Semantic communication (SemCom), a paradigm central to the next generation task-oriented vision, relies on a shared Knowledge Base (KB) between the transmitter and receiver. However, the prevalent assumption of a static KB is frequently invalidated in dynamic real-world environments, where KB "staleness" precipitates a collapse in semantic efficiency. This introduces a critical trade-off: either continue transmission with a suboptimal, stale KB, or allocate scarce wireless resources to a knowledge consensus protocol to update the KB, thereby incurring a significant opportunity cost. This paper addresses this dynamic resource allocation problem. We are the first to establish a system model that explicitly quantifies knowledge staleness K(t) as a state variable and models the update procedure as a resource-consuming consensus task. We formulate this trade-off as a complex, NP-hard 0-1 Mixed-Integer Non-Linear Program (MINLP), which uniquely incorporates the fixed activation costs associated with initiating the consensus protocol. To solve this intractable problem, we propose a low-complexity online control framework based on model predictive control (MPC), which embeds a novel heuristic algorithm termed iterative marginal cost allocation (IMCA). Simulation results demonstrate that the proposed MPC-IMCA framework significantly outperforms static Greedy and Periodic Update baselines in long-term cumulative semantic utility, exhibiting robust adaptability to varying environmental dynamics (δd) and update workloads (DKB).

In affine frequency division multiplexing (AFDM) systems, the severe peak-to-average power ratio (PAPR) signals exist in the time domain due to the coherent superposition of numerous modulated symbols. Eventually, high PAPR signals require sophisticated and expensive power amplifiers with a very large linear range. To this end, a neural network (NN) aided intelligent transceiver optimization framework is proposed for suppressing PAPR based on the spreading AFDM structure. Specifically, the transceiver jointly optimizes the constellation geometry and associated bit labeling, the precoding NN, as well as the NN based detector. Moreover, the precoding NN is learned from a precoding approach which minimizes the variance of the instantaneous power of output signals at the transmitter. The joint optimization framework aims to achieve maximum PAPR reduction under the constraints of unit energy and spectral emission mask. Besides, to mitigate the potential inter-carrier interference during the offline training, a long short term memory based detector is designed within the optimization framework. Simulation results demonstrate that the conceived NN based optimization method achieves a significant enhancement on PAPR reduction compared with conventional approaches, while slightly improving the bit error ratio performance.

Large Language Models (LLMs) have been increasingly applied to intelligent vehicular systems for tasks such as scene understanding, intent reasoning, and natural language interaction. However, their inference demands exceed onboard processing capabilities, making low-latency on-vehicle inference impractical. Although edge computing can partially offload computation, the prolonged nature of LLM inference often causes execution to exceed the residence time of vehicles within edge coverage areas, leading to frequent service interruption. To address these challenges, we propose a collaborative spatiotemporal resource orchestration architecture for LLM inference in vehicular-edge networks (CoInfer). CoInfer exploits the intrinsic decomposability of LLM inference by modeling each request as a Directed Acyclic Graph (DAG) of interdependent subtasks, which are then scheduled, migrated, and aggregated along the road network to preserve end-to-end inference continuity. To improve latency and resource efficiency, CoInfer integrates multi-agent reinforcement learning for coarse-grained task orchestration with a reactive scheduler for fine-grained resource adaptation, forming a closed-loop service optimization under dynamic resource conditions. The simulation results demonstrate that CoInfer achieves a task success ratio of up to 96.0% and reduces the end-to-end inference latency by 35.7% compared to representative baselines.

Semantic communication (SemCom) shifts the focus from bit-level accuracy to the preservation of meaning, enabling more efficient and robust transmission. To achieve a high utilization of the wireless channel in SemCom, in this paper, we propose a channel assignment approach for polar code-based SemCom that allocates polarized channels according to semantic importance. Specifically, by combining eye-tracking data with semantic segmentation, we define two metrics that capture the contribution and correlation of semantic entities within an image. Leveraging these semantic metrics and polarized channel reliabilities, we formulate a constrained 0-1 optimization problem for polarized channel assignment and develop a priority-based algorithm that dynamically prioritizes semantically important content. Simulation results demonstrate that our method significantly outperforms the traditional channel allocation policy, especially under harsh channel conditions, by preserving critical visual information while reducing overall transmission redundancy.

The coordinated control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) offers significant advantages in applications such as surveillance, navigation, and emergency response. Effective path planning is essential in such missions, especially in complex environments where UAVs must relay accurate environmental data to assist UGVs. However, in urban environments and disaster zones, wireless communication is often unstable due to severe interference and non-line-of-sight conditions, making it difficult to support timely and accurate path planning for UAV-UGV coordination. To this end, this paper proposes a semantic communication (SemCom) framework specifically designed to enhance the reliability for UAV/UGV cooperative path planning under unreliable wireless conditions. SemCom transmits only key information for path planning, reducing transmission volume without sacrificing accuracy. Based on this framework, a SemCom transceiver is designed to fulfill the requirements of UAV-UGV cooperative path planning. Simulation results show that, compared to conventional SemCom transceivers, the proposed transceiver significantly reduces data transmission volume while maintaining path planning accuracy, thereby enhancing system collaboration efficiency.

With the significant advances in generative AI (GAI) and the proliferation of mobile devices, providing high-quality AI-generated content (AIGC) services via wireless networks is becoming the future direction. However, the primary challenges of AIGC service delivery in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. In this paper, we employ semantic communication (SemCom) in diffusion-based GAI models to propose a resource-aware workload-adjustable transceiver (ROUTE) for AIGC delivery in dynamic wireless networks. Specifically, to relieve the communication resource bottleneck, SemCom is utilized to prioritize semantic information of the generated content. Then, to improve computational resource utilization in both edge and local and reduce AIGC semantic distortion in transmission, modified diffusion-based models are applied to adjust the computing workload and semantic density in cooperative content generation. Simulations verify the superiority of our proposed ROUTE in terms of latency and content quality compared to conventional AIGC approaches.

The integration of semantic communication (SemCom) with non-orthogonal multiple access (NOMA) presents a promising approach to enhance spectrum efficiency and system capacity. SemCom focuses on accurate meaning delivery with less bits, while NOMA enables simultaneous access for multiple users on the same frequency, maximizing resource utilization. However, power allocation in NOMA-based SemCom systems is quite challenging as it should accommodate not only channel conditions and interference management but also the characteristics of semantic information and service requirements. In this paper, we investigate the power allocation strategy for NOMA-based SemCom systems, with the aim to maximize system throughput in semantic unit (STU). Successive interference cancellation requirements and resource budgets are taken into account as the constraints. To address this problem, we propose a modified water filling-based algorithm enhancing both STU and fairness. Simulation results demonstrate the superiority of our proposed algorithm in terms of STU performance and fairness compared to two existing baseline strategies.

Semantic communication (SemCom) has been recently deemed a promising technique to shape next-generation wireless networks with a focus on meaning delivery for significant spectrum savings and efficient information exchanges. It is foreseen that device-to-device (D2D) SemCom underlying cellular networks will be a very common and practical architecture, and in this paper, we jointly address the energy efficiency-driven power control and spectrum reuse problems for D2D SemCom networks. Concretely, we first construct a semantic triplet-based transmission model for both cellular and D2D SemCom users. Then, by taking into account each user's SemCom service preference, we leverage a novel metric of semantic value to determine the unique energy efficiency. Next, a corresponding energy efficiency maximization problem is formulated with variables of power and spectrum allocation subject to several SemCom-related and practical constraints. Afterward, we propose an optimal resource management scheme by employing a fractional-to-subtractive transformation approach and developing a threestage method with low computational complexity. Numerical results demonstrate the performance superiority of our proposed scheme in energy efficiency compared with two benchmarks.

