To support the development of low altitude economy, the air-ground integrated sensing and communication (ISAC) networks need to be constructed to provide reliable and robust communication and sensing services. In this paper, the sensing capabilities in the cooperative air-ground ISAC networks are evaluated in terms of area radar detection coverage probability under a constant false alarm rate, where the distribution of aggregated sensing interferences is analyzed as a key intermediate result. Compared with the analysis based on the strongest interferer approximation, taking the aggregated sensing interference into consideration is better suited for pico-cell scenarios with high base station density. Simulations are conducted to validate the analysis.

Internet of Things (IoT) has emerged as a key application domain in modern wireless communication systems, where its effectiveness heavily relies on the accuracy of channel state information (CSI). The deployment of large-scale reconfigurable intelligent surfaces (RIS) makes near-field effects non-negligible, bringing great challenges to channel estimation in RIS-assisted IoT scenarios. Existing near-field estimation methods often suffer from modeling errors due to Fresnel approximation or excessive computational complexity arising from dense polar-domain sparse representation. In this paper, we propose a novel RIS-aided near-field channel estimation framework tailored for IoT environments. We first present an accurate sparse channel model that captures the true spherical wavefront propagation and thus eliminates the modeling inaccuracies introduced by Fresnel approximation. Leveraging this model, we further incorporate IoT environmental context to derive a novel dimensionality-reduced sparse representation. Subsequently, we devise a fast sparse Bayesian learning (SBL)-based sparsity recovery scheme with coarse off-grid refinement, and embed generalized approximate message passing (GAMP) to significantly reduce computational complexity. Simulation results demonstrate that the proposed method achieves high estimation accuracy with reduced complexity, owing to the dimensionality-reduced sparse representation and the fast GAMP-embedded SBL framework. These advantages make it highly suitable for RIS-assisted IoT systems.

This paper studies the problem of integrated optical camera communication and scene sensing. Due to the tight coupling between background images and stripe information in low signal-to-noise ratio (SNR) encoded images, existing methods cannot simultaneously achieve high-quality optical signal decoding for LED-to-camera communication and background image reconstruction for scene sensing. To address this challenge, this paper analyzes the adversarial characteristics between stripe information and background images, and proposes GANOCCAS, a generative adversarial learning framework tailored for integrated optical camera communication and scene sensing that effectively resolves mutual interference between stripes and background content. First, we design a generator using a CondConv-based 4-layer U-NET architecture with SimAM modules on the last three residual layers and CondConv+PixelShuffle combinations as upsampling layers. Second, we develop a discriminator that combines multi-scale convolutional networks, pooling layers, and residual networks to output stripe sequences for optical signal decoding. Third, by leveraging pixel loss, multi-scale structural similarity loss, and adversarial loss, we ensure that the generator outputs clean background images suitable for scene sensing while the discriminator decodes optical signals for communication in complex environments. Experiments on synthetic and real-world datasets demonstrate that GANOCCAS effectively reduces communication interference from background images and accurately reconstructs stripe-free background images across various SNR scenarios, outperforming current state-of-the-art methods in both reflected OCC and scene sensing tasks.

Ultra-wideband rigid body localization (RBL) represents a pivotal technology for achieving high-precision indoor localization. However, it encounters significant challenges, particularly non-line-of-sight (NLOS) propagation errors and reduced vertical observability in complex environments, both of which considerably undermine the robustness of existing three-dimensional(3D) RBL techniques. To mitigate these challenges, this paper introduces a novel, robust 3D-RBL methodology based on hierarchical constraint fusion, referred to as HC-RBL. This approach enhances performance with a three-tier optimization framework: 1) at the sensor layer, the Particle Swarm Optimization algorithm is used to derive the globally optimal geometric configuration of sensor anchor nodes and tag nodes, thereby maximizing spatial observability; 2) at the signal layer, a hybrid robust M-estimation coupled with multiple outlier detection techniques is leveraged to effectively suppress the localization error; 3) at the trajectory layer, the Rauch-Tung-Striebel smoothing algorithm is applied, incorporating rigid-body kinematic constraints to ensure the physical consistency of 3D motion trajectory. The dataset was obtained from indoor wheeled robot experiments, covering approximately 500 m of trajectory over 16 min. In indoor NLOS environments, with a ranging accuracy of 0.31 m, experimental results from multiple wheeled robot localization tests demonstrate that HC-RBL significantly outperforms conventional RBL methods, achieving an 76% reduction in root mean square error to 0.103 m and an 80% reduction in the 95% cumulative error to 0.182 m. The proposed HC-RBL method exhibits remarkable robustness in complex indoor environments against pronounced NLOS effects.

