Real-time Range-Angle Estimation and Tag Localization for Multi-static Backscatter Systems

Multi-static backscatter networks (BNs) are strong candidates for joint communication and localization in the ambient IoT paradigm for 6G. Enabling real-time localization in large-scale multi-static deployments with thousands of devices require highly efficient algorithms for estimating key parameters such as range and angle of arrival (AoA), and for fusing these parameters into location estimates. We propose two low-complexity algorithms, Joint Range-Angle Clustering (JRAC) and Stage-wise Range-Angle Estimation (SRAE). Both deliver range and angle estimation accuracy comparable to FFT- and subspace-based baselines while significantly reducing the computation. We then introduce two real-time localization algorithms that fuse the estimated ranges and AoAs: a maximum-likelihood (ML) method solved via gradient search and an iterative re-weighted least squares (IRLS) method. Both achieve localization accuracy comparable to ML-based brute force search albeit with far lower complexity. Experiments on a real-world large-scale multi-static testbed with 4 illuminators, 1 multi-antenna receiver, and 100 tags show that JRAC and SRAE reduce runtime by up to 40X and IRLS achieves up to 500X reduction over ML-based brute force search without degrading localization accuracy. The proposed methods achieve 3 m median localization error across all 100 tags in a sub-6GHz band with 40 MHz bandwidth. These results demonstrate that multi-static range-angle estimation and localization algorithms can make real-time, scalable backscatter localization practical for next-generation ambient IoT networks.

Linearization Explains Fine-Tuning in Large Language Models

Parameter-Efficient Fine-Tuning (PEFT) is a popular class of techniques that strive to adapt large models in a scalable and resource-efficient manner. Yet, the mechanisms underlying their training performance and generalization remain underexplored. In this paper, we provide several insights into such fine-tuning through the lens of linearization. Fine-tuned models are often implicitly encouraged to remain close to the pretrained model. By making this explicit, using an Euclidean distance inductive bias in parameter space, we show that fine-tuning dynamics become equivalent to learning with the positive-definite neural tangent kernel (NTK). We specifically analyze how close the fully linear and the linearized fine-tuning optimizations are, based on the strength of the regularization. This allows us to be pragmatic about how good a model linearization is when fine-tuning large language models (LLMs). When linearization is a good model, our findings reveal a strong correlation between the eigenvalue spectrum of the NTK and the performance of model adaptation. Motivated by this, we give spectral perturbation bounds on the NTK induced by the choice of layers selected for fine-tuning. We empirically validate our theory on Low Rank Adaptation (LoRA) on LLMs. These insights not only characterize fine-tuning but also have the potential to enhance PEFT techniques, paving the way to better informed and more nimble adaptation in LLMs.

Next-Generation Backscatter Networks for Integrated Communications and RF Sensing

This paper provides a comprehensive analysis and theoretical foundation for next-generation backscatter networks that move beyond communication and integrate RF location sensing capabilities. An end-to-end system model for wideband OFDM backscatter systems is derived, including detailed characterization of propagation channels, receiver chain impairments, RF tag operation, and unsynchronized network nodes. The theoretical system model is validated through experimental evaluation using actual hardware, demonstrating the detailed model's accuracy. A practical bistatic ranging method that can operate with unsynchronized nodes is presented, along with the Cram\'er-Rao Lower Bound (CRLB) derived to show the achievable performance limits. Our experimental results demonstrate the system performance for communication, RF sensing, and ranging, while also benchmarking against the derived theoretical limits. This analytical framework and experimental validation establish fundamental understanding of distributed, unsynchronized backscatter systems for future machine-type communication networks that are deployed in massive scale, while remaining energy-efficient.

RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates

We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transformers. Through extensive experiments with medium-size LMs like BERT and RoBERTa, and larger LMs like Bloom-7B, Llama2-7B, and Llama2-13B, we show that our method gives comparable or better accuracies than state-of-art PEFT methods while also being more memory and computation-efficient. We also study the reason behind the effectiveness of our method with tools from neural tangent kernel theory. We empirically demonstrate that our kernel, constructed using a restricted set of row and column parameters, are numerically close to the full-parameter kernel and gives comparable classification performance. Ablation studies are conducted to investigate the impact of different algorithmic choices, including the selection strategy for rows and columns as well as the optimal rank for effective implementation of our method.

Deep Learning-Enabled One-Bit DoA Estimation

Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling in the direction of arrival (DoA) estimation problem when coarse quantization is applied to the measurements. We present a compressed sensing formulation for DoA estimation from one-bit data in which estimating target DoAs requires recovering a sparse signal from a limited number of severely quantized linear measurements. In particular, we exploit covariance recovery from one-bit dither samples. To recover the covariance of transmitted signal, the learned iterative shrinkage and thresholding algorithm (LISTA) is employed fed by one-bit data. We demonstrate that the upper bound of estimation performance is governed by the recovery error of the transmitted signal covariance matrix. Through numerical experiments, we demonstrate the proposed LISTA-based algorithm's capability in estimating target locations. The code employed in this study is available online.

Beyond Diagonal RIS: Key to Next-Generation Integrated Sensing and Communications?

Reconfigurable intelligent surface (RIS) have introduced unprecedented flexibility and adaptability toward smart wireless channels. Recent research on integrated sensing and communication (ISAC) systems has demonstrated that RIS platforms enable enhanced signal quality, coverage, and link capacity. In this paper, we explore the application of fully-connected beyond diagonal RIS (BD-RIS) to ISAC systems. BD-RIS introduces additional degrees of freedom by allowing non-zero off-diagonal elements for the scattering matrix, potentially enabling further functionalities and performance enhancements. In particular, we consider the joint design objective of maximizing the weighted sum of the signal-to-noise ratio (SNR) at the radar receiver and communication users by leveraging the extra degrees-of-freedom offered in the BD-RIS setting. These degrees-of-freedom are unleashed by formulating an alternating optimization process over known and auxiliary (latent) variables of such systems. Our numerical results reveal the advantages of deploying BD-RIS in the context of ISAC and the effectiveness of the proposed algorithm by improving the SNR values for both radar and communication users by several orders of magnitude.