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.

Modern edge cloud platforms must efficiently deploy and route containerized microservice DAGs under strict latency and cost constraints, while adapting to rapidly changing workloads and infrastructure states. Deep Reinforcement Learning (DRL) schedulers adapt well to dynamics but often lack semantic awareness of service intent and task dependencies, resulting in suboptimal decisions in unseen scenarios. To overcome these limitations, we introduce a Retrieval-Augmented Generation-assisted DRL (RAG-DRL) framework that integrates a lightweight DRL agent with a graph-based RAG module powered by a partially frozen LLM. A dynamic memory graph encodes contextual information such as node resources, network latencies, and SLA feedback. The LLM retrieves relevant historical deployments and current service intents to generate soft placement plans and reward estimates, which guide the DRL agent. These priors accelerate convergence, improve generalization across diverse conditions, and ensure real-time responsiveness. Evaluations on a realistic urban-scale edge cloud testbed confirm that RAG-DRL significantly reduces SLA violations, end-to-end latency, and resource imbalance, outperforming modern container-based schedulers. Our framework converges faster, maintains latency below 65 ms on scale, limits SLA violations to 12% under heavy load, and achieves 90 % resource utilization with balanced distribution.

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.

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.

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.

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 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.

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.

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.

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.