We are interested in the mobile target state detection (TSD) based on multi-input-multi-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) communication signals. Yet, communication-based TSD is of challenge, due to complex problem structures and communication symbol randomness. To address this challenge, we exploit structured features of low-speed and narrow-band systems to decouple the target state parameters and random communication symbols, and then use coherent detection to equalize random symbols. As such, a simplified sensing model holding an explicit space-time-frequency-domain correlation structure with respect to target direction angle, radial speed and relative distance, respectively, is obtained. Then, an efficient MIMO OFDM waveform-based TSD method extracting space-time-frequency correlation features is devised. It is verified by simulations that the proposed TSD method outperforms state-of-the-art baselines, due to the above problem-specific algorithm design. In addition, we establish the closed-form boundaries of the maximum detectable speed and maximum detectable range for MIMO OFDM-based TSD, which are essentially subject to the limited coherent time and bandwidth, respectively. The impact of system parameters (e.g., signal bandwidth, subcarrier spacing and carrier frequency) on the detection capability boundaries is analysed to gain insights into the fundamental limits of MIMO OFDM communication-based TSD. This work does not only build a technical foundation for sensing-assisted communication design, but also provide a unified framework for understanding the potentials of MIMO OFDM communication-based sensing.

We focus on joint multi-beam optimization (MBO) on both transmitter and receiver of 6G integrated sensing and communication (ISAC) systems, for achieving on-demand communication and sensing (C&S) performance tradeoff for diverse users. However, MBO is of great challenge due to inevitable self-interference (SI) of full-duplex antenna arrays and its non-convex optimization problem nature. Firstly, in order to address the SI challenge, we absorb SI alleviation requirements into problem modeling, and develop a novel SI-alleviated MBO framework. Secondly, in order to handle the non-convex optimization challenge, we resort to Lagrange dual transformation and fractional transformation for problem simplification, and extract structured models to yield an efficient alternating optimization-type MBO algorithm. We establish the convergence of the proposed MBO algorithm to justify our closed-form iterative optimization design. The proposed SI-alleviated MBO method can address different C&S requirements of diverse users, via joint transmitter and receiver beam steering, which paves the way for on-demand ISAC services. It is corroborated by simulations that our SI-alleviated MBO method outperforms state-of-the-art ISAC beamforming baselines, due to our problem-specific algorithm design.

We focus on self-interference (SI) alleviated beamforming of 6 G full-duplex integrated sensing and communication (ISAC) systems, for achieving an on-demand sensing and communication performance tradeoff with suppressed SI for diverse user devices. However, SI-alleviated ISAC beamforming is of great challenge due to its complex problem structures and nonconvex optimization problem nature. In order to address this challenge, we propose to use the dual transformation framework for problem simplification, and exploit structured components of the problem model, such as convexity, linearity and fraction, for yielding an efficient iterative optimization solution. The proposed SI-alleviated beamforming method can gracefully take care of communication and sensing requirements with suppressed SI for diverse user devices, thus paving the way for an on-demand ISAC service. It is corroborated by numerical simulations that the proposed SI-alleviated beamforming method outperforms state-of-the-art ISAC beamforming baselines, due to our specially-tailored problem modeling and problem-specific algorithm design.

This paper considers downlink power allocation and beamforming (PABF) for integrated sensing and communications (ISAC) against multipath interference. Yet, ISAC-oriented PABF is of great difficulty, due to its parameter-coupling structure and non-convex problem nature. A novel PABF method is proposed to address this issue. Firstly, in order to handle its complex problem structure, the PABF problem is divided into three subproblems, where communication-end beamformer, sensing-end beamformer and multipath power vector are decoupled. Secondly, structured models of the complex problem are extracted to address the non-convexity challenge. An efficient alternating optimization-based PABF algorithm with closed-form iterations is obtained. At the sensing receiver end, our PABF method can focus beams at the line-of-sight direction, while form null beams at reflection directions for suppressing multipath interference. Simultaneously, at the communication transceiver ends, it can smartly adjust beam gains and transmitting power over multiple paths to maximize the communication performance while ensuring a promised sensing performance. The proposed PABF algorithm can strike an on-demand communication and sensing performance tradeoff, via adjusting the sensing performance requirement. We have verified the efficiency of our PABF method by numerical simulations.

