In this paper, we investigate a Multi-layer Aerial system for GenAI inference at the Edge (MAGE). Therein, ground user equipments (UEs) request image synthesis services from a remote base station (BS) that leverages the Latent Diffusion Model (LDM) for image generation. Multiple Unmanned Aerial Vehicles (UAVs) are deployed to serve the UEs for relaying their images and prompts to the BS. To reduce the communication cost and the computation burden at the BS, the UAVs can partially execute the LDM inference, i.e., an image autoencoder and prompt encoder, and offload the diffusion process task to the BS. In this work, we aim to minimize the BRISQUE scores across all the UEs by jointly optimizing the UAVs' positions, UE-UAV associations, the number of denoising steps at the BS, and offloading strategies of the UAVs. The optimization problem is non-convex, in which the objective function based on BRISQUE scores has no closed-form expression. Due to the fixed exploration strategy of Proximal Policy Optimization (PPO), which limits the policy's adaptability in dynamic environments, this leads to sub-optimal solutions. To address these potential drawbacks, we propose an adaptive exploration strategy that dynamically adjusts the exploration rate based on observed improvements in rewards. Specifically, the exploration capability is controlled by modulating the influence of the entropy bonus according to recent reward gains. Simulations based on the COCO-Stuff datasets show that the proposed scheme outperforms baseline schemes in different terms of BRISQUE score, UAVs' energy consumption, and inference latency. In particular, the proposed scheme reduces the BRISQUE score by up to 20-28.57%, inference energy consumption up to 15.98-30.17%, transmission energy consumption by 15.4-18.5%, and the latency by up to 33.33-43.28% compared to the baseline methods, resulting in higher image quality with a noticeably improved level of perceptual naturalness, improved energy efficiency, as well as substantially faster performance.

We explore mission assignment and task offloading in Open Radio Access Network (Open RAN)-enabled intelligent transportation systems (ITS), where autonomous vehicles utilize mobile edge computing for efficient processing. Existing studies often overlook mission dependencies and offloading costs, leading to suboptimal decisions. To address this, we formulate a novel optimization problem that integrates these factors and enhances performance through vehicle cooperation. We then develop the chaotic Gaussian-based global artificial rabbit optimization (CG-GARO) algorithm, a new metaheuristic approach, which significantly improves mission assignment efficiency and resource utilization. Simulation results show that our approach surpasses baseline metaheuristics in both system benefits and mission completion rates, demonstrating strong potential for real-world deployment in dynamic ITS environments.

This paper addresses the challenge of optimizing multi-hop routing in IoT-based digital twin systems, referred to as the MOUNTAIN problem. Multi-hop routing is inherently complex due to the need to balance energy consumption and communication reliability across multiple nodes, especially in dynamic and large-scale IoT networks. In MOUNTAIN, multiple IoT devices in the physical network (PN) frequently transmit data to the digital network twin (DNT), managed by a central server. Given the limited energy resources of IoT devices, our approach considers both energy efficiency and communication reliability. We formulate the MOUNTAIN problem as an optimization task aimed at reducing overall energy consumption while maintaining robust data transmission. Moreover, we address the problem with both single-task optimization and multi-task optimization and propose two corresponding evolution-based metaheuristics that utilize well-designed solution representations and genetic operators to obtain near-optimal solutions to the problem. Among them, the proposed Single-task Evolutionary Algorithm (STEA) solves each problem instance independently, while the proposed Multi-task Evolutionary Algorithm (MTEA) solves multiple instances at the same time to take advantage of exchanging useful solution information during parallel solution searches. Extensive experiments on synthetic datasets demonstrate that our proposed algorithms significantly outperform existing methods, reducing energy consumption and improving network stability. This research contributes to the development of sustainable and efficient IoT infrastructures, which are essential for the operational demands of digital twin applications.

The demand for stringent interactive Quality of Service has intensified in both mobile-edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks in these systems necessitates adherence to specific deadlines or achieving extremely low latency. To optimize task scheduling performance, existing research has mainly focused on reducing the number of late jobs whose deadlines are not met. However, the primary challenge with these methods lies in the total search time and scheduling efficiency. In this article, we present the optimal job scheduling algorithm designed to determine the optimal task order for a given set of tasks. In addition, users are enabled to make informed decisions for offloading tasks based on the information provided by servers. The details of performance analysis are provided to show its optimality and low complexity with the linearithmic time$\mathcal {O}(n\log n)$, where n is the number of tasks. To tackle the uncertainty of the randomly arriving tasks, we further develop an online approach with fast outage detection that achieves rapid acceptance times with time complexity of$\mathcal {O}(n)$. Extensive numerical results are provided to demonstrate the effectiveness of the proposed algorithm in terms of the service ratio and scheduling cost.

