With the evolution of 6G, the procurement of Virtual Network Functions (VNFs) in Space-Ground Integrated Networks (SGINs) faces a complex challenge arising from three conflicting requirements. Specifically, the system must design truthful mechanisms to manage strategic providers with private costs, adapt to highly dynamic network topologies that render static allocation heuristics obsolete, and uphold robust empirical business privacy without subjecting the network to the variance and economic distortion inherent to noise-based cryptographic protection methods. Existing solutions fail to address these interconnected constraints simultaneously. In this paper, we propose MARLA-TGN, a novel framework for dynamic and privacy-preserving VNF auctions that addresses these challenges. Specifically, we model the strategic providers as autonomous agents trained with a Multi-Agent Reinforcement Learning (MARL) algorithm. To ensure privacy and scalability, agents learn from a mean-field signal that we enhance with bid-price standard deviation to accurately capture market volatility. For the auctioneer, we design a truthful winner-determination heuristic that leverages a Temporal Graph Network (TGN) to compute a price-independent quality score for each bid, enabling a provably monotonic and efficient greedy allocation. Rigorous theoretical analysis shows that the proposed mechanism guarantees truthfulness, individual rationality, and computational efficiency. Extensive simulation results verify that MARLA-TGN significantly outperforms state-of-the-art (SOTA) benchmarks in economic efficiency, achieving near-optimal social cost (SC) while upholding its desired economic properties.

Owing to the on-demand deployment, low cost, and flexibility, unmanned aerial vehicles (UAVs) are capable of performing tasks such as data monitoring, collection, and sharing. However, the openness of UAV wireless networks makes data susceptible to security threats such as theft, tampering, and forgery during collaborative data sharing and mission execution. Additionally, the limited resources and high mobility of UAVs further exacerbate challenges related to data security and reliability. To address these issues, this paper proposes a blockchain-based secure data sharing scheme for cloud-assisted multi-UAV networks. Specifically, we leverage cloud-based infrastructure to undertake the storage of massive data, significantly offloading the computational and storage burdens on the UAVs. Simultaneously, blockchain is integrated to establish immutable and traceable trust for the shared data, ensuring strong security, integrity, and retrievability under the constraints of UAV resources. On this basis, a certificateless searchable encryption algorithm is employed to eliminates traditional certificates management overhead and enables lightweight, distributed, and efficient search based on predefined keywords. Furthermore, we introduce an access control list based on geofencing to specify the data sharing permissions of UAVs, strictly limiting the data access permissions within specific area, thereby reducing the misuse or abuse of data. This scheme is proven to achieve ciphertext and trapdoor indistinguishability against keyword guessing attacks. The performance analysis indicates that the efficiency advantages of the proposed scheme become increasingly prominent as the number of UAVs increases.

Sixth generation (6G) networks deploy unmanned aerial vehicles and mobile edge computing to provide collaborative computing and reliable connectivity for resource-limited mobile devices (MDs). However, due to the untrusted and broadcast nature of wireless transmission among communicating MDs and computing resource providers, ensuring the security of resource transactions will be challenging. Blockchain-based resource-sharing systems have been proposed to address security issues. However, these systems use existing consensus mechanisms like Proof-of-Work that consume massive amounts of system resources. In addressing this, some studies attempted to use single-agent deep reinforcement learning (DRL) in leader selection. Nevertheless, these solutions overlooked the intelligence and flexibility of blockchain configuration, and a single-point of failure can cause the system to fail. We propose a multiagent distributed deep deterministic policy gradient (MAD3PG)-assisted consensus mechanism for blockchain-based collaborative resource sharing to address these issues. First, we propose a stochastic game-based incentive-mechanism to encourage consensus nodes to participate in transaction validation. Then, we formulate the optimization problem of node selection and blockchain configuration as a Markov decision process and solve it with the MAD3PG algorithm. With MAD3PG, the agents select consensus nodes based on their experience and available resources and dynamically adjust blockchain settings. The simulation results show that MAD3PG outperforms the benchmarks in maximizing throughput and incentive while minimizing block production latency.

Multi-access edge computing (MEC)-based network slicing (MEC-NS) enables MEC network service providers (MEC-NSPs) to deploy autonomous virtual networks (slices) that deliver customized MEC services to edge Internet of Things devices (EIoTDs) with diverse quality-of-service (QoS) requirements, bringing flexibility to MEC resource management. However, developing an efficient slice-based computation task offloading and slice resource allocation (SCTOSRA) policy remains challenging due to constrained slice resources during slicing periods, evolving dynamics of the slice operating environment, and the difficulty of acquiring global information on connected EIoTDs. This paper proposes a novel adaptive and intelligent SCTOSRA scheme powered by personalized federated dueling double deep Q-learning (PerFedD3QL), which addresses these issues through three key innovations: (i) a dynamic regularization framework that enables robust adaptation across heterogeneous slice operating environments; (ii) an integrated knowledge distillation (KD) mechanism that mitigates non-IID data effects and curbs model drift; and (iii) a two-stage aggregation architecture combining parameter averaging and ensemble distillation to enhance model convergence and cross-slice generalization. PerFedD3QL constructs personalized local D3QL models at each slice and coordinates their training via federated learning to derive globally optimal SCTOSRA policies, which aim to reduce inference latency and energy consumption for connected EIoTDs while protecting data privacy and adapting to changing operational environment states of network slices over time. Simulation results demonstrate the effectiveness of the proposed PerFedD3QL-based SCTOSRA algorithm, which improves performance in reducing time delay and energy consumption compared to baseline methods while maintaining strong personalization and privacy preservation across varying slice scenarios.

Recent technological advancements have led to the emergence of intelligent cruise guidance systems tailored for ride-hailing platforms (RHPs), such as Uber and Didi Chuxing, offering potential solutions to issues like traffic congestion and vehicle emissions. However, they face challenges, such as passenger-driver matching, route, and price optimization, and ensuring safety and fairness. These challenges are exacerbated by heterogeneity in data across multiple RHPs and privacy concerns related to data sharing. In this article, we propose a novel cruise guidance framework, FedCruise, which tackles these issues by using customized federated policy distillation with deep reinforcement learning (DRL). FedCruise enables collaborative model training across different RHPs without exchanging raw data, preserving privacy while addressing nonidentically distributed (non-IID) data challenges. FedCruise employs two models in each DRL agent: 1) a local teacher model and 2) a global student model, enabling bidirectional learning and achieving a global optimum. Our framework optimizes ride-sharing services and addresses data heterogeneity and privacy challenges. The results of our extensive simulations demonstrate the effectiveness and efficiency of FedCruise, proving its superiority in convergence rate, pickup orders, and driver income over other benchmarks.

The increasing demand for high-quality digital content has driven the growth of content exchange among mobile users (MUs) via device-to-device (D2D) communication. However, MUs often face challenges such as limited storage, low computational power, and short battery life, making it very difficult to meet the rising demands for content sharing. UAV-assisted D2D communication has emerged as a promising solution, integrating aerial and ground networks to enable efficient content caching and distribution while reducing latency and communication costs. However, the high mobility of MUs and increasing content size make it challenging to maintain stable communication links between MUs. This increases the complexity of content distribution, caching, and resource allocation in D2D content-sharing frameworks, resulting in higher latency, fluctuating resource demands, and lower QoS, ultimately affecting system efficiency and reliability. To address these challenges, we propose an adaptive and collaborative content-sharing and resource allocation framework integrating multi-agent twin delayed deep deterministic policy gradient (MATD3), blockchain, and a multiple-round distributed double auction (MDDA). MATD3 enables dynamic decision-making for content caching and resource allocation based on user behavior and mobility, while blockchain ensures secure, transparent, and tamper-proof content-sharing transactions. Furthermore, we propose the MDDA-based incentive scheme that allows content sellers, buyers, and the auctioneer to interact and establish optimal pricing strategies. This optimizes the content-sharing capability of MUs and edge devices, enhancing the cache hit rate and average system utility. Finally, the extensive simulation results demonstrate that our proposed scheme outperforms the benchmarks in enhancing cache hit rates, communication latency, and average system utility.

Active wideband millimeter-wave (MMW) systems enable safe and accurate 3-D microwave imaging for various applications, particularly in security screening, while intricate design and implementation are often employed to acquire wideband measurements. Potential imaging methods utilizing single-frequency measurements can be designed to alleviate the hardware requirements. However, applying conventional near-range imaging algorithms directly to single-frequency measurements leads to defocusing issues and degraded imaging results due to the lack of range resolution. Although previous studies have employed autofocusing methods to achieve focused results from defocused images, further enhancement is required for the imaging quality of extended targets. An innovative method based on the conditional diffusion model, named single-frequency autofocusing diffusion model (SFADiff), is proposed in this paper to address the defocusing problem in single-frequency imaging and reconstruct high-quality images effectively. The single-frequency autofocusing encoder network (SFAENet) is first developed to convert 3-D defocused images into 2-D encoded images. Subsequently, SFADiff utilizes these encoded images as conditions guiding image generation towards producing high-quality focused images. Comprehensive experiments are conducted to demonstrate that SFADiff exhibits exceptional performance in terms of image quality, offering a viable solution for achieving single-frequency imaging through autofocusing.

