As intelligent network services continue to diversify, ensuring efficient and adaptive resource allocation in edge networks has become increasingly critical. Yet the wide functional variations across services often give rise to new and unforeseen optimization problems, rendering traditional manual modeling and solver design both time-consuming and inflexible. This limitation reveals a key gap between current methods and human solving — the inability to recognize and understand problem characteristics. It raises the question of whether problem-aware learning can bridge this gap and support effective cross-problem generalization. To answer this question, we propose a problem-aware diffusion (PAD) model, which leverages a problem-aware learning framework to enable cross-problem generalization. By explicitly encoding the mathematical formulations of optimization problems into token-level embeddings, PAD empowers the model to understand and adapt to problem structures. Extensive experiments across ten representative network optimization problems show that PAD generalizes well to unseen problems while avoiding the inefficiency of building new solvers from scratch, yet still delivering competitive solution quality. Meanwhile, an auxiliary constraint-aware module is designed to enforce solution validity further. The experiments indicate that problem-aware learning opens a promising direction toward general-purpose solvers for intelligent network operation and resource management. Our code is open source at https://github.com/qiyu3816/PAD.

To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces a decentralized system master key generation (DSMKG) mechanism without a centralized secret sharer, ensuring that the system secret key remains hidden from any single authority. Based on DSMKG, DCPPA then implements a lightweight verifiable pseudonym self-generation strategy without the system master secret key escrow problem, thus providing flexible pseudonym updating and reliable de-anonymization. Moreover, we implement DCPPA over a hyperelliptic curve cryptosystem (HECC) to balance the system performance. Considering the additional communication processes due to the decentralized feature, we introduce a symmetric balanced incomplete block design (SBIBD) to enhance the communication efficiency. We demonstrate the excellent security of DCPPA through an in-depth security analysis, while demonstrate that the overhead of DCPPA is at ms level through experiments.

Environment sensing and fusion via onboard sensors are envisioned to be widely applied in future autonomous driving networks. This paper considers a vehicular system with multiple self-driving vehicles that is assisted by multi-access edge computing (MEC), where image data collected by the sensors is offloaded from cellular vehicles to the MEC server using vehicle-to-infrastructure (V2I) links. Sensory data can also be shared among surrounding vehicles via vehicle-to-vehicle (V2V) communication links. To improve spectrum utilization, the V2V links may reuse the same frequency spectrum as the V2I links, which may cause severe interference. To tackle this issue, we leverage reconfigurable intelligent computational surfaces (RICSs) to jointly enable V2I reflective links and mitigate interference appearing at the V2V links. Considering the limitations of traditional algorithms in addressing this problem, such as the assumption of quasi-static channel state information, which restricts their ability to adapt to dynamic environmental changes and leads to poor performance under frequently varying channel conditions, in this paper, we formulate the problem at hand as a Markov game. Our novel formulation is applied to time-varying channels subject to multi-user interference and introduces a collaborative learning mechanism among users. The considered optimization problem is solved via a driving safety-enabled multi-agent deep reinforcement learning (DS-MADRL) approach that capitalizes on the RICS presence. Our extensive numerical investigations showcase that the proposed reinforcement learning approach achieves faster convergence and significant enhancements in both data rate and driving safety, as compared to various state-of-the-art benchmarks.

Vertical federated learning (VFL) has been widely adopted in various domains for collaborative decision-making. However, recent studies have revealed critical privacy vulnerabilities in VFL, particularly label inference attacks, which significantly undermine label confidentiality and limit the applicability of VFL in privacy-sensitive scenarios. To mitigate such threats, several defense methods have been proposed by incorporating diverse privacy-preserving techniques. Nevertheless, existing defenses fail to effectively prevent the recently proposed model completion-based label inference attacks. To address this limitation, we propose a novel defense method, termed Label Compression-Based Defense (LCD), to defend against this class of attacks. The core idea of LCD is to train the VFL model using fake labels, thereby decoupling the ground-truth labels from the outputs of the malicious bottom model, which constitute the critical component exploited in the model completion-based attacks. Specifically, we introduce a multi-stage training strategy that decomposes the training process into different stages to deceive the malicious bottom model without affecting the original task. In addition, we design a deep feature-based label compression mechanism to generate fake labels for misleading the attacker. To further enhance the defense effectiveness, we propose an embedding compaction strategy based on center loss, which substantially increases the difficulty of label inference. Moreover, we theoretically prove the effectiveness of LCD from an information-theoretic perspective. Extensive experiments on both tabular and image datasets demonstrate that LCD can effectively defend against label inference attacks. The source code of LCD is publicly available at GitHub:https://github.com/YuanShunJie1/LCD.

