This paper investigates resource allocation for optical intelligent reflecting surfaces (OIRS) on high-altitude platforms (HAPs) supporting multiple UAV-mounted base stations. We propose a rate-optimized spatial resource allocation (R-SRA) scheme that first maximizes the number of QoS-guaranteed UAVs, then allocates remaining OIRS elements to boost the total system rate. Numerical results show R-SRA outperforms conventional methods, even with UAV mobility.

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolarM-pulse amplitude modulation (M-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex multivariate non-convex problem due to the non-convexity of the objective function. To tackle the original non-convex optimization problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima. The FA-based approach, however, suffers from high computational complexity. Thus, using zero-forcing (ZF) precoding, we propose a low-complexity design, which is solved using an alternating optimization approach. Additionally, considering the channel uncertainty, a robust design based on the concept of end-to-end learning with autoencoder (AE) is also presented. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. For instance, the joint design achieves$\mathbf {17.5\%}$and$\mathbf {19.2\%}$higher sum-rate for 8-PAM and 16-PAM, respectively, at 60 dB peak amplitude-to-noise ratio. Some insights into the optimal symbol distributions of the two joint design approaches are also provided. Furthermore, our results show the advantage of the proposed robust design over the non-robust one under uncertain channel conditions.

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.

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar M-pulse amplitude modulation (M-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex non-convex problem due to the non-convexity of the objective function. To tackle the problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima to the original non-convex optimization problem. The FA-based approach, however, suffers from high computational complexity. Therefore, we propose a low-complexity design based on zero-forcing (ZF) precoding, which is solved using an alternating optimization (AO) approach. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. Some insights into the optimal symbol distributions of the two joint design approaches are also provided.

This paper investigates the energy efficiency (EE) of rate-splitting multiple access (RSMA)-based visible light communications (VLC) with confidential private messages. We first formulate an EE maximization problem that takes into account the achievable rate of the common message and the sum secrecy rate of private messages. Due to the fractional non-convex nature of the design problem, an algorithm that leverages the Dinkelbach algorithm and convex-concave procedure (CCP) is proposed to solve local optima. In this study, the channel similarity$S_{C}$and the rate-splitting power allocation ratio$\rho$are jointly considered to evaluate EE performance and trade-offs between the achievable common rate and the sum secrecy rate. Numerical results reveal that when$S_{C}$is below 0.7, the EE saturates at its optimum as$\rho$increases, while when$S_{C}$exceeds 0.7, an optimal$\rho$exists beyond which the EE starts declining. Moreover, it is observed that while the relationship between the sum secrecy rate and$S_{C}$is inversely proportional, it is directly proportional in the case of the common rate.

In this paper, we present a novel coexistence uplink-downlink stochastic mobile edge computing (MEC) system that considers the dynamic characteristics of both small cell base stations (SBSs) and user equipments (UEs). To devise an efficient radio resource management strategy encompassing user association, channel allocation, and power allocation, we formulate an optimization problem that considers time, energy, and achievable rate in the utility function. The formulated problem is a Mixed Integer Nonlinear Program (MINLP) and has been proven to be NP-hard. To address this complexity, we propose a unified nature-inspired optimization framework, which can be deployed for subproblems in various settings and can be integrated with the Whale Optimization Algorithm (WOA), Improved Whale Optimization Algorithm (IWOA), and Particle Swarm Optimization (PSO). Through our rigorous mathematical and numerical analysis, the proposed algorithms show that they can converge to a near-optimal solution while keeping negligible optimality gaps. Our numerical results show the advantages and drawbacks of the proposed algorithms, highlighting their potential for effective resource management in MEC systems. The results also show the performance evaluation of stochastic characteristics on the performance of MEC.