Resource sharing in radio access networks (RAN) can be conceptualized as a resource trading process between infrastructure providers (InPs) and multiple mobile virtual network operators (MVNO), where InPs lease essential network resources, such as spectrum and infrastructure, to MVNOs. Given the dynamic nature of RANs, deep reinforcement learning (DRL) is a more suitable approach to decision-making and resource optimization that ensures adaptive and efficient resource allocation strategies. In RAN slicing, DRL struggles due to imbalanced data distribution and reliance on high-quality training data. In addition, the trade-off between the global solution and individual agent goals can lead to oscillatory behavior, preventing convergence to an optimal solution. Therefore, we propose a collaborative intelligent resource trading framework with a graph-based digital twin (DT) for multiple InPs and MVNOs based on Federated DRL. First, we present a customized mutual policy distillation scheme for resource trading, where complex MVNO teacher policies are distilled into InP student models and vice versa. This mutual distillation encourages collaboration to achieve personalized resource trading decisions that reach the optimal local and global solution. Second, the DT uses a graph-based model to capture the dynamic interactions between InPs and MVNOs to improve resource-trade decisions. DT can accurately predict resource prices and demand from MVNO to provide high-quality training data. In addition, DT identifies the underlying patterns and trends through advanced analytics, enabling proactive resource allocation and pricing strategies. The simulation results and analysis confirm the effectiveness and robustness of the proposed framework to an unbalanced data distribution.

The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines.

Federated Deep Reinforcement Learning (FDRL) for Radio Access Network (RAN) Slicing offers a promising approach for optimizing resource allocation and network performance, while also preserving data privacy for multiple tenants. However, the inherently non-independent and identically distributed (non-IID) nature of data, stemming from the diverse services and unique characteristics of RAN slices, poses significant challenges. This heterogeneity can disrupt the standard assumptions FDRL makes, leading to model training inefficiencies and potentially suboptimal slicing decisions. Addressing this non-IID challenge is imperative to harness the full potential of FDRL in RAN slicing and to ensure seamless, adaptive, and efficient resource sharing among the tenants. Hence, we propose FeDistSlice, a federated distillation slicing framework wherein multiple decision agents collaborate in real time, optimizing resource allocation tailored to each tenant's specific characteristics. Motivated by collaborative intelligence, we introduced a customized mutual policy distillation (MPD) strategy to foster collaboration across multiple tenants. This innovation allows for the creating of personalized models tailored to each agent's unique requirements and context. Through MPD, these models can collaboratively learn and refine their policies by leveraging insights from other agents within the network. Simulation results show that FeDistSlice converges more effectively and achieves increased robustness to non-IID data.

The problem of designing a fast and accurate image reconstruction algorithm for 3-D near-field microwave imaging with sparse multiple-input-multiple-output (MIMO) arrays is discussed in this paper. Time-domain reconstruction algorithms including the backprojection algorithm and the Kirchhoff migration algorithm (KMA) have impractically high computational costs, and wavenumber domain algorithms including range migration algorithms (RMA) are challenging to develop for generic non-uniform ultrasparse MIMO arrays. Based on the fast factorized backprojection algorithms for synthetic aperture radar imaging, the fast factorized Kirchhoff migration algorithm (FFKMA) is proposed. Local spectrum properties of near-field radar images are modeled and exploited to ensure efficient sampling of the subimages in near-field MIMO settings. The proposed algorithm achieves imaging quality close to that of KMA and comparable computational efficiency of fast Fourier transform based RMAs, while still applicable to generic sparse MIMO arrays. Finally, the algorithm is verified with numerical simulations and experiments.