The Society of Automotive Engineers (SAE) J2954 Differential Inductive Positioning System (DIPS) is a ground breaking technology introduced to enable positioning technique for electric vehicle (EV) wireless power transfer (WPT). This technology and its related standard have great potential to bring EV wireless charging to mass production and opens the doors for commercializing autonomous vehicles. Although the DIPS standard has defined the hardware requirements [1], the positioning algorithm design has not been addressed in the literature. In this paper, we introduce the industry's first algorithm for DIPS. We mathematically derive the signal model and parameters estimation algorithm, then evaluate the estimation accuracy of the proposed algorithm using Monte Carlo simulations. The evaluation results have shown the algorithm can achieve centimeter-level accuracy and approach the Cramér-Rao bound.

In space-air-ground-sea networks, the conventional data processing designs separately considering sensing, communication and computation processes lead to severe wastes of radio, energy, and computation resources. To overcome this drawback, an integrated sensing-communication-computation design is pro-posed in this paper, which aims at realizing energy efficient data processing by jointly determining the data offloading ratio together with the sensing and offloading rates according to the processor profiles of mobile devices and servers. It is proved that the data offloading ratio is determined by the server's processor profile, while the string-pulling algorithms are designed to obtain the optimal sensing and offloading rates. Simulations are conducted to verify the effectiveness of the proposed design.

Driven by the rapid development of industrial Internet of Things applications, the wireless networked control system (WNCS) is expected to support real-time control-communication interaction performed in finite-time, which is task-oriented. A WNCS composed of multiple wirelessly inter-connected subsystems (SSs) is considered in this paper. The sensed state information in each SS is transmitted to the controller via wireless links for decision-and-control tasks. After multiple operation periods of state sensing and trans-mission, the system identification (SI) is executed and the optimal control (OC) policy is made. The SI requirement for OC is analyzed via system-level synthesis (SLS) based on robust control theory. A communication and control co-design is investigated, aiming to improve the energy efficiency while guaranteeing the SI performance requirement within the allowed decision-making time. The transmit powers at each sensor and controller, transmission interval length as well as the number of operation periods are jointly optimized. Simulations are conducted to validate the performance of the proposed co-design.

Low peak-to-average power ratio (PAPR) orthogonal frequency division multiplexing (OFDM) waveform design is a crucial issue in integrated sensing and communications (ISAC). This paper introduces an OFDM-ISAC waveform design that utilizes the entire spectrum simultaneously for both communication and sensing by leveraging a novel degree of freedom (DoF): the frequency-domain phase difference (PD). Based on this concept, we develop a novel PD-based OFDM-ISAC waveform structure and utilize it to design a PD-based Low-PAPR OFDM-ISAC (PLPOI) waveform. The design is formulated as an optimization problem incorporating four key constraints: the time-frequency relationship equation, frequency-domain unimodular constraints, PD constraints, and time-domain low PAPR requirements. To solve this challenging non-convex problem, we develop an efficient algorithm, ADMM-PLPOI, based on the alternating direction method of multipliers (ADMM) framework. Extensive simulation results demonstrate that the proposed PLPOI waveform achieves significant improvements in both PAPR and bit error rate (BER) performance compared to conventional OFDM-ISAC waveforms.

For many practical applications in wireless communications, we need to recover a structured sparse signal from a linear observation model with dynamic grid parameters in the sensing matrix. Conventional expectation maximization (EM)-based compressed sensing (CS) methods, such as turbo compressed sensing (Turbo-CS) and turbo variational Bayesian inference (Turbo-VBI), have double-loop iterations, where the inner loop (E-step) obtains a Bayesian estimation of sparse signals and the outer loop (M-step) obtains a point estimation of dynamic grid parameters. This leads to a slow convergence rate. Furthermore, each iteration of the E-step involves a complicated matrix inverse in general. To overcome these drawbacks, we first propose a successive linear approximation VBI (SLA-VBI) algorithm that can provide Bayesian estimation of both sparse signals and dynamic grid parameters. Besides, we simplify the matrix inverse operation based on the majorization-minimization (MM) algorithmic framework. In addition, we extend our proposed algorithm from an independent sparse prior to more complicated structured sparse priors, which can exploit structured sparsity in specific applications to further enhance the performance. Finally, we apply our proposed algorithm to solve two practical application problems in wireless communications and verify that the proposed algorithm can achieve faster convergence, lower complexity, and better performance compared to the state-of-the-art EM-based methods.