Reconfigurable intelligent surfaces (RISs) have demonstrated an unparalleled ability to reconfigure wireless environments by dynamically controlling the phase, amplitude, and polarization of impinging waves. However, as nearly passive reflective metasurfaces, RISs may not distinguish between desired and interference signals, which can lead to severe spectrum pollution and even affect performance negatively. In particular, in large-scale networks, the signal-to-interference-plus-noise ratio (SINR) at the receiving node can be degraded due to excessive interference reflected from the RIS. To overcome this fundamental limitation, we propose in this paper a trajectory prediction-based dynamical control algorithm (TPC) for anticipating RIS ON-OFF states sequence, integrating a long- short-term-memory (LSTM) scheme to predict user trajectories. In particular, through a codebook-based algorithm, the RIS controller adaptively coordinates the configuration of the RIS elements to maximize the received SINR. Our simulation results demonstrate the superiority of the proposed TPC method over various system settings.

In the field of multi-access edge computing (MEC), efficient computation offloading is crucial for improving resource utilization and reducing latency in dynamically changing environments. This paper introduces a new approach, termed as MultiTask Domain Adaptation (MTDA), aiming to enhance the ability of computational offloading models to generalize in the presence of domain shifts, i.e., when new data in the target environment significantly differs from the data in the source domain. The proposed MTDA model incorporates a teacher-student architecture that allows continuous adaptation without necessitating access to the source domain data during inference, thereby maintaining privacy and reducing computational overhead. Utilizing a multitask learning framework that simultaneously manages offloading decisions and resource allocation, the proposed MTDA approach outperforms benchmark methods regarding mean squared error and accuracy, particularly in environments with increasing numbers of users. It is observed by means of computer simulation that the proposed MTDA model maintains high performance across various scenarios, demonstrating its potential for practical deployment in emerging MEC applications.

Real-time sharing of traffic information is crucial for effectively reducing losses after road traffic accidents and disasters. However, traditional methods for collecting and sharing this information are often inefficient, consume considerable resources, and face persistent challenges regarding cost and real- time performance. These issues hinder the ability to achieve real-time sharing of large-scale traffic information. To tackle this problem, this paper presents a new data storage method called the storage counting tree (SCT). Unlike traditional storage methods, where the amount of data is closely tied to the storage space required, the counting tree reuses tree nodes, making its storage space usage independent of the data scale. In this context, the coordinates information of vehicles can be shared efficiently via SCT and thus the density can be evaluated. Accordingly, we propose to optimize the backoff window of the traditional binary exponential backoff (BEB) algorithm based on the density. The experiments show that a 127-byte SCT can accommodate up to 4.2 billion entries, demonstrating impressive storage efficiency. Moreover, the system significantly reduces latency for critical message transmission and shows improved adaptability across various scenarios.

With the development of autonomous driving technology, Multi-Access Edge Computing (MEC) is an effective paradigm to support delay-sensitive applications in vehicular networks. However, achieving the real-time offloading strategy and resource allocation in MEC-empowered vehicular networks becomes a challenge. In this paper, we first formulate an offloading optimization problem to minimize system latency and energy consumption. To obtain the optimal policy in real time, the formulated problem is transformed into a Markov Decision Process (MDP) and then solved by the proposed Attention and Feature Fusion Deep Deterministic Policy Gradient (AFF-DDPG) algorithm, where a multi-head attention mechanism is combined with feature fusion to improve the accuracy of the decision. In addition, the exploration ability and learning efficiency of the AFF-DDPG algorithm are further enhanced by exploiting Ornstein-Uhlenbeck (OU) noise and the priority experience replay mechanism. The simulation results show that the proposed AFF-DDPG algorithm achieves a 9.44 % improvement over the DDPG algorithm.