The millimeter-wave (MMW) 3-D imaging technology with mechanically scanned 1-D non-uniform sparse multiple-input-multiple-output (MIMO) arrays has been extensively researched due to its advantages in near-field applications. In this paper, an accurate and efficient spatial-frequency mixed domain imaging algorithm based on the elliptical bipolar cylindrical coordinates is proposed. The algorithm relaxes the array constraints present in the traditional frequency domain algorithms by decomposing the MIMO synthetic aperture radar (MIMO-SAR) system into bistatic SAR subsystems. Furthermore, an elliptical bipolar cylindrical coordinate system is proposed to address the space-variant characteristics of the spectrum in the bistatic SAR subsystems, thereby enabling efficient imaging of the subsystems using frequency domain techniques without further approximations. The final imaging result is obtained through coherent accumulation of the reconstructed subimages of all bistatic SAR subsystems in the spatial domain. Compared with existing algorithms, the proposed algorithm is applicable to MIMO-SAR systems with scanning 1-D non-uniform sparse MIMO arrays and large field-of-view while maintaining high computational efficiency and image quality. The performance of this algorithm is validated through numerical simulations and experimental results.

The emergence of network slicing as a flagship technology in 5G networks has not only enhanced network expansion and flexibility in resource management for service continuity, but also provided an avenue for establishing a viable market for resource sharing. To optimize the network's resource usage, stakeholders are encouraged to take pragmatic steps toward dynamic resource sharing. This paper designs a techno-economic model for the strategic interactions among multiple competing mobile virtual network operators (MVNOs) and their users in a trading marketplace. We formulate the dynamic pricing problem as a two-stage Stackelberg game, where the MVNOs are leaders, and the users are followers. In the first stage, the MVNOs compete to set their differentiated unit prices using a negotiation mechanism while considering system-level network load. Then, the users decide their purchasing volumes to match the prices of the MVNOs. We transform the game-based optimization problem into a stochastic Markov decision process (MDP) problem and propose a multi-agent deep Q-network (MADQN) method that obtains an optimal solution for the formulated game. Simulation results and analysis reveal that the proposed algorithm achieves convergence under the competitive pricing scheme (CPS) and independent pricing scheme (IPS) while enhancing MVNOs and users’ utilities at acceptable levels.

Radio Access Network (RAN) slicing enables resource sharing among multiple tenants and is an essential feature for next-generation mobile networks. Usually, a centralized controller aggregates available resource pools from multiple tenants to increase spectrum availability. In dynamic resource allocation, a tenant could behave strategically by adjusting its preferences based on perceived conditions to maximize its utility. Slice tenants may lie about the resources needed to gain greater utility. Such behavior could lead to poor resource utilization due to excess resources acquired by lying tenants and resource shortages because slice tenants choose not to purchase high-priced resources to save costs. Furthermore, in a scenario with many slice tenants, the centralized controller can become overwhelmed by the number of requests. This, in turn, can lead to slower response times and higher latency, resulting in poor resource utilization and QoS performance of slice tenants. Therefore, this paper proposes a peer-to-peer (P2P) approach to resource trading, where slice tenants communicate directly instead of relying on a centralized orchestrator. This design is motivated by the need for slice tenants to collaborate effectively. We model the interaction between tenants in a Stackelberg multi-leader and multi-follower game and solve the game with multi-agent deep reinforcement learning with an incentive-reward model to achieve the Stackelberg equilibrium. Furthermore, we propose a decentralized resource trading framework by integrating blockchain technology and federated deep reinforcement learning, enabling network tenants to perform inter-slice resource sharing securely. The simulation results show that the proposed mechanism has significant performance improvements over existing implementations.

Fifth-generation (5G) wireless networks enable gigabit-per-second data speeds, minimal latency, and reliable Internet of Things (IoT) connectivity. Thus, network slicing (NS) has gained enormous interest due to its ability to improve resource allocation. Due to the exponential growth of IoT data, it is difficult for the infrastructure providers (InPs) to determine the appropriate resource to allocate to mobile virtual network operators (MVNOs). In addition, MVNOs and IoT devices may use self-serving tactics that cause MVNOs to violate service level agreements (SLAs). Therefore, a fundamental problem in NS is capturing the interaction between MVNOs and IoT devices and ensuring efficient use of InP resources. This article proposes a two-tier resource allocation technique for NS involving a monopolistic market between an InP, multiple MVNOs, and IoT devices. First, we model the upper tier problem as a Markov decision problem (MDP) and design a federated deep reinforcement learning-based resource allocation algorithm (FDRL-RA) to explore the optimization solution. At the lower tier, we model a trading market between MVNOs and IoT devices as a two-stage Stackelberg game, where MVNOs set their unit prices and IoT devices set their purchase quantities. We use the backward induction method to analyze the proposed Stackelberg game under a competitive pricing scheme (CPS) and independent pricing scheme (IPS), which ensures high MVNOs’ profit and users’ utility at acceptable levels. Simulation results show that our proposed algorithm converges to the optimal solution and effectively maximizes utility under different pricing schemes while providing a high degree of privacy.

