压缩感知在数字电视信号频谱检测中的应用

计算机工程与应用 ›› 2012, Vol. 48 ›› Issue (3): 126-128.

• 数据库、信号与信息处理 • 上一篇下一篇

压缩感知在数字电视信号频谱检测中的应用

唐雅娟，李清

汕头大学电子工程系，广东汕头 515063

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2012-01-21 发布日期:2012-01-21

Application of compressive sensing in spectrum sensing of digital television signal

TANG Yajuan, LI Qing

Department of Electronic Engineering, Shantou University, Shantou, Guangdong 515063, China

Received:1900-01-01 Revised:1900-01-01 Online:2012-01-21 Published:2012-01-21

摘要/Abstract

摘要： 目前频谱资源紧张，有效利用空闲频谱的认知无线电技术应运而生，IEEE802.22开放了VHF/UHF电视频段供认知无线电用户使用。为了合理利用电视频段，首先要对电视信号进行检测，随着数字电视的普及，对数字电视信号的检测尤为重要。传统奈奎斯特采样采样率高，耗费空间和时间，而电视信号在频域具有稀疏性，应用压缩感知技术能以随机采样的方式用更少的数据点来很好地恢复原始信号。鉴于此，应用压缩感知技术，采用能量检测法对数字电视信号进行频谱检测，提高频谱利用率。

关键词: 压缩感知, 地面数字电视广播（DVB-T）, 频谱检测

Abstract: As the spectrum resource is in great shortage currently, cognitive radio has emerged to efficiently use the idle spectrum. As one of its applications, IEEE802.22 has opened the VHF/UHF TV band to cognitive radio users. In order to utilize the TV band properly, it needs to detect the signal first. With the popularity of digital TV, the spectrum sensing of digital television signal becomes particularly important. The sample rate of traditional Nyquist sampling is high, which wastes space and time. Since TV signal is sparse in the frequency domain, compressive sensing can perfectly recover the original signal with less points by random sampling. In view of this, compressive sensing and energy detection are applied to the spectrum sensing of digital TV signals, it improves spectrum efficiency.

Key words: compressive sensing, Digital Video Broadcasting-Terrestrical（DVB-T）, spectrum sensing

唐雅娟，李清. 压缩感知在数字电视信号频谱检测中的应用[J]. 计算机工程与应用, 2012, 48(3): 126-128.

TANG Yajuan, LI Qing. Application of compressive sensing in spectrum sensing of digital television signal[J]. Computer Engineering and Applications, 2012, 48(3): 126-128.

[1]	张珊珊，赵建华. 基于IALM的穿墙雷达成像算法[J]. 计算机工程与应用, 2021, 57(11): 77-83.
[2]	孙泽宇，吕治国. 大规模MIMO加权正交匹配追踪信道估计算法[J]. 计算机工程与应用, 2020, 56(5): 118-124.
[3]	陈子兆，矫文成. 改进的稀疏深度置信网络[J]. 计算机工程与应用, 2020, 56(2): 62-67.
[4]	万东琴，卿朝进，阳庆瑶，蔡斌，余旺. 叠加特征辅助的1-bit CS音频传输[J]. 计算机工程与应用, 2020, 56(16): 69-74.
[5]	邵然，沈军. 二维逐步正交匹配追踪算法[J]. 计算机工程与应用, 2020, 56(1): 209-215.
[6]	朱瑞金，龚雪娇，唐波. 分布式混合压缩感知无线传感器网络数据收集[J]. 计算机工程与应用, 2019, 55(6): 73-80.
[7]	杨赵琪璘，彭定涛，唐琦，罗孝敏. 基于稀疏优化lp正则化的光滑化拟牛顿算法[J]. 计算机工程与应用, 2019, 55(22): 163-171.
[8]	姬晓辉，孙泽宇，阎奔，李传锋. 基于压缩感知的高阶MIMO系统信道估值算法[J]. 计算机工程与应用, 2019, 55(19): 153-159.
[9]	曾春艳，叶佳翔，王志锋，武明虎. 深度学习框架下压缩感知重建算法综述[J]. 计算机工程与应用, 2019, 55(17): 1-8.
[10]	万东琴，卿朝进，阳庆瑶，蔡斌，余旺. 叠加特征信息辅助的语音传输与重构[J]. 计算机工程与应用, 2019, 55(15): 117-122.
[11]	刘玉红，杨丹凤. 光滑[lp]范数压缩感知图像重构优化算法[J]. 计算机工程与应用, 2019, 55(15): 213-218.
[12]	马玉双，刘翠响，郭志涛，王宝珠. 用于CS的广义稀疏度自适应匹配追踪算法[J]. 计算机工程与应用, 2019, 55(13): 207-211.
[13]	沈子钰，汪立新. 步长自适应的测量矩阵迭代优化方法[J]. 计算机工程与应用, 2019, 55(1): 266-270.
[14]	郑成，王国中，范涛，赵海武. 压缩视频感知中量化器的研究与设计[J]. 计算机工程与应用, 2018, 54(9): 172-177.
[15]	刘晓晖，张鑫媛，路利军，冯前进，陈武凡. 磁共振图像的原始-对偶近似迭代重建算法[J]. 计算机工程与应用, 2018, 54(8): 28-35.

压缩感知在数字电视信号频谱检测中的应用

Application of compressive sensing in spectrum sensing of digital television signal

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics