Vehicular edge computing (VEC) involves deploying edge servers in vehicles moving along highways, to provide additional computing resources for low latency and high reliability in computational tasks. However, the dynamic nature of VEC environments, particularly regarding the task offloading of mobile vehicles acting as mobile edge servers, requires further research. To address this challenge, we propose a novel delay optimization algorithm based on deep reinforcement learning in VEC scenarios. It considers intelligent vehicles as mobile service nodes, collaborating with fixed roadside unit service nodes to form an integrated task offloading model. This model facilitates computational task offloading through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. It accounts for the sojourn time routing planning of vehicles and supports divisible tasks, thereby improving offloading efficiency. Then, an Asynchronous Advantage Actor-Critic (A3C) algorithm is used to achieve an effective computational offloading strategy. Simulation results demonstrate that the algorithm significantly outperforms existing methods in terms of the total system delay, while providing a more efficient and effective solution for task offloading in VEC scenarios.
In the wide-spreading netted ISAC systems, time-division pulse sequences have become essential for augmenting station diversity and facilitating collaborative netted sensing applications. This paper introduces an optimization model for pulse sequence (PS) optimization, designed to meet sensing requirements without compromising communication. The numerical results confirm the effectiveness of this method, enhancing surveillance capabilities while adhering to the intricate constraints of new radio (NR) communication requirements.
In this work, we propose a novel direction-of-arrival (DOA) estimation algorithm for sparse linear array via Vandermonde decomposition reconstruction. Unlike virtual array interpolation algorithms, the suggested method performs interpolation directly on the physical array, that is, Nyquist spatial filling. By utilizing the Vandermonde decomposition of the covariance matrix of a uniform linear array (ULA), this filling process is formulated as a structured matrix completion problem via matrix factorization, where the factor matrix is encouraged to exhibit a Vandermonde structure. Subsequently, an iterative approach is developed to solve the resultant problem using the alternating direction method of multipliers (ADMM). Finally, DOAs are retrieved from the reconstructed covariance matrix using subspace-based algorithms. Simulation results demonstrate the superiority of our algorithm over the existing methods.
This paper proposes a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted symbiotic radio (SR) system, in which the transmission functionality of STAR-RIS is used to transmit extra Internet of Things (IoT) data and the reflection functionality of STAR-RIS is adopted to enhance the downlink transmission. STAR-RIS utilizes ON-OFF keying for IoT data modulation, which allows the IoT receiver to decode data using a low-complexity energy detector. The transmit beamforming at the base station and the transmission and reflection beamforming at the STAR-RIS are jointly optimized to maximize the weighted sum-rate (WSR) of downlink users, subject to the received power constraint of IoT transmission. Considering the practical coupled phase shift constraint of STAR-RIS, an efficient penalty dual decomposition-based algorithm is proposed to solve the formulated highly non-convex problem. Based on the obtained solution, the optimal IoT receiver and the theoretical bit error rate are further derived. Simulation results verify the effectiveness of the proposed STAR-RIS-assisted SR system.
With the development of information technologies such as artificial intelligence, cloud computing, and the Internet of Things, Iot devices play an important role in people's lives. As a hardware carrier for carrying devices and transmitting sensitive information, Internet of Things devices may be out of control and leak sensitive information if they are attacked by networks. Therefore, they have urgent security needs in terms of confidential transmission of device data, dynamic command control, real-time picture sharing, and data collaborative analysis. In recent years, the frequent security incidents of Internet of Things devices have made people realize the importance of data security of Internet of Things devices. Aiming at the problems such as low key distribution efficiency and difficulty in distributed multi-party collaborative key distribution in cloud-edge collaborative scenario of existing key distribution methods used by Iot devices, this paper proposes a multi-party collaborative key distribution method based on attribute encryption. The method uses attribute encryption algorithm and national secret SM4 algorithm to achieve secure and efficient distribution of session keys in the environment of multi-user request data, ensure the data security of Iot devices, and achieve fine-grained control of user key distribution. Compared with the existing key distribution methods based on asymmetric cryptographic algorithms, this paper verifies that this method has certain advantages in the efficiency of key distribution, and verifies the effectiveness of this method through simulation experiments, and evaluates the performance.