Federated learning (FL)-enabled digital twin (DT) has recently attracted research attention to bring intelligent applications. However, enabling the FL-enabled DT in vehicular networks becomes challenging due to vehicle mobility’s impact on communication channels. In this regard, we propose to deploy an unmanned aerial vehicle (UAV) as a relay node to support the vehicular network. The objective is to minimize energy consumption under the trade-off with the latency and accuracy constraints of the DT model via a joint optimization of local accuracy, the local computation frequency, relay decision, and transmission power. To do so, we derive instantaneous formulas to update the accuracy and latency constraints, then solve the proposed problem using an iterative algorithm with convex optimization techniques. Numerical results show that the proposed dynamic optimization for UAV-aided vehicular networks can reduce up to 39.9% of consumption energy compared to conventional methods.

Physical layer security (PLS) offers a unique approach to protecting information confidentiality against eavesdropping by malicious users. This paper studies a joint design of adaptive M−ary pulse amplitude modulation (PAM) and precoding for performance improvement of PLS in visible light communications (VLC). It is known that higher-order modulation results in a better secrecy capacity at the expense of a higher bit-error rate (BER). On the other hand, a proper precoding design can also enhance secrecy performance. The proposed design, therefore, aims at the optimal PAM modulation order and precoder to maximize a utility function that takes into account the secrecy capacity and BERs of the legitimate user (Bob)’s and the eavesdropper (Eve)’s channel. Due to the lack of a closed-form expression for the utility function, a Q-learning-based design is proposed and evaluated. Compared to the non-adaptive approach under all different settings of Bob’s and Eve’s positions, simulation results verify that the proposed joint adaptive design achieves a good balance between the secrecy capacity and BER of Bob’s channel while maintaining a sufficiently high BER of Eve’s channel.

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.

In this paper, an energy-efficient precoding scheme is designed for multi-user visible light communication (VLC) systems in the context of physical layer security, where users' messages are kept mutually confidential. The design problem is shown to be non-convex fractional programming, therefore Dinkelbach algorithm and convex-concave procedure (CCCP) based on the first-order Taylor approximation are utilized to tackle the problem. Numerical results are performed to show the convergence behaviors and the performance of the proposed solution for different parameter settings.

This paper addresses the link-layer error-control solutions of high platform altitude (HAP)-aided relaying satellite free-space optical (FSO) systems for the Internet of Vehicles. Specifically, we propose a design of cooperative incremental redundancy (IR) hybrid automatic repeat request (HARQ) protocol to mitigate the transmission errors over the turbulence fading channels. The performance metrics, including average throughput and average frame delay, are analytically derived. The numerical results confirm the effectiveness of our proposed cooperative IR-HARQ protocol in FSO-based satellite-HAP-vehicle systems and support the proper selection of parameters. Monte-Carlo simulations are also performed to validate the correctness of theoretical results.

In this study, the performance of wirelessly powered relay‐aided non‐orthogonal multiple access networks is investigated in terms of outage probability. Specifically, two relay selection strategies, i.e. two‐stage relay selection (TRS) and maximum energy harvesting relay selection (MEHS), and two energy harvesting scenarios, i.e. time switching (TS) and power splitting (PS) are considered. In each setup, outage probabilities' analytical expressions and their asymptotics are derived. Monte‐Carlo simulations are also carried out to verify the correctness of the analysis. The results show that regardless of relay selection and energy harvesting strategies, increasing transmit power can improve the proposed system performance. However, TRS can achieve a full diversity order, while MEHS has a unit diversity order. Besides, the results recommend parameter selections of PS and TS coefficients for optimal performance in term of outage probability.

The explosive growth of end devices and mobile applications calls for novel schemes that can enable computation‐hungry applications at small end‐devices and meet the massive connectivity requirement. Cognitive radio (CR), non‐orthogonal multiple access (NOMA) and multi‐access edge computing (MEC) are envisioned as the key technologies in fifth‐generation and beyond. In this work, the authors introduce the concept of CR‐NOMA in MEC offloading, where a secondary user (SU) can utilise the spectrum allocated to a primary user (PU) to offload its computation task to the MEC server for remote execution. For the spectrum utilisation, an equation to specify the minimum transmit power that must be allocated to the PU is derived. The authors also develop an algorithm to determine the offloading decision (i.e. offloading or not) and the paired PU (i.e. subcarrier used for computation offloading) for SUs, using one‐to‐one matching game. Moreover, through numerical simulations, the authors demonstrate the superior performance of the proposed algorithm compared with several baseline schemes.

