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.