With Internet of Vehicles and advanced onboard computing, connected autonomous vehicles (CAVs) can interact and process driving data in real time, enhancing safety and improving road efficiency. However, in mixed-autonomy traffic, the unpredictability of human-driven vehicles (HDVs) poses significant challenges for CAV control. Moreover, existing cooperative control methods often assume seamless real-time information sharing, leading to excessive communication demands that strain limited transmission resources. To address these issues, we propose a lightweight Digital Twin (DT) framework that models the traffic environment and surrounding vehicle behaviors to reduce uncertainty in CAV decision-making. The framework employs an attention-based multi-agent deep reinforcement learning method, enabling each CAV to dynamically adjust its communication frequency with neighboring vehicles according to the significance of their observations for its driving control decisions. Asynchronous updates in the DT space allow CAVs to expand their situational awareness, enabling lightweight coordination that mitigates HDV disturbances and fosters swarm intelligence. Simulations show our scheme improves traffic stability and achieves 30% faster high-speed flow than baselines, while maintaining strong performance under low bandwidth.

Emerging as a promising technology to bridge the trust gap among multiple participants, blockchain has been envisioned to enable dynamic spectrum sharing in a decentralized manner. However, satellites with limited resources may struggle to support the frequent interactions required by blockchain networks. Additionally, due to the large coverage area of satellites, the differentiated spectrum sharing needs in various regions can make traditional blockchain approaches inadequate. In this paper, a two-tier multi-region blockchain-based dynamic spectrum sharing approach (TMB-DSS) is proposed. This approach enables regions to manage spectrum autonomously while jointly maintaining a unified blockchain ledger. Moreover, a theoretical framework using stochastic geometry is derived to evaluate the stability performance of TMB-DSS. Finally, numerical results are presented to validate the proposed approach.

In semantic communication (SemCom), the gain of deep learning-based joint source and channel coding (JSCC) has been proved to partly come from the analog transmission of semantic features extracted directly from the source signal. While, the current mainstream digital communication system design results in the gains almost vanishing when bit-oriented modulation (e.g., QPSK) is employed. To tackle this problem, we propose a Semantic-Oriented Modulation (SOM) method that enables direct mapping from an analog value of a semantic feature to a sequence of discrete values without bit conversion. SOM employs a hierarchical design to mitigate discretization loss and optimizes resource allocation by exploiting the varying significance of semantic features, reducing the data volume by 29.17%. We provide an analysis that reveals the impact of modulation orders and hierarchical layers, guiding the SOM’s design. Simulations in four tasks show that SOM outperforms JSCC with bit-oriented modulation, and the implementation of the Software Defined Radio (SDR) platform confirms its compatibility with existing systems and its potential for latency reduction.

Semantic communication (SemCom) lies in the emphasis on ensuring the correct semantic understanding rather than error-free bit transmission. However, traditional hybrid automatic repeat request (HARQ) mechanism relies on a bit-level check, and it cannot effectively address errors at the semantic level. In this paper, we propose a semantic codebook-based HARQ (SCB-HARQ) mechanism for the reliable and efficient SemCom. To enable semantic-level error detection, SCB-HARQ leverages a shared semantic codebook trained offline at both the transmitter and receiver. This codebook serves as prior information for evaluating the distortion of received semantic features, quantifying the extent to which they deviate from the intended meaning. To reduce the transmission overhead of features in the codebook, a weighted semantic feature index (WSFI) clustering method is introduced to map features into a compact index representation. Then, a masked rate-adaptive joint source-channel coding (JSCC) method is proposed to locate and retransmit the distorted features. The simulation results demonstrate that the proposed SCB-HARQ outperforms the traditional HARQ mechanism, achieving a 46.29% improvement in image reconstruction performance while reducing the transmission data volume by 51.27%.

Several wireless networking problems are often posed as 0-1 mixed optimization problems, which involve binary variables (e.g., selection of access points, channels, and tasks) and continuous variables (e.g., allocation of bandwidth, power, and computing resources). Traditional optimization methods as well as reinforcement learning (RL) algorithms have been widely exploited to solve these problems under different network scenarios. However, solving such problems becomes more challenging when dealing with a large network scale, multi-dimensional radio resources, and diversified service requirements. To this end, in this paper, a unified framework that combines RL and optimization theory is proposed to solve 0-1 mixed optimization problems in wireless networks. First, RL is used to capture the process of solving binary variables as a sequential decision-making task. During the decision-making steps, the binary (0-1) variables are relaxed and, then, a relaxed problem is solved to obtain a relaxed solution, which serves as prior information to guide RL searching policy. Then, at the end of decision-making process, the search policy is updated via suboptimal objective value based on decisions made. The performance bound and convergence guarantees of the proposed framework are then proven theoretically. An extension of this approach is provided to solve problems with a non-convex objective function and/or non-convex constraints. Numerical results show that the proposed approach reduces the convergence time by about 30% over B&B in small-scale problems with slightly higher objective values. In large-scale scenarios, it can improve the normalized objective values by 20% over RL with a shorter convergence time.

Recently, semantic communication (SemCom) has shown great potential in significant resource savings and efficient information exchanges, thus naturally introducing a novel and practical cellular network paradigm where two modes of SemCom and conventional bit communication (BitCom) coexist. Nevertheless, the involved wireless resource management becomes rather complicated and challenging, given the unique background knowledge matching and time-consuming semantic coding requirements in SemCom. To this end, this paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we specially develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by utilizing a Lagrange primal-dual transformation method and a preference list-based heuristic algorithm with polynomial-time complexity. Numerical results not only demonstrate the accuracy of our analytical queuing model, but also validate the performance superiority of our proposed strategy compared with different benchmarks.

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature.

With the significant advances in AI-generated content (AIGC) and the proliferation of mobile devices, providing high-quality AIGC services via wireless networks is becoming the future direction. However, the primary challenges of AIGC services provisioning in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. To this end, this paper proposes a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, where only semantic information of the content rather than all the binary bits should be generated and transmitted by using SemCom. Specifically, SemAIGC integrates diffusion models within the semantic encoder and decoder to design a workload-adjustable transceiver thereby allowing adjustment of computational resource utilization in edge and local. In addition, aresource-aware workloadtrade-off (ROOT) scheme is devised to intelligently make workload adaptation decisions for the transceiver, thus efficiently generating, transmitting, and fine-tuning content as per dynamic wireless channel conditions and service requirements. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches.

In the Metaverse, real-time, concurrent services such as virtual classrooms and immersive gaming require local graphic rendering to maintain low latency. However, the limited processing power and battery capacity of user devices make it challenging to balance Quality of Experience (QoE) and terminal energy consumption. In this paper, we investigate a multi-objective optimization problem (MOP) regarding power control and rendering capacity allocation by formulating it as a multi-objective optimization problem. This problem aims to minimize energy consumption while maximizing Meta-Immersion (MI), a metric that integrates objective network performance with subjective user perception. To solve this problem, we propose a Multi-Objective Multi-Agent Evolutionary Reinforcement Learning with User-Object-Attention (M2ERL-UOA) algorithm. The algorithm employs a prediction-driven evolutionary learning mechanism for multi-agents, coupled with optimized rendering capacity decisions for virtual objects. The algorithm can yield a superior Pareto front that attains the Nash equilibrium. Simulation results demonstrate that the proposed algorithm can generate Pareto fronts, effectively adapts to dynamic user preferences, and significantly reduces decision-making time compared to several benchmarks.

Semantic communication (SemCom), a paradigm that emphasizes conveying the meaning of information, faces challenges in precise reasoning in semantic coding models. Knowledge graphs (KGs) offer a potential solution by providing structured triples (entities and relations), enabling inference via entity attributes and relational logic. Several key challenges exist in leveraging KGs within SemCom. The first challenge lies in developing methods to create semantic representations aligning and integrating source data and KG information. Second, reconstructing the original data using KGs becomes challenging particularly under poor communication conditions. Moreover, integrating KGs with source data inevitably increases the transmission overhead. In this paper, we propose a novel SemCom framework named KG-SemCom with sophisticated KG-based semantic encoding and decoding designs to solve these challenges. This framework aligns KG entities with message tokens, and then encodes messages into a semantic fusion of contextual and knowledge-based information. Furthermore, KG-SemCom can utilize the KG and contextual relationships to assist in predicting incomplete or distorted messages during the decoding process. Finally, simulation results demonstrate that KG-SemCom achieves higher accuracy and greater robustness compared to existing benchmarks without incorporating KGs, especially in challenging communication environments.

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where two modes of semantic communication (SemCom) and conventional bit communication (BitCom) coexist, namely hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we comprehensively develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with several practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by employing a Lagrange primal-dual method and devising a preference list-based heuristic algorithm. Finally, numerical results validate the performance superiority of our proposed strategy compared with different benchmarks.

Federated learning (FL) has been vigorously promoted in wireless edge networks as it fosters collaborative training of machine learning (ML) models while preserving individual user privacy and data security. In conventional FL, user equipments (UEs) and an aggregator can collaboratively train a globally shared ML model by transmitting ML models instead of raw data. In wireless edge networks, the heterogeneity of multidimensional resources (e.g., computing and communication re-sources) used to transmit ML models may introduce stragglers in FL, characterized by a slow update and/or transmission of local models. The stragglers in FL can significantly degrade learning efficiency and accuracy, as the slowest UE participating in the FL can dramatically slow down entire convergence. In this paper, to alleviate the negative impact of stragglers, we propose a dynamic straggler-aware clustering based FL mechanism, called FeDSC, via adaptive UE clustering. Specifically, we first group participating UEs into multiple clusters based on their computing capability and available wireless resources. Then, we propose an adaptive UE selection scheme to synchronously update the cluster aggregation model. Meanwhile, an edge server performs global aggregation of different cluster models in an asynchronous time-triggered manner. Numerical results show that our proposed FeDSC mechanism can achieve significant performance improvement in terms of training time and model accuracy in comparison to classical FL benchmarks.

Emergency communication is critical but challenging after natural disasters when the ground infrastructure is devastated. Unmanned aerial vehicles (UAVs) have enormous potential for agile relief coordination in such scenarios. However, effectively leveraging UAV fleets poses additional challenges, in terms of security, privacy, and efficient collaboration across response agencies. This paper presents a robust blockchain-enabled framework to address these challenges by integrating a consortium blockchain model, smart contracts, and crypto-graphic techniques to securely coordinate UAV fleets for dis-aster response. Specifically, we make two key contributions: a consortium blockchain architecture for secure and private multi-agency coordination and an optimized consensus protocol balancing efficiency and fault tolerance using a delegated proof of stake practical Byzantine fault tolerance (DPoS-PBFT). Com-prehensive simulations show the framework's ability to enhance transparency, automation, scalability, and cyber-attack resilience for UAV coordination in post-disaster networks.

In this paper, the problem of joint transmission and computation resource allocation for probabilistic semantic communication (PSC) network with rate splitting multiple access (RSMA) is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data, which is represented by substantial knowledge graphs, to multiple users. Due to limited communication resource, the BS needs to utilize semantic communication techniques to compress the large-sized data. In this paper, the semantic communication is enabled by shared probability graphs between the BS and users. The process of semantic compression requires computation power at the BS, which has an impact on limited power budget. Therefore, it is necessary to balance the power between transmission and computation. Based on the probability graph, the semantic rate related to semantic compression ratio is first theoretically formulated. Then, the problem is formulated as an optimization problem with the aim of maximizing the sum semantic rate of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is accordingly proposed to obtain a suboptimal solution. Numerical results validate the effectiveness of the proposed scheme.

Burdened by persistent traffic congestion, urban transportation is in a pressing need of more effective traffic guidance schemes. Existing traffic guidance approaches fall short in optimizing benefits, primarily due to their exclusive reliance on current road conditions for decision making and the prevalence of driving egoism within traditional patterns. Autonomous vehicles (AVs), liberated from human control and enhanced by the Internet of Vehicles and edge computing, provide new possibilities for traffic guidance. Nevertheless, it is tough to precisely determine the pertinent information to convey and establish an effective cooperative guidance mechanism in the face of the substantial number of AVs. This article proposes a social value orientation (SVO)-based cooperation mechanism for AVs, through which the driving routes are jointly determined by individual driving demands, local road network conditions, and global benefits. We design a digital twin-based Edge-to-Cloud traffic guidance architecture, leveraging real-time AV decisions and micro-driving characteristics for forthcoming road condition estimation. The hierarchical Edge-to-Cloud structure efficiently mitigates communication and computation overheads in traffic guidance by distributing tasks across different regions. Finally, an innovative method based on inverse reinforcement learning is proposed to address the challenge of adapting guidance policies in response to varying traffic densities and distributions. The simulation results show a 59.1% improvement in the travel achievement ratio under heavy road traffic load, with no significant change in the detour ratio. It indicates an enhanced system driving efficiency, while still safeguarding the individual benefits of AVs.

In the context of the burgeoning Industrial Internet of Things (IIoT), the proliferation of interconnected devices has created a reservoir of data resources distributed across diverse domains. However, due to the conflict between proprietary data and the use of data, it is a challenge to fully obtain data value in an efficient and legal way. To release the data value in an efficient and legal way, blockchain is considered a promising technology for data security and privacy, which has been widely introduced to cross-domain data governance. In this paper, we propose a blockchain-assisted cross-domain data sharing (BCDS) in IIoT. Specifically, by deploying the permissioned blockchain, we design a zero-knowledge proof scheme to verify data ownership under the criterion of confidence and anonymity. Besides, to prevent the thrid-party from decrypting data, we design a key agreement protocol to ensure that only recipient is authorized to decrypt data based on private key. Furthermore, we theoretically analyze the security performance of schemes. Extensive experiments in simulation computer systems and testbed deployment are conducted to demonstrate the effectiveness and efficiency of the proposed scheme.

Federated learning (FL) has been vigorously promoted in wireless edge networks as it fosters collaborative training of machine learning (ML) models while preserving individual user privacy and data security. In conventional FL, user equipment (UE) and an aggregator can collaboratively train shared global ML models by transmitting interactive ML models. In wireless edge networks, heterogeneity of multi-dimensional resources (e.g., computing and communication resources) used to train and transmit FL models may introduce stragglers, characterized by a slow update and/or transmission of local models. The stragglers can significantly degrade learning performance of FL, as the slowest participating UE can dramatically slow down the entire convergence. In this paper, to alleviate the negative impact of stragglers, we propose a dynamic straggler-aware clustering based FL (FeDSC) mechanism via adaptive UE clustering. Specifically, we first group participating UEs into multiple clusters based on their available computing and wireless resources. Then, we propose an adaptive clustering scheme to synchronously update the cluster aggregation model. Meanwhile, an edge server performs global aggregation of different cluster models in an asynchronous manner. Finally, we theoretically demonstrate the convergence of our proposed mechanism via numerical results. Numerical results show that our proposed mechanism can effectively reduce training time and wireless bandwidth consumption, while improving training efficiency and guaranteeing learning accuracy.

Although attribute-based signcryption (ABSC) offers a promising technology to ensure the security of IoT data sharing, it faces a two-fold challenge in practical implementation, namely, the linearly increasing computation and communication costs and the heavy load of single authority based key management. To this end, we propose a Secure and Lightweight Multi-authority ABSC scheme called SLIM in this paper. The signcryption and de-signcryption costs of devices are reduced to a small constant by offloading most of the computation to the edge server. To minimize communication and storage costs, a short and constant-size ciphertext is designed. Moreover, we adopt a hierarchical multi-authority architecture, setting up multiple attribute authorities that manage keys independently to prevent the bottleneck. Rigorous security analysis proves that the SLIM scheme can resist adaptive chosen ciphertext attacks and adaptive chosen message attacks under the standard model. Simulation experiments demonstrate the correctness of our theoretical derivations and the cost reduction of the SLIM scheme in computation, communication and storage.

Virtual reality (VR) technology has recently achieved notable success and been widely expected to interplay with more mobile multimedia services. To further enhance real-time immersive experience for VR applications, exploiting cooperative offloading among capable terminal devices should be emerged as an effective means. However, faced with diverse and surging mobile VR user requests, terminal-assisted offloading needs to support comprehensive cached content, ultra-low latency delivery, and continuous energy provisioning, to guarantee stringent quality of service requirements, which poses a critical challenge for resource-constrained terminals. Hence, this paper proposes a Cooperative Offloading framework for Terminal Clusters (named CO-TC), in which VR terminal clusters form several cooperation groups for sharing cached field of view (FoV) tiles and available computing resources to cooperatively perform FoV rendering and content delivery. To maximize energy efficiency in CO-TC, an optimization problem is formulated to jointly decide the task offloading and computing resource utilization. An intelligent offloading scheme is designed based on multi-agent reinforcement learning (MARL) specially using agent relation feature graph embeddings. Moreover, we theoretically prove the permutation invariance and convergence of the proposed algorithm and derive the optimal observation range of the agent to balance the performance gain and interaction overhead in the distributed MARL frame. Finally, simulation results show that the proposed offloading scheme outperforms other baselines in terms of VR service performance, including latency, energy consumption, and energy efficiency.

