Reconfigurable intelligent surface (RIS) has emerged as a highly promising technology for future wireless sensing applications, primarily due to its capability to dynamically manipulate the incoming signals through meticulous configuration of the RIS profile. This paper delves into the optimal design strategy for the RIS profile, aiming at maximizing the localization accuracy of the non-line-of-sight user within typical downlink orthogonal frequency division multiplexing (OFDM) systems. Assuming prior knowledge of the user’s location, we first derive the measurement model and corresponding position error bound (PEB) for the considered OFDM systems. Subsequently, under the total power budget constraint, we establish a closed-form solution for the optimal covariance matrix of the RIS profile by leveraging the subspace structure information of the channel states. Building upon this, the design of the optimal RIS profile is executed using the time-sharing technique. Taking into account the hardware limitations, we further formulate a more practical RIS profile design problem by incorporating the unit-modulus constraint for each RIS element, which, however, is non-convex and thus hard to tackle. To address this issue, we employ the alternating minimization technique to compute a suboptimal solution for the constrained RIS profile design problem. Simulation results demonstrate the remarkable PEB performance achieved by both the proposed optimal and constrained RIS profile design strategies. It is also illustrated that the proposed constrained RIS profile design surpasses other state-of-the-art alternatives, exhibiting a comparable performance to our devised optimal benchmark.

Nowadays, unmanned aerial vehicles (UAVs) are deployed to perceive high-definition visuals of ground targets (GTs) for environment reconstruction of virtual reality (VR) by leveraging their high flexibility. Inspired by the classic scalable video coding method, we develop a novel multi-UAV collaborative layered visual perception and transmission scheme for VR named MUL-VR, wherein GTs are divided into multiple overlapped clusters and multiple UAVs are deployed to collaboratively perceive visuals from these clusters. Specifically, our proposed formulation entails maximizing user’s quality of experience (QoE) by optimizing cluster radii, UAV horizontal coordinates, and bandwidth allocation strategy subject to the constraints on visual quality, transmission delay and available bandwidth. To address this issue, we formulate the investigated MUL-VR scheme into an intractable optimization problem, which, however, is difficult to solve due to the non-convexity of the objective function and constraints, as well as the intricate coupling of the variables. To tackle this challenging problem, we first propose an efficient alternating algorithm, which decomposes the original optimization problem into three subproblems, and then derive the optimal closed-form solution to each subproblem. Consequently, the final solution can be obtained by iteratively optimizing the variables associated with each subproblem, while holding the variables in the other two subproblems fixed, until the convergence condition is satisfied. Simulation results demonstrate that the proposed scheme can effectively improve the user’s QoE and enhance the robustness of the system, yielding superior performance compared to other benchmarks. Specifically, compared to the classic K-Means based scheme, the proposed scheme offers a 25.9% enhancement in terms of QoE when the preference coefficient ε = 0.1 and such performance gain progressively expands as ε increases.

Nowadays, unmanned aerial vehicles (UAVs), empowered with the capability of high-definition image transmission, are used to capture the rapidly changing physical environment by leveraging its high flexibility to reconstruct an immersive realistic virtual environment for metaverse users. In this paper, we consider a novel UAV-assisted image acquisition system where a UAV is dispatched to take off from an initial location to capture real-time images of multiple ground targets and then transfer the captured images back to the ground user for virtual environment reconstruction. We aim to minimize the time for the UAV to complete the image acquisition task by optimizing the three-dimensional UAV trajectory under the constraints of image quality, information causality and energy consumption. To this end, we first formulate the investigated scenario into a mixed integer optimization problem, which, however, is difficult to solve due to the infinite time-varying variables closely coupled with each other. Then, a three-stage progressive algorithm is proposed to obtain an efficient solution to the formulated mixed integer optimization problem, where the constraints of image quality, information causality and energy consumption can be sequentially satisfied. Finally, comprehensive performance evaluation is conducted to verify the effectiveness of the proposed three-stage progressive trajectory design algorithm, and the results show that the proposed algorithm significantly outperforms the benchmark schemes.

In dense traffic cases, the channels among vehicles and infrastructures in cellular vehicle-to-everything (C-V2X) network are likely to be congested. Vehicles can select spectrum resources autonomously by adopting sensing-based semipersistent scheduling scheme, which may result in frequent packet collisions due to the random selection process, especially when spectrum resources are limited. So far, there still lacks a definite stochastic expression to characterize the channel selection process for vehicles in C-V2X. In this article, we propose a novel deep reinforcement learning (DRL)-based dynamic spectrum access (DSA) algorithm for C-V2X via imitating Indian buffet process (IBP), aiming to meet the vehicles’ strict communication requirements on high-transmission rate and low-collision probability. First, we explore the correlations among historical spectrum access data to acquire the channel availability list. Then, we exploit DRL to achieve distributive DSA among vehicles. Specifically, the channel state prediction for the distributive DSA is facilitated via imitating the classic IBP, and the spectrum access decisions are made by leveraging the deep$Q$-learning network combined with long short-term memory technique. Finally, comprehensive simulation results are presented to show that the proposed algorithm can improve the transmission rate by 15% and reduce the collision probability by 12% with a false alarm probability of 0.1 compared with other spectrum access methods.