Network optimization is a fundamental challenge in the Internet of Things (IoT) network, often characterized by complex features that make it difficult to solve these problems. Recently, generative diffusion models (GDMs) have emerged as a promising new approach to network optimization, with the potential to directly address these optimization problems. However, the application of GDMs in this field is still in its early stages, and there is a noticeable lack of theoretical research and empirical findings. In this study, we first explore the intrinsic characteristics of generative models. Next, we provide a concise theoretical proof and intuitive demonstration of the advantages of generative models over discriminative models in network optimization. Based on this exploration, we implement GDMs as optimizers aimed at learning high-quality solution distributions for given inputs, sampling from these distributions during inference to approximate or achieve optimal solutions. Specifically, we utilize denoising diffusion probabilistic models (DDPMs) and employ a classifier-free guidance mechanism to manage conditional guidance based on input parameters. We conduct extensive experiments across three challenging network optimization problems. By investigating various model configurations and the principles of GDMs as optimizers, we demonstrate the ability to overcome prediction errors and validate the convergence of generated solutions to optimal solutions. We provide code and data athttps://github.com/qiyu3816/DiffSG.

As a new distributed machine learning framework, vertical federated learning (VFL) has been widely applied in the industry. However, recent studies have demonstrated that VFL faces serious challenges from backdoor attacks, which significantly hinder its further development. Although a few studies have focused on defending against VFL backdoor attacks, these defenses either do not consider the latest attack methods or show limited effectiveness. Moreover, most existing backdoor defense efforts primarily focus on backdoor attacks in horizontal federated learning (HFL) and centralized learning. Due to the unique architecture of VFL models, these methods cannot be directly applied to backdoor defense in VFL. To mitigate the threat of backdoor attacks in VFL, we propose a general backdoor detection (GBD) scheme for backdoor defense, which detects backdoor samples by analyzing the correlation between backdoor samples and the target label, as well as by leveraging the response differences between clean and backdoor samples. Specifically, we propose two backdoor detection metrics: Class Activation Probability (CAP) and Class Activation Contribution (CAC), which are used to calculate the likelihood of a sample being a backdoor sample. We leverage these two metrics to identify backdoor samples during the inference stage. Evaluation results on both tabular and image datasets show that GBD can detect backdoor samples with high accuracy, demonstrating its effectiveness in backdoor defense. The source code of GBD is available at GitHub: https://github.com/YuanShunJie1/GBD.

This paper focuses on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to a multi-access edge computing (MEC) server. Considering that the V2I links sometimes can be reused by vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of the V2I link may suffer from severe interference, causing outages during the task offloading. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Simulation results show that the proposed RICS-assisted offloading framework significantly improves the safety of the autonomous driving network, in which the safety coefficient of the CVs is improved by nearly 34%. The V2V data rate is improved by around 60%, which indicates that the RICS’s adjustment of the signals can effectively mitigate the interference of the V2V link.

Optimization is crucial for the efficiency and reliability of multi-access edge computing (MEC) networks. Many optimization problems in this field are NP-hard and do not have effective approximation algorithms. Consequently, there is often a lack of optimal (ground-truth) data, which limits the effectiveness of traditional deep learning approaches. Most existing learning-based methods require a large amount of optimal data and do not leverage the potential advantages of using suboptimal data, which can be obtained more efficiently. To illustrate this point, we focus on the multi-server multi-user computation offloading (MSCO) problem, a common issue in MEC networks that lacks efficient optimal solution methods. In this paper, we introduce the graph diffusion-based solution generator (GDSG), designed to work with suboptimal datasets while still achieving convergence to the optimal solution with high probability. We reformulate the network optimization challenge as a distribution-learning problem and provide a clear explanation of how to learn from suboptimal training datasets. We develop GDSG, a multi-task diffusion generative model that employs a graph neural network (GNN) to capture the distribution of high-quality solutions. Our approach includes a straightforward and efficient heuristic method to generate a sufficient amount of training data composed entirely of suboptimal solutions. In our implementation, we enhance the GNN architecture to achieve better generalization. Moreover, the proposed GDSG can achieve nearly 100% task orthogonality, which helps prevent negative interference between the discrete and continuous solution generation training objectives. We demonstrate that this orthogonality arises from the diffusion-related training loss in GDSG, rather than from the GNN architecture itself. Finally, our experiments show that the proposed GDSG outperforms other benchmark methods on both optimal and suboptimal training datasets. Regarding the minimization of computation offloading costs, GDSG achieves savings of up to 56.62% on the ground-truth training set and 41.06% on the suboptimal training set compared to existing discriminative methods.