Differential received signal strength (DRSS) provides a practical means of localisation for wireless sensor networks. Closed-form location estimators based on a linearised propagation path loss model are computationally efficient and hence suitable for wireless sensor nodes. However, most existing solutions suffer from a significant bias arising from the injection of noise into the measurement data matrix caused by the linearisation process. The instrumental variable (IV) method can reduce the bias by replacing the data matrix with a matrix that is weakly correlated with the noise and strongly correlated with the data matrix - the latter correlation however can be weakened by large noise. This paper addresses the bias problem when the noise is large by using the method of selective power measurement to maintain correlation between the instrumental variable and data matrix. Simulation results show the proposed solution out-performs other existing solutions in terms of the root mean square error (RMSE) and bias over a wide range of noise levels.

A two-step pseudolinear Kalman filter (2S-PLKF) was previously proposed for angle-of-arrival (AOA) target tracking in three-dimensional space drawing on the pseudolinear rearrangement of nonlinear azimuth and elevation angle measurement equations. Despite enjoying low computational complexity, this algorithm suffers from a severe bias problem arising from the nonlinear-to-linear transformation of the angle measurement equations. This paper presents a new two-step instrumental-variable Kalman filter (2S-IVKF) exploiting the use of pseudolinear estimation, as well as instrumental-variable estimation to overcome the bias problem. The performance advantage of the proposed 2S-IVKF algorithm over the 2S-PLKF, as well as extended and sigma-point Kalman filters is demonstrated with simulation examples.

This letter proposes a novel bias-compensated diffusion pseudolinear Kalman filter algorithm for censored bearings-only target tracking. The proposed algorithm considers two biases caused by the censored bearing angle measurements and the correlation between the measurement vector and the pseudolinear noise. First, the inverse Mills ratio is used to rebuild the uncensored measurements, which can efficiently compensate the bias arising from the censored measurements. Then, we present a bias analysis for the censored diffusion pseudolinear Kalman filter to develop a bias compensation method, leading to the BC-DPLKF-C algorithm. Simulation results show the improved performance of the proposed Kalman filter compared with existing algorithms.

This paper considers the problem of angle-of-arrival (AOA) source localization with Bayesian priors. Extending the so-called pseudolinear estimator (PLE) for AOA localization to the Bayesian framework results in the Bayesian PLE (BPLE). Similar to the PLE, the BPLE suffers from severe bias problems despite enjoying the inherent stability and computational simplicity of a closed-form least-squares estimator. Bayesian priors make bias compensation for the BPLE much more challenging than the PLE. We present a bias analysis for the BPLE from which two direct bias compensation methods are developed. As an alternative to direct bias compensation, a new approach with superior estimation performance and low computational complexity is then proposed by exploiting jointly the use of instrumental variables and 2-D unscented (or cubature) transformation. The resulting estimator, referred to as the bias-compensated Bayesian weighted instrumental variable estimator, enjoys the desirable properties of negligible bias and mean-squared error on par with the computationally demanding an iterative maximum a posteriori estimator. The comparative simulation studies are presented to corroborate the performance advantages of the proposed estimators.

This paper develops a new computationally efficient bias reduction method for the well-known algebraic closed-form solution for time-difference-of-arrival (TDOA) localization developed by Chan and Ho. The noise correlation between the regressor and regressand in the formulation of the linearized least-squares computation is the main cause of bias problems associated with this TDOA localization method. The bias reduction method proposed in this paper, which we call IV- BiasRed, exploits the use of instrumental variables (IV) to eliminate the troublesome noise correlation between the regressor and regressand. The IV- BiasRed method is demonstrated by way of simulations to achieve a significant bias reduction and mean-squared error performance close to the Cramér-Rae lower bound. While producing an estimation performance on par with the maximum likelihood estimator and a recently proposed bias reduction method, the proposed IV-BiasRed method is computationally much more efficient than existing bias reduction methods.

In Doppler-bearing source localization, pseudolinear estimators are appealing alternatives to the divergence-prone and computationally-demanding iterative maximum likelihood estimator. Among the existing pseudolinear estimators, the weighted instrumental variable estimator (WIVE) is the most attractive option as it is asymptotically unbiased and efficient. However, the asymptotic unbiasedness of the WIVE relies on the approximation that the second-order noise term in the Doppler pseudolinear noise is zero, which is only valid for sufficiently small noise. In large noise, the second-order noise term can no longer be neglected, thereby leading to biased estimates. In this paper, we analyze the WIVE bias and propose a new improved version of the WIVE, called the I-WIVE, that overcomes the WIVE bias problems at large noise levels. The superior performance of the I-WIVE over the WIVE and other pseudolinear estimators is demonstrated by way of simulations. Specifically, we observe that the I-WIVE exhibits a negligible bias and produces a mean-squared error closest to the Cramér-Rae lower bound among the simulated estimators.