Network slicing (NS) is envisioned as an emerging paradigm for accommodating different virtual networks on a common physical infrastructure. Considering the integration of blockchain and NS, a secure decentralized spectrum trading platform can be established for autonomous radio access network (RAN) slicing. Moreover, the realization of proper incentive mechanisms for fair spectrum trading is crucial for effective RAN slicing. This paper proposes a novel hierarchical framework for blockchain-empowered spectrum trading for NS in RAN. Specifically, we deploy a consortium blockchain platform for spectrum trading among spectrum providers and buyers for slice creation, and autonomous slice adjustment. For slice creation, the spectrum providers are infrastructure providers (InPs) and buyers are mobile virtual network operators (MVNOs). Then, underloaded MVNOs with extra spectrum to spare, trade with overloaded MVNOs, for slice spectrum adjustment. For proper incentive maximization, we propose a three-stage Stackelberg game framework among InPs, seller MVNOs, and buyer MVNOs, for joint optimal pricing and demand prediction strategies. Then, a multi-agent deep reinforcement learning (MADRL) method is designed to achieve a Stackelberg equilibrium (SE). Security assessment and extensive simulation results confirm the security and efficacy of our proposed method in terms of players’ utility maximization and fairness, compared with other baselines.

We investigate the computing resource allocation in multi-access edge network slicing (NS) in the context of revenue and multi-access edge computing (MEC) resource management. The significant variety of slice resource utilization levels across slice tenants (i.e., Mobile Virtual Network Operators (MVNOs)) challenges MEC resource management in NS with MEC, leading to virtual machine resource (VMR) (i.e., computing resource) wastage or scarcity. As a result, for efficient MEC resource management, the infrastructure provider (InP) encourages dynamic resource sharing and trading (DRST) of unutilized slice VMR quotas. Nevertheless, cellular network security and privacy issues deter MVNOs from collaborating on effective DRST. The security characteristics inherent in blockchain have recently gained much interest for secure resource trading. Thus, this paper proposes a unique hierarchical blockchain-based inter-slice computing resource trading (ISCRT) scheme for peer-to-peer (P2P) MVNOs in an autonomous multi-sliced MEC-based 5G network. For secure ISCRT transactions, a consortium blockchain network with hyperledger smart contracts (SC) is designed. We model the demand and pricing problems of buyer and seller MVNOs for the unutilized VMRs using a two-stage Stackelberg game. Then, to obtain the Stackelberg equilibrium (SE), an enhanced dueling double deep Q-network (D3QN) algorithm is proposed, which intelligently determines the optimal demand and pricing policies of MVNOs for the unutilized VMRs during ISCRT transactions at negotiation intervals. Simulation analysis shows that the proposed enhanced D3QN algorithm outperforms benchmark schemes in terms of the MVNO slice-level satisfaction and VMR utilization while reducing double-spending attacks in ISCRT settings by 16% and increasing both players’ utility.

The emergence of new business and technological models for urban-related transportation has revealed the need for transportation network companies (TNCs). Most research works on TNCs optimize the interests of drivers and passengers, and the operator assuming vehicle resources remain unchanged, but ignore the optimization of resource utilization and satisfaction from the perspective of flexible and controllable vehicle resources. In fact, the load of the scene is variable in time, which necessitates the flexible control of resources. Drivers wish to effectively utilize their vehicle resources to maximize profits. Passengers desire to spend minimum time waiting and the platform cares about the commission they can accrue from successful trips. In this article, we propose an adaptive intelligent cruise guidance and vehicle resource management model to balance vehicle resource utilization and request success rate, while improving platform revenue. We propose an advanced deep reinforcement learning (DRL) method to autonomously learn the statuses and guide the vehicles to hotspot areas where they can pick orders. We assume the number of online vehicles in the scene is flexible and the learning agent can autonomously change the number of online vehicles in the system according to the real-time load to improve effective vehicle resource utilization. An adaptive reward mechanism is enforced to control the importance of vehicle resource utilization and request success rate at decision steps. The simulation results and analysis reveal that our proposed DRL-based scheme balances vehicle resource utilization and request success rate at acceptable levels while improving the platform revenue, compared with other baseline algorithms.

