Mobile edge computing (MEC), integrated with directed acyclic graph (DAG) blockchain technology, has emerged as a promising paradigm for ensuring secure and efficient resource trading between IoT user equipment (UEs) and edge service providers (ESPs). However, due to the open and heterogeneous nature of MEC networks, ESPs are susceptible to malicious attacks, rendering resource trading information potentially unreliable. While DAG blockchains ensure the reliability of on-chain data, they fail to guarantee the trustworthiness of ESPs and cannot effectively incentivize their participation in resource trading and blockchain consensus. To address these challenges, we develop a trust-driven resource trading framework for DAG blockchain-aided MEC networks. In this framework, we first design an off-chain trust-driven resource pricing mechanism, in which the resource price set by each ESP is positively correlated with its trust value. In addition, we design an on-chain trust-driven consensus mechanism, wherein the on-chain security of transactions published by each ESP is positively associated with its trust level. To enable UEs to better evaluate the on-chain transaction security, we design a novel metric termed transaction security satisfaction and incorporate it into the utility function of UEs. Furthermore, we model the resource trading between UEs and ESPs as a multi-leader multi-follower Stackelberg game, and verify the existence and uniqueness of its equilibrium. To maximize the utilities of both ESPs and UEs, we propose a backward induction-based iterative algorithm to jointly optimize resource pricing, resource demand, and offloading strategy. Numerical simulations validate the effectiveness of our proposed scheme, demonstrating its superior performance compared with baseline schemes.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next generation communication systems. In this paper, a dual-mapping SVC (DM-SVC) based short packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme lies in mapping the transmitted information bits onto sparse vectors via block and single-element sparse mappings. The block sparse mapping pattern is able to concentrate the transmit power in a small number of non-zero blocks thus improving the decoding accuracy, while the single-element sparse mapping pattern ensures that the code length does not increase dramatically with the number of transmitted information bits. At the receiver, a two-stage decoding algorithm is proposed to sequentially identify non-zero block indexes and single-element non-zero indexes. Extensive simulation results verify that proposed DM-SVC scheme outperforms the existing SVC schemes in terms of block error rate and spectral efficiency.

Vehicular digital twins (DTs) create virtual representations of physical vehicles, enabling real-time data exchange to enhance intelligence and ensure safe driving. Reducing DT content delivery latency in infrastructure-deficient, sparsely populated areas is crucial. This paper develops a novel Satellite-UAV multi-path content delivery framework for data synchronization in vehicular DT applications. Satellites offer wide coverage but suffer from high latency, while UAVs provide rapid deployment and low-latency communication. The framework leverages these unique characteristics to facilitate simultaneous content downloading through multiple paths, thereby reducing latency. A Stackelberg game model is used to motivate effective resource allocation by UAVs. Given the typically private utility model of DTs, a learning-based algorithm is developed to determine optimal pricing strategies for UAVs. Simulation results demonstrate significant enhancements in UAV utility and reduced DT costs, meeting diverse service requirements.

Vehicular edge computing (VEC) is an emerging computing paradigm that alleviates the limitations of local computing resources for the Internet of Vehicles. However, the lack of trust among distributed nodes and ineffective incentive mechanisms discourage Roadside Units (RSUs) from providing resources. In addition, information asymmetry can lead to the clearing prices of resources failing to accurately reflect the actual value of resources. To address these issues, we propose a trustbased double auction framework in VEC networks. In this framework, to incentivize the RSUs with high trust to participate in resource trading and ensure the clearing prices accurately reflect the actual value of resources, we first design a trustbased resource pricing mechanism. In this mechanism, RSUs with higher trust can set higher resource prices and the clearing prices of resources are closer to the buyers’ bids. Then, we develop a trust-based double auction mechanism that incorporates the Edmonds-Karp algorithm for efficient buyer-seller matching. Considering the time-varying nature of the VEC networks, we employ deep reinforcement learning to optimize decision-making to maximize the social welfare. Finally, we demonstrate that the proposed double auction model satisfies key economic properties such as individual rationality and incentive compatibility. Simulation results validate that our proposed approach outperforms benchmark schemes in terms of social welfare.

Blockchain is considered as a promising technology to ensure the security of resource trading between the IoT user equipment (UEs) and the edge service providers (ESPs) in mobile edge computing (MEC) networks. However, blockchain cannot guarantee the trustworthiness of ESPs and cannot effectively incentivize ESPs to participate in resource trading and blockchain consensus. In addition, the high computational resource demands, energy consumption, and the limited transaction throughput of traditional blockchain pose challenges to IoT applications that require frequent micro transactions. To address these issues, we develop an integrated blockchain and MEC framework based on a directed acyclic graph (DAG) ledger to meet the demands of IoT applications. In this framework, we first model the resource trading between the UEs and the ESP as a multi-follower Stackelberg game. To incentivize the ESP to participate in resource trading and blockchain consensus, we design a trust based resource pricing mechanism, wherein the trust of ESP is evaluated by UEs and the ESP with higher trust can set a higher resource price. Additionally, to enable UEs to better assess the security of transactions on the DAG blockchain, we design a metric called transaction security satisfaction and adopt it as the revenue of UEs. Second, we verify the existence and uniqueness of the Stackelberg equilibrium. Furthermore, we propose a backward induction based iterative algorithm to optimize the resource pricing strategy for ESP and the resource demand strategy for UEs, while maximizing the utilities of both ESP and UEs. Numerical simulations demonstrate the effectiveness of our proposed scheme, showing its superiority over benchmark scheme in terms of transaction security satisfaction and the utilities of ESP and UEs.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next generation mobile communication systems. However, the storage burden of codebook and high decoding complexity limit its application in Internet of Things (loT) devices with constrained storage space and computational capabilities. To tackle this challenge, a semi-tensor SVC (ST-SVC)-based short-packet transmission scheme is proposed in this paper. The core idea behind ST-SVC is that it utilizes the semi-tensor product (STP) model in random spreading process, replacing the matrix multiplication model used in traditional SVC schemes. At the transmitter, a low-dimensional codebook is utilized to perform random spreading on a high-dimensional sparse vector carrying information bits. At the receiver, by exploiting the Kronecker structure induced by the STP model, a low-complexity parallel support identification algorithm is proposed for ST-SVC decoding. The proposed scheme breaks through the dimension matching condition required between the codebook matrix and high-dimensional sparse vector in traditional SVC schemes, allowing the loT devices to store an ultra-low-dimensional codebook, which significantly reduces storage overhead. Simulation results demonstrate that the proposed ST-SVC scheme can achieve a substantial reduction in both storage overhead and decoding latency compared to state-of-the-art SVC schemes, with only a slight performance loss in block error rate.

The integration of directed acyclic graph (DAG) blockchain and mobile edge computing (MEC) has emerged as a promising means to enable computation-intensive, delay-sensitive, and secure task execution in Internet of Things (IoT) applications. However, off-chain task execution results are not credible even if the results have been recorded on the chain, since blockchain cannot extend the trust of on-chain data to off-chain. To make the off-chain and on-chain trust consistent, we first develop a trusted MEC (T-MEC) framework by employing a DAG blockchain-aided decentralized trust management (DAG-DTM) mechanism. Specifically, DAG-DTM evaluates the off-chain trust of edge nodes according to the quality of task execution results, and the trust can be further verified off the chain by any edge node under the same trust management rule. Moreover, the approval time for recording the execution result of the edge node on the chain is positively correlated with the verified off-chain trust, which can further promote on-chain transaction security of trusted edge node. Second, we jointly optimize the bandwidth and computation resource allocation to minimize the system latency that consists of off-chain task execution delay and on-chain transaction confirmation delay. Numerical results compare system latency and security performance between the optimized T-MEC and the benchmark schemes. In particular, the optimized T-MEC can achieve a 33.12% gain of computation delay and a 10.19% gain of system latency at an affordable cost of transaction confirmation delay (i.e., 3.21%) over T-MEC, while meeting the requirements of off-chain task execution latency and on-chain transaction security simultaneously.

In this study, a resource allocation problem considering physical layer security and power consumption for cognitive relay networks is studied. In a secondary network, the ratio of secret rate to power consumption is defined as a secret rate per watt (SRW). Fixed circuit power, dynamic circuit power, and transmit power are all considered in the power consumption model. Under the constraints of the maximum total transmit power of secondary user transmitters and relay nodes, minimum secret rate requirement of secondary user receiver and tolerable interference threshold of each primary user receiver, the authors propose a SRW maximisation algorithm to maximise SRW. The resource allocation problem is formulated as a non‐linear fractional programming and it is transformed into an equivalent subtraction based on the Dinkelbach method. They incorporate the non‐convex constraints into the objective function to convert the feasible region into a convex set and achieve an optimal resource allocation scheme with the nested loop iteration algorithm through the Lagrange dual theory and the difference of convex programming. By the Dinkelbach method and the nested loop iteration algorithm, the optimal SRW is obtained. Simulation results demonstrate the effectiveness and the out‐performance of the proposed algorithm comparing with other algorithms.

Probability density function (PDF) of the modulating retro‐reflector (MRR) turbulence fading channels is crucial for the performance analysis of MRR communication systems. In this study, closed‐form expression for the PDF of normalised MRR free‐space optical (FSO) turbulence fading coefficient is obtained first. Moreover, it is applied to the evaluations of the closed‐form expressions for average capacity and outage probability of MRR FSO links. The effects of the parameters such as atmospheric turbulence conditions, communication distance, receiver aperture diameter and the average signal‐to‐noise ratio on the performance of MRR FSO links are discussed. Results show that in order to achieve successful MRR FSO communication, communication systems employing MRRs are suitable for short‐distance communication in the case of low transmission power, besides quite high transmission power and large receiver aperture diameter are required simultaneously by long‐distance MRR FSO communication.

In this study, the authors study the robust resource allocation problem for orthogonal frequency division multiplexing‐based cooperative cognitive radio networks (CRNs) with decode and forward protocol and consideration of imperfect channel state information. The objective is to maximise the capacity of the cooperative CRN, while the interference to primary user receiver is below a predefined interference threshold and the transmit power of cognitive source and each relay is kept within their power budgets. Considering all possible channel uncertainties, they propose a heuristic robust relay selection scheme and formulate robust power allocation as a semi‐infinite programming (SIP). By the worst‐case approach, the SIP problem is converted into a convex optimisation problem and solved by the Lagrange dual decomposition method. They also analyse feasible regions of the constraints, convergence behaviour and computational complexity of their proposed robust algorithm. Simulation results show the impact of channel uncertainties and the outperformance of the proposed algorithm by comparing with non‐robust algorithms.

In this paper, we study the robust relay selection and power allocation problems for orthogonal frequency division multiplexing (OFDM) based cooperative cognitive radio networks (CRNs) with channel uncertainties. The objective is to maximize the capacity of the cooperative CRN, which is subject to the interference threshold constraints of primary users (PUs) and the total transmit power limitation of secondary user (SU) and relays. We describe all possible channel uncertainties with ellipsoid set and interval set. The robust relay selection and power allocation problems are formulated as semi-infinite programming (SIP) problems, respectively. We convert the SIP problems into their equivalent convex optimization problems with the worst-case approach, which can be solved by the Lagrange dual decomposition method. Simulation results show that the proposed robust relay selection and power allocation algorithm can strictly guarantee the quality of service of PUs under channel uncertainties.