Semantic communication technology conserves spectrum resources, while unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surfaces (RIS), a key component of the low-altitude intelligent networks, enhance communication flexibility. Combining these technologies improves wireless performance. However, due to the openness of wireless channels, high-quality communication links are vulnerable to eavesdropping, which compromises system security. Additionally, maritime communication faces challenges such as dynamic channel variations, and high-altitude UAVs struggle to locate sea surface users and eavesdroppers. To address these issues, we propose a secure UAV-RIS-assisted communication scheme that integrates semantic communication, and integrated sensing and communications (ISAC). This scheme maximizes the secrecy semantic rate, reduces communication and computation energy consumption, and ensures constraints on sensing spectral efficiency and semantic accuracy. We jointly optimize UAV-RIS trajectories, RIS phase shifts, spectrum allocation, and the average number of semantic symbols to enhance security under eavesdropping attacks. We propose an approach that integrates semantic communication, the multi-agent softmax deep double deterministic policy gradient, and the multi-agent dueling deep Q-network (S-MA-SD5), effectively supports UAV-RIS-assisted communication in maritime environments. Performance evaluations reveal that the proposed method exceeds existing methods, achieving higher security semantic rates and lower energy consumption, thereby significantly enhancing security and efficiency in the low-altitude intelligent maritime networks.

Semantic communication optimizes spectrum usage and enhances efficiency. Integrated with uncrewed aerial vehicle (UAV) communication—a core of the low-altitude economy—it boosts data transmission in the power Internet of Things (IoT), a key part of the industrial IoT. However, power IoT wireless transmissions face security and efficiency challenges due to eavesdropping and obstructions. To address this, we propose a secure scheme integrating semantic communication and UAV relays to maximize the secrecy semantic rate while minimizing delay, energy consumption, and ensuring semantic accuracy. By jointly optimizing UAV trajectories, task local computation ratios, and channel selection, the scheme mitigates eavesdropping under cochannel interference. To tackle nonconvexity and dynamic environments, we adopt a deep reinforcement learning-based approach with semantic communication, enabling efficient UAV cooperation and resource allocation. Simulations show the proposed method improves security and energy efficiency in power IoT systems, highlighting the potential of low-altitude intelligent network.

Deep neural networks (DNNs) extract embeddings from multimodal data such as audio, images, and LiDAR, each with heterogeneous computational demands and representation abilities to support multimodal services such as audio-visual speech recognition. Reinforcement learning (RL)-based model selection and splitting schemes determine the model variants from DNNs and the components to offload in unimodal models to reduce inference latency, aiming for a three-fold trade-off among inference accuracy, computation cost, and communication overhead but ignore the heterogeneity of different modalities within multimodal DNNs. In this paper, we propose an RL-based edge-assisted multimodal inference scheme that optimizes model selection at modality level and edge-assisted policies, including collaborative servers and partition points for each feature extractor to perform multimodal DNNs on mobile devices. Based on the information complexity and historical influence of each modality, as well as real-time observations such as channel gain, and previous inference performance, the policy distributions are designed to maximize the utility, as a weighted sum of inference latency and energy consumption, and inference accuracy. Safe policy exploration further mitigates risks such as low inference accuracy, intolerable inference latency, and improper allocation of computational resources to specific modalities. We analyze the computational complexity affected by the number of model variants, edge servers, and partition points and derive performance bounds for inference latency, energy consumption, and utility under specific sample sizes and data rates. Experimental results show that the proposed schemes improve inference performance compared to benchmark schemes.

The rapid proliferation of the Internet of Things has given rise to a multitude of real-time applications, which pose significant computing challenges for resource-constrained users. Air-ground collaborative mobile edge computing (AGMEC) emerges as an innovative solution, integrating aerial and terrestrial computing paradigms to provide flexible, efficient services that significantly enhance data processing capabilities. This paper focuses on the freshness of task data in AGMEC networks, characterized by the emerging metric of age of information (AoI). Due to limited spectrum resources and network coverage gaps, we introduce non-orthogonal multiple access (NOMA) and active reconfigurable intelligent surface (RIS) technologies to facilitate efficient task offloading. We formulate a joint optimization problem of uncrewed aerial vehicle trajectory, active RIS beamforming, and task offloading strategy to minimize the network’s average AoI under multidimensional constraints. Considering the non-convex nature and the dynamic characteristics of the AGMEC environment, we develop an action adjuster-based deep deterministic policy gradient (AADDPG) algorithm. The innovative design of the action adjuster enables the algorithm to not only achieve efficient processing of hybrid action spaces but also effectively protect UAV battery performance. Simulation results demonstrate that the proposed AADDPG algorithm significantly improves AoI performance compared to other benchmark algorithms. Additionally, the results corroborate the efficacy of both NOMA and active RIS in minimizing AoI for AGMEC networks.

Uncrewed aerial vehicles (UAVs)-assisted low-altitude communication networks have emerged as a promising solution for extending air-to-ground communication coverage and services. However, UAV-assisted communications are highly susceptible to jamming attacks due to its high probability of line-of-sight links. In this paper, we design an embodied intelligence-enhanced low-altitude communication network under malicious jammers, where multiple UAVs act as embodied intelligent agents to collaborate and jointly optimize power allocation and spectrum allocation to minimize transmission delay, while guaranteeing quality of service requirements against jamming attacks. Considering the non-convex problem and highly dynamic wireless environments, we propose an embodied multiagent deep reinforcement learning (E-MA-DRL)-based intelligent resource allocation approach to jointly optimize the communication resource, where embodied intelligent agents (UAVs) sense communication states, learn to make decisions and perform resource allocation actions. To enhance learning efficiency and performance, we then design prioritized experience replay (PER) and transfer learning (TL) in a double deep Q-network (DDQN) algorithm, to smartly schedule the communication resource and reduce the effect of jamming attacks and inter-channel interference. Simulation results show that the proposed approach significantly reduces communication delay and improves the probability of successful transmission in low-altitude communication networks against jamming attacks.