As the third generation of neural networks, spiking neural networks (SNNs) have gained much attention recently because of their high energy efficiency on neuromorphic hardware. However, training deep SNNs requires many labeled data that are expensive to obtain in real-world applications, as traditional artificial neural networks (ANNs). In order to address this issue, transfer learning has been proposed and widely used in traditional ANNs, but it has limited use in SNNs. In this article, we propose an effective transfer learning framework for deep SNNs based on the domain in-variance representation. Specifically, we analyze the rationality of centered kernel alignment (CKA) as a domain distance measurement relative to maximum mean discrepancy (MMD) in deep SNNs. In addition, we study the feature transferability across different layers by testing on the Office-31, Office-Caltech-10, and PACS datasets. The experimental results demonstrate the transferability of SNNs and show the effectiveness of the proposed transfer learning framework by using CKA in SNNs.

The advent of radio access network (RAN) slicing is envisioned as a new paradigm for accommodating different virtualized networks on a single infrastructure in 5G and beyond. Consequently, infrastructure providers (InPs) desire virtualized networks to share their subleased resources for effective resource management. Nonetheless, security and privacy challenges in the wireless network deter operators from collaborating with one another for resource trading. Lately, blockchain technology has received overwhelming attention for secure resource trading thanks to its security features. This paper proposes a novel hierarchical framework for blockchain-based resource trading among peer-to-peer (P2P) mobile virtual network operators (MVNOs), for autonomous resource slicing in 5G RAN. Specifically, a consortium blockchain network that supports hyperledger smart contract (SC) is deployed to set up secure resource trading among seller and buyer MVNOs. With the aim of designing a fair incentive mechanism, we model the pricing and demand problem of the seller and buyers as a two-stage Stackelberg game, where the seller MVNO is the leader and buyer MVNOs are followers. To achieve a Stackelberg equilibrium (SE) for the formulated game, a dueling deep Q-network (Dueling DQN) scheme is designed to achieve optimal pricing and demand policies for autonomous resource allocation at negotiation interval. Comprehensive simulation results analysis prove that the proposed scheme reduces double spending attacks by 12% in resource trading settings, and maximizes the utilities of players. The proposed scheme also outperforms deep Q-Network (DQN), Q-learning (QL) and greedy algorithm (GA), in terms of slice and system level satisfaction and resource utilization.

In the fifth-generation (5G) wireless networks, Edge-Internet-of-Things (EIoT) devices are envisioned to generate huge amounts of data. Due to the limitation of computation capacity and battery life of devices, all tasks cannot be processed by these devices. However, mobile-edge computing (MEC) is a very promising solution enabling offloading of tasks to nearby MEC servers to improve quality of service. Also, during emergency situations in areas where network failure exists, unmanned aerial vehicles (UAVs) can be deployed to restore the network by acting as Aerial Base Stations and computational nodes for the edge network. In this article, we consider a central network controller who trains observations and broadcasts the trained data to a multi-UAV cluster network. Each UAV cluster head acts as an agent and autonomously allocates resources to EIoT devices in a decentralized fashion. We propose model-free deep reinforcement learning (DRL)-based collaborative computation offloading and resource allocation (CCORA-DRL) scheme in an aerial to ground (A2G) network for emergency situations, which can control the continuous action space. Each agent learns efficient computation offloading policies independently in the network and checks the statuses of the UAVs through Jain’s Fairness index. The objective is minimizing task execution delay and energy consumption and acquiring an efficient solution by adaptive learning from the dynamic A2G network. Simulation results reveal that our scheme through deep deterministic policy gradient, effectively learns the optimal policy, outperforming A3C, deep$Q$-network and greedy-based offloading for local computation in stochastic dynamic environments.

The Internet of Things (IoT) edge network has connected lots of heterogeneous smart devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging applications. Limited computational capacity and energy availability have been major factors hindering the performance of edge user equipment (UE) and IoT devices in IoT edge networks. Besides, the edge base station (BS) with the computation server is allowed massive traffic and is vulnerable to disasters. The UAV is a promising technology that provides aerial base stations (ABSs) to assist the edge network in enhancing the ground network performance, extending network coverage, and offloading computationally intensive tasks from UEs or IoT devices. In this paper, we deploy a clustered multi-UAV to provide computing task offloading and resource allocation services to IoT devices. We propose a multi-agent deep reinforcement learning (MADRL)-based approach to minimize the overall network computation cost while ensuring the quality of service (QoS) requirements of IoT devices or UEs in the IoT network. We formulate our problem as a natural extension of the Markov decision process (MDP) concerning stochastic game, to minimize the long-term computation cost in terms of energy and delay. We consider the stochastic time-varying UAVs’ channel strength and dynamic resource requests to obtain optimal resource allocation policies and computation offloading in aerial to ground (A2G) network infrastructure. Simulation results show that our proposed MADRL method reduces the average costs by 38.643%, and 55.621% and increases the reward by 58.289% and 85.289% compared with the different single agent DRL and heuristic schemes, respectively.