In this paper, we investigate a multi-user offloading problem in the overlapping domain of a multi-server mobile edge computing system. We divide the original problem into two stages: the offloading decision-making stage and the request scheduling stage. To prevent the terminal from going out of the service area during offloading, we consider the mobility parameter of the terminal according to the human behaviour model when making the offloading decision, and then introduce a server evaluation mechanism based on both the mobility parameter and the server load to select the optimal offloading server. In order to fully utilise the server resources, we design a double deep Q-network (DDQN)-based reward evaluation algorithm that considers the priority of tasks when scheduling offload requests. Finally, numerical simulations are conducted to verify that our proposed method outperforms traditional mathematical computation methods as well as the DQN algorithm.

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.

Connected and Automated Vehicles (CAVs) are characterized by diverse communication attributes, embodying the trajectory of future automotive progress. Meanwhile, the transportation system will be in a mixed stage of CAVs and Human-Driven Vehicles (HDVs) for a long time. The study of communication capacity and strategies for mixed traffic systems is of great significance for the popularization of CAVs and the deployment of communication infrastructures. However, current research mainly focuses on the communication capacity analysis in the scenario with full penetration of CAVs, while the influence caused by HDVs on Vehicle-to-Vehicle (V2V) communications and the capacity analysis of connected vehicles in mixed traffic systems need further understanding. To address this issue, this paper considers the shadow fading caused by HDVs on wireless communication links and analyzes the communication capacity in mixed traffic systems. Specifically, we first synthesize the V2V and Vehicle-to-Infrastructure (V2I) communication modes to propose an analytical framework for vehicular network communication capacity in mixed traffic. Then, a predictive communication strategy is also provided that caches the required content at infrastructure in advance according to predicted vehicle trajectories to improve the capacity of vehicular networks in mixed traffic. Furthermore, the derived capacity analysis theorems reveal the communication capacity of mixed traffic is closely related to the CAV penetration rate, the vehicle arrival rate, and the infrastructure deployment interval. Simulation results prove the effectiveness of the proposed framework, and the proposed predictive communication strategy can increase the mixed traffic communication capacity compared to existing communication strategies. The theoretical results herein can guide the implementation of vehicular network applications and the design of communication strategies in mixed traffic systems.

With the rapidly increasing number of bandwidth-intensive terminals capable of intelligent computing and communication, such as smart devices equipped with shallow neural network (NN) models, the complexity of multiple access (MA) for these intelligent terminals is increasing due to the dynamic network environment and ubiquitous connectivity in sixth-generation (6G) systems. Traditional MA design and optimization methods are gradually losing ground to artificial intelligence (AI) techniques that have proven their superiority in handling complexity. AI-empowered MA and its optimization strategies aimed at achieving high quality-of-service (QoS) are attracting more attention, especially in the area of latency-sensitive applications in 6G systems. In this work, we aim to: 1) present the development and comparative evaluation of AI-enabled MA; 2) provide a timely survey focusing on spectrum sensing, protocol design, and optimization for AI-empowered MA; and 3) explore the potential use cases of AI-empowered MA in the typical application scenarios within 6G systems. Specifically, we first present a unified framework of AI-empowered MA for 6G systems by incorporating various promising machine learning (ML) techniques in spectrum sensing, resource allocation, MA protocol design, and optimization. We then introduce AI-empowered MA spectrum sensing related to spectrum sharing and spectrum interference management. Next, we discuss the AI-empowered MA protocol designs and implementation methods by reviewing and comparing the state of the art and further explore the optimization algorithms related to dynamic resource management, parameter adjustment, and access scheme switching. Finally, we discuss the current challenges, point out open issues, and outline potential future research directions in this field.

Computation offloading has become a popular solution to support computationally intensive and latency-sensitive applications by transferring computing tasks to mobile edge servers (MESs) for execution, which is known as mobile/multi-access edge computing (MEC). To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i.e., whether offloading or not) and computational resource allocation of MEC. The design can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which is generally NP-hard and its effective solution can be obtained by performing online inference through a well-trained deep neural network (DNN) model. However, when the system environments change dynamically, the DNN model may lose efficacy due to the drift of input parameters, thereby decreasing the generalization ability of the DNN model. To address this unique challenge, in this paper, we propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs). Specifically, the shared backbone will be invariant during the PHs training and the inferred results will be ensembled, thereby significantly reducing the required training overhead and improving the inference performance. As a result, the joint optimization problem for offloading decision and resource allocation can be efficiently solved even in a time-varying wireless environment. Experimental results show that the proposed MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.

