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This paper studies the cooperative localization problem for mobile nodes. Different from the previous work which highly relies on the synchronized time-slotted systems, the cooperative localization framework we establish does not need any synchronization for the communication links and measurement processes in the entire wireless network. More specifically, the mobility of nodes is modeled by the stochastic differential equation, which allows the modeling of each node utilizing the asynchronously received messages and measurements in the continuous-time domain. To solve the cooperative localization problem, first we propose the centralized localization algorithm based on the global information. Then, we rigorously prove under what condition a localization estimation with partial information has a small performance gap from the one with global information. Finally, by applying this result at each node, the distributed prior-cut algorithm is designed to solve this asynchronous localization problem. Two cooperative localization examples are presented to corroborate the effectiveness of the distributed prior-cut algorithm in dealing with asynchronous communications and measurements.

Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in the current multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In order to address this issue, model-free localization techniques using deep learning frameworks have been lately proposed, where mainly classification methods were applied. In this paper, instead of classification based mechanism, we propose a logistic regression based scheme with the deep learning framework, combined with Cramér-Rao lower bound (CRLB) assisted robust training, which achieves more robust sub-meter level accuracy (0.97m median distance error) in the standard laboratory environment and maintains reasonable online prediction overhead under the single WiFi AP settings.

In this paper, multi-leak identification based on a transient wave model with physical parameter uncertainties for smart water supply systems is studied. We formulate multi-leak identification under uncertain parameters (such as friction factor, wave speed, and source–end discharge oscillation) as a sparse signal recovery problem with inaccurate parameters in the measurement matrix, by using spatial sampling in leak location space. A stochastic sparse variational Bayesian inference (SSVBI) algorithm to jointly learn the spatial samples, sparse signal, and uncertain parameters is proposed for multi-leak identification. In addition, we establish the convergence of the SSVBI algorithm to an approximate minimum means squared estimate. The proposed approach can be applied to an arbitrary number of leaks, and its computational complexity is insensitive to the number of leaks. This is a significant technical improvement over existing approaches. Finally, simulations show that the SSVBI-based joint learning of uncertain parameters and sparse model can achieve a huge performance gain over existing methods.

Light-induced photoluminescence (PL) switching using azobenzene-doped liquid crystal (LC)-dispersed quantum dots (QDs) is demonstrated in a format of LC cell and LC-infiltrated tube. Before light irradiation, excitation photons undergo multiple scattering events before emission out of the LC cell, and PL intensity is high. Upon the light irradiation, the trans-to-cis photoisomerization of azobenzene makes the QD-dispersed LCs highly transparent, thus allowing most excitation photons to pass through LCs and decrease PL intensity. This demonstration may allow a new modulating technique for future development of light-switchable, QD-emission-based display or photonic devices.

In this paper, we consider a wireless multihop device-to-device (D2D) based mobile edge computing (MEC) system, where the destination wireless device (WD) is scheduled to compute nomographic functions. Under the MapReduce framework and motivated by reducing communication resource overhead, we propose a new multi-level over-the-air (OTA) aggregation scheme for the destination WD to collect the individual partially aggregated intermediate values (IVAs) for reduction from multiple source WDs in the data shuffling phase. For OTA aggregation per level, the source WDs employ a truncated channel-inverse structure multiplied by their individual transmit coefficients in transmission over the same time-frequency resource blocks, and the destination WD finally uses a receive filtering factor to construct the aggregated IVA. Under this setup, we develop a unified transceiver design framework that minimizes the mean squared error (MSE) of the aggregated IVA at the destination WD subject to the source WDs’ individual power constraints, by jointly optimizing the individual transmit coefficients of the source WDs and the receive filtering factor of the destination WD. The formulated power-constrained MSE minimization problem is non-convex. First, based on the primal decomposition method, we derive the closed-form solution under the special case of a common transmit coefficient. This shows that the common transmit coefficient of the source WDs is determined by the minimal transmit power budget among them. Next, for the general case, we transform the original problem into a quadratic fractional programming problem, and then develop a low-complexity algorithm to obtain the (near-) optimal solution by leveraging Dinkelbach’s algorithm along with the Gaussian randomization method. Numerical results are provided to demonstrate the significant performance gains achieved by the proposed multi-level OTA aggregation scheme over various existing schemes. [ABSTRACT FROM AUTHOR]