The introduction of 5G will see exponential growth in the amount of data generated in mobile networks. This huge growth in data volume will put great pressure on not only the wireless access network but also the backhaul. In-network caching as a key component of 5G targets faster download speeds and reduction in latency through efficient content placement to avoid contents being transmitted repeatedly. In addition, the reduction in latency will require an effective resource allocation scheme to improve radio resource utilization. This paper investigates the problem of delay-aware content distribution in a multi-tenant network. We propose a content placement scheme to minimize the average visiting time of all users and a novel heuristic graph-partitioning algorithm via cell clustering to maximize the user transmission rates. Finally, simulations are conducted to evaluate the proposed scheme with QoE satisfaction and resource utilization for multi-tenants.

The next generation network, 5G, is expected to provide service-oriented networks where different applications are served in isolation according to their own requirements. It is challenging to have an efficient common resource allocation mechanism for virtual networks (VNs) that have different objectives. In this work, we propose a dynamic reservation and deep reinforcement learning based autonomous virtual resource management. The infrastructure provider periodically reserves the unused resource to the VNs based on their ratio of minimum resource requirements. Then, the VNs autonomously control their resource allocation by using deep reinforcement learning based on the average quality of service utility and resource utilization of their users. With the defined design pattern in this paper, virtual operators can customize their own utility function and objective function based on their own requirements. We simulate our work to show resource utilization and satisfaction of the VNs.

In this paper, we investigate dynamic network slicing strategies with mixed traffics and multiple base stations in virtualized radio access network. Considering versatile user's QoS requirements on delay and rate, low complexity resource slicing algorithms and shape-based heuristic algorithm for user resource customization are devised in order to improve resource utilization and QoS satisfaction. To validate the advantage, a system-level simulation based on the 5G air interface design is conducted. Results show performances of the proposed algorithm outperform existing benchmarks.

In this paper, we propose a content-aware cache resource slicing framework in software-defined information-centric virtualized wireless device-to-device (D2D) networks. In incorporating D2D communications, we attain the benefits of reuse and proximity gains, and by using the software defined network as a platform, we simplify the computational overhead. In this framework, we devise a cache allocation solution aimed at the latency-sensitive applications in the next-generation cellular networks. As the formulated problem is NP-hard, we evaluate four algorithms until we arrive at the most optimal solution. The heuristic solutions we provide are intuitive, yet efficient, and offer very low computational complexity.

In recent times, there has been an increase in the number of mobile devices to access a variety of services on radio access network, and the trend is expected to continue. In addition, ultra-low latency services require much bandwidth and often characterized by having extremely short delay constraints. Hence, satisfying required strong QoS requirement becomes challenging task. Existing scheduling methods to solve this problem exhibit very poor performance in terms of transmission latency. In this paper, a scheduling-based resource reservation mechanism is proposed for cloud UE. Unlike other methods, the proposed algorithm in this paper considers various traffic parameters to calculate the effective bandwidth of the flow and always gives priority to delay sensitive flows under a software defined network framework. Simulation results show that the proposed scheduling algorithm improves the average throughput of ultra-low latency flows.

The ultra-low latency transmission for emergency services needs an effective resources management scheme to deliver content in few milliseconds. The traditional solutions can't guarantee the ultra-low latency performance required by such traffic. In this paper, we propose an air-interface slice based dynamic resource reservation schema for a massive number of sensors with emergency flows in the context of the next generation cellular networks. The proposed schema allows ultra-low latency flows to be transported by guaranteed-rate radio link connection with a content name as identifier and it achieves air-interface latency in few milliseconds. The dynamic bit-map update, silence probability and window-based re-transmission are introduced based on the Frame Slotted Aloha, which can schedules the delay-sensitive flows immediately from one or many groups of connected terminals. Furthermore, a probability theory based analytic model is provided and evaluated with Monte-Carlo simulation results.

To address the challenge of unprecedented growth in mobile data traffic, ultra-dense network deployment is a cost efficient solution to offload the traffic over some small cells. The overlapped coverage areas of small cells create more than one candidate access points for one mobile station. Signal strength based user association in IEEE 802.11 results in a significantly unbalanced load distribution among access points. However, the bandwidth demand of each user actually differs vastly due to their different preferences on mobile applications. In this paper, we formulate a non-linear integer programming model for joint user association and user bandwidth demand guarantee problem. In this model, we try to maximize the system capacity and guarantee the effective bandwidth demand for each user by soft-association control. Finally, we evaluate the proposed algorithm performance for the edge users with dynamic and heterogeneous bandwidth demands. Simulation results show that the proposed soft-association control performs better than the distributed ones and improves the individual quality of user experience with a little price on system throughput.

