On Performance of RIS-Aided Fluid Antenna Systems

This letter studies the performance of reconfigurable intelligent surface (RIS)-aided communications for a fluid antenna system (FAS) enabled receiver. Specifically, a fixed singleantenna base station (BS) transmits information through a RIS to a mobile user (MU) which is equipped with a planar fluid antenna in the absence of a direct link.We first analyze the spatial correlation structures among the positions (or ports) in the planar FAS, and then derive the joint distribution of the equivalent channel gain at the user by exploiting the central limit theorem. Furthermore, we obtain compact analytical expressions for the outage probability (OP) and delay outage rate (DOR). Numerical results illustrate that using FAS with only one activated port into the RIS-aided communication network can greatly enhance the performance, when compared to traditional antenna systems (TAS).

Vision Reimagined: AI-Powered Breakthroughs in WiFi Indoor Imaging

Indoor imaging is a critical task for robotics and internet-of-things. WiFi as an omnipresent signal is a promising candidate for carrying out passive imaging and synchronizing the up-to-date information to all connected devices. This is the first research work to consider WiFi indoor imaging as a multi-modal image generation task that converts the measured WiFi power into a high-resolution indoor image. Our proposed WiFi-GEN network achieves a shape reconstruction accuracy that is 275% of that achieved by physical model-based inversion methods. Additionally, the Frechet Inception Distance score has been significantly reduced by 82%. To examine the effectiveness of models for this task, the first large-scale dataset is released containing 80,000 pairs of WiFi signal and imaging target. Our model absorbs challenges for the model-based methods including the non-linearity, ill-posedness and non-certainty into massive parameters of our generative AI network. The network is also designed to best fit measured WiFi signals and the desired imaging output. For reproducibility, we will release the data and code upon acceptance.

Channel Estimation for FAS-assisted Multiuser mmWave Systems

This letter investigates the challenge of channel estimation in a multiuser millimeter-wave (mmWave) time-division duplexing (TDD) system. In this system, the base station (BS) employs a multi-antenna uniform linear array (ULA), while each mobile user is equipped with a fluid antenna system (FAS). Accurate channel state information (CSI) plays a crucial role in the precise placement of antennas in FAS. Traditional channel estimation methods designed for fixed-antenna systems are inadequate due to the high dimensionality of FAS. To address this issue, we propose a low-sample-size sparse channel reconstruction (L3SCR) method, capitalizing on the sparse propagation paths characteristic of mmWave channels. In this approach, each fluid antenna only needs to switch and measure the channel at a few specific locations. By observing this reduced-dimensional data, we can effectively extract angular and gain information related to the sparse channel, enabling us to reconstruct the full CSI. Simulation results demonstrate that our proposed method allows us to obtain precise CSI with minimal hardware switching and pilot overhead. As a result, the system sum-rate approaches the upper bound achievable with perfect CSI.

Reconciling Radio Tomographic Imaging with Phaseless Inverse Scattering

Radio Tomographic Imaging (RTI) is a phaseless imaging approach that can provide shape reconstruction and localization of objects using received signal strength (RSS) measurements. RSS measurements can be straightforwardly obtained from wireless networks such as Wi-Fi and therefore RTI has been extensively researched and accepted as a good indoor RF imaging technique. However, RTI is formulated on empirical models using an assumption of light-of-sight (LOS) propagation that does not account for intricate scattering effects. There are two main objectives of this work. The first objective is to reconcile and compare the empirical RTI model with formal inverse scattering approaches to better understand why RTI is an effective RF imaging technique. The second objective is to obtain straightforward enhancements to RTI, based on inverse scattering, to enhance its performance. The resulting enhancements can provide reconstructions of the shape and also material properties of the objects that can aid image classification. We also provide numerical and experimental results to compare RTI with the enhanced RTI for indoor imaging applications using low-cost 2.4 GHz Wi-Fi transceivers. These results show that the enhanced RTI can outperform RTI while having similar computational complexity to RTI.

An Efficient Ratio Detector for Ambient Backscatter Communication

Ambient backscatter communication (AmBC) leverages the existing ambient radio frequency (RF) environment to implement communication with battery-free devices. One critical challenge of AmBC systems is signal recovery because the transmitted information bits are embedded in the ambient RF signals and these are unknown and uncontrollable. To address this problem, most existing approaches use averaging-based energy detectors and consequently the data rate is low and there is an error floor. Here we propose a new detection strategy based on the ratio between signals received from a multiple-antenna Reader. The advantage of using the ratio is that ambient RF signals are removed directly from the embedded signals without averaging and hence it can increase data rates and avoid the error floor. Different from original ratio detectors that use the magnitude ratio of the signals between two Reader antennas, in our proposed approach, we utilize the complex ratio so that phase information is preserved and propose an accurate linear channel model approximation. This allows the application of existing linear detection techniques from which we can obtain a minimum distance detector and closed-form expressions for bit error rate (BER). In addition, averaging, coding and interleaving can also be included to further enhance the BER. The results are also general, allowing any number of Reader antennas to be utilized in the approach. Numerical results demonstrate that the proposed approach performs better than approaches based on energy detection and original ratio detectors.