Sixth-Generation (6G) technologies will revolutionize the wireless ecosystem by enabling the delivery of futuristic services through satellite-terrestrial integrated networks (STINs). As the number of subscribers connected to STINs increases, it becomes necessary to investigate whether the edge computing paradigm may be applied to low Earth orbit satellite (LEOS) networks for supporting computation-intensive and delay-sensitive services for anyone, anywhere, and at any time. Inspired by this research dilemma, we investigate a LEOS edge-assisted multi-layer multi-access edge computing (MEC) system. In this system, the MEC philosophy will be extended to LEOS, for defining the LEOS edge, in order to enhance the coverage of the multi-layer MEC system and address the users’ computing problems both in congested and isolated areas. We then design its operating offloading framework and explore its feasible implementation methodologies. In this context, we formulate a joint optimization problem for the associated communication and computation resource allocation for minimizing the overall energy dissipation of our LEOS edge-assisted multi-layer MEC system while maintaining a low computing latency. To solve the optimization problem effectively, we adopt the classic alternating optimization (AO) method for decomposing the original problem and then solve each sub-problem using low-complexity iterative algorithms. Finally, our numerical results show that the offloading scheme conceived achieves low computing latency and energy dissipation compared to the state-of-the-art solutions, a single layer MEC supported by LEOS or base stations (BS).

The envisioned wireless networks of the future entail the provisioning of massive numbers of connections, heterogeneous data traffic, ultra-high spectral efficiency, and low latency services. This vision is spurring research activities focused on defining a next generation multiple access (NGMA) protocol that can accommodate massive numbers of users in different resource blocks, thereby, achieving higher spectral efficiency and increased connectivity compared to conventional multiple access schemes. In this article, we present a multiple access scheme for NGMA in wireless communication systems assisted by multiple reconfigurable intelligent surfaces (RISs). In this regard, considering the practical scenario of static users operating together with mobile ones, we first study the interplay of the design of NGMA schemes and RIS phase configuration in terms of efficiency and complexity. Based on this, we then propose a multiple access framework for RIS-assisted communication systems, and we also design a medium access control (MAC) protocol incorporating RISs. In addition, we give a detailed performance analysis of the designed RIS-assisted MAC protocol. Our extensive simulation results demonstrate that the proposed MAC design outperforms the benchmarks in terms of system throughput and access fairness, and also reveal a trade-off relationship between the system throughput and fairness.

In this article, we propose a transfer learning (TL) enabled edge convolutional neural network (CNN) framework for 5G industrial edge networks with privacy-preserving characteristic. In particular, the edge server can use the existing image dataset to train the CNN in advance, which is further fine-tuned based on the limited datasets uploaded from the devices. With the aid of TL, the devices that are not participating in the training only need to fine-tune the trained edge-CNN model without training from scratch. Due to the energy budget of the devices and the limited communication bandwidth, a joint energy and latency problem is formulated, which is solved by decomposing the original problem into an uploading decision subproblem and a wireless bandwidth allocation subproblem. Experiments using ImageNet demonstrate that the proposed TL-enabled edge-CNN framework can achieve almost 85% prediction accuracy of the baseline by uploading only about 1% model parameters, for a compression ratio of 32 of the autoencoder.

Increasing concerns on intelligent spectrum sensing call for efficient training and inference technologies. In this paper, we propose a novel federated learning (FL) framework, dubbed federated spectrum learning (FSL), which exploits the benefits of reconfigurable intelligent surfaces (RISs) and overcomes the unfavorable impact of deep fading channels. Distinguishingly, we endow conventional RISs with spectrum learning capabilities by leveraging a fully-trained convolutional neural network (CNN) model at each RIS controller, thereby helping the base station to cooperatively infer the users who request to participate in FL at the beginning of each training iteration. To fully exploit the potential of FL and RISs, we address three technical challenges: RISs phase shifts configuration, user-RIS association, and wireless bandwidth allocation. The resulting joint learning, wireless resource allocation, and user-RIS association design is formulated as an optimization problem whose objective is to maximize the system utility while considering the impact of FL prediction accuracy. In this context, the accuracy of FL prediction interplays with the performance of resource optimization. In particular, if the accuracy of the trained CNN model deteriorates, the performance of resource allocation worsens. The proposed FSL framework is tested by using real radio frequency (RF) traces and numerical results demonstrate its advantages in terms of spectrum prediction accuracy and system utility: a better CNN prediction accuracy and FL system utility can be achieved with a larger number of RISs and reflecting elements.