In this paper, we consider a new unmanned aerial vehicle (UAV)-assisted oblique image acquisition system where a UAV is dispatched to take images of multiple ground targets (GTs). To study the three-dimensional (3D) UAV trajectory design for image acquisition, we first propose a novel UAV-assisted oblique photography model, which characterizes the image resolution with respect to the UAV’s 3D image-taking location. Then, we formulate a 3D UAV trajectory optimization problem to minimize the UAV’s traveling distance subject to the image resolution constraints. The formulated problem is shown to be equivalent to a modified 3D traveling salesman problem with neighbourhoods, which is NP-hard in general. To tackle this difficult problem, we propose an iterative algorithm to obtain a high-quality suboptimal solution efficiently, by alternately optimizing the UAV’s 3D image-taking waypoints and its visiting order for the GTs. Numerical results show that the proposed algorithm significantly reduces the UAV’s traveling distance as compared to various benchmark schemes, while meeting the image resolution requirement.

The explosive demands for high quality mobile video services have caused heavy overload to the existing cellular networks. Although the small cell has been proposed to alleviate such a problem, the network operators may not be interested in deploying numerous base stations (BSs) due to expensive infrastructure construction and maintenance. The unmanned aerial vehicles (UAVs) can provide the low-cost and quick deployment, which can support high-quality line-of-sight communications and have become promising mobile BSs. In this paper, we propose a quality-of-experience (QoE)-driven UAV-enabled pseudo-analog wireless video broadcast scheme, which provides mobile video broadcast services for ground users (GUs). Due to limited energy available in UAV, the aim of the proposed scheme is to maximize the minimum peak signal-to-noise ratio (PSNR) of GUs’ video reconstruction quality by jointly optimizing the transmission power allocation strategy and the UAV trajectory. Firstly, the reconstructed video quality at GUs is defined under the constraints of the UAV's total energy and motion mechanism, and the proposed scheme is formulated as a complex non-convex optimization problem. Then, the optimization problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent model and the successive convex approximation model. Finally, the experimental results are presented to show the effectiveness of the proposed scheme. Specifically, the proposed scheme can achieve over 1.6 dB PSNR gains in terms of GUs’ minimum PSNR, compared with the state-of-the-art schemes, e.g., DVB, SoftCast, and SharpCast.

The optimal spectrum access strategy is investigated for multi-user multi-channel scenario in cognitive radio networks. At first, an online learning method based on Dirichlet Process is adopted to predict the channel usage based on ACK/NACK feedbacks, which can avoid frequent information exchange among users. Based on the prediction result, the delay performance can be computed when a user transmits certain percentage of multimedia packets on a specific channel. Second, the packet delivery ratio (PDR) is derived from the prediction result of channel usage to reflect the accessing competition among multiple users. Finally, the quality of service (QoS) of multimedia applications is defined as the joint delay and throughput performances. Moreover, a dynamic spectrum access scheme is proposed to optimize the QoS metrics. The simulation results demonstrate that the QoS and the peak-signal-to-noise ratio (PSNR) of the proposed spectrum access algorithm outperform the three existing spectrum access algorithms, i.e., cognitive cross-layer algorithm, dynamic learning algorithm, and dynamic least interference algorithm. The proposed algorithm achieves more than 21.8%, 5.4%, and 3.9% PDR enhancement and over 3.23 dB, 0.82 dB, and 0.50 dB PSNR gains, compared with those three algorithms, given the transmission power as 10, 20, and 30 units, respectively.

In cognitive radio networks (CRNs), the spectrum utilization can be dramatically improved with the secondary users (SUs) accessing the unoccupied licensed channels opportunistically. However, how to fully utilize the spectrum holes to meet the quality of service requirements of SUs is still an open issue. In this paper, we focus on multi-user, multi-channel case, and analyze the stable channel state after allocating packets over a licensed channel. We first assume that the SUs' packet arrival rate obeys Poisson distribution, and the lost packets will be retransmitted with exponential backoff delay. Then, we analyze the stable channel state when a SU selects and allocates certain percentage of its packets over one channel. The theoretic analysis shows that such stable channel state can be solved by the steady-state equations. Based on such stable channel state analysis, we propose a novel greedy packet allocation scheme in multi-user and multi-channel S-ALOHA system. The proposed packet allocation scheme will obtain a maximal spectrum utilization, which will support more SUs to share the spectrum holes or cause less access conflict with PUs. In the simulation part, we assume the PUs' channel access pattern follows Markov model, and study the spectrum efficiency after packet loading over licensed channels and make comparisons with different packet allocation schemes. The simulation results show that the proposed packet allocation scheme outperforms other related works in terms of successful packet delivery ratio, the number of collision packets, and spectrum efficiency.