The integration of mobile edge computing (MEC) with semantic communication (SemCom) has emerged as a promising solution to address the growing demand for computing services. However, challenges remain in balancing task delay and energy consumption for unmanned aerial vehicles (UAVs) providing dynamic edge services, as the diversity of tasks complicates offloading and resource allocation. This paper investigates a multi-modal task offloading problem for Task-Oriented SemCom (TOSC)-based low-altitude MEC networks. We aim to minimize the weighted sum of ground devices’ average task delay and UAV’s energy consumption by jointly optimizing task offloading indicators, as well as the three-dimensional (3D) trajectory and computing resources of the UAV. To solve the formulated nonconvex optimization problem, which involves discrete and continuous variables, we propose an exact Penalty-based Alternative and soft Penalty-based Proximal Policy Optimization (PA-P3O) algorithm. Specifically, we develop an Exact Penalty (EP)-based algorithm to address the equilibrium-constrained task offloading problem with the discrete offloading indicators. Then, the coupling between the 3D trajectory and computing resources of the UAV is modeled as a Markov decision process (MDP), and a Soft Penalty (SP)-based deep reinforcement learning (DRL) algorithm is proposed to address the high-dimensional action space. Simulation results demonstrate that our proposed algorithm outperforms benchmark schemes and reveal an elevation-angle-distance tradeoff.

Reinforcement learning (RL) based flying ad-hoc network (FANET) routing enables unmanned aerial vehicles (UAVs) to choose the next-hop, but the quality of service (QoS) and energy efficiency have to be enhanced against jamming due to the inaccurate path quality estimation. In this paper, we propose an RL based anti-jamming FANET routing with QoS guarantee to optimize the transmit power and the originator message broadcast interval for the route discovery to estimate the path quality to select the next-hop from the routing table. Based on the path availability history, the channel conditions, the received jamming power and the transmission quality regarding the number of the received originator messages and the bit error rate during route discovery, the routing policy is selected to enhance the throughput and the energy efficiency. The backward estimation of the throughput in the utility evaluation addresses the delayed feedback from the destination. The performance bound is derived in terms of network topology and channel gain based on the Nash equilibrium of the cooperative game among the UAVs. Simulation results provide the performance gain of the throughput and energy consumption over the benchmarks.

Reinforcement learning (RL) based flying ad-hoc network (FANET) routing enables unmanned aerial vehicles (UAVs) to choose the next-hop to forward the packets, but the quality of service (QoS) and energy efficiency have to be enhanced due to the inaccurate path quality estimation under jamming attacks. In this paper, we propose an RL based energy-efficient anti-jamming FANET routing scheme with QoS guarantee to optimize both the transmit power and the originator message broadcast interval for the route discovery to estimate the path quality to select the next-hop from the routing table. Based on the path availability history, the channel conditions and the received jamming power, as well as the transmission quality regarding the number of the received originator messages and the bit error rate during route discovery, the routing policy is selected to enhance the throughput and the energy efficiency under jamming attacks with changing power. The backward estimation of the throughput in the utility evaluation addresses the delayed feedback from the destination under large-scale networks. The deep neural networks are further designed to address the quantization error of the transmission quality and the channel gain for UAVs with high mobility to enhance the path exploration efficiency. In addition, the upper bound in terms of network topology and channel gain is derived based on the Nash equilibrium of the anti-jamming routing game. The proposed routing scheme is implemented to improve the image transmission quality against jamming in outdoor environments. Experimental results based on UAVs equipped with Raspberry Pi show the performance gain of the throughput and the energy consumption.

Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) in space-air-ground integrated networks (SAGINs) provide a promising solution for enhancing communication, computing, and storage services for the increased number of Internet of Things (IoT) devices. However, jamming attacks and co-channel interference severely degrade the network performance due to wide field of line of sight. In this work, we propose a resource scheduling method to jointly optimize the channel selection, UAV deployment and task offloading to minimize both the communication and computing latency, under the malicious jamming attacks and resource constraints. Considering the highly dynamic and complex nature of the SAGIN environment and the multi-UAV collaboration framework, we then design an advanced anti-jamming-driven multi-agent deep reinforcement learning (MADRL) method based on the multi-agent twin-delayed deep deterministic policy gradient (MATD3) algorithm. This method adaptively adjusts the resource scheduling strategy to enhance the network’s ability to withstand jamming attacks, reduce total network latency, and maintain real-time services even under unfavorable conditions. Simulation results show that our proposed method significantly outperforms existing benchmark methods in terms of latency reduction, signal-to-interference-plus-noise ratio (SINR) improvement, and overall network robustness under jamming attacks. For example, the proposed MATD3-SAGAJ method reduces latency by about 10% and improves the SINR satisfaction ratio from around 91% to nearly 95% compared to the current optimal benchmark method.

In next-generation wireless networks, integrated satellite-terrestrial networks are regarded as a pivotal solution for supporting seamless coverage and elevated data rates, but the physical layer security performances are severely degraded under both jamming and eavesdropping attacks due to wide field of line of sight. Thus, this paper designs an aerial hybrid active-passive reconfigurable intelligent surface (aerial hybrid RIS) communication system to enhance secure and reliable communication for integrated satellite-terrestrial networks, where an active eavesdropper aims to jam legitimate channels and eavesdrop on any data stream from RIS simultaneously. Specifically, we propose a resource scheduling approach that jointly optimizes the position of the aerial RIS, the hybrid beamforming matrix, the satellite beamforming design, and the satellite transmission power to maximize the ground users’ (GUs) secrecy rate under quality of service (QoS) requirements. To address the optimization problem in complex and dynamic communication environments, we reformulate the problem as a reinforcement learning (RL) problem and propose a secure resource scheduling method based on the relay hindsight experience replay-softmax deep double deterministic policy gradients (RHER-SD3) algorithm. The proposed RHER-SD3 algorithm effectively schedules the secure hybrid active-passive beamforming matrix, the aerial position of the RIS, the satellite beamforming vectors, and the satellite power allocation to avoid both jamming and eavesdropping attacks, even though the behavior information of the attacker is imperfect. Simulation results demonstrate that the proposed method outperforms existing approaches in improving system secrecy performance and QoS satisfaction against hybrid attacks.