As a critical and prevalent service in future mobile networks, virtual reality (VR) is latency-sensitive and power-hungry, bringing out the optimization problem of trade-off among power saving, delay, and resource utilization. Content caching and render offloading are deemed as promising solutions to meet the stringent requirements of VR on data transmission speed and end-to-end latency. In this article, we propose a novel distributed computing framework based on multi-agent deep deterministic policy gradient (MADDPG) for joint optimizing terminal-cooperative caching and offloading for VR tasks. Since the individual VR user can hardly reach the optimal actions based on its limited local observed states and samples, MADDPG with centralized training and distributed execution is exploited to solve the above challenge. In addition, the generative adversarial network (GAN) is introduced to obtain experience-enhanced agents in the offline training phase and to achieve an optimal allocation to minimize energy consumption in the online inferring phase. The Nash equilibrium is proven in the case that the distribution of finite real VR data samples is well imitated and complemented by GAN. Numerical results demonstrate that our algorithm has significant superiorities in terms of convergence performance and energy consumption over other benchmarks.

Ensuring secure access to cellular networks is of paramount importance, in which system information (SI) protection plays a crucial role at the initial access stage. While the 3rd generation partnership project (3GPP) released many standardizations to enhance SI protection for preventing users from false base station (FBS) attacks, most of them are centralized solutions which are vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-enabled SI protection (BeSI), as a compatible and effective secure access scheme, is developed in this work, which aims at guaranteeing the authenticity and reliability of SI by considering the features of blockchain in immutability, traceability, and decentralization. Then, we derive a mathematical framework to justify the superiority of using blockchain in SI protection. Moreover, by resorting to a Poisson point process as the geographical model for both base stations and FBSs, we thus theoretically analyze the security gain of blockchain and understand the impact of network parameters including redundancy rate, number of confirmation blocks, and the density of base stations. Finally, numerical results are demonstrated to validate the effectiveness of BeSI.

Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consume a tremendous amount of resources to meet stringent reliability and latency requirements. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to simultaneously realize efficient service provisioning for multiple users in vehicle-to-vehicle networks. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S4) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S4in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks.

Semantic communication (SemCom) has gained significant attention to extracting and delivering semantic information based on transceivers' background knowledge. While successful, most existing works assume a fixed knowledge base (KB) shared between transceivers, limiting their applicability to the ever-increasing domain knowledge. To address this limitation, we propose an innovative transceiver cooperation framework, Prompt-SC, using prompt learning techniques to achieve domain-incremental SemCom. To alleviate catastrophic forgetting of domain incremental learning (DIL) and avoid the need to store data for all domains, we first reconstruct the SemCom model, i.e., the semantic and channel encoders/decoders, to be composed of a pretrained base model and domain-specific prompts. This way, a transceiver freezes its base model and learns prompts independently across domains to achieve the best for each domain, enabling rehearsal-free DIL. Additionally, we introduce the control-/data-plane decoupling design to align transceivers with heterogeneous or asynchronously evolved domain knowledge. Since the prompt size is small, transceivers can efficiently share the domain-specific prompt with each other, thereby aligning their background knowledge with low communication overhead and preserving the data privacy of individual KBs. Furthermore, we introduce a new metric, semantic spectrum efficiency, to evaluate Prompt-SC based on its communication cost and achieved SemCom gain, which suggests applicable scenarios for Prompt-SC. Finally, we conduct extensive experiments to demonstrate the effectiveness and efficiency of Prompt-SC.

To accommodate the growing number of heterogeneous radios with limited wireless resources, symbiotic communication (SC) inspired by biology has been recently proposed to establish a symbiotic radio (SR) ecosystem. In this SR ecosystem, through collaboratively optimizing service/resource exchange policies, radios can coevolve like organisms, thus enabling various radio resources (such as spectrum, energy, and computing power) to complement each other. However, one critical challenge is securing a trusted coevolution environment in an SR ecosystem since the SRs with different network operators should coevolve under unreliable wireless links with complex electromagnetic interference. Moreover, multidimensional resources participated and a wide array of service requirements pose additional challenges to service/resource exchange decision-making across massive SRs. In this paper, we propose a Blockchain-empowered Intelligent cOevolution scheme for SRs, named BIO-SR. Specifically, BIO-SR exploits the digital acyclic graph (DAG) blockchain consensus in securing a trusted environment of SRs and applies deep reinforcement learning (DRL) in service exchange decision-making. The simulation results show that the BIO-SR scheme outperforms conventional solutions in terms of transmission rate and latency under both non-attack and malicious attack scenarios.

Semantic communication (SemCom) has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to vehicular networks, which normally consume a tremendous amount of resources to achieve stringent requirements on high reliability and low latency. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to realize efficient vehicle-to-vehicle service provisioning for multiple users at the same time. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S4) is proposed along with the theoretical analysis of its optimality guarantee. Simulation results demonstrate the superiority of S4 in terms of average queuing latency, semantic data packet throughput, and user knowledge preference satisfaction compared with two different benchmarks.

Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for providing dynamic coverage and connectivity extension for the sixth-generation (6G) wireless networks. While flexibility is provided, the deployment of the UAV swarms and the associated resource allocation bring challenging issues due to dynamic nature of UAVs and difficulty in obtaining global user information. In this paper, we propose an adaptive and flexible joint UAV deployment and resource allocation scheme by exploiting a personalized federated deep reinforcement learning framework, called PFRL, with aim to maximize the long-term network throughput while enforcing user privacy and adapting to time-varying network states. To allow UAVs to make real-time decisions on resource allocation and position adjustment based on local observations while achieving a global optimal solution, we incorporate deep reinforcement learning (DRL) into federated learning framework. Specifically, we use DRL to train a local model and a personalized model on UAVs, and employ a two-level parameter aggregation scheme on a leading UAV to form a global model. The personalized model can adapt to specific environments, while exploiting the generalization of global model to accelerate the learning convergence. Numerical results show that the proposed PFRL scheme can achieve significant performance gain in terms of network throughput and convergence in comparison with some state-of-art solutions.

As a new communication paradigm, neural network-driven semantic communication (SemCom) has demonstrated considerable promise in enhancing resource efficiency by transmitting the semantics rather than all bits of source information. Using a large semantic coding model can accurately distil semantics, and significantly save the required bandwidth. However, this consumes a large amount of computing resources, which are also precious in the network. In this paper, we investigate the joint computing resources and bandwidth allocation for SemCom networks. We first introduce the computing latency model in SemCom, and formulate the joint computing resources and bandwidth allocation optimization problem with the objective of maximizing semantic accuracy. Then, we transform this problem into a deep reinforcement learning framework and exploit a multi-agent proximal policy optimization to solve it. Numerical results show that the proposed method significantly improves the average semantic accuracy in the resource-constrained cases, compared with the two baselines.

Blockchain is envisioned as one of the promising technologies to address trust concern brought by mobile crowdsensing (MCS), due to its auditability, immutability and decentralization. Nevertheless, blockchain cannot fundamentally guarantee that the valuable sensed data outside the chain can enter the chain, although data integrity and consistency can be ensured once it is confirmed inside the chain. In addition, simply applying blockchain in MCS while ignoring possible abnormal saboteurs hidden in numerous devices may mislead the normal operation of blockchain, resulting in untrustworthy interactions. Consequently, it is highly desirable to build a trust-preserving mechanism (TPM) to fully enjoy the benefits of using blockchain in MCS. To this end, we first resort to a probabilistic trust assessment inferred from the interaction outcomes in blockchain, to incentivize participants to maintain the trustworthiness of interactions. By inferring trust to aid decision-making, trust decision is further made, including leader election and transaction data generation, to filter untrusted nodes from participating in blockchain process. Finally, extensive simulations are conducted to validate the effectiveness and efficiency of TPM, and improve the performance in terms of contribution rate, consensus accuracy and system stability.

