Related papers: Asymptotic Task-Based Quantization with Applicatio…
Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads to high memory…
The deployment of large-scale antenna arrays for cellular base stations (BSs), termed as `Massive MIMO', has been a key enabler for meeting the ever-increasing capacity requirement for 5G communication systems and beyond. Despite their…
Receivers with one-bit analog-to-digital converters (ADCs) are promising for high bandwidth millimeter wave (mmWave) systems as they consume less power than their full resolution counterparts. The extreme quantization in one-bit receivers…
Conventional analog and mixed-signal (AMS) circuit designs heavily rely on manual effort, which is time-consuming and labor-intensive. This paper presents a fully automated design methodology for Successive Approximation Register (SAR)…
We investigate massive multiple-input-multiple output (MIMO) uplink systems with 1-bit analog-to-digital converters (ADCs) on each receiver antenna. Receivers that rely on 1-bit ADC do not need energy-consuming interfaces such as automatic…
In this article, we investigate channel estimation for wideband millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) under hybrid architecture with lowprecision analog-to-digital converters (ADCs). To design channel…
In many quantization problems, the distortion function is given by the Euclidean metric to measure the distance of a source sample to any given reproduction point of the quantizer. We will in this work regard distortion functions, which are…
Massive MIMO (Multiple Input Multiple Output) has demonstrated as a potential candidate for 5G-and-beyond wireless networks. Instead of using Gaussian signals as most of the previous works, this paper makes a novel contribution by…
This paper investigates the design of analog beamforming at the receiver in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, aided by full digital chains featuring 1-bit ADCs. We advocate utilizing these full digital…
Compute-in-memory (CIM) is an efficient method for implementing deep neural networks (DNNs) but suffers from substantial overhead from analog-to-digital converters (ADCs), especially as ADC precision increases. Low-precision ADCs can reduce…
We study the spectral efficiency (SE) of a mixed-ADC massive MIMO system in which K single-antenna users communicate with a base station (BS) equipped with M antennas connected to N high-resolution ADCs and M-N one-bit ADCs. This…
The practical deployment of massive multiple-input multiple-output (MIMO) in future fifth generation (5G) wireless communication systems is challenging due to its high hardware cost and power consumption. One promising solution to address…
This paper considers multiaccess multiple-input multiple-output (MIMO) systems with finite rate feedback. The goal is to understand how to efficiently employ the given finite feedback resource to maximize the sum rate by characterizing the…
Recently, transformer has achieved remarkable performance on a variety of computer vision applications. Compared with mainstream convolutional neural networks, vision transformers are often of sophisticated architectures for extracting…
Even if Application-Specific Integrated Circuits (ASIC) have proven to be a relevant choice for integrating inference at the edge, they are often limited in terms of applicability. In this paper, we demonstrate that an ASIC neural network…
Diffusion Models (DM) have democratized AI image generation through an iterative denoising process. Quantization is a major technique to alleviate the inference cost and reduce the size of DM denoiser networks. However, as denoisers evolve…
Multiple-input multiple-output (MIMO) wireless systems conventionally use high-resolution analog-to-digital converters (ADCs) at the receiver side to faithfully digitize received signals prior to digital signal processing. However, the…
Massive MIMO is a variant of multiuser MIMO in which the number of antennas at the base station (BS) $M$ is very large and typically much larger than the number of served users (data streams) $K$. Recent research has illustrated the…
Fixed low-resolution Analog to Digital Converters (ADC) help reduce the power consumption in millimeter-wave Massive Multiple-Input Multiple-Output (Ma-MIMO) receivers operating at large bandwidths. However, they do not guarantee optimal…
This paper studies the optimal bit allocation for shape-gain vector quantization of wireless channels in multiuser (MU) multiple-input multiple-output (MIMO) downlink systems based on linear precoding. Our design minimizes the mean…