This paper studies a green paradigm for the underlay coexistence of primary users (PUs) and secondary users (SUs) in energy harvesting cognitive radio networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the energy of PUs to enhance spectrum efficiency and green energy utilization. To lower the transmit powers of SUs, we employ multi-hop transmission with time division multiple access, by which SUs first harvest energy from the RF signals of PUs, and then, transmit data in the allocated time concurrently with PUs, all in the licensed spectrum. In this way, the available transmit energy of each SU mainly depends on the harvested energy before the turn to transmit, namely energy causality. Meanwhile, the transmit powers of SUs must be strictly controlled to protect PUs from harmful interference. Thus, subject to the energy causality constraint and the interference power constraint, we study the end-to-end throughput maximization problem for optimal time and power allocation. To solve this nonconvex problem, we first equivalently transform it into a convex optimization problem and then propose the joint optimal time and power allocation (JOTPA) algorithm that iteratively solves a series of feasibility problems until convergence. Extensive simulations evaluate the performance of EH-CRNs with JOTPA in three typical deployment scenarios and validate the superiority of JOTPA by making comparisons with two other resource allocation algorithms.

With the interdisciplinary advances of mobile communication and edge computing, massive heterogeneous tasks are accessing wireless networks and competing for the edge-end computing and communication resources. Digital twin (DT), which establishes the digital models of physical objects for simulation, analysis and optimization, provides a promising method for network scheduling and management. This paper proposes a DT-driven edge-end collaborative scheduling algorithm for heterogeneous tasks and heterogeneous computing/communication resources. Specifically, multiple end devices (EDs) cooperate with each other to accomplish a complex job, where each ED can offload individual task to multiple edge servers (ESs) for parallel computing. By fully considering deadline requirements of heterogeneous tasks, maximum computing capabilities of ESs and EDs, computing resource estimation deviations of DT, maximum transmit powers of EDs and tolerable peak interference powers to coexisting EDs, we formulate a job completion time minimization problem to jointly optimize the edge-end task division, transmit power control, computing resource type matching and allocation. To solve this non-convex problem, we first reformulate it by multi-agent Markov decision process, where a compound reward leveraging latency reward and deadline reward according to the task criticality is designed. Then, we propose a multi-agent deep reinforcement learning-based scheduling algorithm, where Actor-Critic framework with estimation and target networks is designed for policy and value iterations. Meanwhile, a step-by-step ϵ-greedy algorithm is proposed to balance exploration and exploitation, avoiding local optimal trap. Through offline centralized training by DT and online distributed execution by EDs, we realize edge-end collaborative computing for heterogeneous tasks. Experimental results demonstrate that, comparing with typical benchmark algorithms, the proposed algorithm converges with the highest reward and achieves the smallest job completion time, where the deadlines of heterogeneous tasks can be well satisfied respectively.

This letter studies the outage performance of multihop cognitive relay networks with energy harvesting in underlay paradigms, wherein the secondary users are powered by a dedicated power beacon (PB) and their transmit powers are subject to the harvested energy from PB and the interference constraint from the primary user. We derive the exact outage probability for Rayleigh block fading and prove that the outage probability is monotonically decreasing with respect to the transmit power of PB. Furthermore, we derive the asymptotic outage probability to study the outage saturation phenomenon and propose an iterative algorithm that jointly optimizes the transmit power of PB and the harvest-to-transmit ratio to approximate the minimum outage probability. Simulation results validate the theoretical results.