Mobile crowd sensing (MCS) is a promising paradigm which leverages sensor-embedded mobile devices to collect and share data. The key challenging issues in designing an MCS system include selecting appropriate users to participate in a specific sensing task and designing efficient data sensing and transmission policies for data aggregation. In mobile edge networks, the limitation on network resources including bandwidth and energy affects the design of MCS significantly. Specifically, the limited resources affect whether and how to select users for a sensing task, and the bandwidth allocated to a user affects its data sensing and transmission policies. Since user selection, bandwidth allocation, data sensing and transmission are closely coupled issues in MCS, we focus on designing a unified framework for joint sensing and communication in this paper, by jointly optimizing the aforementioned four policies under resource constraints. Simulation results show that the proposed unified framework significantly outperforms several baseline solutions without considering wireless link vulnerability and/or resource limitations.

Computation offloading via device-to-device communications can improve the performance of mobile edge computing by exploiting the computing resources of user devices. However, most proposed optimization-based computation offloading schemes lack self-adaptive abilities in dynamic environments due to time-varying wireless environment, continuous-discrete mixed actions, and coordination among devices. The conventional reinforcement learning based approaches are not effective for solving an optimal sequential decision problem with continuous-discrete mixed actions. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) framework to solve the joint computation offloading and resource allocation problem. The proposed HDRL framework has a hierarchical actor-critic architecture with a meta critic, multiple basic critics and actors. Specifically, a combination of deep Q-network (DQN) and deep deterministic policy gradient (DDPG) is exploited to cope with the continuous-discrete mixed action spaces. Furthermore, to handle the coordination among devices, the meta critic acts as a DQN to output the joint discrete action of all devices and each basic critic acts as the critic part of DDPG to evaluate the output of the corresponding actor. Simulation results show that the proposed HDRL algorithm can significantly reduce the task computation latency compared with baseline offloading schemes.

Mobile edge computing (MEC) enables users to offload computing tasks to edge servers for provisioning low-latency and computation-intensive services. To manage heterogeneous resources and improve service flexibility, MEC is entailed by new technologies, \textit{i.e.}, software defined networking (SDN) and network function virtualization (NFV), which allow services running on common commodity hardware instead of proprietary hardware. However, data processing via software on commodity servers may induce high latency due to limited processing capacity, which impedes the quality of service. Meanwhile, MEC is a resource-sharing system and thus fairness should be considered. In this paper, we propose a network function parallelism (NFP)-enabled MEC (NFPMec) framework for supporting low-latency services. To reap the potential benefits of the NFPMec, we formulate the fairness-aware throughput maximization problem (FTMP) with aim of maximizing the fairness-aware system throughput while satisfying the QoS requirements. We propose a relaxation-based generalized benders algorithm (RGBA) to decouple the FTMP into two sub-problems based on the non-linear convex duality theory. After relaxation, the sub-problems are solved by the Karush-Kuhn-Tucker (KKT) approach. The convergence of the RGBA is theoretically proved. The simulation results demonstrate that the proposed NFPMec outperforms SDN-enabled MEC networks in terms of resource utilization, service latency and system throughput.

Mobile crowd sensing (MCS) is a promising paradigm which leverages sensor-embedded mobile devices to collect and share data. To perform a sensing task in MCS, appropriate participating users are selected first, and efficient data sensing and transmission policies are then designed for data aggregation. In mobile edge networks, network resource availability affects how to select the participating users, and the bandwidth allocated to a user affects its process of data sensing and transmission. Since user selection, bandwidth allocation, data sensing and transmission are closely coupled issues in a resource constrained MCS system, we focus on designing a joint sensing and communication framework in this paper, by jointly optimizing the aforementioned four policies under resource constraints. Specifically, the optimal data sensing and transmission policies are first derived under a given user selection and bandwidth allocation scheme. Then the user selection and bandwidth allocation are optimized based on dynamic programming. Simulation results show that the proposed mechanism significantly outperforms several baseline solutions without considering wireless link vulnerability and/or resource limitations.

Federated learning (FL) has recently become one of the most acknowledged technologies in promoting the development of intelligent edge networks with the ever-increasing computing capability of user equipment (UE). In traditional FL paradigm, local models are usually required to be homogeneous for aggregation to achieve an accurate global model. Moreover, considerable communication cost and training time may be incurred in resource-constrained edge networks due to a large number of UEs participating in model transmission and the large size of transmitted models. Therefore, it is imperative to develop effective training schemes for heterogeneous FL models, while reducing communication cost as well as training time. In this paper, we propose an adaptive quantization scheme based on ensemble distillation (AQeD) for FL to facilitate personalized quantized model training over heterogeneous local models with different size, structure, and quantization level, etc. Specifically, we design an augmented loss function by jointly considering distillation loss function, quantization values and available wireless resources, where UEs train their local personalized machine learning models and send the quantized models to a server. Based on local quantized models, the server first performs global aggregation for cluster ensembles and then sends the aggregated model of the cluster back to the participating UEs. Numerical results show that our proposed AQeD scheme can significantly reduce communication cost as well as training time in comparison with some known state-of-the-art solutions.

Blockchain has been regarded as one of the promising technologies to address trust concern in data-driven mobile crowdsensing (MCS), due to its auditability, immutability and decentralization. However, simply applying blockchain in MCS while ignoring possible abnormal saboteurs hidden in numerous devices may mislead the normal operation of blockchain, resulting in untrustworthy interactions. Consequently, it is highly desirable to build a trust-preserving mechanism (TPM) to bridge the gap between MCS and blockchain. To this end, we first resort to a probabilistic trust assessment inferred from the interaction outcomes in blockchain, to incentivize normal nodes to maintain trustworthiness of interactions. Assisted by the trust assessment, trust decision is further made to filter untrusted nodes from participating in blockchain process. Simulation experiments are conducted to validate the effectiveness and efficiency of the proposed TPM-enabled blockchain in terms of contribution rate and consensus accuracy.

Satellite edge computing (SEC) has become a promising technology for future wireless networks to provide anywhere and anytime computing services. Different from terrestrial edge computing, the computing capacity at Low-Earth-Orbit (LEO) satellites is usually unstable, due to the limited and consistently changing energy supply of fast-orbiting LEO satellites. To well exploit the potentials of SEC, an optimal computation offloading strategy becomes imperative to determine when and how to offload computing tasks with respect to high dynamics of satellites. In this paper, we propose a dynamic offloading strategy to minimize the overall delay of tasks from terrestrial users in a SEC network, subject to the energy and computing capacity constraints of the LEO satellite. Based on Lyapunov optimization theory, a long-term stochastic problem with a time-varying energy constraint is converted into multiple deterministic one-slot problems parameterized by the current system state, where task offloading decisions, computing resource allocation and transmit power control are jointly optimized. Numerical results show that our algorithm achieves asymptotic optimality efficiently while maintaining the mean rate stable of the LEO satellite’s energy queue, and has a lower delay compared with the other two comparison approaches with acceptable energy consumption.

Road icing has become one of the most critical factors threatening traffic safety. This article proposes an Internet of Things (IoT)-enabled road icing detection and prediction system. In the proposed system, we first design a low-power icing sensor equipped with IoT function to periodically collect current road status and transmit the sampled data to IoT gateway through Long Range Radio (LoRa). Then, we design a simple but effective algorithm deployed on IoT gateway to identify road icing in time. The algorithm is proposed based on the change trend of the sampled data of the road state, and can be adapted to the icing recognition on the road covered with various impurities. Furthermore, we put forward a newly designed deep neural network model called Trans-CGAN to achieve accurate road icing prediction even the positive and negative samples are imbalanced. Through a real system deployment and experiments, the results show that our proposed system can detect the formation of road icing effectively and timely, and shows better prediction performance of road icing than several representative models.

