Journal of Information and Intelligence最新文献

Integrated sensing and communication (ISAC) technology enhances the spectrum utilization of the system by interchanging the spectrum between communication and sensing, which has gained popularity in scenarios such as vehicle-to-everything (V2X). With the aim of providing more dependable services for vehicles in high-speed mobile scenarios, we propose a scheme based on sense-assisted polarisation coding. Specifically, the base station acquires the vehicle's positional information and channel strength parameters through the forward time slot echo information. This information informs the creation of the coding architecture for the following time slot. This approach not only optimizes resource consumption but also enhances system dependability. Our simulation results confirm that the introduced scheme displays a notable improvement in the bit error rate (BER) when compared to traditional communication frameworks, maintaining this advantage across both unimpeded and compromised channel conditions.

The combination of integrated sensing and communication (ISAC) with mobile edge computing (MEC) enhances the overall safety and efficiency for vehicle to everything (V2X) system. However, existing works have not considered the potential impacts on base station (BS) sensing performance when users offload their computational tasks via uplink. This could leave insufficient resources allocated to the sensing tasks, resulting in low sensing performance. To address this issue, we propose a cooperative power, bandwidth and computation resource allocation (RA) scheme in this paper, maximizing the overall utility of Cramér-Rao bound (CRB) for sensing accuracy, computation latency for processing sensing information, and communication and computation latency for computational tasks. To solve the RA problem, a twin delayed deep deterministic policy gradient (TD3) algorithm is adopted to explore and obtain the effective solution of the RA problem. Furthermore, we investigate the performance tradeoff between sensing accuracy and summation of communication latency and computation latency for computational tasks, as well as the relationship between computation latency for processing sensing information and that of computational tasks by numerical simulations. Simulation demonstrates that compared to other benchmark methods, TD3 achieves an average utility improvement of 97.11% and 27.90% in terms of the maximum summation of communication latency and computation latency for computational tasks and improves 3.60 and 1.04 times regarding the maximum computation latency for processing sensing information.

The integration of communications, sensing and computing (I-CSC) has significant applications in vehicular ad hoc networks (VANETs). A roadside unit (RSU) plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication. Due to the constraints of limited resources, RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method. However, direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue. In this paper, we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks. The renormalization method is compared with two deployment schemes: genetic algorithm (GA) and memetic framework-based optimal RSU deployment (MFRD), to verify the improvement of communication performance. Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.

In modern Wi-Fi systems, channel state information (CSI) serves as a foundational support for various sensing applications. Currently, existing CSI-based techniques exhibit limitations in terms of environmental adaptability. As such, optimizing the utilization of subcarrier CSI stands as a critical avenue for enhancing sensing performance. Within the OFDM communication framework, this work derives sensing outcomes for both detection and estimation by harnessing the CSI from every individual measured subcarrier, subsequently consolidating these outcomes. When contrasted against results derived from CSI based on specific extraction protocols or those obtained through weighted summation, the methodology introduced in this study offers substantial improvements in CSI-based detection and estimation performance. This approach not only underscores the significance but also serves as a robust exemplar for the comprehensive application of CSI.

As the phenomenon of an aging population gradually becomes common worldwide, the pressure on the elderly has seen a notable increase. To address this challenge, fall detection systems are important in ensuring the safety of the elderly population, particularly those living alone. Wi-Fi sensing, as a privacy-preserving method of perception, can be deployed indoors for detecting human activities such as falls, based on the reflective properties of electromagnetic waves. Signals generated by transmitters experience reflections from various objects within indoor environments, leading to distinct propagation paths. These signals eventually aggregate at the receiver, incorporating details about the objects’ orientation and their activity states. In this study, within practical experimental environments, we collect dataset and utilize deep learning method to classify the falling events.

Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources. However, the overwhelming upload traffic may lead to unacceptable uploading time. To tackle this issue, for tasks taking environmental data as input, the data perceived by roadside units (RSU) equipped with several sensors can be directly exploited for computation, resulting in a novel task offloading paradigm with integrated communications, sensing and computing (I-CSC). With this paradigm, vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading. By optimizing the computation mode and network resources, in this paper, we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task. Although this non-convex problem can be handled by the alternating minimization (AM) algorithm that alternatively minimizes the divided four sub-problems, it leads to high computational complexity and local optimal solution. To tackle this challenge, we propose a creative structural knowledge-driven meta-learning (SKDML) method, involving both the model-based AM algorithm and neural networks. Specifically, borrowing the iterative structure of the AM algorithm, also referred to as structural knowledge, the proposed SKDML adopts long short-term memory (LSTM) network-based meta-learning to learn an adaptive optimizer for updating variables in each sub-problem, instead of the handcrafted counterpart in the AM algorithm. Furthermore, to pull out the solution from the local optimum, our proposed SKDML updates parameters in LSTM with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.