With the rapid evolution towards Beyond 5G and future 6G networks, multi-access edge computing (MEC)-enabled wireless networks are expected to support massive device connectivity, ultra-low latency, and high network capacity. However, meeting these stringent requirements in multi-server wireless multihop networks essentially requires the joint orchestration of server selection, multihop routing, and interference management. This paper develops a novel three-stage optimization scheme named broad learning system with Q-learning (BLSQ), consisting of a broad learning system-based server allocation stage, a signal-to-interference-plus-noise ratio-driven Q-learning-based multihop path selection stage, and a consensus transmit power control stage for adaptive interference mitigation. Furthermore, a consensus transmit power control mechanism is incorporated to adaptively adjust the transmit power of user devices, aiming to balance interference mitigation and throughput enhancement. The proposed scheme is particularly suitable for various mission-critical and dynamic scenarios, such as emergency communication in disaster-stricken areas, multihop data exchange between rescue teams and command centers, and flexible network deployment in large-scale events using unmanned aerial vehicles. Extensive simulation results demonstrate that the proposed BLSQ schemes outperforms existing related approaches in terms of network capacity, task completion time, interference management, and quality of servers, validating the superiority and robustness of our design for future MEC-enabled wireless networks.

Mobile Edge Caching (MEC) can potentially alleviate Internet transmission congestion by delivering content at the network edge. However, current MEC solutions suffer from low resource utilization efficiency and often fail to meet user Quality of Experience (QoE), primarily due to dynamic user requests and obsessive pursuit of direct caching hits. Given the prevalence of recommendation systems, users often lack precise requests when using recommendation-based applications like TikTok and Taobao, insted passively enjoying recommended content. In this paper, we introduce a recommendation-enabled MEC architecture to enhance resource utilization and QoE. We develop a recommendation-enabled soft caching model and formulate the optimization problem as maximizing joint system revenue. To address this, we propose an attention-assisted federated learning deep Q-network algorithm. We conduct the simulations by using the real-world MIND dataset. The results demonstrate that our proposed algorithm outperforms existing baselines, demonstrating its effectiveness in improving resource utilization and QoE.

Edge computing has promoted various applications driven by artificial intelligence (AI). However, upgrading AI models during system operation may change resource and performance features. Then, the service management controller (SMC) faces an unprecedented environmental condition and has limited prior knowledge, resulting in high probabilities of policy mismatches. With the proliferation of AI applications, it is an urgent necessity that SMCs can adapt to different conditions to ensure quality of service (QoS) and resource efficiency. Therefore, this paper studies the problem of dynamic edge AI service adaptation and formulates it as a multi-task scenario adaptation problem. After that, we proposed an approach based on off-policy meta-reinforcement learning and digital twin (DT) technology. The DT system emulates a set of encountered conditions, and a meta-policy is obtained by interacting with these DTs. The executed policy is initialized as the meta-policy once AI models are upgraded. Then, it adapts to new service conditions by drawing salient information from limited transition contexts collected from a newly encountered environmental condition. Simulation results reveal that our approach can optimize QoS and adapt to different service situations.

Addressing the pressing requirement for dynamic and intelligent allocation of slicing resources, the dynamic provisioning of resources based on traffic predictions has emerged. Although this method favours proactive scheduling of network slices, more complexities are introduced by the prediction uncertainty. In addition, because multi-tenant networks are always changing in terms of technology and business model, profit-aware network slicing is becoming an important topic of study in the field of resource provision. This paper focuses on profit-aware slicing resource provisioning amid traffic uncertainty in multi-tenancy flexible Ethernet over wavelength division multiplexing networks. Specifically, we develop a profit model for multi-tenant network slicing, accounting for the impact of network prediction uncertainty, and formulate the problem as maximizing the profit of users primarily. To solve this problem, we propose a profit-aware resource provisioning approach that first checks if the slice requests are made by pruning algorithms and then determines the service relationship between slices and tenants by matching games. Simulation results demonstrate the superiority of the proposed algorithm over benchmarks in terms of user profit, total benefit, and denial ratio of service.

