21 accepted papers
This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy. The mixed quantization precision significantly reduces the data amount that needs to be shared from ra…
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 su…
This paper argues that generating output tokens is more effective than using pooled representations for prediction tasks because token-level generation retains more mutual information. Since LLMs are trained on massive text corpora using next-token prediction, generation aligns naturally with their…
We propose Row-Column Fine-Tuning(RoCoFT), a parameter-efficient fine-tuning method for large language models based on updating only a few rows and columns of the weight matrices in transformers. Through extensive experiments with medium-sized LMs like RoBERTa and DeBERTa, and larger LMs like Bloom-…
Recently, one-bit modulo sampling, also known as unlimited one-bit (UNO), has been proposed as a bridge between modulo sampling and coarse quantization. This approach successfully combines the benefits of both techniques by providing efficient, low-cost quantization for modulo sampling while also of…
In this study, we develop a holistic framework for space-time adaptive processing (STAP) in connected and automated vehicle (CAV) radar systems. We investigate a CAV system consisting of multiple vehicles that transmit frequency-modulated continuous-waveforms (FMCW), thereby functioning as a multist…
To facilitate target localization, active radar signals or sequences are designed to have low auto-correlation. This goal is typically achieved by the minimization of the auto-correlation integrated side-lobe level (ISL) metric, or the weighted more general version of ISL, known as the WISL metric.…
Intelligent reflecting surface (IRS) technology has recently attracted a significant interest in non-light-of-sight radar remote sensing. Prior works have largely focused on designing single IRS beamformers for this problem. For the first time in the literature, this paper considers multi-IRS-aided…
In this work, we study a deep learning-based initialization approach for unimodular quadratic programs (UQPs), that are concerned with the maximization of a quadratic form over a set of complex unimodular vectors. UQPs have shown prevalent presence in many signal processing and design problems such…
One-bit quantization has attracted considerable attention in signal processing for communications and sensing. The arcsine law is a useful relation often used to estimate the normalized covariance matrix of zero-mean stationary input signals when they are sampled by one-bit analog-to-digital convert…
Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to its capability of providing enhanced degrees of freedom for DoAs that can be resolved. Additionally, deployment of one-bit Analog-to-Digital…
Parameter estimation from noisy and one-bit quantized data has become an important topic in signal processing, as it offers low cost and low complexity in the implementation. On the other hand, Direction-of-Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable i…
Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference. In this paper, we consider the application of the deep unfolding technique in the problem of signal reconstruction from its one-bit noisy measurements. Namely, we propo…
Sequences with good correlation and distribution properties play a central role in various areas of signal processing. In this paper, we propose an efficient computational framework for designing sequences with two key properties: (i) an impulse-like auto-correlation, and (ii) a probability distribu…
Big data analysis is a fundamental research topic with extensive technical obstacles yet to be overcome. Graph clustering has shown promise in addressing big data challenges by categorizing otherwise unlabeled data-thus giving them meaning. In this paper, we propose a set of non-convex programs, gen…
In this paper, we study the problem of low-rank matrix recovery based on the information obtained by comparing matrix entries (where each comparison is represented by one-bit) and not the entries themselves. This is highly relevant in the context of recommendation systems, due to the fact that users…
In this paper, we present a consensus-based framework for decentralized estimation of deterministic parameters in wireless sensor networks (WSNs). In particular, we propose an optimization algorithm to design (possibly complex) sensor gains in order to achieve an estimate of the parameter of interes…
Restoration of shredded signals remains a relevant and significant challenge in archaeological and forensic efforts. In this work, we present a novel approach for reconstruction of shredded signals (including text documents and images) within a context of general multidimensional sparse signals. To…
In this paper, we present an optimization framework for designing precoding (a.k.a. beamforming) signals that are instrumental in achieving a fair user performance through the networks. The precoding design problem in such scenarios can typically be formulated as a non-convex max-min fractional quad…
We consider the problem of sum-rate maximization in massive MIMO two-way relay networks with multiple (communication) operators employing the amplify-and-forward (AF) protocol. The aim is to design the relay amplification matrix (i.e., the relay beamformer) to maximize the achievable communication s…
In this work, a directional modulation-based technique is devised to enhance the security of a multi-antenna wireless communication system employing M-PSK modulation to convey information. The directional modulation method operates by steering the array beam in such a way that the phase of the recei…