Reconfigurable intelligent surface (RIS) is a promising reflective radio technology for improving the coverage and rate of future wireless systems by reconfiguring the wireless propagation environment. The current work mainly focuses on the physical layer design of RIS. However, enabling multiple devices to communicate with the assistance of RIS is a crucial challenging problem. Motivated by this, we explore RIS-assisted communications at the medium access control (MAC) layer and propose an RIS-assisted MAC framework. In particular, RIS-assisted transmissions are implemented by prenegotiation and a multidimension reservation (MDR) scheme. Based on this, we investigate RIS-assisted single-channel multiuser (SCMU) communications. Wherein the RIS regarded as a whole unity can be reserved by one user to support the multiple data transmissions, thus achieving high efficient RIS-assisted connections at the user. Moreover, under frequency-selective channels, implementing the MDR scheme on the RIS group division, RIS-assisted multichannel multiuser (MCMU) communications are further explored to improve the service efficiency of the RIS and decrease the computation complexity. Besides, a Markov chain is built based on the proposed RIS-assisted MAC framework to analyze the system performance of SCMU/MCMU. Then the optimization problem is formulated to maximize the overall system capacity of SCMU/MCMU with energy-efficient constraint. The performance evaluations demonstrate the feasibility and effectiveness of each.

The empowering unmanned aerial vehicles (UAVs) have been extensively used in providing intelligence such as target tracking. In our field experiments, a pretrained convolutional neural network (CNN) is deployed at UAV to identify a target (a vehicle) from the captured video frames and enable the UAV to keep tracking. However, this kind of visual target tracking demands a lot of computational resources due to the desired high inference accuracy and stringent delay requirement. This motivates us to consider offloading this type of deep learning (DL) tasks to a mobile-edge computing (MEC) server due to the limited computational resource and energy budget of the UAV and further improve the inference accuracy. Specifically, we propose a novel hierarchical DL tasks distribution framework, where the UAV is embedded with lower layers of the pretrained CNN model while the MEC server (MES) with rich computing resources will handle the higher layers of the CNN model. An optimization problem is formulated to minimize the weighted-sum cost, including the tracking delay and energy consumption introduced by communication and computing of UAVs while taking into account the quality of data (e.g., video frames) input to the DL model and the inference errors. Analytical results are obtained and insights are provided to understand the tradeoff between the weighted-sum cost and inference error rate in the proposed framework. Numerical results demonstrate the effectiveness of the proposed offloading framework.

The aerial-terrestrial communication system constitutes an efficient paradigm for supporting and complementing terrestrial communications. However, the benefits of such a system cannot be fully exploited, especially when the line-of-sight (LoS) transmissions are prone to severe deterioration due to complex propagation environments in urban areas. The emerging technology of reconfigurable intelligent surfaces (RISs) has recently become a potential solution to mitigate propagation-induced impairments and improve wireless network coverage. Motivated by these considerations, in this paper, we address the coverage and link performance problems of the aerial-terrestrial communication system by proposing an RIS-assisted transmission strategy. In particular, we design an adaptive RIS-assisted transmission protocol, in which the channel estimation, transmission strategy, and data transmission are independently implemented in a frame. On this basis, we formulate an RIS-assisted transmission strategy optimization problem as a mixed-integer non-linear program (MINLP) to maximize the overall system throughput. We then employ multi-task learning to speed up the solution to the problem. Benefiting from multi-task learning, the computation time is reduced by about four orders of magnitude. Numerical results show that the proposed RIS-assisted transmission protocol significantly improves the system throughput and reduces the transmit power.

Multi-access edge computing (MEC) has already shown great potential in enabling mobile devices to bear the computation-intensive applications by offloading some computing jobs to a nearby access point (AP) integrated with a MEC server (MES). However, due to the varying network conditions and limited computational resources of the MES, the offloading decisions taken by a mobile device and the computational resources allocated by the MES can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which may not be optimized with the lowest cost. In this paper, we propose a novel offloading framework for the multi-server MEC network where each AP is equipped with an MES assisting mobile users (MUs) in executing computation-intensive jobs via offloading. Specifically, we formulate the offloading decision problem as a multiclass classification problem and formulate the MES computational resource allocation problem as a regression problem. Then a multi-task learning based feedforward neural network (MTFNN) model is designed and trained to jointly optimize the offloading decision and computational resource allocation. Numerical results show that the proposed MTFNN outperforms the conventional optimization method in terms of inference accuracy and computational complexity.