The enormous data distributed at the network edge and ubiquitous connectivity have led to the emergence of the new paradigm of distributed machine learning and large-scale data analytics. Distributed principal component analysis (PCA) concerns finding a low-dimensional subspace that contains the most important information of high-dimensional data distributed over the network edge. The subspace is useful for distributed data compression and feature extraction. This work advocates the application of over-the-air federated learning to efficient implementation of distributed PCA in a wireless network under a data-privacy constraint, termed AirPCA. The design features the exploitation of the waveform-superposition property of a multi-access channel to realize over-the-air aggregation of local subspace updates computed and simultaneously transmitted by devices to a server, thereby reducing the multi-access latency. The original drawback of this class of techniques, namely channel-noise perturbation to uncoded analog modulated signals, is turned into a mechanism for escaping from saddle points during stochastic gradient descent (SGD) in the AirPCA algorithm. As a result, the convergence of the AirPCA algorithm is accelerated. To materialize the idea, descent speeds in different types of descent regions are analyzed mathematically using martingale theory by accounting for wireless propagation and techniques including broadband transmission, over-the-air aggregation, channel fading and noise. The results reveal the accelerating effect of noise in saddle regions and the opposite effect in other types of regions. The insight and results are applied to designing an online scheme for adapting receive signal power to the type of current descent region. Specifically, the scheme amplifies the noise effect in saddle regions by reducing signal power and applies the power savings to suppressing the effect in other regions. From experiments using real datasets, such power control is found to accelerate convergence while achieving the same convergence accuracy as in the ideal case of centralized PCA. [ABSTRACT FROM AUTHOR]

In massive multiple-input multiple-output (MIMO) systems employing hybrid analog-digital precoder, there are two commonly used antenna structures that have their own pros and cons, namely, the fully-connected antenna structure (FCAS) and the partially-connected antenna structure (PCAS). The FCAS achieves better spectrum efficiency (SE) even with reduced radio frequency (RF) chains, but the hardware cost and power consumption are still grievous. Contrarily, the PCAS has less hardware cost and power consumption, but suffers from severe performance loss. In this paper, by combining the advantages of both structures, we first propose a sparse adaptive antenna structure (SAAS) for the implementation of the hybrid precoder, which can jointly control the on/off state of all phase shifters (PS) and RF chains through a switch network. Then, a sparse hybrid precoding (SHP) optimization problem based on the proposed SAAS is established aiming at minimizing the total power consumption under individual average data rate requirements. To tackle the challenging non-smooth non-convex stochastic optimization (NSO) emerged with the SHP design and reduce the power consumption of PSs and RF chains, we propose a sparse smooth approximation based an online algorithm to find a stationary point of the NSO problem and establish its convergence. Simulations verify that the proposed antenna structure and algorithm achieve a better balance between power consumption and system throughput than the existing schemes. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) massive antenna array has shown great potential in gaining high-accuracy localization for user equipments (UEs). Yet, mmWave signals suffer from non-line-of-sight (NLOS) propagation and fast time-scale fading in practice, which will affect UE localization performance. It is non-trivial to reveal the impact of multipath propagation and fast time-scale fading on mmWave-based localization, and gain guidelines for harnessing NLOS propagation. In this paper, we aim to provide a unified framework for performance analysis of mmWave-based UE localization and channel estimation. Firstly, closed-form Cramer-Rao lower bounds for UE localization and channel estimation, respectively, are derived to shed lights on their performance limits. Secondly, the effect of system elements (e.g., channel correlation and fast time-scale fading) on UE localization performance is quantitatively analyzed. In addition, information contribution from NLOS channel is quantified to reveal the impact of multipath propagation on UE localization. Finally, we show that the NLOS propagation-caused localization error can be alleviated by using a NLOS model folding-based localization scheme1, which strikes a balance between computational complexity and localization performance. [ABSTRACT FROM AUTHOR]