Semantic communication technology, which transmits only relevant semantic information, can significantly conserve communication resources and reduce service time. This technology is particularly promising for unpilotedaerial vehicle (UAV)-assisted mobile edge computing (MEC) in the internet of vehicles (IoV). However, integrating semantic communication with UAV-assisted vehicle MEC is susceptible to malicious jamming. This paper introduces a reliable communication method that combines multi-modal semantic communication with UAV-assisted vehicle MEC to minimize delays in communication and computation while maintaining semantic accuracy during jamming attacks. Our approach optimizes UAV trajectories, user associations, and channel selections, enabling the UAV to select optimal positions when associating with different modal users and reducing the impact of jammers during multi-modal task reception. Due to the non-convex nature of the optimization problem and the highly dynamic environment, we employ the semantic communication combined with the multi-agent twin delayed deep deterministic policy gradient (SC-MA-TD3) approach, a multi-agent deep reinforcement learning (DRL) strategy that fosters UAV cooperation for efficient resource allocation. Simulation results show that our approach outperforms existing approaches in reducing delays and enhancing semantic accuracy.

Unmanned aerial vehicles (UAVs) have been widely employed as airborne base stations to enhance terrestrial communications. However, the growing demand for communications, spectrum and energy resources are increasingly in short supply, while malicious jamming from jammers threatens the communications reliability. To address these challenges, we propose a joint reliable channel selection and power control approach for multi-UAV-enabled communications networks under malicious jamming attacks, with the goal of maximizing user communication capacity under limited energy constraint and avoiding malicious jamming from jammers. Due to the dynamic and time-varying nature of communication environments, we propose an intelligent resource optimization algorithm based on game theory guided reinforcement learning. To be specific, we employ a hierarchical learning algorithm based on the Stackelberg game to help users in the follower layer cooperatively select channels to against co-channel interference and jamming, and develop a deep reinforcement learning-based algorithm for dynamic power control to maintain communication efficiency. Simulation results demonstrate that our proposed approach can significantly improve the user communication rate and achieves faster convergence compared with existing algorithms.

The space-air-ground integrated network (SAGIN) is a crucial technology for sixth-generation (6G) wireless communication networks to achieve seamless coverage and high throughput. In this paper, we propose an unmanned aerial vehicle (UAV)-assisted SAGIN structure, where the UAV is responsible for collecting data from ground users (GUs) and transmitting it to low-earth orbit (LEO) satellites. This paper also formulates a joint energy-efficient and fair resource scheduling optimization problem under jamming attacks and limited energy constraints, where the line-of-sight (LoS) links between the UAV and GUs are susceptible to being jammed. Due to the non-convex problem and dynamic environments, a deep reinforcement learning (DRL)-based twin delayed deep deterministic policy gradient (TD3) is developed to search optimal UAV trajectory to maximize energy efficiency (EE) and fairness against jamming. Simulation results verify that the proposed intelligent resource scheduling algorithm outperforms the baseline algorithms in terms of EE and fairness index in different settings.

Reliable port communications support maritime applications such as vessel navigation and cargo tracking for a large number of mobile users on ships, but the quality of services (QoS) such as data rate and energy consumption is severely degraded by inter-cell interference. In this paper, we propose a deep reinforcement learning (RL)-based interference coordination scheme for port communications to reduce the transmission latency and energy consumption, and improve the data rate. Based on the signal-to-interference plus noise ratio, the channel gains, the estimated interference levels and the transmission latency, the base station chooses the transmit power and downlink bandwidth constraint to avoid choosing risk policies that cause the communication performance degradation. In addition, a two-level hierarchical structure with two convolution networks and four fully connected layers is designed to reduce the algorithm complexity and enhance the convergence speed. Simulation results verify the performance gain of the proposed scheme in terms of the data rate, the transmission latency, and the energy consumption compared with the benchmark.

Cellular-connected unmanned aerial vehicles (UAVs) play an essential role in cellular networks. Combined with non-orthogonal multiple access (NOMA) technique, UAVs can provide better performance in various communication scenarios. In this paper, we investigate a NOMA-enhanced UAV-assisted cellular network where multiple UAVs are deployed as aerial base stations to provide communication services for mobile ground users in the presence of a malicious jammer. We propose a two-step learning-based resource scheduling approach. First, an algorithm based on K-means clustering is proposed to partition ground users (GUs) to reduce mutual interference. Moreover, a cooperative multi-agent twin delayed deep deterministic algorithm is proposed to jointly optimize UAVs' trajectories, power allocation and GU association to maximize the system energy efficiency (EE) while guaranteeing minimum quality-of-service (QoS) requirements. Extensive results demonstrate that the proposed solution can efficiently improve EE and QoS performances under jamming attacks compared with existing popular approaches.

Unmanned aerial vehicle (UAV) plays an important role in fifth and sixth (5G/6G) communication systems, garnering significant research attention. This paper investigates a dynamic UAV-enabled secure ultra-reliable and low-latency communication (URLLC) system, where a UAV sends confidential data to a mobile device where another UAV cooperatively transmits jamming interference to confuse a potential eavesdropper. The transmission latency constraint, secure communication requirement and dynamic channel characteristics are considered into real-word communication environments. Then, a joint communication transmit power, jamming power, and trajectory optimization approach is proposed to maximize the system secrecy rate, simultaneously guaranteeing the URLLC requirement. To effectively address the non-convex problem, the URLLC constraint is transformed into a data rate constraint to ensure that the optimization problem is more manageable, and then present an efficient solution by employing an alternating approximation and successive convex optimization. Finally, simulation results verify that the presented joint optimization approach substantially outperforms other popular benchmarks in secrecy rate performance.

Device authentication plays a key role in securing Internet of Things (IoT), where radio frequency fingerprinting (RFF) identification is an emerging physical layer security technique by exploiting intrinsic and unique hardware impairments of wireless devices. However, recent works mainly focus on the identification of authorized devices, while neglecting the harm misidentification of illegal devices. Thus, this paper proposes a normalizing flow-based RFF method (NFRFF) for illegal device identification. The proposed NFRFF designs parallel flows and a fusion flow to handle the distributions of radio frequency signal samples and employs the dual attention mechanism to enhance the efficiency of information fusion and the ability to recognize illegal wireless devices. Ultimately, NFRFF is capable of assigning lower likelihoods to signal samples from illegal devices and higher ones to those from authorized devices. Simulation results verify that after extracting multi-scale radio frequency feature maps using VGG-16, NFRFF achieves an AUROC of 0.991 under 120 authorized devices and 30 illegal devices, achieving excellent identification performance.