We are interested in multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication-based vehicle state detection (VSD) (including vehicle location, velocity and pose angle) in multipath interference scenarios, towards 6G integrated sensing and communications. Yet, communication-based VSD is challenging, since its signals undergo multipath interference and random fading, while channel state and reflector locations are even unknown, in addition to vehicle state. To address these challenges, a novel multipath information fusion-assisted VSD scheme is devised to smartly aggregate geometric knowledge from both direct and reflection paths, thus yielding a robust VSD solution against multipath interference. In addition, we propose to divide the complex VSD problem into four subproblems: (i) angle-of-arrival detection, (ii) time-of-flight estimation, (iii) joint reconstruction of angle-of-departure, radial speed and channel state, and (iv) vehicle-and-reflector state detection. An efficient four-step cascaded VSD method is devised by exploiting linearity, quadratic, orthogonality and space-time-domain correlation of MIMO OFDM signals, which finally achieves simultaneous VSD, reflector positioning and channel estimate. It is verified by simulations that our VSD scheme outperforms state-of-the-art baselines due to our specially-tailored problem decoupling and multipath information fusion, which builds a technical foundation for designing environment sensing-assisted communication strategies.

We focus on visible light communication-based simultaneous position and orientation detection (SPAO) for user devices (UDs) using photodiodes, which is challenging due to scattering interference and small-scale fading. To address this challenge, a novel SPAO approach is proposed, which can jointly estimate UD location parameters and scattering channel states. As such, the disturbance of diffuse scattering and random fading on SPAO will be alleviated via scattering channel equalization. In addition, SPAO is non-convex in nature, and hence brute-force application of conventional optimization methods will lead to a poor SPAO solution. To address this issue, we devise a majorization minimization (MM)-based SPAO algorithm, where hidden convex structure of the non-convex SPAO problem is exploited, which renders an efficient closed-form iteration rule for joint SPAO and diffuse channel estimation. Due to the cross-layer cooperation between “VLC” and “ranging”, a robust SPAO solution against diffuse scattering and small-scale fading is achieved. It is corroborated by our simulations that the proposed MM-based SPAO algorithm achieves a large performance gain over state-of-the-art baseline methods.

Visible Light Positioning (VLP), a promising indoor positioning technique, has gained wide popularity worldwide because of its ubiquitous infrastructure, low power consumption, and high positioning precision. However, VLP systems based on photodiodes (PDs) often suffer from varying ambient light with time and space, which seriously degrades their positioning precision and robustness. In this article, we carefully evaluate the influence of the ambient light on the VLP system, which includes the reduction of positioning accuracy by varying ambient light with time and the inaccurate parameter calibration by unevenly distributed ambient light. Then, we figure out that the influence of ambient light on the Received Signals Strength (RSS) values is determined by the ambient light intensity and PD, which is independent of external factors, including distance, frequency, LED, etc. Next, we propose a new positioning framework, RatioVLP, where a ratio model that is more robust to varying ambient light with time is used. However, the ratio model is severely dependent on the Lambert parameters that are vulnerable to ambient light, which reduces the framework's precision when the calibration area is unevenly covered by ambient light. Thus, we design new parameters that are less sensitive to ambient light, calledR parameter, to connect the RSS ratio and its corresponding distance ratio, which can strengthen the ratio model's robustness and effectively reduce the influence of ambient light on the parameter calibration process. Experimental results show that the positioning precision of the proposed method is improved by more than 50 % when compared to the conventional Lambert model in scenes influenced by ambient light.

In this paper, we focus on visible light-based position and pose detection (VLP) for user devices in dynamical environments. Traditional model-based VLP methods usually depend on a perfect signal propagation model (SPM) with fixed parameters, and hence their performance will be seriously decreased when localization environment varies over time, e.g., due to diffuse scattering and reflections. To address this challenge, in this paper we propose a novel branch-structured residual convolutional neural network (RCNN)-based VLP method, without any requirement on perfect SPM knowledge. We observe that there are environment-invariant texture features in received visible light signal samples, which can be exploited for VLP performance enhancement. A branch-structured RCNN-based VLP scheme is devised for exploiting diverse-level stable texture features from received measurement samples, rendering a reliable VLP solution against environmental dynamics. It is verified by simulations that our branch-structured RCNN-based VLP solution outperforms existing machine learning-based VLP methods.

