the event caused by either real missing tags or the detection error. Performance analysis will be investigated to find optimal parameter settings for the protocols. Computer simulation results are also provided to validate our analysis as well as to show the merit of the proposed protocols in comparison with the conventional protocols.