认知无线电中子谱缺失下的似然恢复算法

doi:10.19678/j.issn.1000-3428.0054376

计算机工程 ›› 2020, Vol. 46 ›› Issue (2): 201-206,213. doi: 10.19678/j.issn.1000-3428.0054376

认知无线电中子谱缺失下的似然恢复算法

管亮^1a, 郑霖^1a,2, 张文辉^1b

1. 桂林电子科技大学 a. 广西无线宽带通信与信号处理重点实验室;b. 广西云计算与大数据协同创新中心, 广西桂林 541004;
2. 通信网信息传输与分发技术重点实验室, 石家庄 050081

收稿日期:2019-03-26 修回日期:2019-04-29 发布日期:2019-06-06
作者简介:管亮(1992-),男,硕士研究生,主研方向为卫星通信、信号处理;郑霖,教授、博士;张文辉,副教授、博士。
基金资助:
国家自然科学基金（61571143，61371107）；广西云计算与大数据协同创新中心-广西高校云计算与复杂系统重点实验室项目（1716）。

Likelihood Recovery Algorithm for Subspectrum Deficiency in Cognitive Radio

GUAN Liang^1a, ZHENG Lin^1a,2, ZHANG Wenhui^1b

1a. Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing;1b. Guangxi Cloud Computing and Big Data Collaborative Innovation Center, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China;
2. Key Laboratory of Communication Network Information Transmission and Distribution Technology, Shijiazhuang 050081, China

Received:2019-03-26 Revised:2019-04-29 Published:2019-06-06

摘要/Abstract

摘要： 在使用滤波器组对信号频谱进行分割、插空的过程中，如果用户信号频谱带宽大于空余频带的总和或者插空信道出现强干扰，用户的信号检测会受到干扰。针对该问题，提出一种子谱缺失下的似然恢复算法。结合频谱分割技术，构建子谱缺失场景下的等效信道模型，并采用自适应似然信号恢复算法抑制非理想信道的干扰。仿真结果表明，该算法能够有效恢复失真信号，改善认知无线电中子谱缺失下的信号检测性能。

关键词: 认知无线电, 滤波器组, 频谱分割, 频带利用率, 似然信号恢复

Abstract: When a filter bank is used for signal spectrum division and interpolation,user signal detection will be interfered if the spectrum bandwidth of user signals is greater than the sum of free spectrum bandwidth or the interpolation channel is highly interfered.To address the problem,this paper proposes a likelihood recovery algorithm for subspectrum deficiency.The spectrum division technology is used to construct an equivalent channel model in scenarios of subspectrum deficiency,and then the adaptive likelihood signal recovery algorithm is used to suppress interference from non-ideal channels.Simulation results show that the algorithm can effectively recover distorted signals and improve the signal detection performance in the absence of subspectrum in cognitive radio.

Key words: cognitive radio, filter bank, spectrum division, frequency utilization efficiency, likelihood signal recovery

中图分类号:

TN911

管亮, 郑霖, 张文辉. 认知无线电中子谱缺失下的似然恢复算法[J]. 计算机工程, 2020, 46(2): 201-206,213.

GUAN Liang, ZHENG Lin, ZHANG Wenhui. Likelihood Recovery Algorithm for Subspectrum Deficiency in Cognitive Radio[J]. Computer Engineering, 2020, 46(2): 201-206,213.

https://www.ecice06.com/CN/Y2020/V46/I2/201

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