The timeliness of information is of capital importance for numerous Internet of Things (IoT) services. To improve the information freshness in large-scale distributed IoT systems, this paper focuses on the Peak Age of Information (PAoI) optimization of slotted Aloha networks. Specifically, by assuming the first-come-first-served (FCFS) service discipline and Bernoulli packet arrival model, the mean PAoI is characterized and then optimized by either individually tuning the channel access probability or jointly tuning the channel access probability and packet arrival rate of each sensor. The explicit expressions of optimal parameter settings and the corresponding minimum PAoI are obtained, based on which the age-throughput tradeoff is evaluated. The analysis is verified by simulations. It is found that in the massive access scenarios, the minimum PAoI linearly increases with the network scale in both individual optimization and joint optimization cases, while the latter attains a lower increasing rate, better age performance, and less throughput loss.

For battery-limited IoT networks, the energy efficiency and Age of Information (AoI) are two key performance metrics. Yet the tradeoff between energy efficiency and AoI remains unclear for large-scale networks since the analysis becomes challenging due to the couple queue problem. This paper aims to address this issue by studying the performance limit of energy efficiency under AoI constraint.Specifically, we evaluate the energy efficiency via the expected number of successfully transmitted packets during each transmitter’s life time for which the explicit expression is derived based on the spatio-temporal analytical framework in [1]. By further taking the AoI constraint into consideration, explicit expressions of the Maximum Expected Number of Successfully Transmitted Packets (MENSTP) and the corresponding channel access probability are obtained. The analysis reveals that if the Power Ratio of the Transmission state and the Waiting state (PRTW) equals one, i.e., the energy consumption per time slot of the transmission state equals to that of the waiting state, then the expected number of successfully transmitted packets during each transmitter’s life time and the peak AoI can be optimized simultaneously; otherwise, the MENSTP declines with a stringent AoI constraint. Moreover, the performance gap enlarges when the PRTW or the node distribution density increases which reveals a crucial tradeoff between the energy efficiency and AoI. It is therefore of importance to properly tuning the channel access probability to strike an optimal energy-age tradeoff in battery-limited large-scale IoT networks.

Millimeter-wave (mmWave) massive multiple input multiple input (MIMO) has shown great potential in user equipment (UE) localization of 5G wireless communication systems. However, mmWave signals usually suffer from non-line-of-sight (NLOS) propagation, which will affect mmWave MIMO-based UE localization performance. Hence, it is non-trivial to reveal how NLOS propagation affect mmWave-based UE localization performance. In this paper, we give a unified analysis framework for UE localization performance gain from harnessing NLOS propagation. Firstly, a closed-form Cramer-Rao lower bound on mmWave MIMO-based UE localization is derived to shed lights on its performance limit. Secondly, NLOS propagation-caused localization error for conventional UE localization methods without harnessing multipath effect is analysed. Finally, the information contribution from NLOS channel is quantified, which sheds light on how to smartly harness NLOS propagation and the associated UE localization performance gain.

In this paper, we are interested in the joint estimate of user equipment (UE) location and orientation for millimeter-wave multi-input-multi-output (mmWave MIMO) systems. In practice, mmWave signals suffer from small-scale fading, which degrades UE localization. Moreover, mmWave MIMO-based UE localization is a non-convex optimization problem, and the bruteforce application of conventional optimization methods will result in a poor solution or lead to large computational cost. In order to address the above challenges, we propose a novel simultaneous localization and channel estimate (SLCE) algorithm, where the UE location parameters and small-scale fading coefficients are jointly optimized. In such a case, the disturbance of small-scale fading on UE localization is alleviated. Thanks to our problem-specific update rule design, the proposed SLCE algorithm achieves a large performance gain over existing baseline methods.

As a key enabler in the green transport system, the popularity of Electric Vehicles (EV) has attracted attention from academia and industrial communities. However, the driving range of EVs is inevitably affected by the insufficient battery volume, as such EV drivers may experience trip discomfort due to a long battery charging time (under traditional plug-in charging service). One feasible alternative to accelerate the service time to feed electricity is the battery switch technology, by cycling switchable (fully-recharged) batteries at Battery Switch Stations (BSSs) to replace the depleted batteries from incoming EVs. Along with recent advance of vehicle cooperation through emerging Information Communication Technology (ICT), in this paper we propose a Mobile Edge Computing (MEC) driven architecture to gear the intelligent battery switch service management for EVs. Here, the decision making on where to switch battery is operated by EVs in a distributed manner. Besides, the Vehicle-to-Vehicle (V2V) communication in line with public transportation bus system is applied to operate flexible information exchange between EVs and BSSs. Dedicated MEC functions are positioned for bus system to efficiently disseminate BSSs status and aggregate EVs’ reservations, concerning the massive signalling exchange cost. The Global Controller (GC) is positioned as cloud server to gather BSSs (service providers) status and EVs’ reservations (clients), and predict the service availability of BSS (e.g., whether/when a battery can be switched). We conduct performance evaluation to show the advantage of MEC system in terms of reduction of communication cost, and BSS service management scheme regarding reduction of service waiting time (e.g., how long to wait for battery switch) and increase of service satisfaction rate (e.g., how many batteries to switch for EVs).

