Hardware-Limited Task-Based Quantization
arXiv:1807.08305 · doi:10.1109/TSP.2019.2935864
Abstract
Quantization plays a critical role in digital signal processing systems. Quantizers are typically designed to obtain an accurate digital representation of the input signal, operating independently of the system task, and are commonly implemented using serial scalar analog-to-digital converters (ADCs). In this work, we study hardware-limited task-based quantization, where a system utilizing a serial scalar ADC is designed to provide a suitable representation in order to allow the recovery of a parameter vector underlying the input signal. We propose hardware-limited task-based quantization systems for a fixed and finite quantization resolution, and characterize their achievable distortion. We then apply the analysis to the practical setups of channel estimation and eigen-spectrum recovery from quantized measurements. Our results illustrate that properly designed hardware-limited systems can approach the optimal performance achievable with vector quantizers, and that by taking the underlying task into account, the quantization error can be made negligible with a relatively small number of bits.
References in corpus (10)
- Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems
- Fixed-length lossy compression in the finite blocklength regime
- Information Theoretic Proofs of Entropy Power Inequalities
- Mixed-ADC Massive MIMO Detectors: Performance Analysis and Design Optimization
- Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications
- Performance Analysis for Channel Estimation with 1-bit ADC and Unknown Quantization Threshold
- Quantization of Prior Probabilities for Hypothesis Testing
- Analog-to-Digital Compression: A New Paradigm for Converting Signals to Bits
- Hybrid Analog-Digital Beamforming for Massive MIMO Systems
- Estimating an Inverse Gamma distribution
Cited by in corpus (13)
- UVeQFed: Universal Vector Quantization for Federated Learning
- Deep Signal Recovery with One-Bit Quantization
- RF Chain Reduction for MIMO Systems: A Hardware Prototype
- Asymptotic Task-Based Quantization with Application to Massive MIMO
- LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers
- Task-Based Analog-to-Digital Converters
- Practical Vertical Federated Learning with Unsupervised Representation Learning
- BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization
- Deep Task-Based Analog-to-Digital Conversion
- Design and Analysis of Hardware-limited Non-uniform Task-based Quantizers
- Exploring the Synergy: A Review of Dual-Functional Radar Communication Systems
- Quantization Games on Social Networks and Language Evolution
- Capacity Bounds for One-Bit MIMO Gaussian Channels with Analog Combining