Traditional grid-based compressed sensing algorithms usually suffer from the base mismatch effect in channel estimation problems. To address this, we propose a novel gridless uplink/downlink (UL/DL) channel estimation strategy for millimeter wave (mmWave) massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. By exploiting inherent sparsity in the angle-delay domain of the mmWave channel, we first formulate the UL channel estimation problem as a joint sparse signal recovery problem. Then, we introduce the reweighted atomic norm for enhancing angular resolution of the mmWave channel on continuous Fourier dictionaries; we suggest a novel reweighted atomic norm minimization (NRAM) algorithm to solve the channel estimation problem by leveraging the Hankel-Toeplitz block model with multiple measurement vectors (MMVs), and the original NRAM problem is approximated by the solution of a semi-definite programming (SDP) problem with structured sparsity, which is efficiently solved by a low-complexity alternating direction multiplier method (ADMM). Subsequently, in the frequency division duplex (FDD) system, we design a simplified DL channel estimation scheme by leveraging the angle-delay reciprocity of UL and DL channels. This scheme reconstructs the DL channel matrix using the angle and path delay estimated from the UL channel, along with the channel gain obtained through least squares (LS). Finally, simulation results validate that our proposed approach achieves superior channel estimation accuracy and reduces pilot overhead compared to conventional UL/DL channel estimation techniques.

Periodic data collection from numerous vehicular on-board sensors is necessary for aiding decision making in complex navigation and autonomous driving applications. The temporal freshness of data, represented by the Age of Information (AoI), thus holds critical significance. Integrating Unmanned Aerial Vehicle (UAV) relays with Reconfigurable Intelligent Surface (RIS) emerges as a promising strategy to establish reliable communication links between vehicles and data processing centers. Despite this potential, the current body of literature on the integration of UAV relays and RIS is insufficient, particularly in studying AoI. This paper addresses this gap by achieving a comprehensive optimization of the phase shifts at the RIS, spectrum allocation, and the UAV trajectory. The objective is to minimize the average AoI while adhering to the constraints associated with UAV energy consumption. This joint optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. It is tackled using an approach based on Multi-Step Dueling Double Deep Q Network (MSD3QN). Extensive simulations conducted across diverse scenarios prove the effectiveness of our proposed approach and demonstrate its ability in improving the timeliness of making decisions, reducing average AoI, and enhancing network coverage.

This article proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating in the millimeter-wave band. Specifically, the ISAC system consists of a radar subsystem and a communication subsystem to detect multiple targets and communicate with the users simultaneously. The IRS is used to configure the radio propagation environment by changing the phase of the radio signal to enhance the communication transmission rate. In the proposed scheme, we first derive a closed-form solution for the radar signal covariance matrix to generate a radar beampattern in the angle of interest. Then, we jointly optimize the beamforming vector of the communication subsystem and the IRS phase shifts to enhance the communication transmission rate. To decouple the multiple variables to be optimized, the alternating optimization and quadratic transformation methods are applied to determine the communication beamforming vector and the IRS phase shifts. Specifically, we utilize the majorization minimization and the complex circle manifold methods to compute the IRS phase shifts. Simulation results verify the effectiveness of the proposed algorithm and demonstrate that an IRS can improve the performance of ISAC systems.

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. Recognizing the inherent tradeoff between energy harvesting and sensing performance, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first algorithm involves the semi-positive definite programming techniques, and the second algorithm is based on the successive convex approximations and majorization minimization to design the closed form solutions of the optimization variables, which can effectively reduce the computational complexity. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. This research contributes to the integration of wireless sensing and wireless energy in intelligent transportation systems and underscores the optimization of system performance through the introduction of IRS.

The proliferation of Internet of Things (IoT) applications has led to the interconnection of multiaccess edge computing (MEC) systems through metro optical networks. To cater to these diverse applications, network slicing has become a popular tool for creating specialized virtual networks. However, the uneven utilization of multidimensional resources can result in resource fragmentation, thereby reducing the utilization of limited edge resources. This article focuses on mitigating multidimensional resource fragmentation in virtual network embedding (VNE) to maximize the profit of the infrastructure provider (InP). The problem is converted into a bilevel optimization problem, taking into account the interdependence between virtual node embedding and virtual link embedding. To solve this problem, we propose a nested bilevel VNE approach named BiVNE. BiVNE leverages an ant colony system (ACS) algorithm for the upper layer problem and utilizes the Dijkstra algorithm and an exact-fit spectrum slot assignment method for the lower layer problem. Evaluation results demonstrate that BiVNE can greatly improve the profit of the InP by increasing the acceptance ratio and avoiding resource fragmentation simultaneously.

With the incoming era of 5G communication, the number of mobile devices is anticipated to increase dramatically. Flexible network resource allocation is required urgently to meet the mobility needs of a large number of users, accelerating the rise of network virtualization. However, the existing researches on virtual network embedding (VNE) consider less the virtual node migration caused by user mobility. In this paper, we attempt to address the problem of VNE supporting user mobility. The concepts of interruption penalty and blocking penalty are proposed to quantify the impact of virtual node mobility on infrastructure providers (InPs) and refine the revenue model of InPs. Then we propose a location-constrained 5G VNE algorithm, where a virtual node and virtual link pair embedding method is designed to increase the probability of successful VNE. Based on the proposed VNE algorithm, we further propose a virtual network re-embedding algorithm that can dynamically migrate the embedding of virtual nodes following user mobility. The virtual node migration is triggered by predicting the locations of virtual nodes and selecting the target physical nodes with the minimum number of re-embedding. Simulation results show that the proposed algorithm outperforms the existing VNE algorithms with higher InP revenue.