In this paper, we aim to give a unified performance limit analysis of the visible light-based positioning (VLP) for a vehicular user equipment (UE), which will help to understand the essence of time-domain localization cooperation and gain insights into how to improve the performance limit of the vehicular VLP system. This is challenging due to the complex system models and the complex dependency between UE location performance and orientation performance. To achieve the above goal, we will first characterize the closed-form error bounds of the UE location and orientation at each time slot, respectively, in terms of Fisher information. Generally, the VLP error will propagate over time as the vehicular UE moves, and hence the VLP error at the current time slot is affected by the VLP performance at the previous time slot, the UE mobility and the channel quality. Based on the obtained VLP error bounds, we then reveal the impact of prior UE location knowledge, UE mobility and signal-to-noise-ratio on the VLP performance. Furthermore, the time-domain evolution of the VLP error is studied, where the convergence of the time-domain VLP error evolution is established and its closed-form stable state is quantified, which will shed light on the long-term performance of the vehicular VLP system.

In this paper, we focus on wireless sensor network-based localization for user devices (UDs). Prior to UD localization, signal propagation model (SPM) is required to calibrate using training samples from a number of location grids. However, SPM usually suffers from measurement noise and inevitable error in calibration grid (CG) locations. This will significantly degrade UD localization performance. Nevertheless, the impact of CG location error, CG layout and the number of CGs on SPM calibration performance has not been characterized. Furthermore, the effect of SPM calibration error on UD localization performance has not been developed. In this paper, we aim to provide a unified framework for performance analysis of SPM calibration and UD localization. Firstly, we establish a closed-form Cramér-Rao lower bound on SPM calibration error and UD localization error, respectively. Secondly, the impact of measurement noise, CG location error and the number of CGs on SPM calibration performance is revealed. Thirdly, the influence of SPM calibration error, CG location error and measurement noise on UD localization performance is studied. The effect of modeling mismatch is also studied. The obtained analysis framework builds a theoretical basis for the design of efficient system optimization strategies, including resource allocation and CG deployment optimization, for UD localization performance enhancement.

In this article, we are interested in leak identification (LI) for water supply pipelines using transient-wave (pressure) measurement data. This is challenging since water pipeline system conditions are usually uncertain in practice. For instance, the pipeline diameter, the friction factor, and the pipeline shape will vary. The conventional signal propagation model-based LI methods rely on a deterministic system model with perfectly known and fixed-value parameters, which limits their application in general cases. To address this challenge, we design a novel deep neural network (DNN)-based machine learning approach to solve the LI problem. First, we propose a novel fusion-enhanced stochastic optimization algorithm for the DNN training, which can greatly improve the DNN training performance and hence the LI accuracy, without increasing the computational cost. Second, we design a novel convolutional-based pooling network to extract the stable texture feature of transient-wave samples, thus achieving a reliable LI solution against the pipeline system dynamics. It is shown in experiments that, thanks to the above system design, the proposed DNN-based LI method can achieve a failure rate lower than $6\times 10^{-4}$ when the signal-to-noise ratio is 0 dB, which outperforms the conventional LI methods.

