Integrated sensing and communication (ISAC) has been esteemed as a pivotal driver for the next-generation wireless networks in achieving dual-function spectrum efficiency. In typical ISAC systems, user equipment and targets are treated as distinct entities, operating without interaction between the communication and sensing functionalities. However, in scenarios where the user and target align as a unified entity, current solutions underperform due to the coupled performance metrics involving the achievable rate and the sensing Cramér–Rao Bound (CRB). To address this issue, focusing on this particular scenario, this paper delves into the design of optimal transmission precoder and conducts a fundamental trade-off analysis between the achievable rate and the target sensing CRB. Firstly, we derive a compact expression of the CRB, characterized by the angle and delay parameters of the sensing channel, thereby obtaining the subsequent position error bound (PEB) by exploiting the inherent connection between the PEB and CRB. Next, we aim to design the optimal precoder matrix to minimize the PEB, subject to constrains on the minimum communication rate and total transmit power budget. By leveraging the structural properties of the precoder covariance matrix, we develop an efficient algorithm to devise a closed-form optimal precoder design. Numerical results demonstrate that our proposal yields a favorable CRB-Rate trade-off across various scenarios, closely aligning with the performance of semi-definite relaxation (SDR)-based optimization scheme.

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.

Estimating air-to-ground (A2G) channel for a cellular-connected unmanned aerial vehicle (UAV) requires frequent pilot transmission due to its high mobility. To reduce the pilot overhead, researchers have attempted to predict future channels based on the historical ones by leveraging learning techniques. However, most existing works are limited to sequentially forecasting subsequent channels, suffering from the error accumulation problem that consequently hampers the prediction accuracy. To address this issue, this paper proposes a novel learning-based estimate-then-predict scheme for A2G channel tracking. In this scheme, the UAV transmits limited pilots, and the base station (BS) first performs channel estimation by exploiting the received pilots and then predicts a series of subsequent channels concurrently. Specifically, in the estimation phase, we propose a least-squares feedforward neural network (LS-FNN) to fuse the benefits of LS in high signal-to-noise ratio (SNR) regime and FNN in low SNR regime. In the prediction phase, a multi-time-interval long-short-term-memory (MTI-LSTM) network is proposed for concurrent channel prediction. A distinctive difference from prior works is that layer normalization is employed to greatly increase the prediction accuracy at no cost of additional neurons. Simulation results corroborate the superior performance of our proposed scheme over the state-of-the-art benchmarks.

Orthogonal time frequency space (OTFS) modulation has been considered as one of the most promising candidates to support reliable data transmission especially in high-mobility networks, wherein the performance of communications strongly relies on timely and accurate tracking of the relevant user state parameters. In this context, the problem of integrated sensing and communication (ISAC) assisted user state refinement is addressed in the framework of OTFS systems. In particular, by exploiting the initial yet coarse angle estimate provided by the typical codebook-based user state sensing algorithm, we judiciously design a hybrid digital-analog architecture to output the nested array structured low dimensional observations. In this way, the corresponding nested array based technique is employed to perform angle refinement by fully utilizing the degrees of freedom provided by the measurements. Next, based on the refined angle estimate, we develop a two-stage joint delay and Doppler shifts estimation scheme to update the corresponding coarse estimates. Numerical results validate the effectiveness of the proposed algorithm in various scenarios, showing that our well designed user state refinement scheme is able to improve the performance of the considered ISAC-assisted OTFS systems in term of both radar and communication metrics.

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 this letter, we consider the coherent direction-ofarrival estimation problem in impulsive noise. An ℓp-norm-based variant of the classical relaxation technique is proposed to tackle this problem. The proposed method successively minimizes the cost function along block coordinate directions. Specifically, at each iteration, only one block of the signal component is updated, while the remaining blocks are kept fixed. Then, instead of solving each block exactly, the proposed method optimizes the parameters in the block iteratively by solving a surrogate function that upper bounds the ℓp-norm. Numerical results show that the proposed scheme offers substantial performance improvement over the state-of-theart algorithms.

In this letter, we consider the problem of underdetermined direction-of-arrival estimation of wideband signals using nested arrays in the framework of sparse signal recovery. The problem is recast into recovering multiple nonnegative sparse signals, which share the same sparse support but correspond to dictionaries of different frequency bins. By constructing parameterized dictionaries and exploiting the joint sparsity structure, we develop an iterative minimization method that can jointly estimate the sparse signal and refine the parameterized dictionaries. Numerical results are provided to verify the practical effectiveness of the proposed scheme.

In this paper, a noncircular deterministic maximum likelihood (NC-DML) estimator for direction-of-arrival estimation of strictly NC signals is devised. Unlike the conventional DML solution for arbitrary signals, the NC-DML exploits the NC properties of the sources by reconstructing the parameter set, significantly decreasing the number of parameters to be considered. For computing the NC-DML, we present a novel NC alternating projection (NC-AP) approach. The NC-AP solution is carried out based on an augmented virtual array structure. Moreover, it also takes the impact of the initial phase shift of the NC signals into account. Simulation results are included to illustrate the superiority of the proposed method.

Joint estimation of the directions-of-arrival (DOAs) and frequencies of multiple signals is addressed in this paper. By constructing a set of joint diagonalization matrices, two cost functions that do not require a priori information of the source number are devised for DOA and frequency estimation in a separate manner. This enables us to estimate DOAs and frequencies via two one-dimensional search steps in their corresponding spatial and frequency domains. Thus, the tremendous two-dimensional search required in the standard approaches can be avoided. Simulation results demonstrate the effectiveness of the proposed approach.

Spectrum sensing is a fundamental problem in cognitive radio. In this paper, we introduce two spectrum sensing methods based on Gerschgorin disk. The Gerschgorin radii contain the information of signal subspace, whereas the Gerschgorin centers capture the signal energy. The first proposal only relies on the Gerschgorin radii and thereby is robust against nonuniform noise. The second one, utilizing both the Ger-schgorin radii and centers, can significantly improve the detection performance. Simulation results are included to illustrate the superiority of the proposed methods.

A structured least squares based ESPRIT method is devised for joint direction-of-arrival and frequency estimation. By considering the errors in the estimated signal subspace and employing an iterative minimization procedure, the proposed approach is able to efficiently refine the estimated signal subspace, leading to significant enhancement in estimation performance. Simulation results demonstrate the effectiveness of the proposed approach.