Unrolled Optimization with Deep Learning-based Priors for Phaseless Inverse Scattering Problems

Inverse scattering problems, such as those in electromagnetic imaging using phaseless data (PD-ISPs), involve imaging objects using phaseless measurements of wave scattering. Such inverse problems can be highly non-linear and ill-posed under extremely strong scattering conditions such as when the objects have very high permittivity or are large in size. In this work, we propose an end-to-end reconstruction framework using unrolled optimization with deep priors to solve PD-ISPs under very strong scattering conditions. We incorporate an approximate linear physics-based model into our optimization framework along with a deep learning-based prior and solve the resulting problem using an iterative algorithm which is unfolded into a deep network. This network not only learns data-driven regularization, but also overcomes the shortcomings of approximate linear models and learns non-linear features. More important, unlike existing PD-ISP methods, the proposed framework learns optimum values of all tunable parameters (including multiple regularization parameters) as a part of the framework. Results from simulations and experiments are shown for the use case of indoor imaging using 2.4 GHz phaseless Wi-Fi measurements, where the objects exhibit extremely strong scattering and low-absorption. Results show that the proposed framework outperforms existing model-driven and data-driven techniques by a significant margin and provides up to 20 times higher validity range.

Using Loaded N-port Structures to Achieve the Continuous-Space Electromagnetic Channel Capacity Bound

A method for achieving the continuous-space electromagnetic channel capacity bound using loaded N-port structures is described. It is relevant for the design of compact multiple-input multiple-output (MIMO) antennas that can achieve channel capacity bounds when constrained by size. The method is not restricted to a specific antenna configuration and a closed-form expression for the channel capacity limits are provided with various constraints. Furthermore, using loaded N-port structures to represent arbitrary antenna geometries, an efficient optimization approach is proposed for finding the optimum MIMO antenna design that achieves the channel capacity bounds. Simulation results of the channel capacity bounds achieved using our MIMO antenna design with one square wavelength size are provided. These show that at least 18 ports can be supported in one square wavelength and achieve the continuous-space electromagnetic channel capacity bound. The results demonstrate that our method can link continuous-space electromagnetic channel capacity bounds to MIMO antenna design.

A New Correction to the Rytov Approximation for Strongly Scattering Lossy Media

We propose a correction to the conventional Rytov approximation (RA) and investigate its performance for predicting wave scattering under strong scattering conditions. An important motivation for the correction and investigation is to help in the development of better models for inverse scattering. The correction is based upon incorporating the high frequency theory of inhomogeneous wave propagation for lossy media into the RA formulation. We denote the technique as the extended Rytov approximation for lossy media (xRA-LM). xRA-LM significantly improves upon existing non-iterative linear scattering approximations such as RA and the Born approximation (BA) by providing a validity range for the permittivity of the objects of up to 50 times greater than RA. We demonstrate the technique by providing results for predicting wave scattering from piece-wise homogeneous scatterers in a two-dimensional (2D) region. Numerical investigation of the performance of xRA-LM for solving direct problem show that xRA-LM can accurately predict wave scattering by electrically large, low-loss scatterers with high complex permittivity ($\epsilon_r> 50+5j$). To the best of our knowledge, this is the first non-iterative, linear approximate wave scattering model which has a large validity range in terms of both permittivity and electrical size.

Optimal Grouping Strategy for Reconfigurable Intelligent Surface Assisted Wireless Communications

The channel estimation overhead of reconfigurable intelligent surface (RIS) assisted communication systems can be prohibitive. Prior works have demonstrated via simulations that grouping neighbouring RIS elements can help to reduce the pilot overhead and improve achievable rate. In this paper, we present an analytical study of RIS element grouping. We derive a tight closed-form upper bound for the achievable rate and then maximize it with respect to the group size. Our analysis reveals that more coarse-grained grouping is important-when the channel coherence time is low (high mobility scenarios) or the transmit power is large. We also demonstrate that optimal grouping can yield significant performance improvements over simple `On-Off' RIS element switching schemes that have been recently considered.

Physics Assisted Deep Learning for Indoor Imaging using Phaseless Wi-Fi Measurements

A physics assisted deep learning framework to perform accurate indoor imaging using phaseless Wi-Fi measurements is proposed. It is able to image objects that are large (compared to wavelength) and have high permittivity values, that existing radio frequency (RF) inverse scattering techniques find very challenging, making it suitable for indoor RF imaging. The technique utilizes a Rytov based inverse scattering model with a deep learning framework. The inverse scattering model is based on an extended Rytov approximation (xRA) that pre-reconstructs the RF measurements. Under strong scattering conditions, this pre-reconstruction is related to the actual permittivity profile by a non-linear function, which is learned by a modified U-Net model to obtain the permittivity profile of the object. Thus, our proposed approach not only reconstructs the shape of objects, but also estimates their permittivity values accurately. We demonstrate its imaging performance using simulations as well as experimental results in an actual indoor environment using 2.4 GHz Wi-Fi phaseless measurements. For incident wavelength $\lambda_0$, the proposed framework can reconstruct objects with relative permittivity as high as 77 and electrical size as large as $40 \lambda$, where $\lambda =\lambda_0/\sqrt{77}$. This is in contrast to existing phaseless imaging techniques which cannot reconstruct permittivity values beyond 3 or 4. Thus, our proposed method is the first inverse scattering-based deep learning framework which can image large scatterers with high permittivity and achieve accurate indoor RF imaging using phaseless Wi-Fi measurements.