Vehicular Metaverses represent emerging paradigms arising from the convergence of vehicle road cooperation, Metaverse, and augmented intelligence of things. Users engaging with Vehicular Metaverses (VMUs) gain entry by consistently updating their Vehicular Twins (VTs), which are deployed on RoadSide Units (RSUs) in proximity. The constrained RSU coverage and the consistently moving vehicles necessitate the continuous migration of VTs between RSUs through vehicle road cooperation, ensuring uninterrupted immersion services for VMUs. Nevertheless, the VT migration process faces challenges in obtaining adequate bandwidth resources from RSUs for timely migration, posing a resource trading problem among RSUs. In this paper, we tackle this challenge by formulating a game-theoretic incentive mechanism with multi-leader multi-follower, incorporating insights from social-awareness and queueing theory to optimize VT migration. To validate the existence and uniqueness of the Stackelberg Equilibrium, we apply the backward induction method. Theoretical solutions for this equilibrium are then obtained through the Alternating Direction Method of Multipliers (ADMM) algorithm. Moreover, owing to incomplete information caused by the requirements for privacy protection, we proposed a multi-agent deep reinforcement learning algorithm named MALPPO. MALPPO facilitates learning the Stackelberg Equilibrium without requiring private information from others, relying solely on past experiences. Comprehensive experimental results demonstrate that our MALPPO-based incentive mechanism outperforms baseline approaches significantly, showcasing rapid convergence and achieving the highest reward.

Industrial systems require reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. Existing studies rely on heuristic models which may struggle to capture complex temporal dependencies. This paper introduces an integrated framework that leverages the capabilities of the Transformer and Deep Reinforcement Learning (DRL) algorithms to optimize system maintenance actions. Our approach employs the Transformer model to effectively capture complex temporal patterns in IoT sensor data, thus accurately predicting the Remaining Useful Life (RUL) of equipment. Additionally, the DRL component of our framework provides cost-effective and timely maintenance recommendations. Numerous experiments conducted on the NASA C-MPASS dataset demonstrate that our approach has a performance similar to the ground-truth results and could be obviously better than the baseline methods in terms of RUL prediction accuracy as the time cycle increases. Additionally, experimental results demonstrate the effectiveness of optimizing maintenance actions.

In recent years, jointly optimizing unmanned aerial vehicle (UAV) hover point selection and resource management for UAV-assisted mobile edge computing (MEC) is a hot research topic. Unlike previous studies, this paper investigates the optimization problem of hover point selection and resource management under dynamic jamming attacks, where the objective is to maximize overall communication and computing efficiency while taking into account constraints on total UAV power and the availability of channels. Due to the non-convex problem and highly dynamic environments, we then propose an advanced deep reinforcement learning (DRL) algorithm to jointly optimize UAV hover point selection, task collection time ratio, transmission power, channel selection, and task offloading ratio to improve the efficiency of UAV-assisted MEC. Specifically, the algorithm optimizes UAV hover point selection to minimize the negative effect of jamming attacks, and then manages resources to improve UAV task processing capacity and reduce energy consumption while mitigating jamming. Simulation results demonstrate that our proposed learning-based algorithm significantly enhances the computing and offloading efficiency in complex and dynamic UAV-assisted MEC environments against jamming compared to other existing algorithms.

In mobile edge computing (MEC) systems, multiple unmanned aerial vehicles (UAVs) can be utilized as aerial servers to provide computing, communication, and storage services for edge users, called UAV-assisted MEC, which has emerged as a promising technology to improve both the computing and communication performances. Unlike existing works without considering jamming attacks, we investigate a multi-UAV-assisted-MEC scenario under multiple malicious jammers and then propose a resource management approach with the objective of minimizing both the system energy consumption and latency. Due to the time-varying nature of communication environments, we design a multi-agent deep reinforcement learning (MADRL)-based resource management approach to dynamically adjust the CPU frequency, communication bandwidth, and channel access selection of UAVs to enhance the system performance against jamming attacks. On this basis, in order to enhance the algorithm learning efficiency, we propose a multi-agent twin-delayed deep deterministic policy algorithm in combination with the prioritized experience replay mechanism (PER-MATD3) to effectively search for the joint resource management strategy under high-dimensional state and action spaces, where the time-varying channel state information and imperfect attack behavior information are also effectively trained to improve the learning capacity and convergence speed. Simulation and experimental results verify that the proposed approach can significantly decrease the overall system latency (i.e., computing and communication latency) and energy consumption compared to other benchmark algorithms under different real-world settings.

The integration of semantic communication with mobile edge computing (MEC) has emerged as a prominent research area. In this paper, we explore a novel scenario where semantic communication is integrated with unmanned aerial vehicles (UAVs) to enhance MEC, particularly in the face of jamming attacks. Our research focuses on addressing the resource management challenge to minimize task completion time and maximize semantic spectral efficiency (SSE) while adhering to quality of service requirements and resource constraints. Given the non-convexity of this problem and the dynamic behavior of jamming attacks, this paper proposes a deep reinforcement learning (DRL) algorithm by jointly optimizing UAV trajectories, user associations, and channel selections against jamming. In detail, the proposed anti-jamming DRL-based resource management approach can effectively capture the jammer’s behavior, and learn to adjust semantic task and resource scheduling strategies with the objective to minimize the negative effect of jamming attacks on task offloading and semantic communication. Simulation results demonstrate that the proposed approach outperforms baseline algorithms in terms of task completion time and total SSE under different real-world settings.

Mobile edge computing (MEC) enables communication users with limited computation power to offload computation-intensive tasks to the edge server, thus dramatically enhancing the limited computing capabilities of the users. As the reality of scarce spectrum resources and the energy-constrained nature of communication users, this paper introduces non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT) techniques to achieve more efficient task offloading in MEC. To minimize the number of computationally failed tasks while simultaneously satisfying different quality of service (QoS) requirements of users, a joint resource management problem of the spectrum, computation, and energy resources is formulated. Due to the non-convexity of the offloading optimization problem and the stochastic nature of the constructed MEC environment, a multiple agents deep deterministic policy gradient (MADDPG)-based resource management algorithm is proposed to manage each user’s multidimensional resources without collaborating. The simulation results show that compared to other benchmark schemes, the proposed algorithm can effectively improve both the communication and computational performances in MEC.