Time sensitive networks support deterministic schedules over Ethernet networks. Due to their high determinism, high reliability and high bandwidth, they have been considered as a good choice for the backbone network of industrial internet of things. In industrial applications, the backbone network connects multiple industrial field networks together and has to carry massive real-time packets. However, the off-the-shelf time-sensitive network (TSN) switches can deterministically schedule no more than 1024 real-time flows due to the limited number of schedule table entries. The excess real-time flows have to be delivered by best-effort services because the switch only supports the two scheduling services. The best-effort services can reduce average delay, but cannot guarantee the hard real-time constraints of industrial applications. To make the limited number of schedule table entries support more real-time flows, first, we relax scheduling rules to reduce the requirement for schedule table entries and formulate the process of transmitting packets as a satisfiability modulo theories (SMT) specification. Then, we divide the SMT specification into multiple optimization modulo theories (OMT) specifications so that the execution time of solvers can be reduced to an acceptable range. Second, we propose fast heuristic algorithms that combine schedule tables and packet injection control to eliminate scheduling conflicts. Finally, we conduct extensive evaluations. The evaluation results indicate that, compared to existing algorithms, our proposed algorithm requires only one-twentieth the number of schedule entries to schedule the same flow set.

Blockchain-based vehicular edge computing (VEC) is regarded as a promising computing paradigm that can enhance the computing capabilities of mobile vehicles while ensuring security during task offloading. However, the blockchain consensus for secure task offloading inevitably increases the communication and computation resource consumption. More importantly, the frequent handover among roadside units during the fast movement of vehicles also raises the communication cost for blockchain consensus. To address these issues, this article proposes intelligent secure task offloading and caching (ISTOC) scheme for VEC networks. Specifically, we first establish a digital twin-assisted VEC network that migrates the blockchain consensus process from the physical space to the cyber space, supporting the dynamic handover of vehicles. Correspondingly, we propose a lightweight blockchain scheme named diffused delegated Byzantine fault tolerance (d2BFT). Then, aiming at simultaneously reducing the task processing latency and improving the blockchain transaction throughput, we formulate the joint blockchain, communication, computation, and caching (B3C) optimization problem subject to task division, communication bandwidth, computing frequency, cache storage, task deadline, and blockchain stability. Due to the nonconvexity of B3C, we transform it into a Markov decision process, and propose a multiagent double actor-critic (MADAC) algorithm in light of the distributed characteristic of blockchain. Through offline training and online execution, we jointly optimize the task division, communication bandwidth, computing frequency and cache storage allocation, block size, and block generation interval for ISTOC. Experimental results show that the proposed MADAC-based ISTOC scheme can stably converge with a much higher reward than the benchmark schemes based on MADDPG, soft actor-critic, deep deterministic policy gradient, and TD3. The improvement of MADAC-ISTOC over SAC-ISTOC is more than 25.93%.

Industrial wireless networks (IWNs) are generally time-sensitive and interference-limited to guarantee real-time and reliability for critical industrial tasks. However, the highconcurrent access of heterogeneous industrial tasks poses great challenges for IWNs which are generally resource-limited. By employing multi-access edge computing (MEC) to enhance the computing capability, this paper proposes a multi-priority computation offloading scheme to realize end-edge orchestrated computing for time-sensitive and interference-limited IWNs based on deep reinforcement learning. Specifically, we study a general scenario that multiple industrial end devices offload tasks to multiple MEC-enhanced industrial base stations to cooperatively accomplish a complex industrial work. By fully considering different task deadlines, edge computing capabilities, maximum transmit power and peak co-channel interference power, we formulate an overall task delay minimization problem with respect to computing decisions, offloading ratios and transmit powers. Due to the non-convexity of the problem, we reformulate it by Markov decision process and design a priority-driven reward, where multiple priorities are assigned according to different deadline requirements. To approximate the optimum solution in the explosive state space, we employ the double and dueling architectures on the basis of deep Q-network (namely D3QN), and propose the D3QN-based multi-priority computation offloading scheme (D3QN-MPCOS). Extensive experiments are performed to validate the suitability and superiority of D3QN-MPCOS for IWNs, where eight benchmark schemes are compared. The results show that D3QN-MPCOS can converge with a higher reward and a smaller overall task delay than other schemes, and satisfy the deadline requirements of heterogeneous industrial tasks under different interference constraints.