Sum-Rate Optimisation of a Multi-User STAR-RIS-Aided System with Low Complexity

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. However, the conventional RIS can only reflect the incident signal. Hence, it provides a limited coverage, as compared to a simultaneously transmitting and reflecting RIS (STAR-RIS). Prior works on the STAR-RIS address the power minimisation or the sum-rate maximisation problem by reformulating the objective problem as a convex optimisation problem and then employing numerical tools like CVX to obtain the solution, which introduces significant computational complexity leading to a huge runtime, making the algorithms impractical for real-world implementation. In this paper, we propose a low complexity solution for the optimisation of a multi-user STAR-RIS system, where the non-convex optimisation problem is decomposed into multiple convex sub-problems with closed-form optimal solutions. The simulation results illustrate that our proposed algorithm achieves similar performance to CVX-based solutions in the literature while being computationally efficient.

Semi-Blind Strategies for MMSE Channel Estimation Utilizing Generative Priors

This paper investigates semi-blind channel estimation for massive multiple-input multiple-output (MIMO) systems. To this end, we first estimate a subspace based on all received symbols (pilot and payload) to provide additional information for subsequent channel estimation. We show how this additional information enhances minimum mean square error (MMSE) channel estimation. Two variants of the linear MMSE (LMMSE) estimator are formulated, where the first one solves the estimation within the subspace, and the second one uses a subspace projection as a preprocessing step. Theoretical derivations show the superior estimation performance of the latter method in terms of mean square error for uncorrelated Rayleigh fading. Subsequently, we introduce parameterizations of this semi-blind LMMSE estimator based on two different conditional Gaussian latent models, i.e., the Gaussian mixture model and the variational autoencoder. Both models learn the underlying channel distribution of the propagation environment based on training data and serve as generative priors for semi-blind channel estimation. Extensive simulations for real-world measurement data and spatial channel models show the superior performance of the proposed methods compared to state-of-the-art semi-blind channel estimators with respect to the MSE.

On the Asymptotic MSE-Optimality of Parametric Bayesian Channel Estimation in mmWave Systems

The mean square error (MSE)-optimal estimator is known to be the conditional mean estimator (CME). This paper introduces a parametric channel estimation technique based on Bayesian estimation. This technique uses the estimated channel parameters to parameterize the well-known LMMSE channel estimator. We first derive an asymptotic CME formulation that holds for a wide range of priors on the channel parameters. Based on this, we show that parametric Bayesian channel estimation is MSE-optimal for high signal-to-noise ratio (SNR) and/or long coherence intervals, i.e., many noisy observations provided within one coherence interval. Numerical simulations validate the derived formulations.

Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation

Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-Aware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world Vehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles.

Sparse Bayesian Generative Modeling for Joint Parameter and Channel Estimation

Leveraging the inherent connection between sensing systems and wireless communications can improve their overall performance and is the core objective of joint communications and sensing. For effective communications, one has to frequently estimate the channel. Sensing, on the other hand, infers properties of the environment mostly based on estimated physical channel parameters, such as directions of arrival or delays. This work presents a low-complexity generative modeling approach that simultaneously estimates the wireless channel and its physical parameters without additional computational overhead. To this end, we leverage a recently proposed physics-informed generative model for wireless channels based on sparse Bayesian generative modeling and exploit the feature of conditionally Gaussian generative models to approximate the conditional mean estimator.

Statistical Precoder Design in Multi-User Systems via Graph Neural Networks and Generative Modeling

This letter proposes a graph neural network (GNN)-based framework for statistical precoder design that leverages model-based insights to compactly represent statistical knowledge, resulting in efficient, lightweight architectures. The framework also supports approximate statistical information in frequency division duplex (FDD) systems obtained through a Gaussian mixture model (GMM)-based limited feedback scheme in massive multiple-input multiple-output (MIMO) systems with low pilot overhead. Simulations using a spatial channel model and measurement data demonstrate the effectiveness of the proposed framework. It outperforms baseline methods, including stochastic iterative algorithms and Discrete Fourier transform (DFT) codebook-based approaches, particularly in low pilot overhead systems.

Decoupling Networks and Super-Quadratic Gains for RIS Systems with Mutual Coupling

We propose decoupling networks for the reconfigurable intelligent surface (RIS) array as a solution to benefit from the mutual coupling between the reflecting elements. In particular, we show that when incorporating these networks, the system model reduces to the same structure as if no mutual coupling is present. Hence, all algorithms and theoretical discussions neglecting mutual coupling can be directly applied when mutual coupling is present by utilizing our proposed decoupling networks. For example, by including decoupling networks, the channel gain maximization in RIS-aided single-input single-output (SISO) systems does not require an iterative algorithm but is given in closed form as opposed to using no decoupling network. In addition, this closed-form solution allows to analytically analyze scenarios under mutual coupling resulting in novel connections to the conventional transmit array gain. In particular, we show that super-quadratic (up to quartic) channel gains w.r.t. the number of RIS elements are possible and, therefore, the system with mutual coupling performs significantly better than the conventional uncoupled system in which only squared gains are possible. We consider diagonal as well as beyond diagonal (BD)-RISs and give various analytical and numerical results, including the inevitable losses at the RIS array. In addition, simulation results validate the superior performance of decoupling networks w.r.t. the channel gain compared to other state-of-the-art methods.

Sparse Bayesian Generative Modeling for Compressive Sensing

This work addresses the fundamental linear inverse problem in compressive sensing (CS) by introducing a new type of regularizing generative prior. Our proposed method utilizes ideas from classical dictionary-based CS and, in particular, sparse Bayesian learning (SBL), to integrate a strong regularization towards sparse solutions. At the same time, by leveraging the notion of conditional Gaussianity, it also incorporates the adaptability from generative models to training data. However, unlike most state-of-the-art generative models, it is able to learn from a few compressed and noisy data samples and requires no optimization algorithm for solving the inverse problem. Additionally, similar to Dirichlet prior networks, our model parameterizes a conjugate prior enabling its application for uncertainty quantification. We support our approach theoretically through the concept of variational inference and validate it empirically using different types of compressible signals.

Robust Precoding for FDD MISO Systems via Minorization Maximization

In this work, we propose an approach to robust precoder design based on a minorization maximization technique that optimizes a surrogate function of the achievable spectral efficiency. The presented method accounts for channel estimation errors during the optimization process and is, hence, robust in the case of imperfect channel state information (CSI). Additionally, the design method is adapted such that the need for a line search to satisfy the power constraint is eliminated, that significantly accelerates the precoder computation. Simulation results demonstrate that the proposed robust precoding method is competitive with weighted minimum mean square error (WMMSE) precoding, in particular, under imperfect CSI scenarios.

Bilinear Precoder Based Efficient Rate Splitting Method in FDD Systems

In this work, we propose a low-cost rate splitting (RS) technique for a multi-user multiple-input single-output (MISO) system operating in frequency division duplex (FDD) mode. The proposed iterative optimisation algorithm only depends on the second-order statistical channel knowledge and the pilot training matrix. Additionally, it offers a closed-form solution in each update step. This reduces the design complexity of the system drastically as we only need to optimise the precoding filters in every coherence interval of the covariance matrices, instead of doing that in every channel state information (CSI) coherence interval. Moreover, since the algorithm is based on closed-form solutions, there is no need for interior point solvers like CVX, which are typically required in most state-of-the-art techniques.