With the rapid development of maritime activities, efficient and reliable maritime communications have attracted ever-increasing attention, and mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, can provide flexible and adaptable services for maritime communications. In this paper, we investigate a UAV-RIS-assisted maritime communication system under a malicious jammer, where a UAV-RIS is deployed to jointly adjust its placement and RIS surface elements to maximize the system energy efficiency (EE) and guarantee quality of service requirements against jamming attacks. In addition, an adaptive energy harvesting scheme is developed for information transmission (IT) and energy harvesting (EH) simultaneously to enhance the endurance of the UAV by deploying different IT times for each RIS element. Considering the non-convex optimization problem and highly complex maritime environments, an intelligent resource management approach based on deep reinforcement learning is proposed to jointly optimize the base station’s transmit power, placement of UAV-RIS, and RISs reflecting beamforming. Furthermore, hindsight experience replay is adopted to improve the learning efficiency and performance. The simulation results demonstrate that the proposed approach achieves the better EE and EH performances under different real-world settings compared with existing popular approaches.

Mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, combines the benefits of these two techniques, which can further improve the communication performance. However, high-quality air-ground channel links are more vulnerable to both the adversarial eavesdropping and the malicious jamming. Therefore, this paper proposes a reliable and secure communication approach assisted by the UAV-RIS to maximize the secrecy rate, while ensuring the quality of service (QoS) requirement of the legitimate user against both the eavesdroppers and the jammer. Specifically, with the imperfect channel state information and behaviors of mixed attacks, we try to maximize the achievable worst-case secrecy rate by jointly designing the transmit beamforming, artificial noise, UAV-RIS placement, and RIS’s passive beamforming. As the optimization problem is non-convex and the environment is highly dynamic, a post-decision state deep Q-network combined with Fourier feature mapping algorithm (called PDS-DQN-FFM) is further designed to effectively achieve the robust anti-attack transmission strategy. Simulation results demonstrate that our proposed learning based reliable and secure transmission approach significantly enhances both the secrecy rate and QoS satisfaction level as compared with existing approaches.

Based on the efficient and reliable exchange of learning messages containing both the policy selection experiences such as the learning parameters and observations among the learning agents, multi -agent reinforcement learning (RL) has to address adversarial communications that send fake learning messages to learning agents with the goal of decreasing the RL rewards or even failing the learning tasks. In this paper, we propose a multi-agent RL (MARL) communication framework for wireless networks against adversarial communications, in which each learning agent chooses the cooperative agents to share learning messages based on the agent reputation that indicates the probability to send fake learning messages. By comparing with the learning history, each learning agent authenticates the received learning messages before integrating them in the RL task state formulation and the learning parameter update for robust task learning. The learning factor that increases with the correlation between the local and the shared observation is calculated to update the task Q-values and neural network weights based on the shared learning parameters. As a case study, the multi-agent deep Q-network within our proposed MARL communication framework is implemented in the UAV swarm video transmission system and the performance gain over the benchmark is provided in the simulation results based on 5-UAV swarm against an attacker that sends fake observations and neural network weights.

Unmanned aerial vehicles (UAVs) have been increasingly employed as aerial servers in mobile edge computing (MEC) systems, providing essential computing, communication, and storage services for edge users. This UAV-assisted MEC paradigm shows great promise in enhancing both computing and communication performances. However, the presence of malicious jammers poses significant challenges to the system's reliability and efficiency. In this study, we explore the resource management problem in a multi-UAV-assisted MEC scenario under the influence of multiple malicious jammers. To mitigate the impact of jamming attacks, we propose a resource management approach with the primary objective of minimizing system energy consumption and latency while adhering to UAV energy constraints. Due to the dynamic and time-varying nature of the communication environment, we present a deep reinforcement learning (DRL)-based algorithm that dynamically adjusts the CPU frequency and communication bandwidth of the UAV to optimize the system performance even under jamming attacks. Through simulations, we demonstrate the effectiveness of the proposed algorithm in significantly reducing the overall system latency (both computational and communication latency) as well as minimizing energy consumption.

Cognitive unmanned aerial vehicle (UAV) communication systems combine benefits of both the cognitive radio and UAV, which improves the spectral efficiency and communication coverage area. However, high-quality air-ground channel links maybe more vulnerable to potential eavesdropping or jamming attacks. Thus, this paper proposes a secure transmission approach assisted by deploying a cooperative UAV to transmit artificial noise to jam an active eavesdropper, in order to maximize the system secrecy rate under the quality of service (QoS) requirement of a primary device. Specifically, we jointly optimize the flight trajectory and transmission power of the cooperative jammer to maximize the system's secrecy rate under strict constraints. To achieve this, we convert the non-convex problem into an approximately convex problem using the block coordinate descent algorithm and successive convex approximation method. Simulation results show that compared to existing algorithms, the proposed algorithm in this study can significantly improve the system's secrecy rate.

The ultra-high speed (e.g., typically moving with 10–20 Mach) of the hypersonic vehicle (HSV) causes a plasma sheath, which severely degrades the communication performance and results in communication blackouts. In this paper, we propose a deep reinforcement learning (RL)-based HSV reliable communications scheme against jamming, which enables the HSV to select the carrier frequency and transmit power according to the signal quality, the estimated voltage standing wave ratio, flight altitude, flight speed, and angle of attack. Specifically, we design a deep two-level hierarchical structure to compress the high-dimensional state and action space, with the added advantage of leveraging transfer learning to reduce initial exploration and expedite the optimization process. To optimize the learning speed, the dueling architecture is implemented in the deep network to measure the state value and the advantage function of the policies. In contrast to the benchmark, the simulation results indicate that the proposed scheme yields a significant reduction in both bit error rate and transmit power.

Rate-splitting multiple access (RSMA) is promising to be developed as a key enabling technology for 6G. This paper investigates the resource allocation problem in a downlink multi-user and multiple-input single-output (MU-MISO) RSMA system with successive interference cancellation (SIC) errors for ultra-reliable and low-latency communications (URLLC). The single-carrier RSMA (SC-RSMA) resource allocation scheme with URLLC is first given, where the beamforming vector and the transmission rate are optimized to maximize the effective throughput (ET). To solve the non-convex optimization problem formulated in the SC-RSMA scheme, an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) is proposed to alternately optimize the beamforming factor and the transmission rate. Furthermore, the multicarrier RSMA (MC-RSMA) scheme is developed for the URLLC access of large-scale users. Based on the results of the SC-RSMA scheme, a low-complexity three-step optimization algorithm is proposed to solve the resource allocation problem formulated in the MC-RSMA scheme. Finally, the simulation results show that the RSMA scheme and MC-RSMA scheme can respectively achieve higher ET than the non-orthogonal multiple access (NOMA) scheme and multi-carrier NOMA (MC-NOMA) scheme in the URLLC scenario, and verify that RSMA can reduce the latency and improve the reliability of the system.

