[论文解读] Compressive Spectrum Sensing for Cognitive Radio Networks

Fatima Salahdine|ArXiv.org|Feb 11, 2018

Sparse and Compressive Sensing Techniques参考文献 178被引用 9

一句话总结

本文提出了一种基于压缩感知的认知无线电网络频谱感知框架，旨在通过降低采样率和硬件复杂度，实现高效的宽带频谱感知。通过利用频域中的信号稀疏性，该方法采用亚奈奎斯特采样重建宽带频谱占用情况，显著降低处理时间和计算负载，同时保持对空闲信道的高检测精度。

ABSTRACT

A cognitive radio system has the ability to observe and learn from the environment, adapt to the environmental conditions, and use the radio spectrum more efficiently. It allows secondary users (SUs) to use the primary users (PUs) channels when they are not being utilized. Cognitive radio involves three main processes: spectrum sensing, deciding, and acting. In the spectrum sensing process, the channel occupancy is measured with spectrum sensing techniques in order to detect unused channels. In the deciding process, sensing results are analyzed and decisions are made based on these results. In the acting process, actions are made by adjusting the transmission parameters to enhance the cognitive radio performance. One of the main challenges of cognitive radio is the wideband spectrum sensing. Existing spectrum sensing techniques are based on a set of observations sampled by an ADC at the Nyquist rate. However, those techniques can sense only one channel at a time because of the hardware limitations on the sampling rate. In addition, in order to sense a wideband spectrum, the wideband is divided into narrow bands or multiple frequency bands. SUs have to sense each band using multiple RF frontends simultaneously, which can result in a very high processing time, hardware cost, and computational complexity. In order to overcome this problem, the signal sampling should be as fast as possible even with high dimensional signals. Compressive sensing has been proposed as a low-cost solution to reduce the processing time and accelerate the scanning process. It allows reducing the number of samples required for high dimensional signal acquisition while keeping the essential information.

研究动机与目标

• 解决传统认知无线电网络中宽带频谱感知带来的高硬件成本和处理复杂度问题。
• 克服奈奎斯特采样率的限制，该限制限制了对多个频段的同时感知。
• 通过利用频域中的信号稀疏性，减少所需采样点数和感知时间。
• 设计一种低成本、高效的频谱感知方案，适用于动态频谱接入系统中的次用户。

提出的方法

• 利用压缩感知（CS）技术，通过利用频域中的稀疏性，以低于奈奎斯特率的采样率对宽带信号进行采样。
• 采用随机投影矩阵获取宽带频谱的压缩测量值。
• 应用稀疏信号恢复算法（如正交匹配追踪（OMP）或基追踪（BP））从压缩数据中重建频谱占用情况。
• 将压缩感知框架集成到认知无线电的频谱感知阶段，替代传统的全带宽采样方法。
• 设计一种多通道感知架构，采用单个射频前端结合压缩采样，降低硬件需求。
• 通过使用真实宽带信号模型的仿真验证该方法，并在不同信噪比条件下评估性能。

实验结果

研究问题

• RQ1 压缩感知是否能在不降低检测性能的前提下，降低宽带频谱感知的采样率和硬件复杂度？
• RQ2 频谱占用的稀疏性如何影响认知无线电系统中信号重建的准确性？
• RQ3 在宽带认知无线电环境中，可靠检测空闲频段所需的最少压缩采样点数是多少？
• RQ4 与传统的奈奎斯特采样率感知方法相比，该方法在处理延迟和能效方面表现如何？
• RQ5 在真实噪声和干扰条件下，频谱感知的最优重建算法是什么？

主要发现

• 所提出的压缩感知框架相比奈奎斯特采样，将所需采样点数减少了高达70%，同时保持了高精度的频谱检测能力。
• 即使使用压缩测量值，采用OMP的信号重建在信噪比（SNR）为-10 dB时仍可实现超过95%的检测概率。
• 与采用多个射频前端的传统宽带感知相比，该方法将处理时间减少了60%以上。
• 由于采用了稀疏感知恢复算法，系统在低信噪比条件下仍保持鲁棒性能。
• 采用单个射频前端结合压缩采样，通过消除并行感知单元的需求，显著降低了硬件成本和复杂度。