Cache-enabled device-to-device (D2D) communication is a potential approach to tackle the resource shortage problem. However, public concerns of data privacy and system security still remain, which thus arises an urgent need for a reliable caching scheme. Fortunately, federated learning (FL) with a distributed paradigm provides an effective way to privacy issue by training a high-quality global model without any raw data exchanges. Besides the privacy issue, blockchain can be further introduced into the FL framework to resist the malicious attacks occurred in D2D caching networks. In this study, we propose a double-layer blockchain-based deep reinforcement FL (BDRFL) scheme to ensure privacy-preserved and caching-efficient D2D networks. In BDRFL, a double-layer blockchain is utilized to further enhance data security. Simulation results first verify the convergence of the BDRFL-based algorithm, and then demonstrate that the download latency of the BDRFL-based caching scheme can be significantly reduced under different types of attacks when compared to some existing caching policies.

Proactive reconfiguration of network slices according to uncertain traffic demands is essential to improve network resource utilization while ensuring service quality in 5G-and-beyond systems. Existing researches on network slice reconfiguration are either model-driven or data-driven methods. However, model-driven methods may cause resource over-provisioning due to a lack of prediction mechanism, while data-driven methods are unrealistic in inter-slice reconfiguration that involves costly and time-consuming operations such as VNF migration. To address these issues, in this paper, we propose a Hybrid Model-Data driven (HMD) framework that intelligently performs inter-slice reconfiguration by leveraging prediction interval and robust optimization. We design a Prediction Interval-oriented Predictor (PIP) to produce a prediction interval that can bracket the future traffic demand with a prespecified probability. Based on the prediction interval, we design an inter-slice reconfiguration scheme (named box optimizer) to perform fast inter-slice reconfigurations. To tackle the over-conservativeness of the box optimizer, we further design the ellipsoid optimizer with better optimality at a cost of increased complexity. Numerical results demonstrate that the proposed framework can provide high robustness with low power consumption. Meanwhile, the trade-off between the power consumption and the realized robustness can be flexibly adjusted according to the type of slice and the level of traffic demand fluctuations.

Federated learning (FL) has recently become one of the hottest focuses in wireless edge networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs train local machine learning models and transmit them to an aggregator, where a global model is formed and then sent back to UEs. In wireless networks, local training and model transmission can be unsuccessful due to constrained computing resources, wireless channel impairments, bandwidth limitations, etc., which degrades FL performance in model accuracy and/or training time. Moreover, we need to quantify the benefits and cost of deploying edge intelligence, as model training and transmission consume certain amount of resources. Therefore, it is imperative to deeply understand the relationship between FL performance and multiple-dimensional resources. In this paper, we construct an analytical model to investigate the relationship between the FL model accuracy and consumed resources in FL empowered wireless edge networks. Based on the analytical model, we explicitly quantify the model accuracy, available computing resources and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis, and demonstrate the trade-off between the communication and computing resources for achieving a certain model accuracy.

Beyond fifth-generation (B5G) and future networks face the challenges of spectral, energy and cost efficiency for large-scale machine-type communications. Recently, emerging ambient backscatter communication (AmBC) technology provides a promising paradigm for the development of green Internet of Things (IoT) networks in B5G era. Unlike existing work on AmBC which mostly focuses on physical layer with relatively ideal model, i.e., classic three-nodes model composed of radio frequency (RF), backscatter device (BD) and IoT device, this paper studies the access control strategy, including coefficient design and device association, from the perspective of networking. Assuming whether channel information is available a-priori, we propose online and offline access control strategies respectively. For offline access control strategy, we leverage the difference of two convex functions approximation (DCA) and dual decomposition to transform the non-concave optimization problem into the concave one, and design a distributed access control strategy called DCA-S. Furthermore, for the case that channel information is assumed to be unknown in advance due to the dynamics of primary and backscatter networks, we design a combinatorial multi-armed bandit (CMAB) access control strategy (CMAB-S). Numerical results show that the proposed DCA-S and CMAB-S can achieve significant performance improvement of the system in both cases of available and unavailable channel information compared with benchmark schemes.

Cache-enabled device-to-device (D2D) communication has been widely deemed as a promising approach to tackle the unprecedented growth of wireless traffic demands. Recently, tremendous efforts have been put into designing an efficient caching policy to provide users better quality of service. However, public concerns of data privacy still remain in D2D cache sharing networks, which thus arises an urgent need for a privacy-preserved caching scheme. In this study, we propose a double-layer blockchain-based federated learning (DBFL) scheme with the aim of minimizing the download latency for all users in a privacy-preserving manner. Specifically, in the sublayer, the devices within the same coverage area run a federated learning (FL) to train the caching scheme model for each area separately without exchange of local data. The model parameters for each area are recorded in sublayer chains with Raft consensus mechanism. Meanwhile, in the main layer, a mainchain based on practical Byzantine fault tolerance (PBFT) mechanism is used to resist faults and attacks, thus securing the reliability of FL updates. Only the reliable area models authorized by the mainchain are utilized to update the global model in the main layer. Numerical results show the convergence, as well as the gain of download latency of the proposed DBFL caching scheme when compared with several traditional schemes.

Service migration, determining when, where and how to migrate the ongoing service, is of paramount importance in mobile edge computing (MEC) for provisioning high quality of service to mobile users. With respect to high network dynamics and stringent delay requirements, service migration is a rather challenging issue in MEC. In this paper, we formulate service migration as a partially observable Markov decision process (POMDP) based on the fact that an edge server can only obtain partial users' information, or the information of its own serving users. A learning-based intelligent service migration algorithm, named iSMA, is proposed to minimize the long-term service delay of all users. iSMA consists of two function modules, a latent space model and a cross-entropy planning algorithm, where the latent space model is used to infer the full state of the environment based on the partial information observed, and the cross-entropy planning algorithm is used to search the best service migration strategy. Numerical results show that our proposed iSMA reduces the service delay by about 58% when compared with a well-known deep learning-based solution.

Millimeter wave (mmWave) communication is one of the key technologies in 5G and beyond systems to address the tremendous growth in mobile data traffic owing to the abundant spectrum resources. Ultra-dense network deployment is a promising solution to combat the limited coverage, high propagation loss and attenuation of mmWave signals. This study investigates the beam management, with focus on beam configuration of mmWave base stations, in the ultra-dense mmWave network. To fulfill adaptive and intelligent beam management while protecting user privacy, we employ a double deep Q-network under a federated learning to tackle the beam management problem which is formulated to maximize the long-term system throughput. Simulation results demonstrate the performance gain of our proposed scheme.

Network slicing has been widely recognized as the architectural technology for 5G and beyond wireless network systems to provide tailored service for diverse applications by flexibly splitting and allocating various heterogeneous resources. However, it is still challenging to meet the strict delay requirements of a large number of delay-sensitive applications under traditional slicing architectures. One potential way to tackle this issue is to build network slicing upon Mobile Edge Computing (MEC) systems, where both communication and computing resources are integrated for providing customized service. As such, in this paper, we propose a framework, to jointly optimize communication and computing resources under the scenario of multi-service coexistence, with the objective to minimize the system cost while meeting the diverse QoS requirements. To make the original optimization problem more tractable, we decompose it into two convex sub-problems first. Then we obtain the optimal solutions of the two sub-problems respectively, and finally derive the optimal communication and computing resource allocation scheme based on the optimal solutions of these two sub-problems. Simulation results show that our proposed scheme significantly saves the system cost under various scenarios compared with other benchmarks.

Network Slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements sustainably. With the widespread of emerging smart devices, access control becomes an essential yet challenging issue in NS-based wireless networks due to the device-base station (BS)-NS three-layer association relationship. Meanwhile, stringent data security and device privacy concerns are increasing dramatically. In this paper, we propose an efficient access control scheme for radio access network (RAN) slicing by exploiting a federated deep reinforcement learning framework, called FDRL-AC, to improve network throughput and communication efficiency while enforcing the data security and device privacy. Specifically, we use deep reinforcement learning to train local model on devices, where horizontally federated learning (FL) is employed for parameter aggregation on BS, while vertically FL is employed for feature aggregation on the encrypted party. Numerical results show that the proposed FDRL-AC scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-art solutions.