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance.

Collaborative inference in mobile edge computing (MEC) enables mobile devices to offload the computation tasks for the computation-intensive perception services, and the inference policy determines the inference latency and energy consumption. The optimal inference policy depends on the inference performance model of deep learning, the data generation model and the network model that are rarely known by mobile devices in time. In this paper, we propose a multi-agent reinforcement learning (RL) based energy-efficient MEC collaborative inference scheme, which enables each mobile device to choose both the partition point of deep learning and the collaborative edge of each mobile device based on the image quantity, the channel conditions and the previous inference performance. A learning experience exchange mechanism exploits the Q-values of the neighboring mobile devices to accelerate the inference policy optimization with less energy consumption. We also provide a deep multi-agent RL based inference scheme to accelerate learning for large-scale MEC networks, in which an actor network yields the collaborative inference policy probability distribution and a critic network guides the weight update of the actor network to enhance sample efficiency. We provide the inference performance bound and analyze the computational complexity. Both simulation and experimental results show that our proposed schemes reduce the inference latency and save the MEC energy consumption.

Visible light communication (VLC) is an emerging wireless technology to support high transmission rate for indoor devices by using existing lighting infrastructure, and VLC-based indoor localization is capable of providing high-accuracy localization. However, current VLC-based localization systems suffer from several key challenges such as sensitivity to random tilting of the receiver, which limits its full potential in real-world applications. In this paper, we design an integrated visible light communication and localization (VLCL) system to simultaneously support accurate real-time localization and communication services for indoor devices. To achieve this, an advanced differential phase difference of arrival (A-DPDOA) localization design is developed to simplify hardware and improve tracking robustness. In addition, a joint adaptive modulation, subcarrier and power allocation scheme is also proposed, which aims to improve the communication data rate and localization accuracy. Extensive experiments are performed to demonstrate that the proposed integrated VLCL system achieves higher localization accuracy and transmission data rate, compared to existing systems and schemes. Experiments also illustrate that the localization algorithm is more robust against the random tilting of the receiver under device movement in two-dimensional and three-dimensional scenarios.

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has recently received significant research attention due to its outstanding ability of serving grant-free (GF) users with grant-based (GB) users’ spectrum, which greatly improves the spectrum efficiency and effectively relieves the massive access problem of 5G and beyond networks. In this paper, we first study the outage performance of the greedy best user scheduling SGF scheme (BU-SGF) by considering the impacts of Rayleigh fading, path loss, and random user locations. In order to tackle the admission fairness problem of the BU-SGF scheme, we propose a fair SGF scheme by applying cumulative distribution function (CDF)-based scheduling (CS-SGF), in which the GF user with the best channel relative to its own statistics will be admitted. Moreover, by employing the theories of order statistics and stochastic geometry, the outage performances of both BU-SGF and CS-SGF schemes are analyzed. Theoretical results show that both schemes can achieve full diversity orders only when the served users’ data rate is capped, which severely limits the rate performance of SGF schemes. To further address this issue, we propose a distributed power control strategy to relax such data rate constraint, and derive analytical expressions of the two schemes’ outage performances under this strategy. Finally, simulation results validate the fairness performance of the proposed CS-SGF scheme, the effectiveness of the power control strategy, and the accuracy of the theoretical analyses.

The combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency and spectral efficiency of the upcoming beyond fifth generation network (B5G), especially for support the wireless sensor communications in Internet of things (IoT) system. However, how to realize intelligent frequency, time, and energy resource allocation to support better performances is an important problem to be solved. In this paper, we study joint spectrum, energy, and time resource management for the EH-CR-NOMA IoT systems. Our goal is to minimize the number of data packets losses for all secondary sensing users (SSU), while satisfying the constraints on the maximum charging battery capacity, maximum transmitting power, maximum buffer capacity, and minimum data rate of primary users (PU) and SSUs. Due to the non-convexity of this optimization problem and the stochastic nature of the wireless environment, we propose a distributed multidimensional resource management algorithm based on deep reinforcement learning (DRL). Considering the continuity of the resources to be managed, the deep deterministic policy gradient (DDPG) algorithm is adopted, based on which each agent (SSU) can manage its own multidimensional resources without collaboration. In addition, a simplified but practical action adjuster (AA) is introduced for improving the training efficiency and battery performance protection. The provided results show that the convergence speed of the proposed algorithm is about 4 times faster than that of DDPG, and the average number of packet losses (ANPL) is about 8 times lower than that of the greedy algorithm.

Wireless body area networks (WBANs) are vulnerable to active eavesdropping that simultaneously perform sniffing and jamming to raise the sensor transmit power, and thus steal more healthcare data. In this paper, we propose an intelligent reflecting surface (IRS)-aided reinforcement learning (RL) based secure WBAN transmission scheme that enables the coordinator to jointly optimize the sensor encryption key and transmit power, as well as the IRS phase shifts against active eavesdropping. A Dyna architecture is designed to improve the learning efficiency with the simulated transmission experiences and safe exploration is applied to avoid the risky policies that result in severe data leakage. A deep RL based WBAN transmission scheme is proposed to further improve the secure transmission with lower eavesdropping rate, intercept probability, sensor energy consumption and transmission latency for the coordinators that support deep learning. We analyze the computational complexity and investigate the equilibrium of the secure transmission game between the coordinator and the eavesdropper to provide the performance bounds, which is verified via the simulation results, showing the efficacy of our proposed schemes.

The proliferation of the Internet of Things pushes the visible light positioning (VLP) system research. However, the existing positioning systems still have the following problems: 1) when the smartphone (receiver) is rotated or tilted during the positioning, existing collaborative LEDs’ positioning algorithms fail and 2) for different smartphone application scenarios, there is not an effective resource management solution between the server and the client. Therefore, in this article, we design and implement a robust and flexible indoor VLP system based on collaborative LEDs and edge computing. First, we propose the enhanced collaborative LEDs’ positioning algorithm, which uses the indoor hidden location information to obtain the rotation angle and tilt angle of the receiver, to achieve the robust system positioning. Then, we use the edge computing solution to balance between bandwidth resources and computing resources and propose a flexible functional segmentation scheme for different smartphone application scenarios. Finally, we conduct experimental tests to evaluate the positioning system performance by landmark decoding rate, positioning accuracy, and segmentation analysis. Test results show that the designed positioning system can achieve centimeter-level positioning. Meanwhile, the smartphone can exchange the least bandwidth resources for the most computing resources under Scheme-3.

This paper investigates the problem of distributed resource management in two-tier heterogeneous networks, where each cell selects its joint device association, spectrum allocation, and power allocation strategy based only on locally-observed information without any central controller. As the optimization problem with devices’ quality-of-service (QoS) constraints is non-convex and NP-hard, we model it as a Markov decision process (MDP). Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

With the advent of the Internet of Everything era, communication data has exploded, which requires more communication resources, such as frequency, time, and energy. In this context, this paper presents a machine learning-based data packet scheduling scheme to achieve efficient data packet transmission in the 5G/6G communication systems. To minimize the average number of packet overflows (APNO), we propose distributed deep deterministic policy gradient (DDPG)-based algorithm for multidimensional resource scheduling. To improve the algorithm stability and training efficiency, the strategy of centralized training and distributed execution is adopted, and an Action Adjuster is designed. The proposed algorithm enables the multidimensional resource management of the 5G/6G commu-nication systems without any information interaction between each agent. Simulation results show that the proposed Action Adjuster DDPG algorithm achieves faster convergence and less data overflow compared to other benchmark algorithms.

This paper investigates the problem of distributed resource management (i.e., joint device association, spectrum allocation, and power allocation) in two-tier heterogeneous networks without any central controller. Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

The Industrial Internet of Things (IIoT) has attracted tremendous interest from both industry and academia as it can significantly improve production efficiency and system intelligence. However, with the explosive growth of various types of user equipment (UE) and data flow, IIoT experiences spectrum resource scarcity for wireless applications. In this article, we propose a solution for spectrum resource management for the IIoT network, with the objective of facilitating the limited spectrum sharing between different kinds of UEs. To overcome the challenges of unknown dynamic IIoT environments, a modified deep $Q$ -learning network (MDQN) is developed. Considering the cost effectiveness of IIoT devices, the base station (BS) acts as a single agent and centrally manages the spectrum resources, which can be executed without coordination or exchange between UEs. In this article, we first built a realistic IIoT model and design a simple medium access control (MAC) frame structure to facilitate the environment state observation. Then, a new reward function is designed to drive the learning process, which takes into account the different communication requirements of various types of UEs. In addition, to improve the learning efficiency, we compress the action space and propose a priority experience replay strategy based on decreasing temporal difference (TD) error. Finally, simulation results show that the proposed algorithm can successfully achieve dynamic spectrum resource management in the IIoT network. Compared with other algorithms, it can achieve superior network performance with a faster convergence rate.

Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, machine learning (ML) model training, and wireless communications. However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training. Moreover, due to the dynamic channel condition and heterogeneous computing capacity of devices in UAV-enabled networks, the reliability and efficiency of data sharing require to be further improved. In this paper, we develop an asynchronous federated learning (AFL) framework for multi-UAV-enabled networks, which can provide asynchronous distributed computing by enabling model training locally without transmitting raw sensitive data to UAV servers. The device selection strategy is also introduced into the AFL framework to keep the low-quality devices from affecting the learning efficiency and accuracy. Moreover, we propose an asynchronous advantage actor-critic (A3C) based joint device selection, UAVs placement, and resource management algorithm to enhance the federated convergence speed and accuracy. Simulation results demonstrate that our proposed framework and algorithm achieve higher learning accuracy and faster federated execution time compared to other existing solutions.

Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLF-CPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLF-CPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, a modified PDS scheme is presented to trace the channel dynamic and adjust the beamforming policy against channel uncertainty accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.

With the rapid deployment of the Internet of Things (IoT), fifth-generation (5G) and beyond 5G networks are required to support massive access of a huge number of devices over limited radio spectrum radio. In wireless networks, different devices have various quality-of-service (QoS) requirements, ranging from ultra-reliable low latency communications (URLLC) to high transmission data rates. In this context, we present a joint energy-efficient subchannel assignment and power control approach to manage massive access requests while maximizing network energy efficiency (EE) and guaranteeing different QoS requirements. The latency constraint is transformed into a data rate constraint which makes the optimization problem tractable before modelling it as a multi-agent reinforcement learning problem. A distributed cooperative massive access approach based on deep reinforcement learning (DRL) is proposed to address the problem while meeting both reliability and latency constraints on URLLC services in massive access scenario. In addition, transfer learning and cooperative learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. Simulation results clearly show that the proposed distributed cooperative learning approach outperforms other existing approaches in terms of meeting EE and improving the transmission success probability in massive access scenario.

Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLFCPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the the IRS-assisted system rate and transmission protection level compared with existing solutions.

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Simulation results demonstrate that the proposed deep learning based secure beamforming approach can significantly improve the system secrecy performance compared with other approaches.

This paper experimentally demonstrates a new integrated visible light communication and positioning (VLCP) system to support both the communication and positioning services. To maximize the system transmission data rate while meeting different quality-of-service (QoS) requirements of devices (minimum data rate and positioning accuracy constraints), aQoS-driven joint the adaptive modulation, subcarrier allocation and pre-equalization is presented to improve the system performance. The experimental results indicate that the presented integrated VLCP system achieve the higher positioning accuracy than the existing integrated VLCP system, and also verify that the proposed QoS-driven optimized design archives higher data rate, positioning accuracy and QoS satisfied probability, compared with other existing designs.

