Integrated sensing and communication (ISAC) will become an important feature in the future wireless communication networks. To achieve the high-rate communication and high-precision sensing, joint design of beamforming and power allocation is essential in the ISAC-assisted vehicular networks. This paper studies the downlink ISAC-assisted beamforming design and power allocation in the vehicle-to-infrastructure (V2I) communication networks to maximize the achievable sum-rate while guaranteeing the targeted sensing accuracy. The Cramer-Rao lower bounds (CRLBs) are introduced to characterize the estimation performance of the angle and distance between the target vehicle and the roadside unit (RSU). Due to the non-convex CRLBs sensing constraints, we propose an intelligent scheme based on deep reinforcement learning (DRL) to design the beamforming and allocate the power. In this scheme, the reward function is related to the communication sum-rate and CRLBs sensing constraints. Simulation results show that the proposed scheme significantly improves both communication and sensing performance compared to the benchmark schemes.

This paper studies the integration of over-the-air computation (AirComp) and wireless power transfer (WPT) for achieving sustainable wireless data aggregation (WDA). In such wireless powered AirComp system, a multi-antenna hybrid access point (HAP) employs the transmit energy beamforming to charge multiple single-antenna low-power wireless devices (WDs) in the downlink, and the WDs utilize their harvested energy to simultaneously send messages to the HAP for AirComp in the uplink. Under this setup, our objective is to minimize the computation mean square error (MSE) by jointly optimizing the transmit en-ergy beamforming and the receive AirComp beamforming at the HAP, as well as the transmit power control at the WDs, subject to the wireless energy harvesting constraints at individual WDs. To tackle the non-convex computation MSE minimization problem, we present an efficient algorithm to find a converged high-quality solution by using the alternating optimization technique, in which the transmit energy beamforming (together with WDs' power control) and the receive beamforming are alternately optimized. Simulation results show that the proposed joint WPT-AirComp scheme significantly decreases the system's MSE, as compared to conventional designs without such joint optimization.

This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help the base station (BS) provide ISAC services at separate line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive uniform linear array (ULA) IRSs for sensing, in which each IRS is integrated with dedicated sensors for processing echo signals, and each IRS simultaneously serves one sensing target and multiple communication users (CUs) in its coverage area. We consider two cases with point and extended targets, in which each IRS aims to estimate the target direction-of-arrival (DoA) and the complete target response matrix, respectively. Under this setup, we first derive the closed-form Cramér-Rao bounds (CRBs) for parameter estimation under the two target models. Then, we assume that the BS sends combined information and dedicated sensing signals for ISAC, and accordingly consider two different types of CU receivers that can and cannot cancel the interference from dedicated sensing signals. Under this setup, we minimize the maximum CRB at all IRSs, via jointly optimizing the transmit beamformers at the BS and the reflective beamformers at the multiple IRSs, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs, the maximum transmit power constraint at the BS, and the unit-modulus constraints at the multiple IRSs. To tackle the highly non-convex SINR-constrained max-CRB minimization problems, we propose efficient algorithms based on alternating optimization and semi-definite relaxation, to obtain converged solutions. Finally, numerical results are provided to verify the benefits of our proposed designs over various benchmark schemes based on separate or heuristic beamforming designs.

This paper studies a multi-intelligent-reflecting-surface (IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help a base station (BS) provide ISAC services at the line-of-sight (LoS) blocked areas. In particular, we consider the case with semi-passive uniform linear array (ULA) IRSs each integrated with dedicated sensors for receiving echo signals, in which each IRS simultaneously senses one point target and communicates with one communication user (CU) within its coverage area. Under this setup, we first derive the closed-form Cramér-Rae bound (CRB) for the targets' direction-of-arrival (DoA) estimation at the corresponding IRSs. Then, to achieve fair and optimal sensing performance, we minimize the maximum CRB for targets' DoA estimation at all IRSs, by jointly optimizing the transmit beamformers at the BS and the reflective beamformers at the IRSs, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs, the maximum transmit power constraint at the BS, and the unit-modulus constraints at the IRSs. To tackle the highly non-convex SINR-constrained max-CRB minimization problem, we propose an efficient algorithm based on alternating optimization and semi-definite relaxation, to obtain a converged solution. Finally, numerical results are provided to verify the effectiveness of our proposed design over various benchmark schemes based on separate or heuristic beamforming designs.

Mobile edge computing (MEC) is a promising technique to meet the demand of computation resources in unmanned aerial vehicle (UAV)-assisted vehicle-to-everything (V2X) networks by offloading computation-intensive tasks to UAV base stations (UBSs). In this paper, we consider an uplink UAV-assisted V2X communication system and characterize the information freshness between UAV and vehicles by applying the new age of information (Aol) metric. To solve the non-convex Aol minimization problem in the high-dimensional action space, a deep Q-network (DQN)-based scheme is proposed to optimize the transmit power and offloading ratio based on the stored experience, in which the action space consists of independent transmit power and offloading ratio. Meanwhile, each UBS selects the beneficial action based on the reward function related to Aol to guarantee the information freshness. The simulation results show that the flight height of UBS and the number of vehicles have a negative impact on Aol. Compared with the benchmark schemes, the proposed scheme can reduce Aol by 40.6%. In addition, the simulation results show that the offloading has a significant effect on Aol compared with the transmit power.

The emergence of connected vehicle (CV) technologies has created new traffic control opportunities, among them, is the potential to estimate volume without approach lane detection. Rather than requiring the expense and effort to install and maintain detector systems, this new “detector-free” method permits traffic volume to be estimated from CV GPS trajectory data. Unfortunately, however, CV GPS methods are limited not only to locations where CV GPS data can be recorded, but also limited to time when CV GPS data is recorded. The goal of this research was to overcome these limitations and permit volume estimation to be accomplished under any location or condition, including low-penetration CV environments. The contributions made by this work are significant in two respects. First, it creates an improved queue-based method to estimate intersection approach volumes during each signal cycle with sparse CV data. Second, the research demonstrates the application of a Bayesian deduction method to approximate volume with no CV trajectory data. To accomplish this, traffic volumes are assumed to be time-dependent Poisson distributed throughout the day, and CV data were used to estimate CV volume and further set as prior to deduce the time-dependent Poisson arrival rate. To verify and evaluate the accuracy and effectiveness of this new method under a range of potential traffic conditions, a simulation case study and a NGSIM case study were implemented. Results of both case studies resulted in estimated-to-actual arrival rate average errors as low as 4.2 percent and volume estimation errors as low as 0.9 percent.

Location-aware publish/subscribe is an important location-based service based on server-initiated model. Often times, the owner of massive spatio-textual messages and subscriptions outsources its location-aware publish/subscribe services to a third-party service provider, for example, cloud service provider, who is responsible for delivering messages to their relevant subscribers. The issue arising here is that the messages delivered by the service provider might be tailored for profit purposes, intentionally or not. Therefore, it is essential to develop mechanisms which allow subscribers to verify the correctness of the messages delivered by the service provider. In this paper, we study the problem of authenticating messages in outsourced location-aware publish/subscribe services. We propose an authenticated framework which not only can deliver the messages efficiently but also can make the subscribers’ authentication available with low cost. Extensive experiments on a real-world dataset demonstrate the effectiveness and efficiency of our proposed authenticated framework.