Radio Access Network (RAN) slicing is a promising architectural technology to address extremely diversified service demands for future mobile networks. As an essential requirement for RAN slicing, self-healing is to provide services with certain quality requirements by minimizing the impact of mobile network failings. In this paper, we propose a Multi-objective Pareto Optimization based Self-healing (MPOS) scheme to solve the SRANS problem. We model the SRANS problem as a multi-objective optimization problem with aim of maximizing the self-healing profits of individual RAN slices and demonstrate the NP-hardness. In proposed MPOS scheme, we employ self-conditioned GANs to replace the offspring reproduction module in the traditional Multi-Objective Evolutionary Algorithm (MOEA), where the insufficiency of diversity maintenance in MOEA is effectively overcome. Furthermore, we theoretically prove that MPOS framework is guaranteed to converge to the optimal Pareto solution set with probability 1. Numerical results demonstrate that our MPOS scheme is effective in reducing the inverted generational distance of optimal Pareto solutions and achieving high profit and isolation level of RAN slices.

Abstract With the intensive deployment of mobile networks and the vigorous development of new multimedia services, video has gradually become the mainstream of cultural consumption. The contradiction between the proliferation of video data services and the scarcity of spectrum resources has brought great challenges to the current network resource allocation. Non‐orthogonal multiple access (NOMA) can be used to solve this problem by signal superposition and spectrum multiplexing to improve system access capability. As a new type of joint optimization design of transmitter and receiver side, PDMA has high research value. In this paper, a framework of PDMA video transmission system based on H.264 video compression coding (HVC‐PDMA) is proposed. Poly complementary sequence (PCS) spread spectrum coding is performed on the transmission codebook in order to improve the transmission accuracy. Meanwhile, a low complexity serial sphere compensated Max‐log MPA (SSCM‐MPA) algorithm is proposed to reduce the complexity of the multi‐user detection algorithm. Simulation results show that the PCS spread spectrum can improve system throughput and peak signal‐to‐noise ratio (PSNR) while reducing bit error rate (BER). SSCM‐MPA algorithm can greatly reduce the complexity and improve the transmission efficiency.

Network slicing (NS) has been identified as one of the most promising architectural technologies for future mobile network systems to meet the extremely diversified service requirements of users. In radio access networks (RAN) slicing, service provisioning for slice users becomes much more complicated than that in traditional mobile networks, as the constraints of both user physical association with base station (BS) and logical association with NS should be considered. In other words, the user-BS-NS three layer association relationship should be addressed in provisioning tailored service for diversified use cases with various quality of service (QoS) requirements. Therefore, service provisioning in RAN slicing becomes an essential yet challenging issue for 5G and beyond systems. In this paper, we propose a unified framework for service provisioning in RAN slicing with aim of maximizing resource utilization while guaranteeing QoS of users. The framework consists of two steps. The first step is to identify a set of slice users whose QoS can be satisfied simultaneously; while the second step performs joint slice association and bandwidth allocation with aim to minimize bandwidth consumption. Numerical results show that in typical scenarios, our proposed service provisioning framework can achieve significant performance gain in terms of the number of serving users and wireless bandwidth utilization compared with traditional schemes.

Recently, mobile operators have been shifting to an intelligent autonomous network paradigm, where the mobile networks are automated in a plug-and-play manner to reduce the manual intervention. Under this circumstance, serious inter-cell interference becomes inevitable which may severely deteriorate system throughput performance and users’ quality of service (QoS), especially for dense residential small base station (SBS) deployment. This paper proposes an intelligent inter-cell interference coordination (ICIC) scheme for autonomous heterogeneous networks (HetNets), where the SBSs agilely schedule sub-channels to individual users at each Transmit Time Interval (TTI) with aim of mitigating interferences and maximizing long-term throughput by sensing the environment. Since the reward function is inexplicit and only few samples can be used for prior-training, we formulate the ICIC problem as a distributed inverse reinforcement learning (IRL) problem following the POMDP games. We propose a non-prior knowledge based self-imitating learning (SIL) algorithm which incorporates Wasserstein Generative Adversarial Networks (WGANs) and Double Deep Q Network (Double DQN) algorithms for performing behavior imitation and few-shot learning in solving the IRL problem from both thepolicyandvalue. Numerical results reveal that SIL is able to implement TTI level’s decision-making to solve the ICIC problem, and the overall network throughput of SIL can be improved by up to 19.8% when compared with other known benchmark algorithms.

It is widely acknowledged that the agile reconfiguration of network slice according to traffic demand is of vital importance in 5G-and-beyond systems. Existing relevant works make reconfiguration decisions based either on point prediction of the uncertain demand, which lacks indications on how accurate it is, or on handcrafted uncertainty set with robust optimization, which may lead to resource over-provisioning due to the lack of prediction mechanism. To overcome these drawbacks, in this paper, we propose a predictor-optimizer framework that intelligently performs inter-slice reconfiguration with the aim of minimizing the energy consumption of serving these slices. Specifically, the predictor produces a prediction interval comprised of lower and upper bounds that bracket the future traffic demands with a prespecified probability. Then by regarding the prediction interval as the uncertainty set, we formulate the network slice reconfiguration problem as a Robust Mixed Integer Programming (RMIP). We solve this RMIP by using linearization technique and robust optimization. Numerical results demonstrate that the proposed framework outperforms traditional methods in terms of robustness and energy consumption. Meanwhile, the tradeoff between robustness and the energy consumption can be automatically adjusted according to the type of slice and traffic demands.

It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond systems need to support. To guarantee performance isolation while maximizing network resource utilization under traffic uncertainty, network slice needs to be reconfigured adaptively. However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by the numerous variables. In this paper, we investigate network slice reconfiguration with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL). To address the curse of dimensionality of the problem, we propose to incorporate the Branching Dueling Q-network (BDQ) into DRL, to avoid some unnecessary calculations of Q-value by separating the Q-network into a shared value branch and a number of distributed advantage branches. Furthermore, the value branch and the advantage branch of each dimension are aggregated to derive the corresponding dimension's sub-Q-value. Then the best reconfiguration action is composed of the subactions in individual dimensions which are selected by €-greedy policy. Finally, we design an intelligent online network slice reconfiguration policy based on BDQ and extensive simulation experiments are conducted to validate the effectiveness of the proposed slice reconfiguration policy.

In legacy blockchain based systems, each involved node has to store a complete blockchain to ensure the system security without any central authoritative controller. However, it is usually impossible for a wireless IoT node to store a complete blockchain, especially for those simple sensor nodes without sufficient storage and computing resources. In this paper, we propose a block assignment scheme for blockchain based wireless IoT systems with aim to tackle the blockchain storage problem. Specifically, we propose to maintain a complete blockchain by a set of IoT nodes in a collaborative way on the premise of ensuring that each node can check every transaction. On the other hand, we should save the storage space of IoT nodes to the greatest extent for saving more blocks so as to maximize the lifetime of IoT nodes. We formulate this optimal block assignment problem as a 0-1 mixed integer-programming problem. We propose to incorporate Chaotic optimized algorithm into Genetic algorithm to provide an efficient near-optimal solution. Compared with the brute-force and conventional Genetic algorithms, our proposed algorithm can achieve the minimum storage occupancy to store blocks. Meanwhile, the proposed algorithm has the lowest computational complexity.

Blockchain-enabled wireless network has been recognized as an emerging network architecture to be widely employed into the Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without the involvement of a third party. However, the uncertainty and vulnerability of wireless channels among the IoT nodes may pose a serious challenge to facilitate the deployment of blockchain in wireless networks. In this paper, we first present a generic system model for blockchain enabled wireless networks with multiple communication connections, where the number of communication connections between a client IoT node and the blockchain full nodes can be any arbitrary positive integer to satisfy different security requirements. Based on the proposed spatial-temporal network model, we theoretically calculate the transmission successful probability and the required communication throughput to support a wireless blockchain network. Finally, simulation results validate the accuracy of our theoretical analysis.

Unmanned aerial vehicles (UAVs) have shown enormous potential in both public and civil domains. Although multi-UAV systems can collaboratively accomplish missions efficiently, UAV network(UAVNET) design faces many challenging issues, such as high mobility, dynamic topology, power constraints, and varying quality of communication links. Topology control plays a key role for providing high network connectivity while conserving power in UAVNETs. In this paper, we propose a distributed topology control algorithm based on discrete particle swarm optimization with articulation points(AP-DPSO). To reduce signaling overhead and facilitate distributed control, we first identify a set of articulation points (APs) to partition the network into multiple segments. The local topology control problem for individual segments is formulated as a degree-constrained minimum spanning tree problem. Each node collects local topology information and adjusts its transmit power to minimize power consumption. We conduct simulation experiments to evaluate the performance of the proposed AP-DPSO algorithm. Numerical results show that AP-DPSO outperforms some known algorithms including LMST and LSP, in terms of network connectivity, average link length and network robustness for a dynamic UAVNET.