Ambient backscatter communication enabling device-to-device (D2D) communications via the ambient radio frequency (RF) signal has revealed its numerous application potential in the Internet-of-Things (IoT) networks. However, the work on the link layer for the backscatter communication is in its infancy due to the constraints of ultralow power and cost in such a system, especially a channel access protocol in the backscatter communication system for IoT networks is rarely mentioned. In this article, a distributed multiple access control (MAC) protocol is presented, which allows multiple backscatter devices (BDs) to connect with each other by relying on the ambient RF signal. By combining an analog channel sensing strategy with the dual-backoff mechanism, each BD can switch among the transmission, receiving, and energy harvesting (EH) states. Specifically, each BD starts a randomly designated time for EH once the channel is sensed to be busy. As the timer expires, the BD ceases the EH and continues its sensing and backoff procedures. With the consideration of the false alarm and the miss detection problems occurring while sensing, an enhanced 3-D Markov model is built to analyze the saturation throughput performance of the proposed MAC protocol. Extensive simulations verify the analysis and demonstrate the advantage of the proposed backscatter MAC protocol.

In this article, we propose a novel framework of mobile edge computing (MEC)-based hierarchical machine learning (ML) tasks distribution for the Industrial Internet of Things. It is assumed that a batch of ML tasks, such as anomaly detection, need to be executed timely in an MEC setting, where the devices have limited computing capability while the MEC server (MES) has rich computing resources. Thus, a small ML model for the device and a deep ML model for the MES are pretrained offline using historical data, and then they are deployed accordingly. However, offloading tasks to the MES introduces communications delay. Thus, each device must decide the portion of the tasks to offload to minimize the processing delay. Since the delay and the error of data processing are incurred by communications and ML computing, a joint optimization problem is formulated to minimize the total delay subject to the ML model complexity and inference error rate, data quality, computing capability at the device and MES, and communications bandwidth. A closed-form solution is derived analytically and an optimal offloading strategy selection algorithm is proposed. Insights are provided to understand the tradeoff between communications and ML computing in offloading decisions, and the effects of key parameters in the proposed algorithm are investigated. The numerical results demonstrate the effectiveness of the proposed algorithm.

Long-term evolution (LTE) deployment in the unlicensed band has been a promising solution to handle the ever-increasing data traffic growth. However, spectrum sharing on unlicensed band poses a significant challenge regarding the interaction between LTE licensed assisted access (LAA) and Wi-Fi. In this paper, a radio access technology (RAT) heterogeneous network that consists of an LAA tier and a Wi-Fi tier is constructed, to achieve the coexistence and alleviate the intra-RAT and inter-RAT interference, a listen-and-talk (LAT) scheme is utilized in Wi-Fi while LAA adopts the listen-before-talk (LBT). By leveraging the full-duplex (FD) techniques in Wi-Fi, the collision can be avoided and the utilization of unlicensed spectrum can be improved. Furthermore, the sensing errors are derived based on the FD strategy and an enhanced Markov model is presented to analyze the performance of the heterogeneous network with consideration of the residual self-interference (RSI). The fairness between LAA and Wi-Fi is also investigated. At last, to ensure the performance of Wi-Fi when serious RSI exists, a switched MAC (S-MAC) that can adaptively switch between the FD mode and half-duplex (HD) mode is presented.

In this paper, we propose a distributed deep reinforcement learning (DRL) based medium access control (MAC) protocol, termed DRL-MAC, which is used to assist the backscatter communications for Internet-of-Things (IoT) networks. By leveraging the current Wi-Fi infrastructure, the backscatter communications can be reserved in advance to avoid the interference from Wi-Fi communications. In the proposed MAC protocol, the deep reinforcement learning is further introduced to learn the reserved information and make decisions such as 1) which backscatter device (TAG) will be serviced, and 2) the reservation step for the serviced TAG. In addition, the utility function is defined and the optimization problem is formulated to balance the backscatter communications and Wi-Fi communications. Moreover, a DRL algorithm is proposed to obtain the optimal strategy. The numerical results show the effectiveness of the proposed MAC for backscatter communications.