The deployment of federated learning in a wireless network, called federated edge learning (FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model while preserving data privacy. In this work, we study the spatial (i.e., spatially averaged) learning performance of FEEL deployed in a large-scale cellular network with spatially random distributed devices. Both the schemes of digital and analog transmission are considered, providing support of error-free uploading and over-the-air aggregation of local model updates by devices. The derived spatial convergence rate for digital transmission is found to be constrained by a limited number of active devices regardless of device density and converges to the ground-true rate exponentially fast as the number grows. The population of active devices depends on network parameters such as processing gain and signal-to-interference threshold for decoding. On the other hand, the limit does not exist for uncoded analog transmission. In this case, the spatial convergence rate is slowed down due to the direct exposure of signals to the perturbation of inter-cell interference. Nevertheless, the effect diminishes when devices are dense as interference is averaged out by aggressive over-the-air aggregation. In terms of learning latency (in second), analog transmission is preferred to the digital scheme as the former dramatically reduces multi-access latency by enabling simultaneous access. [ABSTRACT FROM AUTHOR]

We consider remote state estimation for an internet-of-things (IoT) system, where a number of distributed IoT sensors monitor a dynamic plant and deliver the state measurements to a remote state estimator over an unreliable wireless network. We propose novel radix-partition-based over-the-air aggregation to coordinate the transmissions of the IoT sensors, which strongly enhances the spectral efficiency of radio resources and enables a smaller state estimation mean square error (MSE) at the remote state estimator, taking into consideration the quantization effect and radio resource allocation for the IoT sensors. We consider a fixed-filtering design at the remote state estimator, which significantly reduces the signal processing overhead at the remote state estimator compared to using the conventional Kalman-filtering-based state estimation approach. We show that the proposed low-complexity state estimation scheme enables state estimation stability via the linear matrix inequality (LMI) technique for on-off fading channels and via Lyapunov stability analysis for random fading channels. Based on this, we further propose efficient algorithms for the offline design of the filtering gain, connection topology and radix representation for IoT systems. Numerical results show that the proposed scheme has superior scalability performance and can achieve a smaller state estimation MSE compared to various state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Sparse coding is a class of unsupervised methods for learning a sparse representation of the input data in the form of a linear combination of a dictionary and a sparse code. This learning framework has led to state-of-the-art results in various signal processing tasks. However, classical methods learn the dictionary and the sparse code based on alternating optimizations, usually without theoretical guarantees for either optimality or convergence due to the non-convexity of the problem. Recent works on sparse coding with a complete dictionary provide strong theoretical guarantees thanks to the development of non-convex optimization. However, initial non-convex approaches learned the dictionary in the sparse coding problem sequentially in an atom-by-atom manner, which led to a long execution time. More recent works  have sought to directly learn the entire dictionary at once, which substantially reduces the execution time. However, the associated recovery performance is degraded with a finite number of data samples. In this paper, we propose an efficient sparse coding scheme with a two-stage optimization. The proposed scheme leverages the global and local Riemannian geometry of the two-stage optimization problem and facilitates fast implementation for superb dictionary recovery performance by a finite number of samples. We further prove that, with high probability, the proposed scheme can exactly recover any atom in the target dictionary with a finite number of samples. Experiments on both synthetic and real-world data verify the efficiency and robustness of the proposed scheme.1 [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel joint trajectory and communication scheduling scheme for multiple unmanned aerial vehicles (UAVs) enabled wireless caching networks. To exploit the favorable propagation of air-to-ground channels, we consider an ultra dense UAVs enabled content-centric wireless transmission network, where massive UAVs are deployed to transmit cached contents to a group of random distributed ground users. We formulate the problem as an infinite horizon ergodic stochastic differential game (SDG) for optimizing the users’ quality-of-experience (QoE). In particular, stochastic dynamics of channel states, UAVs’ mobility, energy queues and content request queues are modeled in this game. To deal with the state coupling between the UAVs, we consider a limiting problem for large number of UAV based on mean field analysis. A reduced-complexity decentralized solution can be obtained through mean-field equilibrium analysis. To further reduce the solution complexity on each UAV, we propose a model-specific deep neural network (DNN) to learn the optimal control solution in an online manner. The DNN is not arbitrarily generated but tailored to the structural properties of the value function and stationary distribution based on the homotopy perturbation method analysis. Finally, simulation results are provided to show that the proposed solution can achieve significant gain over the existing baselines. [ABSTRACT FROM AUTHOR]

This paper presents large-angle color dispersion using a holographically fabricated 2-D subwavelength transmission grating. A dispersion angle of 68° is observed within continuous spectrum dispersion from 360 to 700 nm at an incident angle of 20 ° using white light as a probe beam. By changing the angle between the sample normal and the probe beam, the dispersed spectrum can be tuned at a spectral resolution of 3.6 nm per degree.