With the rapid development of the Internet of Things (IoT) in the smart city, smart grid, and smart industry, indoor communication and positioning are important for IoT. However, radio-frequency (RF)-based wireless networks may fail to guarantee different quality-of-service (QoS) requirements of devices, due to the limited bandwidth, severe interference, and multipath reflections. Hence, this article presents a new integrated visible light communication (VLC) and VLC positioning (VLCP) network for IoT to provide both high-speed communication and high-accuracy positioning services. As the network consists of multiple VLC access points (APs), we propose jointly optimizing the AP selection, bandwidth allocation, adaptive modulation, and power allocation approach to satisfy different QoS requirements of indoor devices while maximizing the network data rate. A low-complexity iterative algorithm is presented to solve the resource management (RM) optimization problem by decomposing it into two subproblems. Finally, a robust handover mechanism and a pedestrian dead reckoning (PDR)-assisted VLCP scheme are presented to maintain good performance under line-of-sight (LOS) blockages. The simulation results verify that the proposed solutions outperform other existing solutions in terms of effectively enhancing the data rate, improving the positioning accuracy, and guaranteeing devices' QoS requirements. In detail, the mean position error is reduced from 20 to 4.3 cm by using our presented integrated VLCP model. The proposed RM approach achieves a satisfied QoS level improvement of up to 20.3% compared with the non-QoS-driven RM approach, and it achieves the high data rate up to 1.31 Gb/s.

Internet of Things (IoT) has attracted much interest due to its wide applications, such as smart city, manufacturing, transportation, and healthcare. Social and cognitive IoT is capable of exploiting social networking characteristics to optimize network performance. Considering the fact that the IoT devices have different Quality-of-Service (QoS) requirements [ranging from ultrareliable and low-latency communications (URLLCs) to minimum data rate], this article presents a QoS-driven social-aware-enhanced device-to-device (D2D) communication network model for social and cognitive IoT by utilizing social orientation information. We model the optimization problem as a multiagent reinforcement learning formulation, and a novel coordinated multiagent deep-reinforcement-learning-based resource management approach is proposed to optimize the joint radio block assignment and the transmission power control strategy. Meanwhile, the prioritized experience replay (PER) and the coordinated learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. The simulation results corroborate the superiority in the performance of the presented resource management approach, and it outperforms other existing approaches in terms of meeting the energy efficiency and the QoS requirements.

Smart factory under Industry 4.0 and industrial Internet of Things (IoT) has attracted much attention from both academia and industry. In wireless industrial networks, industrial IoT and IoT devices have different quality-of-service (QoS) requirements, ranging from ultra-reliable low-latency communications (URLLC) to high transmission data rates. These industrial networks will be highly complex and heterogeneous, as well as the spectrum and energy resources are severely limited. Hence, this article presents a heterogeneous radio frequency (RF)/visible light communication (VLC) industrial network architecture to guarantee the different QoS requirements, where RF is capable of offering wide-area coverage and VLC has the ability to provide high transmission data rate. A joint uplink and downlink energy-efficient resource management decision-making problem (network selection, subchannel assignment, and power management) is formulated as a Markov decision process. In addition, a new deep post-decision state (PDS)-based experience replay and transfer (PDS-ERT) reinforcement learning algorithm is proposed to learn the optimal policy. Simulation results corroborate the superiority in performance of the presented heterogeneous network, and verify that the proposed PDS-ERT learning algorithm outperforms other existing algorithms in terms of meeting the energy efficiency and the QoS requirements.

With the rapid development of Internet of Things (IoT) in the smart city, smart grid and smart industry, indoor communication and positioning are important fields of applications for indoor IoT. This paper presents an integrated visible light communication and positioning (VLCP) system for indoor IoT, in order to provide the high-speed data rate and high-accuracy positioning for IoT devices. where the filter bank multicarrier-based subcarrier multiplexing (FBMC-SCM) technique is exploited to effectively reduce the out-of-band interference (OOBI) on both adjacent communication and positioning subcarriers. After that, we propose a coordinated resource allocation approach for the system with the purpose of maximizing the sum rate while guaranteeing the minimum data rates and positioning accuracy requirements of devices. To this end, we solve the optimization problem by decomposing it into two subproblems, where a low-complexity suboptimal subcarrier allocation approach is proposed and the sequential quadratic programming (SQP) method is adopted to solve the non-linearly constrained power allocation optimization problem. Numerical results verify the superiority in performance of the presented integrated VLCP system for indoor IoT, and the results also reveal that the proposed coordinated resource allocation approach can effectively improve the sum rate and the positioning accuracy compared with other resource allocation approaches.

In this paper, we propose a novel non-orthogonal multiple access (NOMA) technique from a spatial domain (SD) perspective for indoor multiple-input multiple-output visible light communication (MIMO- VLC) systems. By fully exploiting the spatial distributions of light-emitting diode (LED) transmitters in the ceiling and users over the receiving plane, SD-NOMA is achieved by assigning all the users to different LEDs in the MIMO-VLC system. Hence, each user only receives data from a specific LED and users assigned to the same LED can use the overall modulation bandwidth of the system. Moreover, a signal-to-noise ratio (SNR) based LED selection scheme is further proposed for each user to efficiently select its desired LED. The achievable rates of a general indoor MIMO-VLC system using conventional MIMO orthogonal frequency division multiple access (MIMO-OFDMA) and the proposed SD-NOMA are analytically derived. The superiority of SD-NOMA over conventional MIMO-OFDMA for multi-user MIMO-VLC systems is successfully verified by detailed analytical results.

In this paper, we firstly propose a joint subcarrier and power allocation approach for multi-user integrated visible light communication and positioning (VLCP) systems in the presence of practical unique optical constraints. The purpose of the proposed resource allocation approach is to maximize the sum rate of users and meanwhile guarantee the different minimum data rates and positioning accuracy requirements of users. Then, we solve the optimization problem by decomposing it into two subproblems, where a low-complexity suboptimal subcarrier allocation approach is proposed and the sequential quadratic programming (SQP) method is adopted to solve the non-linearly constrained power allocation optimization problem. Numerical results show that the proposed resource allocation approach can effectively improve the sum rate and the positioning accuracy of users compared with other resource allocation approaches.

This paper proposes an optimal non-cooperative power allocation game scheme for multi-user multiple-input multiple-output (MU-MIMO) with signal to leakage and noise ratio (SLNR) precoding. The proposed game scheme sets the value of per user SLNR and allocated power as reference for punishment price. Considering the effect of channel correlation and channel estimation error, an alternative robust power allocation scheme is presented based on the game model for per user in order to guarantee the desired QoS of the users and achieve Nash Equilibrium (NE). Simulation results show that both the schemes considerably improve the average bit error rate performance and obtain excellent sum capacity performance than other schemes in the presence of channel correlation and incomplete channel state information.