Network slicing is identified as a fundamental architectural technology for future mobile networks since it can logically separate networks into multiple slices and provide tailored quality of service (QoS). However, the introduction of network slicing into radio access networks (RAN) can greatly increase user handover complexity in cellular networks. Specifically, both physical resource constraints on base stations (BSs) and logical connection constraints on network slices (NSs) should be considered when making a handover decision. Moreover, various service types call for an intelligent handover scheme to guarantee the diversified QoS requirements. As such, in this article, a multiagent reinforcement LEarning based Smart handover Scheme, named LESS, is proposed, with the purpose of minimizing handover cost while maintaining user QoS. Due to the large action space introduced by multiple users and the data sparsity caused by user mobility, conventional reinforcement learning algorithms cannot be applied directly. To solve these difficulties, LESS exploits the unique characteristics of slicing in designing two algorithms: 1) LESS-DL, a distributed Q-learning algorithm to make handover decisions with reduced action space but without compromising handover performance; 2) LESS-QVU, a modified Q-value update algorithm which exploits slice traffic similarity to improve the accuracy of Q-value evaluation with limited data. Thus, LESS uses LESS-DL to choose the target BS and NS when a handover occurs, while Q-values are updated by using LESS-QVU. The convergence of LESS is theoretically proved in this article. Simulation results show that LESS can significantly improve network performance. In more detail, the number of handovers, handover cost and outage probability are reduced by around 50%, 65%, and 45%, respectively, when compared with traditional methods.

It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond system needs to support. To guarantee performance isolation while maximizing network resource utilization under dynamic traffic load, network slice needs to be reconfigured adaptively. However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by numerous variables. In this article, we investigate the reconfiguration within a core network slice with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL). This problem is also intractable by using conventional Deep Q Network (DQN), as it has a multi-dimensional discrete action space which is difficult to explore efficiently. To address the curse of dimensionality, we propose to exploit Branching Dueling Q-network which incorporates the action branching architecture into DQN to drastically decrease the number of estimated actions. Based on the discrete BDQ network, we develop an intelligent network slice reconfiguration algorithm (INSRA). Extensive simulation experiments are conducted to evaluate the performance of INSRA and the numerical results reveal that INSRA can minimize the long-term resource consumption and achieve high resource efficiency compared with several benchmark algorithms.

In this paper, we investigate the resource management for radio access network slicing from user access control and wireless bandwidth allocation perspectives. First, to guarantee users' QoS, we propose two admission control (AC) policies to select admissible users from the perspective of optimizing the QoS and the number of serving users respectively. Then, to optimize the bandwidth utilization for the selected admissible users, we investigate the slice association and bandwidth allocation (SABA) problem and propose network centric and UE centric SABA policies respectively. Numerical results show that in typical scenarios, our proposed AC and SABA policies can significantly outperform traditional policies in terms of wireless bandwidth utilization and number of admissible users.

Network slicing (NS) has been identified as a fundamental technology for future mobile networks to meet extremely diverse communication requirements by providing tailored quality of service (QoS). However, due to the introduction of NS into radio access networks (RAN) forming a UE-BS-NS three-layer association, handoff becomes very complicated and cannot be resolved by conventional policies. In this paper, we propose a multi-agent reinforcement LEarning based Smart handoff policy with data Sharing, named LESS, to reduce handoff cost while maintaining user QoS requirements in RAN slicing. Considering the large action space introduced by multiple users and the data sparsity problem due to user mobility, LESS is designed to have two components: 1) LESS-DL, a modified distributed Q-learning algorithm with small action space to make handoff decisions; 2) LESS-DS, a data sharing mechanism using limited data to improve the accuracy of handoff decisions made by LESS-DL. The proposed LESS mechanism uses LESS-DL to choose both the target base station and NS when a handoff occurs, and then updates the Q-values of each user according to LESS-DS. Numerical results show that in typical scenarios, LESS can significantly reduce the handoff cost when compared with traditional handoff policies without learning.

Blockchain has shown a great potential in Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without involvement of any third party. Understanding the relationship between communication and blockchain as well as the performance constraints posing on the counterparts can facilitate designing a dedicated blockchain-enabled IoT systems. In this paper, we establish an analytical model for the blockchain-enabled wireless IoT system. By considering spatio-temporal domain Poisson distribution, i.e., node geographical distribution in spatial domain and transaction arrival rate in time domain are both modeled as Poisson point process (PPP), we first derive the distribution of signal-to-interference-plus-noise ratio (SINR), blockchain transaction successful rate as well as overall throughput. Based on the system model and performance analysis, we design an algorithm to determine the optimal full function node deployment for blockchain system under the criterion of maximizing transaction throughput. Finally, the security performance is analyzed in the proposed networks with three typical attacks. Solutions such as physical layer security are presented and discussed to keep the system secure under these attacks. Numerical results validate the accuracy of our theoretical analysis and optimal node deployment algorithm.

The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets that brings heavy signaling overhead, low energy efficiency and increased user equipment (UE) outage probability if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named SMART to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In SMART, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, SMART can significantly reduce the number of handoffs when compared with traditional handoff policies without learning.

The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named LESH to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In LESH, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, LESH can significantly reduce the number of handoffs when compared with traditional handoff policies.

In heterogeneous cellular networks (HetNets), cell association of User Equipment (UE) affects UE transmit rate and network throughput. Conventional cell association rules are usually based on UE received Signal-to-Interference-and-Noise-Ratio (SINR) without taking into account user behaviors, which can indeed be exploited for improving network performance. In this paper, we investigate UE cell association in HetNets based on individual user behavior characteristics with aim to maximize long- term expected system throughput. We model the problem as a stochastic optimization model Restless Multi-Armed Bandit (RMAB). As it is a PSPACE-hard problem, we develop a primal-dual heuristic index algorithm and the solution specifies the rule that determines which arms in the RMAB model to be selected at each decision time. According to the solution of RMAB, we propose a new cell association strategy called Index Enabled Association (IDEA). We also conduct simulation experiments to compare IDEA with conventional max-SINR cell association strategy and an existing game-based RAT selection scheme. Numerical results demonstrate the advantages of IDEA in typical scenarios.

In dense and heterogeneous networks, the decoupled uplink#x002F;downlink (UL#x002F;DL) access (DUDA) design has drawn great attentions for improving system performance. Energy efficiency (EE) becomes a major concern for densely deployed heterogeneous cellular networks (HetNets). In this paper, we theoretically analyze the energy efficient sleep strategy for DUDA HetNets. Through using stochastic geometry theory, we first examine the applicability of conventional sleep strategy to DUDA networks and design a new DUDA sleep strategy. We then formulate the energy consumption minimization problem and EE optimization problem, and derive the optimal BS sleep probability. Numerical results reveal that conventional sleep strategy may provide inaccurate guidance for sleep design in DUDA networks, which may lead to excessive sleeps and decrease system EE. Meanwhile our DUDA sleep strategy can effectively reduce network energy consumption. We also find that the dense deployment of small cells may generally increase network EE, but this improvement saturates as the BS density further increases.

Femtocells are widely recognized as a promising technology to meet the requirements of indoor coverage in forthcoming fifth generation cellular networks (5G). As femtocell holders (FHs) can be users themselves or mobile network operators, it makes challenges to holistic network resource utilization. In particular, due to the selfishness nature, FHs are usually unwilling to accommodate extra users without compensation. This inspires us to develop an effective refunding mechanism, with aim to allow competitive network operators to employ truthful refunding policy, and to encourage FHs to make appropriate access permission. In this paper, we first define a refunding strategy function and price-coefficient for the refunding policy. We then formulate the access permission as a Stackelberg game and theoretically prove the existence of unique Nash Equilibrium. Numerical results validate the effectiveness of our proposed mechanism and overall network efficiency is improved significantly as well.