In this paper, visible light-based positioning (VLP) is studied. VLP is greatly challenging because (i) it is essentially a non-convex optimization problem since the visible-light received signal strength (RSS) is nonlinear with the user equipment (UE) position; and (ii) in addition to the UE location, the visible light RSS also depends on the UE orientation and small-scale channel gains, which are unknown in practice. This complicates the VLP problem due to the enlarged searching space. To address these challenges, we propose a location-domain grid sampling scheme. Specifically, the location-domain grid sampling can potentially partition the location space into small cells, and hence the non-convexity challenge of RSS-based VLP is mitigated. In addition, using the location-domain grid sampling, we transform VLP into a sparse recovery problem. A novel group sparse learning (GSL) algorithm with self-adaptive location-domain grids is proposed to achieve an efficient RSS-based VLP solution, via exploring the inherent sparse structure. The convergence of our GSL algorithm is established. Thanks to the adaptivity of dynamic location-domain grids, the required number of location-domain grids can be significantly reduced, compared with conventional fixed-grid-based GSL solutions. Moreover, the proposed GSL-based VLP method jointly learns the UE location, orientation and channel gain, thus achieving a robust RSS-based VLP solution against parameter uncertainties. Finally, our simulation result verifies the large performance gain of the proposed RSS-based VLP solution over state-of-the-art VLP baselines, thanks to our self-adaptive grid sampling and problem-specific group sparse learning.

In practice, visible light signals undergo non-line-of-sight (NLOS) propagation, and in visible light-based positioning (VLP) methods, the NLOS links are usually treated as disturbance sources to simplify the associated signal processing. However, the impact of NLOS propagation on VLP performance is not fully understood. In this paper, we aim to reveal the performance limits of VLP systems in an NLOS propagation environment via Fisher information analysis. Firstly, the closed-form Cramer-Rao lower bound (CRLB) on the estimation error of user detector (UD) location and orientation is established to shed light on the NLOS-based VLP performance limits. Secondly, the information contribution from the NLOS channel is quantified to gain insights into the effect of the NLOS propagation on the VLP performance. It is shown that VLP can gain additional UD location information from the NLOS channel via leveraging the NLOS propagation knowledge. In other words, the NLOS channel can be exploited to improve VLP performance in addition to the line-of-sight (LOS) channel. The obtained closed-form VLP performance limits can not only provide theoretical foundations for the VLP algorithm design under NLOS propagation, but also provide a performance benchmark for various VLP algorithms.

In this paper, we are interested in the joint estimate of user equipment (UE) position and orientation for visible light communication (VLC) with uncertain emission power. This joint estimation is a non-convex problem with a huge search space. To address this challenge, a novel VLC localization algorithm is proposed, which converges to the stationary solution of the joint estimation problem at an asymptotic quadratic convergence rate due to our problem-specific surrogate function design. A closed-form update rule is obtained via exploiting the hidden convex structure of the non-convex optimization problem. Hence, our algorithm has low complexity compared with the particle swarm-based optimization methods. In addition, the closed-form Cramer-Rao lower bounds (CRLBs) on the estimation errors of the respective UE location, orientation and LED emitting power are derived. Moreover, the effect of critical parameters such as signal-to-noise ratio (SNR), the number of LED transmitters, transmission distance and non-line-of-sight propagation on the VLC localization performance are revealed. The simulation result verifies that the proposed VLC localization algorithm under unknown VLC emitting power can achieve a huge performance gain over the state-of-the-art localization baselines.

In this paper, we aim at providing a unified performance analysis framework for visible light-based positioning (VLP) using received signal strength (RSS), which can be used to gain insights into improving the performance of RSS-based VLP systems. Specifically, we first obtain a closed-form Cramer-Rao lower bound (CRLB) for the user equipment (UE) location and orientation, respectively. Then, we reveal the impact of the signal-to-noise ratio (SNR), transmission distance, prior knowledge and the number of LED sources on the RSS-based VLP performance. Moreover, the impact of the non-line-of-sight (NLOS) propagation on the RSS-based VLP performance is studied. It is shown that the RSS-based VLP performance will hit an error floor caused by the unknown NLOS links. These NLOS-caused UE location and orientation error floors in the high SNR region are analyzed. Finally, the information contribution of each LED source is studied to give an intuitive understanding on the impact of the LED array geometry on the RSS-based VLP performance. The obtained CRLB and the associated VLP performance analysis form a theoretical basis for the design of efficient VLP algorithms and VLP performance optimization strategies (e.g., resource allocation and smart LED source selection).

In this paper, the identification of multiple leaks in pipes based on transient waves is studied, which is, however, quite challenging due to its nonconvex nature with lots of local optima. Existing approaches need the number of leaks and suffer from a huge computational complexity that is increased exponentially with the number of leaks. To provide a scalable solution, we propose a compressive sensing (CS) framework to solve the multileaks identification. We first exploit the sparseness nature of leak locations through spatial sampling. Then, we formulate the multileak identification as a CS problem, where the spatial sample-dependent components form a basis matrix and the leak sizes are viewed as a sparse signal. We establish the convergence of spatial sampling mismatch and the two-restricted isometry property of basis matrix to justify the proposed CS framework. The proposed CS framework renders a superior-performance solution to multileak identification and its computational complexity is linear with the number of leaks, which is a significant technical improvement over existing approaches. In addition, a closed-form Cramer-Rao lower bound (CRLB) on the leak localization errors is derived. A geometric insight of CRLB evolution is presented to give us an intuitive understanding of the contribution of new measurements to leak localization performance.