In massive MIMO systems, the effective channel state information (CSI) is an essential prerequisite for the beamforming (BF) design. While there is a handful of previously proposed compressive sensing (CS)-based channel estimation algorithms in the literature, the large BF gain provided by the massive MIMO array has not been fully exploited in the channel estimation stage. In order to obtain higher BF gain and better estimation performance with less pilot overhead, we study the joint design of the transmitting and receiving hybrid pilot BF as well as the associated channel tracking scheme. Specifically, a Markov prior is used to model the temporal correlation in massive MIMO channels over different time slots. Then, the hybrid pilot BF is optimized by maximizing the mutual information between the channel measurements and the corresponding downlink sparse channels with the Markov prior. Following, we derive an efficient channel tracking algorithm called Turbo Bayesian Inference (Turbo-BI) to solve the resulting CS problem and generate the channel prior information required to calculate the mutual information for the optimization of hybrid pilot BF in the next time slot. The proposed Turbo-BI can exploit both the sparsity and the temporal correlation of massive MIMO channels to enhance the estimation performance. Finally, simulations show that our proposed algorithm can achieve significant gain over the existing state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

We propose a Bayesian deep learning framework for model driven online sparse channel estimation task in Multi-user MIMO systems. Tools from Bayesian neural network and stochastic variational Bayesian Inference are utilized to capture aleatoric and epistemic uncertainty estimates. We treat the network prediction as an auxiliary variable to allow inference performance to be unaffected by the stage of training of the network. In addition to providing uncertainty estimates, being Bayesian, the framework enables us the possibility to marginalize over penalty parameters and is well suited for online scenario with changing environments. Our simulations show that the framework is robust to model mismatch, and efficiently captures uncertainty in the predictions. [ABSTRACT FROM AUTHOR]

Channel estimation is crucial to modern wireless systems and becomes more and more challenging with the growth of user throughput in sub-6 GHz multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms. Moreover, in order to extend the estimation scheme into the non-stationary environment which is especially attractive in the coming 6G, we utilize the recurrent network structure to approximate the non-linear channel statistic correlation to model the non-stationary behavior which is difficult to accomplish in the theoretical way. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks and directly apply them in different environments. As shown in this paper, the proposed unified super-resolution based channel estimation scheme can outperform the conventional approaches in both stationary and non-stationary scenarios, which we believe can significantly change the current channel estimation method in the near future. [ABSTRACT FROM AUTHOR]

In this paper, we consider a mission-critical remote state estimation system with asynchronous massive access of the IoT sensors. We focus on remote state estimation stability of the system in the presence of asynchronous access of the sensors. Exploiting the sparsity in the observation matrix induced by the asynchronous access, we propose a low complexity 2-D message passing state estimation algorithm, where the cyclic loops in the 2-D factor graphs are removed based on the Gaussian-elimination-based quasi-diagonalization of the oversampled aggregated channel matrix of the IoT sensors. As a result, the proposed state estimation scheme is of low complexity and can achieve exact MAP estimation. Using Lyapunov drift analysis, we derive closed-form necessary and sufficient conditions for stability of the mission-critical remote state estimation system. We show that our proposed scheme can achieve significant performance gain over various state-of-the-art baselines for the large-scale system under asynchronous massive access. [ABSTRACT FROM AUTHOR]

Massive MIMO has been regarded as a key enabling technique for 5G and beyond networks. Nevertheless, its performance is limited by the large overhead needed to obtain the high-dimensional channel information. To reduce the huge training overhead associated with conventional pilot-aided designs, we propose a novel blind data detection method by leveraging the channel sparsity and data concentration properties. Specifically, we propose a novel $\ell _{3}$ -norm-based formulation to recover the data without channel estimation. We prove that the global optimal solution to the proposed formulation can be made arbitrarily close to the transmitted data up to a phase-permutation ambiguity. We then propose an efficient parameter-free algorithm to solve the $\ell _{3}$ -norm problem and resolve the phase-permutation ambiguity. We also derive the convergence rate in terms of key system parameters such as the number of transmitters and receivers, the channel noise power, and the channel sparsity level. Numerical experiments will show that the proposed scheme has superior performance with low computational complexity. [ABSTRACT FROM AUTHOR]