In this paper, the secrecy performance of a massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network is studied in the presence of a multiple-antenna eavesdropper. The ergodic secrecy rates for the downlink transmission in the considered system are derived to provide important insights. Then, by using these results, a joint power allocation scheme is proposed for both uplink training and downlink data transmission phases to maximize the sum ergodic secrecy rates. Because the utility function of interest is non-concave and the involved constraints are non-convex, a new iterative algorithm is proposed, which can find at least a local optimum. The obtained results reveal that the secrecy performance of NOMA networks benefits from deploying massive MIMO techniques. They also indicate that the proposed optimization algorithm enhances the secrecy performance of the considered system.

This paper studies the error-control protocol design for free-space optical (FSO) burst transmission in satellite communication systems. Specifically, we model and analyze the throughput performance of LEO-to-ground FSO systems over atmospheric turbulence channels when incremental redundancy hybrid automatic repeat request (IR-HARQ) protocols, which combine rate-compatible punctured convolutional (RCPC) code and sliding window ARQ, are employed. For this purpose, the time-varying behavior of atmospheric turbulence channels is first captured by a finite-state Markov chain. Then, the channel model is used to develop the burst loss model for IR-HARQ in order to analytically derive the system throughput. The results quantitatively show the impact of atmospheric turbulence on the throughput performance and support the optimal selection of system parameters. Monte Carlo simulations are also performed to validate the accuracy of theoretical derivations.

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.

Long Term Evolution (LTE) networks are expected to play a key role in providing connections to billions of Machine-Type Devices (MTDs) in the 5G big picture. Since the ALOHA-based access framework of LTE-A alone cannot support bursty massive access scenarios caused by the MTDs, the 3GPP has additionally employed the Access Class Barring (ACB) scheme as the baseline traffic control method. While the scheme certainly improves access success probability of the devices, corresponding delay degradation may be unacceptable. In this paper, we renounce the ALOHA-based framework of LTE to propose a new protocol, namely the Free Access Distributed Queue (FADQ), accompanied by an estimation method to resolve the massive synchronized access issue in a more efficient manner. Simulations under the 3GPP's reference setup show that FADQ protocol significantly reduces access delay while maintaining an access success probability on par with the ACB.

Hidden‐tag problem is one of the most important issues in the implementation of radio‐frequency identification (RFID) systems. Due to effects of imperfect wireless channels, RFID tags can be hidden during the identification process by either another tag or an unsuccessful detection. The former is known as the capture effect (CE) while the latter is the detection error (DE). This study newly proposes two modified tree‐based identification protocols, namely tweaked binary tree (TBT) and tweaked query tree (TQT), which are able to tackle the hidden‐tag problem caused by both the CE and DE. The performance of the proposed TBT and TQT protocols, in terms of the average number of slots required to detect a tag, and the tag‐loss rate, is evaluated in comparison with that of previously proposed ones. Computer simulations and numerical results confirm the effectiveness of the proposed protocols.

This paper proposes a new RFID binary tree-based identification protocol, namely Tweaked Binary Tree (TBT), to cope with hidden tag problem caused by capture effect and detection error phenomena. In TBT, the whole identification process is divided into multiple binary tree cycles, and the hidden tags in a cycle are checked and re-transmitted in the first slot of the next one. The average number of slots for a successful detection of a tag, and the tag loss rate, defined as a ratio between the number of missing tags and the whole tag cardinality, are theoretically analyzed. Computer simulations are also performed to validate the theoretical analysis. We also confirm the superiority of the proposed method in comparison with a conventional General Binary Tree (GBT) one.

Anonymous tag set cardinality estimation problem of Radio Frequency IDentification (RFID) using Maximum A Posteriori (MAP) approach is studied in this paper. The posterior probability of the total number of tags, given the frame size and the observed number of non-empty slots, is firstly determined. Then, the total number of tags is estimated to maximize the posterior probability. Computer simulation results demonstrate the effectiveness of the proposed approach.