Simultaneous localization and tracking (SLAT) in wireless sensor networks (WSNs) involves tracking the mobile target while calibrating the nearby sensor node locations. In practice, localization error propagation (EP) phenomenon will arise, due to the existence of the latest tracking error, target mobility, measurement error, and reference node location errors. In this case, the SLAT performance limits are crucial for the SLAT algorithm design and WSN deployment, and the study of localization EP principle is desirable. In this paper, we focus on the EP issues for the received signal strength-based SLAT scheme, where the measurement accuracy is assumed to be spatial-temporal-domain doubly random due to the target mobility, environment dynamics, and different surroundings at different reference nodes. First, the Cramer-Rao lower bound (CRLB) is derived to unveil both the target tracking EP and the node location calibration EP. In both cases, the EP principles turn out to be in a consistent form of the Ohm's Law in circuit theory. Second, the asymptotic CRLB analysis is then presented to reveal that both EP principles scale with the inverse of sensor node density. Meanwhile, it is shown that, the tracking and calibration accuracy only depends on the expectation of the measurement precision. Third, the convergence conditions, the convergence properties, and the balance state of the target tracking EP and the location calibration EP are examined to shed light on the EP characteristics of the SLAT scheme Finally, numerical simulations are presented to corroborate the EP analysis.

Cooperative network localization plays an important role in wireless sensor network (WSN), wherein neighboring sensor nodes will help each other to calibrate their locations. However, due to the dynamic wireless propagation environment and different surroundings, the measurement accuracy at different network nodes is different and varies overtime. In this paper, the uncertainties in both measurement accuracy and reference node locations are considered to account for the impact of different surrounding environments and the initial node location errors on the cooperative network localization. A mean-field variational inference-based positioning (VIP) algorithm is proposed for cooperative network localization. The mechanism of the proposed VIP algorithm, the convergence properties, implementation complexity, and the parallel implementation structure are presented to show that the VIP algorithm provides an effective mechanism to incorporate and share the localization information among all network nodes for an improved localization performance. Finally, a concise Cramer-Rao lower bound (CRLB) is derived to reveal the principle of localization error propagation. It is disclosed that the localization error propagation principle is similar to the Ohm's Law in circuit theory, which provides a new insight into the impact of the measurement accuracy, the reference node location errors and the number of reference nodes on the cooperative network localization performance.

The channel estimate and carrier frequency offset (CFO) acquisition are two essential bases for multiple-input-multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) techniques. In high-mobility scenario, the fast time varying channel and the serious Doppler frequency shift will make the channel tracking and CFO estimation much more challenging. In this paper, the achievable limits of joint basis expansion-model (BEM)-based channel tracking and CFO estimation are derived in terms of the Bayesian Cramer-Rao lower bound (BCRLB) to reveal the impact of dependent factors, like the pilot and BEM modeling error. The BCRLB analysis shows that, the measurement reliability, BEM modeling error, channel correlation and the statistical channel dynamics will dominate the achievable limits. And smaller pilot space seems to be an imperative choice to improve the joint estimate performance.

The wireless cooperative localization plays a key role in location-aware service. However, its objective function, e.g., the posteriori probability function, is commonly nonconvex due to nonlinear measurement function and/or non-Gaussian system disturbance. Moreover, due to the unavoidable reference node location error, the associated objective function is commonly intractable, which further complicates the cooperative localization. In this paper, a novel particle-assisted stochastic search (PASS) algorithm is proposed to realize the cooperative localization. Given a nonconvex objective function, the proposed PASS method can find out the global optimum in probability, assisted with its search particles, detection particles, and proposal particles. In addition, the PASS algorithm can harness the reference node location uncertainties in cooperative localization, by employing its proposal particles. The associated Cramer-Rao lower bound (CRLB), localization error propagation, computational complexity, and convergence properties are also presented to assess the proposed PASS-based cooperative localization. Finally, received signal strength-based localization is simulated to validate the effectiveness of the proposed PASS approach.