This letter presents a new three-dimensional (3-D) instrumental variable based Kalman filter (3D-IVKF) algorithm for angle-of-arrival target tracking from azimuth and elevation angle measurements. First, a 3-D pseudolinear Kalman filter (KF) algorithm is derived by applying the classical linear KF to a pseudolinear state-space model. To counter the severe bias problems with this algorithm, bias compensation and recursive instrumental variable (IV) methods are considered. A selective-angle-measurement strategy is also adopted to satisfy requisite conditions for IV estimation. The resulting 3D-IVKF algorithm inherits the low computational complexity and robust performance of pseudolinear estimation techniques. It is shown through simulation examples that the 3D-IVKF algorithm can achieve a similar tracking performance to the sigma-point KFs by producing a negligible bias and a mean-square error close to the posterior Cramér-Rao lower bound at a much reduced computational complexity, while outperforming the extended KFs at high noise levels.

This paper presents new closed-form estimators for 3-D source localization using Doppler measurements collected by a moving sensor platform. Two estimators, based on bias compensation and instrumental variables, are developed with significant performance improvement over a previously proposed least-squares estimator (LSE). First, a refined variant of the LSE, called bias-compensated weighted LSE (BCWLSE), is presented utilizing a weighting matrix and a bias compensation step. A weighted instrumental variable estimator is then developed based on a modified pseudolinear equation for the LSE to avoid pseudolinear noise amplification that may deteriorate the BCWLSE performance at large noise. Comparative simulation studies are presented to demonstrate the performance advantages of the proposed estimators over the severely biased LSE and divergence-prone maximum likelihood estimator. The proposed estimators are observed to produce almost no bias and achieve a mean-squared-error performance close to the Cramér-Rao lower bound.

Closed-form pseudolinear estimators are computationally attractive alternatives to iterative nonlinear techniques. For time-of-arrival (TOA) based localization, several pseudolinear estimators have been proposed such as the least squares calibration (LSC) estimator, the linear least squares (LLS) estimator, and their best linear unbiased estimator (BLUE) variants (namely, the BLUE-LSC and BLUE-LLS estimators). Despite their stable performance and low computational complexity, these pseudolinear estimators suffer from bias problems due to the nonzero mean of the pseudolinear noise vectors. In this paper, we present a bias analysis for the TOA-based pseudolinear estimators. Based on the bias analysis we develop bias compensation methods that lead to new bias-compensated versions of the LSC, LLS, BLUE-LSC and BLUE-LLS estimators. The superior performance of the proposed bias-compensated estimators is demonstrated via numerical simulations. The new estimators are observed to exhibit negligible estimation bias even at high measurement noise levels.

This paper presents a new algebraic solution for the Doppler positioning problem where the position of a stationary emitter is estimated from Doppler frequency measurements collected by a single low-earth-orbit (LEO) satellite. The proposed algebraic solution can be used for effective initialization of more sophisticated iterative algorithms as it produces estimates sufficiently close to the actual position of the emitter to ensure convergence. It is computationally more efficient than existing initialization techniques, based on the point of closest approach, which require expensive nonlinear curve fitting procedures. The effectiveness of the proposed algebraic solution is demonstrated by way of numerical simulations.

This paper derives the optimal angular geometry for elliptic time-of-arrival localization by a multistatic radar consisting of multiple independent bistatic channels. The optimal geometry analysis is conducted based on minimization of the area of the estimation confidence region which is equivalent to maximizing the determinant of the Fisher information matrix. It is shown that the optimal angular geometry is obtained when the transmitter and receiver of each bistatic channel are collinear with the target where the target is placed at the either end. The optimization problem therefore boils down to determination of the optimal angular separation between different bistatic channels which is mathematically equivalent to the optimal angular sensor separation for angle-of-arrival localization. These analytical results are confirmed by simulation examples.

An adaptive waveform scheduling algorithm is presented for target tracking by a multistatic radar system with two key components: (i) a distributed multistatic tracking algorithm for target tracking in cluttered environments, and (ii) the next transmitted waveform selected to minimize the tracking mean squared error. The scheduling algorithm is developed based on the minimization of the trace of the expected tracking error covariance matrix. A simulation example is presented to demonstrate the superiority of the proposed waveform scheduling algorithm over conventional fixed waveforms.