Quantized Compressive Spectrum Sensing (QuaCoSS)

量化压缩频谱传感 (QuaCoSS)

• 批准号: 273202924
• 负责人: Professor Dr. Rudolf Mathar
• 金额: --
• 依托单位: Mathematisches Institut
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/273202924?language=en
• 关键词: Quantized; Compressive; Spectrum; Sensing; QuaCoSS

Spectrum sensing aims at detecting non-occupied frequency bands in the radio spectrum in order to enable unlicensed (secondary) users to opportunistically communicate over these bands. The goal is to increase communication rates for the secondary users without creating interference for licensed (primary) users. However, this demands to monitor a very large bandwidth so that sampling at the Nyquist rate may result in unacceptably large amount of samples. It is well-known that some of the test statistics used in spectrum sensing feature sparsity, i.e., the frequency spectrum has been shown to be sparsely occupied and the cyclic spectra of man-made signals are sparse. By exploiting this sparsity, one may use compressed sensing techniques instead of traditional Shannon-Nyquist sampling in order to significantly reduce the number of samples, while still ensuring reliable detection of non-occupied bands. In practice, samples have to be quantized before exchanging these.This project aims to explore the effect of quantization in compressed sensing on spectrum sensing. We will explore both the extreme case of one-bit quantization, where only the sign of a measurement is retained, as well as multi-bit quantization schemes. A particular focus is put on structured random measurements such as the random partial Fourier matrix, which are highly relevant in practical applications. While initial theoretical results on quantized compressed sensing are available for Gaussian random measurement matrices, structured random matrices remain completely unexplored in this context up to now. Moreover, we plan to investigate two open fundamental problems in the practical application of quantized compressive spectrum sensing. Firstly, given a fixed bit-budget for communication we will investigate the tradeoff concerning the quantization resolution and the number of measurements taken by secondary users in order to achieve optimal detection performance. Secondly, we will analyze the tradeoff between the occupancy decision frequency and the bit-budget available per decision in order to minimize wasted transmission opportunities.The research group of Mathar will dedicate its efforts to the development, implementation and simulation of quantized compressed sensing algorithms and their application in the spectrum sensing context. The focus of the research group of Rauhut and Dirksen will be on the theoretical analysis of quantized compressed sensing with the aim of deriving rigorous error guarantees and sharp bounds on the required number of measurements. The interaction of the two groups is expected to be crucial for achieving significant progress on the understanding and practical implementation of quantized compressive spectrum sensing.

频谱感应旨在侦测无线电频谱中未被占用的频带，使未领牌（辅助）用户可以在这些频带上进行通讯。目标是在不干扰授权（主）用户的情况下提高辅助用户的通信速率。然而，这需要监控一个非常大的带宽，以便以奈奎斯特速率采样可能导致不可接受的大量样本。众所周知，用于频谱感知的一些测试统计量具有稀疏性，即频谱被稀疏占用，人造信号的循环谱是稀疏的。通过利用这种稀疏性，人们可以使用压缩感知技术来代替传统的Shannon-Nyquist采样，以显着减少样本数量，同时仍然确保可靠地检测未占用的频带。在实践中，样品必须在交换这些之前进行量化。本项目旨在探讨压缩感知中量化对频谱感知的影响。我们将探讨一比特量化的极端情况，其中只保留测量的符号，以及多比特量化方案。一个特别的重点放在结构化随机测量，如随机部分傅立叶矩阵，这是高度相关的实际应用。虽然对高斯随机测量矩阵的量化压缩感知已经有了初步的理论结果，但到目前为止，结构化随机矩阵在这方面还没有得到充分的研究。此外，我们计划在量化压缩频谱感知的实际应用中研究两个开放的基本问题。首先，给定一个固定的通信位预算，我们将研究量化分辨率和次要用户采取的测量次数之间的权衡，以实现最佳的检测性能。其次，我们将分析占用决策频率和每个决策可用的比特预算之间的权衡，以尽量减少浪费的传输机会。Mathar的研究小组将致力于量化压缩感知算法的开发、实现和仿真及其在频谱感知环境中的应用。Rauhut和Dirksen研究小组的重点将放在量化压缩感知的理论分析上，目的是获得严格的误差保证和所需测量次数的明确界限。预计这两个小组的相互作用对于在量化压缩频谱感知的理解和实际实施方面取得重大进展至关重要。