Rui Tang

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.

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 paper, we consider a multi-unmanned aerial vehicle (UAV)-assisted Internet-of-things (IoT) sensor system with non-orthogonal multiple access (NOMA) and aim to maximize the sum utility of bit rates of all IoT sensors through the joint optimization of user association, three-dimensional (3-D) UAV placement, decoding ordering, and power allocation. To cope with the formulated mixed-integer non-convex problem, we decompose it into four subproblems relating to each dimension of radio resources under the alternating optimization framework. For the user association or decoding ordering subproblems involving only binary variables, a novel algorithm is proposed by leveraging variable relaxation, fractional programming, and efficient bounding. For the non-convex 3-D UAV placement subproblem, the sequential quadratic programming algorithm is applied to obtain an efficient sub-optimal solution by solving a sequence of quadratic programming problems that are iteratively modified. For the power allocation subproblem, the global-optimal solution is achieved based on auxiliary variables, variable transformation, and convex optimization. Simulation results show that the proposed resource allocation strategy outperforms the state-of-the-art benchmarks that are based on the greedy algorithm, the K-means algorithm, the geometric center-based algorithm, and the deep reinforcement learning, and strikes a good balance between performance and complexity in contrast to those based on the genetic algorithm and the brute-force search.

In this paper, we investigate a resource allocation problem for a multi-unmanned aerial vehicle (UAV) assisted integrated sensing and communication system (ISAC), where a group of dual-functional UAVs perform simultaneous radar sensing of a target and data communication with multiple ground users. In particular, the trajectory of UAVs, user association, and beamforming design are jointly considered to maximize the sum weighted bit rate of all ground users while ensuring the sensing beampattern gain of the target. To cope with the above mixed-integer non-convex 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 ground users and UAVs by leveraging matching theory and fractional programming theory. For the non-convex UAV trajectory subproblem, we apply the sequential quadratic programming to obtain a sub-optimal 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.

In this paper, we investigate a resource allocation problem for a multi-unmanned aerial vehicle (UAV)-assisted full-duplex wireless-powered Internet-of-things (IoT) network, where the slot partition, power allocation, user association, and three dimensional (3D) UAV placement are jointly considered to maximize the sum bit rate of all IoT devices under the imperfect self-interference cancellation and generalized probabilistic air-ground channel model. To deal with the formulated mixed-integer non-convex problem, we propose a novel resource allocation strategy with three nested parts by integrating the model-based optimization theory with the data-based learning theory. Particularly, the data-based deep deterministic policy gradient algorithm is only explicitly used to train the 3D UAV placement policy, while the model-based Lagrange dual theory and matching theory are implicitly used to explore the hidden tractability of the rest two parts and design efficient algorithms, where the optimization results are passed onto the data-based part through reward values. Simulation results show that the proposed strategy greatly cuts down the execution time of the exhausting search-based genetic algorithm by 4 orders of magnitude at the cost of less than 5.1 percent performance loss.

The detection of fine-grained objects in remote sensing images has been recognized as a challenging issue, which cannot be well addressed by the existing deep learning-based methods due to their inadaptability to multi-scale objects, slow convergence speed, and limitations to scarce datasets. To cope with the above issue, we propose a deep convolutional neural network (CNN)-based detection method for multi-scale fine-grained objects in complex remote sensing scenarios. Specifically, we adopt a deep CNN with a residual structure as the backbone network, to extract deep-level details from the image. Besides, we introduce a multi-scale region generation network to overcome the limitations of fixed receptive field convolution kernels and enable multi-scale object detection. Lastly, we replace fully connected layers in the fully convolutional region classification network with $1\times 1$ convolutional layer to enhance detection efficiency and detection speed. To overcome the limitation of scarce datasets, we conducted experiments on the FAIR1M dataset, which is currently the largest fine-grained object detection dataset in the remote sensing field. Simulation results show that the proposed detection method achieves the highest average precision (35.86%) among all benchmarks and outperforms the classic Faster R-CNN-based method by 3.44%. Furthermore, our method demonstrates significantly improved detection speed compared to the Faster R-CNN-based methods.

In this letter, we consider a non-orthogonal multiple access (NOMA)-based uplink cellular system assisted by an unmanned aerial vehicle (UAV) through dual connectivity. To balance efficiency and fairness, we aim to maximize the sum weighted bit rate by jointly optimizing the three-dimensional UAV placement and power allocation. Considering the non-convexity of the original problem, we propose a hybrid offline-online algorithm: In the offline training, when the UAV placement is given, the property of fractional programming is utilized to obtain a sub-optimal power allocation solution; Based on the results of power allocation, the deep deterministic policy gradient algorithm is leveraged to learn the optimal UAV trajectory policy that can be deployed in an online fashion. Finally, simulations are conducted to show the efficiency of the proposed algorithm.

In order to relieve the traffic burden of the ground base station (BS) and improve system operation efficiency, we study the energy-efficient optimization problem in a non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-assisted data collection system, where a UAV assists a ground BS for traffic offloading. Particularly, the joint optimization problem of the placement of the UAV and transmit power of the sensor nodes is formulated to maximize the energy efficiency of all sensor nodes under the quality-of-service constraints and the probabilistic ground-to-air channel model. To balance the optimized performance and online operation time, we propose an alternating-based offline optimization algorithm for obtaining the optimal online UAV trajectory policy. Under the given UAV placement, the formulated problem is first transformed into a convex power allocation subproblem. Based on the power-allocation solutions, the deep deterministic policy gradient algorithm is then leveraged to approach the optimal UAV placement. Simulations show that the proposed algorithm can obtain an efficient performance while consuming an average online operation time of fewer than 0.2 seconds.