High-precision and low-latency wireless localization is a key objective for future networks. The majority of existing localization methods rely on multi-antenna arrays (MAAs) to estimate directions of arrival (DoAs), but the size and cost of MAAs limit their application in portable electronic devices. Virtual antenna arrays (VAAs), constructed from signals received at different positions by a moving single-antenna receiver, offer a promising alternative. However, VAA-based localization is challenged by the local oscillator frequency offset (LOFO) in transceivers, the irregular array geometry caused by the receiver’s movement, and the non-line-of-sight (NLoS) environments due to physical blockage. To address the above challenges, this paper proposes a VAA-based online localization approach. Specifically, we employ a manifold separation technique based on the Jacobi-Anger expansion to maintain a uniform linear array-like channel representation for the irregular VAA geometry. This enables us to transform the joint estimation of multi-path DoAs, times of arrival (ToAs), and LOFO into a modified two-dimensional atomic norm minimization problem, which is solved using an alternating convex search algorithm. Additionally, we introduce an unscented Kalman filter-based simultaneous localization and mapping algorithm for real-time position tracking in NLoS environments. Extensive simulations validate the effectiveness of our approaches, showing a 70.15% reduction in DoA estimation error and a 54.02% reduction in localization error compared to benchmark methods.

Previous studies on time-of-arrival (ToA) estimation with phase ambiguity have largely relied on correlation matrices constructed from multi-shot signals. However, only a single-shot signal with binary phase ambiguity is available in Bluetooth low energy (BLE) ranging systems, rendering traditional methods ineffective. To address the above limitation, this paper proposes an efficient method for the joint estimation of ToA and phase ambiguity in BLE ranging systems. By leveraging the idea of beamforming among multiple frequency channels, the proposed method is comprised of three phases. In the first phase, we extract the one-way time differences of arrival (TDoAs) from the two-way channel frequency response measurements based on a modified atomic norm minimization algorithm by further addressing a component-dominating issue. In the second phase, we solve the matching problem related to the topological order of TDoAs by employing an alternating optimization framework and the Hungarian matching algorithm. In the final phase, we cope with the remaining first-arriving delay estimation problem by solving a series of low-rank MaxCut semidefinite programs. In particular, we prove the uniqueness and the global optimality of the obtained solution under certain conditions that can be easily satisfied in practice. Simulation shows that the proposed method can achieve the nanosecond-level accuracy in ToA estimation at a signal-to-noise ratio of 25 dB, with approximately 90% of ToA estimations exhibiting errors of less than 18 nanoseconds.

In this paper, we consider a generalized space-air-ground integrated Internet-of-remote-things system with multiple unmanned aerial vehicles (UAVs) and low earth orbit satellites. To explore the diverse channel propagation conditions and adapt to the practical transmission environment, we investigate the three-dimensional node association among sensors, UAVs, and satellites, the spectrum partition between two-hop data collection links, and the multi-UAV deployment under the probabilistic ground-to-air channel model. Unlike existing works, we address the issue of user fairness by maximizing the minimum amount of collected data among all sensors. To cope with the formulated mixed-integer non-convex problem, we decompose it into two subproblems: a node association and spectrum partition subproblem, and a UAV deployment subproblem. To enhance optimized performance, the above two subproblems are solved alternately using the Lagrange dual decomposition and sequential quadratic programming. Simulations show that the proposed strategy converges within 15 iterations and yields an efficient solution, incurring an average loss of approximately 0.2 percent compared to the result of a brute-force search-based algorithm. Additionally, it outperforms benchmarks based on variable relaxation, successive convex approximation, and deep reinforcement learning under various parameter settings.

To utilize the limited acoustic spectrum while combating the harsh underwater propagation, we incorporate partial spectrum sharing into an underwater acoustic sensor network and aim to maximize the minimum data collection rate among all underwater sensor nodes through joint power allocation and spectrum assignment. To cope with the non-convex optimization problem, we propose a Hybrid Model-based and Data-based Resource Allocation (HMDRA) scheme: 1) Under any given spectrum assignment strategy, we analyze the impact of the partial spectrum sharing and imperfect successive interference cancellation on baseband signal processing, and formulate a power allocation problem that is solved by the bisection method and Lagrange dual theory. 2) Based on the optimal power allocation strategy, the gradient-free genetic algorithm (GA) is first adopted to approach the optimal solution of the model-less spectrum assignment problem by nearly enumerating the solution space. To reduce complexity, we further propose a deep reinforcement learning (DRL)-based algorithm and obtain an efficient solution by traversing a deep neural network-based policy learned from the training stage. Simulation results show that compared with the GA-based algorithm, the average execution time of the DRL-based algorithm is substantially reduced by 5 orders of magnitude to 0.7076 seconds at the cost of approximately 6 percent performance loss.

In this article, we investigate a resource allocation problem for a multiunmanned aerial vehicle (UAV) assisted integrated sensing and communication (ISAC) system, where a group of dual-functional UAVs perform simultaneous radar sensing of a target and data communication with multiple ground users (GUs). In particular, the trajectory of UAVs, user association, and beamforming design are jointly considered to maximize the sum weighted bit rate of all GUs while ensuring the sensing beampattern gain of the target. To cope with the above mixed-integer nonconvex optimization problem, we propose an efficient strategy by decomposing the original problem into two subproblems under the alternating optimization framework. For the user association and beamforming design, we propose a novel algorithm to circumvent the coupling relationship among GUs and UAVs by leveraging matching theory and fractional programming theory. For the nonconvex UAV trajectory subproblem, we apply the sequential quadratic programming to obtain a suboptimal solution by solving a sequence of quadratic programming problems. The above two subproblems are iteratively solved and a stable solution is obtained upon convergence. Simulation results show that the proposed strategy outperforms various benchmark schemes that are based on the deferred acceptance algorithm, K-means algorithm, and a heuristic algorithm. It is demonstrated that the proposed strategy efficiently improve the sensing beampattern gain and communication rate.