Edge computing (EC) is a promising technology to support a variety of performance-sensitive intelligent applications, especially in the Industrial Internet of Things (IIoT). The sensing-data-driven applications whose task processing requires sensing data from various sensors are typical applications in IIoT systems. The placement of caching and computing edge service functions for such applications is vital to ensure system performance and resource utilization in EC-enabled IIoT systems. Therefore, this paper investigates the joint caching and computing edge service placement (JCCESP) for multiple sensing-data-driven IIoT applications in an EC-enabled IIoT system. The JCCESP problem is formulated as a Markov Decision Process (MDP). Then, a deep reinforcement learning (DRL)-based approach is proposed to address the challenges like limited prior knowledge and the heterogeneity of such IIoT systems. Under such an approach, the policy network of the DRL agent is constructed based on an encoder-decoder model to tackle various applications requiring different numbers of service functions. A REINFORCE-based method is further employed to train the policy network. Simulation results indicate that the performances achieved by our proposed approach can converge after training and are significantly superior to benchmarks.

Edge computing (EC) extends the ability of cloud computing to the network edge to support diverse resource-sensitive and performance-sensitive IoT applications. However, due to the limited capacity of edge servers (ESs) and the dynamic computing requirements, the system needs to dynamically update the task allocation policy according to real-time system states. Service migration is essential to ensure service continuity when implementing dynamic task allocation. Therefore, this article investigates the long-term dynamic task allocation and service migration (DTASM) problem in edge-cloud IoT systems where users’ computing requirements and mobility change over time. The DTASM problem is formulated to achieve the long-term performance of minimizing the load forwarded to the cloud while fulfilling the seamless migration constraint and the latency constraint at each time of implementing the DTASM decision. First, the DTASM problem is divided into two subproblems: 1) the user selection problem on each ES and 2) the system task allocation problem. Then, the DTASM problem is formulated as a Markov decision process (MDP) and an approach based on deep reinforcement learning (DRL) is proposed. To tackle the challenge of vast discrete action spaces for DTASM task allocation in the system with a mass of IoT users, a training architecture based on the twin-delayed deep deterministic policy gradient (DDPG) is employed. Meanwhile, each action is divided into a differentiable action for policy training and one mapped action for implementation in the IoT system. Simulation results demonstrate that the proposed DRL-based approach obtains the long-term optimal system performance compared to other benchmarks while satisfying seamless service migration.

By placing edge service functions in proximity to IoT facilities, edge computing can satisfy various IoT applications’ resource and latency requirements. Sensing-data-driven IoT applications are prevalent in IoT systems, and their task processing relies on sensing data from sensors. Therefore, to ensure the Quality of Service (QoS) of such applications in an edge-enabled IoT system, dedicated caching functions (CFs) are required to cache necessary sensing data. This article considers an edge-enabled IoT system and investigates the joint caching and computing service placement (JCCSP) problem for sensing-data-driven IoT applications. Then, deep reinforcement learning (DRL) is exploited to address the problem since it can adapt to a heterogeneous system with limited prior knowledge. In the proposed DRL-based approaches, a policy network based on the encoder–decoder model is constructed to address the issue of varying sizes of JCCSP states and actions caused by different numbers of CFs related to applications. Then, an on-policy REINFORCE-based method is adopted to train the policy network. After that an off-policy training method based on the twin-delayed (TD) deep deterministic policy gradient (DDPG) is proposed to enhance the training efficiency and experience utilization. In the proposed DDPG-based method, a weight-averaged twin-$Q$-delayed (WATQD) algorithm is introduced to reduce the bias of$Q$-value estimation. Simulation results show that our proposed DRL-based JCCSP approaches can achieve converged performance that is significantly superior to benchmarks. Moreover, compared with the original TD method, the proposed WATQD method can significantly improve the training stability.

In In-band Full-duplex (IBFD) wireless networks, the RTS/CTS mechanism is unable to establish an asymmetric dual link and to recognize the transmission mode of communication nodes to capture more opportunities of IBFD transmission, which limits total network throughput. In this paper, we propose a novel distributed IBFD MAC mechanism to establish symmetric/asymmetric dual link in wireless networks. Here we fully consider the two modes of asymmetric dual transmission. By medium access, the neighbors of communication nodes can clearly know network transmission status, which will provide extra opportunities of asymmetric IBFD dual communication. Finally, we develop a Markov model to characterize the non-saturation throughput of our proposed mechanism in IBFD wireless networks. The numerical results show that the throughput of IBFD network with our scheme nearly doubles that of HD network with RTS/CTS. Moreover, the non-saturation degree of the network has little influence on the throughput of our mechanism.