Multi-access edge computing (MEC) has already shown the potential in enabling mobile devices to bear the computation-intensive applications by offloading some tasks to a nearby access point (AP) integrated with a MEC server (MES). However, due to the varying network conditions and limited computation resources of the MES, the offloading decisions taken by a mobile device and the computational resources allocated by the MES may not be efficiently achieved with the lowest cost. In this paper, we propose a dynamic offloading framework for the MEC network, in which the uplink non-orthogonal multiple access (NOMA) is used to enable multiple devices to upload their tasks via the same frequency band. We formulate the offloading decision problem as a multiclass classification problem and formulate the MES computational resource allocation problem as a regression problem. Then a multi-task learning based feedforward neural network (MTFNN) model is designed to jointly optimize the offloading decision and computational resource allocation. Numerical results illustrate that the proposed MTFNN outperforms the conventional optimization method in terms of inference accuracy and computation complexity.

In this paper, a joint latency and energy minimization problem is considered for hierarchical machine learning tasks distribution (HMLTD) with mobile edge computing (MEC). Firstly, we propose a MEC based HMLTD framework enabling mobile devices embedded with shallow neural network (SNN) model to offload latency-sensitive computing-intensive tasks to a nearby MEC server (MES), which has a more powerful deep neural network (DNN) model. Then, we formulate the offloading strategy as a piecewise convex optimization problem to minimize the weighted-sum of latency and energy. A closed-form solution of the optimal tasks partition strategy is derived analytically for different scenarios, and then an optimal partial offloading strategy (OPOS) is proposed. As proof of concept, some insights are gained to demonstrate the key parameters affecting the task partition strategy. Numerical results are given to illustrate that the proposed offloading scheme outperforms the baseline scheme.

To address the problem of imaging instability when space-frequency (SF) time-reversal (TR) imaging algorithm chooses different ranks of signal subspaces in TR imaging for multiple targets, a modified SF-TR is proposed in this letter, termed W-SF-TR, in which the vectors of noise subspace are weighted to provide the imaging stability. The simulation results show that W-SF-TR significantly reduces the sensitivity of imaging to different ranks, and improves the image resolution as well.

Nowadays, an Internet-of-Things (IoT) connected system brings a tremendous paradigm shift into the medium access control (MAC) design. In this paper, we present a distributed MAC framework assisted by machine learning for the Heterogeneous IoT system, where the IoT devices coexist with the WiFi users in the unlicensed industrial, scientific, and medical (ISM) spectrum. Specifically, the superframe is divided into two phases: a rendezvous phase and a transmission phase. During the rendezvous phase, the gateway that is capable of machine learning predicts the number of WiFi users and the IoT devices by performing a triangular handshake on the primary channel. The prediction takes advantage of the deep neural network (DNN) model which is pretrained on our universal software radio peripheral (USRP2) testbed offline. The gateway allocates the frequency channels to the WiFi and IoT systems based on the inference results. Then, the IoT devices and WiFi users initiate data transmissions during the transmission phase. Furthermore, system throughput is analyzed and optimized in two typical scenarios, respectively. An optimized MAC framework is proposed to maximize the total system throughput by finding the key design parameters. The analytical and simulation results that are conducted using the ns-2 demonstrate the effectiveness of the proposed MAC framework.

The vision of the Internet-of-Things (IoT) networks calls for a large number of power constrained devices communicating with the gateway. To achieve the channel coordination in IoT, IEEE 802.15.4 standard has been considered as one of the most competitive technologies. However, the length of the Contention Access Period (CAP) of the superframe can hardly adapt to the variation of network traffic, so the performance of IoT is restricted. To resolve the problem, we propose a scalable MAC framework assisted by Machine Learning, called MML. With the implementation of machine learning algorithms such as Neural Network Predictor (NNP), the gateway can detect the number and type of devices from the overlapped signals, as demonstrated in our Universal Software Radio Peripheral (USRP2) testbed. Therefore, MML can dynamically adjust the CAP length based on the knowledge of the number of active devices and a stable throughput can be achieved. Moreover, the throughput of MML is analyzed, which is verified by conducting simulations using network simulator (ns-2.35). The analytical and simulation results demonstrate the superiority of the proposed MML.

A multi‐channel medium access control (MAC) is proposed for dense wireless ad‐hoc networks, called MSR‐MMAC, which aims to enhance throughput by introducing multi‐slot reservation into multiple channels environment. Based on the integration of carrier sense multiple access with collision avoidance and time division multiple access, MSR‐MMAC alternately implements contention‐based channel access and multiple reservation‐based data transmission on a certain frequency channel. Besides, the authors build an enhanced Markov model to analyse the throughput performance of the proposed MSR‐MMAC protocol, which is further validated by extensive simulations using ns‐2. Simulation results show that the proposed MSR‐MMAC protocol reduces the average collision probability and improves throughput significantly.