quantization plays a critical role in digital signal processing systems, allowing the representation of continuous-amplitude signals with a finite number of bits. However, accurately representing signals requires a la...
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quantization plays a critical role in digital signal processing systems, allowing the representation of continuous-amplitude signals with a finite number of bits. However, accurately representing signals requires a large number of quantization bits, which causes severe cost, power consumption, and memory burden. A promising way to address this issue is task-based quantization. By exploiting the task information for the overall system design, task-based quantization can achieve satisfying performance with low quantization costs. In this work, we apply task-based quantization to multiuser signal recovery and present a hardware prototype implementation. The prototype consists of a tailored configurable combining board, and a software-based processing and demonstration system. Through experiments, we verify that with proper design, the task-based quantization achieves a reduction of 25 fold in memory by reducing from 16 receivers with 16 bits each to 2 receivers with 5 bits each, without compromising signal recovery performance.
Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be compressed in order to be stored and conv...
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ISBN:
(纸本)9781728176055
Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be compressed in order to be stored and conveyed. The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation. In this work we study the joint design of graph signal sampling along with the quantization of these samples, for graph signal compression. We focus on bandlimited graph signals, and show that the compression problem can be represented as a task-based quantization setup, in which the task is to recover the spectrum of the signal. based on this equivalence, we propose a joint design of the sampling and recovery mechanisms for a fixed quantization mapping, and present an iterative algorithm for dividing the available bit budget among the discretized samples. Our numerical evaluations demonstrate that the proposed scheme achieves reconstruction accuracy within a small gap of that achievable with infinite resolution quantizers, while compressing high-dimensional graph signals into finite bit streams.
task-oriented communication exploits the task to improve communication efficiency. Most existing works on task-oriented communication transmit analog signals without quantization, which limits its application in digit...
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ISBN:
(纸本)9798350344868;9798350344851
task-oriented communication exploits the task to improve communication efficiency. Most existing works on task-oriented communication transmit analog signals without quantization, which limits its application in digital communication systems. This paper studies digital task-oriented communication systems with hardware-limited scalar quantization (TOC-SQ) for computation-limited scenarios such as the Internet of Things, where the source data is encoded by a source encoder and then quantized by hardware-limited quantizers for digital transmission. Accordingly, the receiver contains a source decoder to decode the received signal. Our goal is to minimize the mean squared error (MSE) between the transmitted and received task-relevant signals to achieve optimal task performance under a certain bit budget. In particular, we first establish a theoretical analysis framework for TOC-SQ. Then, the closed-form expressions of the source encoder and decoder are derived for linear tasks. Finally, the lower bound of the MSE between the transmitted and received task-relevant information is analyzed to evaluate the task performance. Simulation results verify that the proposed TOC-SQ achieves 6.9 dB MSE gains in frequency-selective channels compared with analog TOC systems.
In this work, we investigate the gridless parameter estimation of pulse-Doppler radar targets using a reduced number of samples under a limited bit budget. We propose a hybrid analog and digital (HAD) acquisition syst...
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In this work, we investigate the gridless parameter estimation of pulse-Doppler radar targets using a reduced number of samples under a limited bit budget. We propose a hybrid analog and digital (HAD) acquisition system integrating a tunable analog component, low-resolution quantizers, and a digital filter. Under the framework of task-based quantization, the HAD architecture is designed to optimize target parameter estimation within the constraints of the bit budget. Specifically, a small subset of the received signal samples is observed and the low-rank parameter matrix is recovered using matrix completion techniques. The atomic norm minimization method is applied to reconstruct the complete parameter matrix, enabling gridless estimation of the parameters. Numerical experiments are conducted to validate the effectiveness of the proposed receiver in gridless parameter estimation.
Pulse-Doppler (PD) radars, which prefer to operate with large bandwidth signals to attain superior range resolution, are widely used due to their superior performance in target detection and parameter estimation. Howe...
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Pulse-Doppler (PD) radars, which prefer to operate with large bandwidth signals to attain superior range resolution, are widely used due to their superior performance in target detection and parameter estimation. However, the increasing data flow of such radar systems induced by large bandwidth presents significant challenges to signal processing and hardware implementation, particularly concerning analog-to-digital converters (ADCs), which are tasked with converting high-bandwidth inputs into digital representations at or above the Nyquist rate. In this work, we introduce a hybrid analog-digital processing architecture, which implements sub-Nyquist sampling as well as low-bit quantization, specifically tailored for PD radar applications. We refer to the designed architecture as the bit-limited sub-Nyquist pulse-Doppler radar (BiLiPD) receiver, designed with cost-efficiency in mind, utilizing low-rate and low-resolution ADCs to reduce both cost and power consumption. Specifically, the received radar echoes are acquired under the sub-Nyquist sampling framework, and then, quantized with low-resolution ADCs. The building modules of the proposed BiLiPD receiver, including the analog preprocessing, the ADCs, and the digital processing, are jointly designed to mitigate the challenges posed by sub-Nyquist sampling and low-bit quantization. We incorporate structural constraints into the design problem, formulating it under the task-based quantization framework and employing gradient descent methods for its solution. Our simulation results illustrate that the proposed BiLiPD receiver operating under low-rate low-bit constraints is capable of accurately recovering target parameters, with its target recovery performance approaching that of classic PD processing operating with unlimited resolution ADCs while notably outperforming that employing task-ignorant low-bit quantization.
This survey paper examines recent advancements in low-resolution signal processing, emphasizing quantized compressed sensing. Rising costs and power demands of high-sampling-rate data acquisition drive the interest in...
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This survey paper examines recent advancements in low-resolution signal processing, emphasizing quantized compressed sensing. Rising costs and power demands of high-sampling-rate data acquisition drive the interest in quantized signal processing, particularly in wireless communication systems and Internet of Things sensor networks, as 6G aims to integrate sensing and communication within cost-effective hardware. Motivated by this urgency, this paper covers novel signal processing algorithms designed to address practical challenges arising from quantization and modulo operations, as well as their impact on system performance. We begin by introducing the framework of one-bit compressed sensing and discuss relevant theories and algorithms. We explore the application of quantized compressed sensing algorithms to sensor networks, radar, cognitive radio, and wireless channel estimation. We highlight how generic methods can be tailored to an application using specific examples from wireless channel estimation. Additionally, we review other low-resolution techniques beyond one-bit compressed sensing along with their applications. We also provide a brief overview of the emerging concept of unlimited sampling. While this paper does not aim to be exhaustive, it selectively highlights results to inspire readers to appreciate the diverse algorithmic tools (convex optimization, greedy methods, and Bayesian approaches) and sampling techniques (task-based quantization and unlimited sampling).
Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be compressed in order to be stored and tran...
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Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be compressed in order to be stored and transmitted. The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation. In this work, we study the joint design of graph signal sampling along with quantization, for graph signal compression. We focus on bandlimited graph signals, and show that the compression problem can be represented as a task-based quantization setup, in which the task is to recover the spectrum of the signal. based on this equivalence, we propose a joint design of the sampling and recovery mechanisms for a fixed quantization mapping, and present an iterative algorithm for dividing the available bit budget among the discretized samples. Furthermore, we show how the proposed approach can be realized using graph filters combining elements corresponding the neighbouring nodes of the graph, thus facilitating distributed implementation at reduced complexity. Our numerical evaluations on both synthetic and real world data shows that the joint sampling and quantization method yields a compact finite bit representation of high-dimensional graph signals, which allows reconstruction of the original signal with accuracy within a small gap of that achievable with infinite resolution quantizers.
Multiple-input multiple-output (MIMO) radar is known to achieve high performance by probing with multiple orthogonal waveforms. However, implementing a low cost low power MIMO radar is challenging. In this work we stu...
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ISBN:
(纸本)9781728176055
Multiple-input multiple-output (MIMO) radar is known to achieve high performance by probing with multiple orthogonal waveforms. However, implementing a low cost low power MIMO radar is challenging. In this work we study reduced cost MIMO radar receivers restricted to operate with low resolution ADCs. A hybrid analog-digital architecture, referred to as bit-limited MIMO radar (BiLiMO) receivers, which are capable of accurately recovering their targets while operating under strict resolution constraints is designed. This is achieved by applying an additional analog filter to the acquired waveforms, and designing the overall hybrid analog-digital system to facilitate target identification using task-based quantization methods. Our numerical results demonstrate that the proposed BiLiMO receiver operating with strict bit budget achieves target recovery performance which approaches that of costly MIMO radars operating with unlimited resolution ADCs.
Dual function radar and communications (DFRC) systems are the focus of growing research attention. The common DFRC setup considers simultaneous probing and information transmission to a remote receiver, typically invo...
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ISBN:
(纸本)9781728176055
Dual function radar and communications (DFRC) systems are the focus of growing research attention. The common DFRC setup considers simultaneous probing and information transmission to a remote receiver, typically involving complex radar-oriented waveforms, whose detection can induce a notable burden on the receiver. In many DFRC applications, the communication receivers are devices which are limited in terms of hardware, power, and memory resources. These receivers are required to extract the desired information from the received dual-function waveform, while operating with a given bit budget. In this paper, we design bit constrained communication receivers in dual-function systems, by considering hybrid analog/digital architectures and treating their operation as task-based quantization. We study two forms of analog processing in these hybrid receivers, allowing to combine inputs in different time instances and antennas or only in different antennas at the same time instance. Simulation results demonstrate that the proposed task-based quantization strategy outperforms receivers operating only in the digital domain with the same total number of quantization bits.
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