IET Communications最新文献

Edge caching at access points (APs) is a promising approach to alleviate the fronthaul burden and reduce user-perceived delay. However, the edge caching placement is still challenging considering the coupling between caching and AP-user association, limited fronthaul capacity, and multi-AP deployment in the cell-free (CF) massive MIMO systems. To this end, the authors establish a framework for the joint problem of AP-user association and caching to minimize the content delivery delay which considers both cooperation delivery delay and radio access delay. It is an integer nonlinear programming problem and NP-hard. The optimization problem is first decomposed into an AP-user association sub-problem and a caching placement sub-problem to address this problem. A two-stage matching algorithm is further proposed to achieve AP-user association and a modified genetic algorithm to determine caching placement. A computationally efficient iterative algorithm is developed to solve the joint optimization problem. Finally, the global convergence and computational complexity of the proposed strategy are analyzed theoretically. Simulation results reveal that the proposed strategy can achieve better delivery delay performance than benchmark schemes.

This article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual-queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density-based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV-assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances.

With the continuous development of wireless communication and artificial intelligence technology, Internet of Things (IoT) technology has made great progress. Deep learning methods are currently used in IoT technology, but deep neural networks (DNNs) are notoriously susceptible to adversarial examples, and subtle pixel changes to images can result in incorrect recognition results from DNNs. In the real-world application, the patches generated by the recent physical attack methods are larger or less realistic and easily detectable. To address this problem, a Generative Adversarial Network based on Visual attention model and Style transfer network (GAN-VS) is proposed, which reduces the patch area and makes the patch more natural and less noticeable. A visual attention model combined with generative adversarial network is introduced to detect the critical regions of image recognition, and only generate patches within the critical regions to reduce patch area and improve attack efficiency. For any type of seed patch, an adversarial patch can be generated with a high degree of stylistic and content similarity to the attacked image by generative adversarial network and style transfer network. Experimental evaluation shows that the proposed GAN-VS has good camouflage and outperforms state-of-the-art adversarial patch attack methods.

Time-sensitive networking (TSN) is considered one of the most promising solutions to address real-time scheduling in in-vehicle network due to its capabilities for providing deterministic service. The TSN working group proposed various traffic shaping mechanisms, while deterministic scheduling of hybrid traffic is still not effectively solved since the traffic requirements are difficult to satisfy by standalone or combined mechanisms with fixed time slot divisions. This article presents a time-aware multi-cyclicqueuing and forwarding scheduling model, that integrates the no-wait enabled time-aware shaper and multi-cyclic queuing and forwarding shaping models. Then, a scheduling solution, dubbed “TSN scheduling optimizer” (TSO) is proposed that combines optimization methods and incremental techniques. TSO aims to balance the load to maximize flow schedulability while guaranteeing the service requirements of hybrid traffic. Simulation evaluations through OMNeT++ provide a performance assessment of this proposed scheduling model, which can satisfy multiple types of traffic transmission requirements. Furthermore, TSO is compared with other baseline scheduling solutions, and TSO shows efficacy regarding execution time and schedulability.

This paper proposes a novel machine learning (ML) assisted low-latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block-length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look-up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look-up table using signal-to-noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k-nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low-latency communications scenarios, where short block-length LDPC codes are utilized. On the other hand, given the short block-length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML-LDPC-AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML-LDPC-AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER.

Mobile networks are expanding quickly as a result of significant advancements in wireless technologies and solutions, especially with the recent introduction of the Fifth Generation New Radio. The growth in mobile networks requires the installation of massive numbers of base stations that bring concerns about increasing overall electromagnetic field (EMF) radiation exposure levels. The International Commission on Non-Ionizing Radiation Protection (ICNIRP) has published guidelines that have been adopted by many regulators in many countries to control the overall radiation emitted from EMF transmitters. This paper studies the compliance boundary for a single site operating with multiple technologies including from the second generation (2G) to 5G colocated in the same site. The analysis is performed using a typical site configuration setup for the boundary calculations in the form of the Compliance Distance (CD). The calculation uses the power reduction factor and system load for more realistic results, and in situ measurements are conducted to validate the calculation's formula. The study also investigated the CD for four types of sites, macro, micro, small cell, and indoor sites. Additionally, the study analyzed the power densities (PDs) and total exposure ratio (TER) for the general public and occupational workers at each site. The results show that CD has shorter distances when the power factor is considered, and 5G makes the highest contribution to the TER at the CD in the main directions of the antenna.

As a key component of high-performance switches and routers, the packet forwarding engine (PFE) is mainly responsible for selecting the appropriate output port for tens of thousands of packets within an extremely short time frame. However, the performance of PFE is determined by the selected group membership algorithm. This paper puts forth a hybrid strategy–caching scalar-pair and vectors routing and forwarding (CSVRF), consisting of virtual output port bitmap caching (VOPBC) and fractional-N SVRF to address major multicast forwarding issues such as scalability by using content addressable memory. In CSVRF, a virtual output port bitmap cache is introduced, which includes the most popular combinations of output port bitmap and divides the big scalar-pair into N sub-groups to achieve parallel compute and the reusability of less bit-length prime. The results demonstrate that the memory space and the forwarding latency are effectively reduced compared with previous work. In space efficiency, it only required 10% memory space compared with the original SVRF/fractional-N SVRF, decreased 10% memory usage compared with pure VOPBC and nearly improved 1 to 4 orders of magnitude of packet processing time compared with the original SVRF and the fractional-N SVRF respectively.

Mobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience for the user. However, it is difficult to obtain the optimal migration policy in real time due to the huge state space. Considering delay-sensitive data-intensive applications run by mobile terminals with limited battery power, an efficient service migration policy should be able to make a good tradeoff among service cost, service delay and terminal energy consumption. Here, an online Monte Carlo-based service migration (MCSM) policy is proposed to minimize service cost under constraints of deadline and terminal energy consumption. A penalty mechanism is designed to update reward when partial or all constraints are not meet. State-action value estimation and policy improvement are triggered only on the completion of each episode. Each episode is traversed reversely to calculate the average cumulative reward so as to improve policy. Experimental results show that the proposed approach can improve service success ratio and reduce average service cost compared to the existing service migration policies.