Orthogonal frequency-division multiplexing (OFDM) is an ideal transmission technique for cognitive radio (CR) systems because of its flexible nature to support dynamic spectrum access. However, the out-of-band (OOB) radiation of OFDM signals from different CR users must be strictly controlled to protect licensed users operating in the adjacent frequency bands. In this paper, we propose a spectral precoding approach for multiple OFDM-based CR users to reduce OOB leakage and enhance spectrum compactness. By constructing individual precoders to render selected spectrum nulls, our approach suppresses the overall OOB radiation without sacrificing bit-error rate performance of CR users. The proposed approach also ensures user independence thus with low encoding and decoding complexities. Furthermore, our approach can improve bandwidth efficiency by carefully selecting notched frequencies. As a comprehensive application of the proposed approach, two simplified multiuser spectral precoding schemes are provided to reduce the computational complexity. Simulation results demonstrate that our spectral precoding schemes effectively limit OOB radiation and enable efficient spectrum sharing.

Orthogonal frequency-division multiplexing (OFDM) is an ideal transmission technique for dynamic spectrum access in cognitive radio (CR) systems. In this letter, we propose low-complexity spectrum shaping to enable fast decaying of power spectral sidelobes and enhance spectral compactness for OFDM-based CR systems. Based on a basic scheme mapping antipodal symbol pairs onto adjacent subcarriers, we present two modified schemes to further balance sidelobe suppression and system throughput. Compared with existing spectrum shaping schemes, ours exhibit their advantage of both simplicity and flexibility.

In this paper, we propose a cognitive interference management approach, called fractional frequency donation, to alleviate co-channel interference among selfish femtocells. In such networks, our approach allows each femtocell to access all available bands but requires good femtocells with high throughput to "donate" some bands to poor ones. When the donors and the corresponding donated bands are properly selected, both good performance on average- and 5% edge-throughputs can be achieved. Simulation results show that in femtocell networks, the proposed fractional frequency donation approach is more suitable than the conventional fractional frequency reuse ones.

Orthogonal frequency-division multiplexing (OFDM) is a candidate transmission technique for cognitive radio (CR) because of its flexible nature to support spectrum sharing. However, the out-of-band (OOB) radiation of OFDM signal from CR users must be strictly controlled to protect licensed users in adjacent bands. In this paper, we propose a spectral precoding scheme for multiple OFDM-based CR users to reduce OOB emission and enhance spectrum compactness. By constructing individual precoders to render selected spectrum nulls, our scheme suppresses the overall OOB radiation without sacrificing the bit-error rate performance of CR users. The proposed scheme ensures user independence with low encoding and decoding complexity. We also study the selection of notched frequencies to further increase the bandwidth efficiency and implementation flexibility. Simulation results demonstrate that our spectral precoding scheme effectively limits OOB radiation and enables efficient spectrum sharing.

Cognitive radio (CR) technology provides great flexibility in spectrum utilization with orthogonal frequency-division multiplexing (OFDM) as its candidate transmission technique. In this paper, we propose a simple spectrum shaping scheme for OFDM-based CRs to enhance spectral compactness and ensure bandwidth efficiency. By mapping antipodal symbol pairs onto adjacent subcarriers at the edges of the utilized spectrum band, our scheme enables fast power spectral sidelobe decaying without bringing much extra complexity to the transmitter or receiver. Sidelobe suppression and system throughput can be well balanced by adjusting the coding rate while power control on different sets of subcarriers will further deepen the sidelobes. The proposed scheme is also validated by our simulation.

The problem of locating a mobile terminal has received significant attention in the field of wireless communications. The wireless location problem is made difficult by nonsymmetric contamination of measured time of arrival (TOA) data caused by non-line-of-sight (NLOS) propagation. In this paper, a novel robust NLOS error mitigation algorithm based on minimum entropy estimation is proposed without prior statistics knowledge of NLOS propagation error.We compare the proposed algorithm with two additional ones, the normal least-squares estimator and the Huber estimator, through MATLAB simulation in different COST 259 channel environment. Results reveal that the proposed algorithm is more robust to NLOS error than the other two, although it is not always superior to the other two on location accuracy.