【摘 要】
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1 IntroductionrnThe fact that the spectrum resource is underutilised in cer-tain bands has motivated the dynamic spectrum access (DSA)approach,which enables unlicensed secondary users (SUs)equipped with cognitive radio (CR) devices to access the spec-trum
【机 构】
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School of Computer Science and Electronic Engineering,University of Surrey,Guildford,Surrey GU27XH,U
论文部分内容阅读
1 IntroductionrnThe fact that the spectrum resource is underutilised in cer-tain bands has motivated the dynamic spectrum access (DSA)approach,which enables unlicensed secondary users (SUs)equipped with cognitive radio (CR) devices to access the spec-trum without causing significant interference to primary users(PUs).Nowadays,the increasing bandwidth for wireless com-munication in millimetre-wave and Terahertz frequency bands puts higher requirements on the performance of spectrum sensing technique,the primary enabler of DSA.Traditional Nyquist-rate sampling and processing tend to be impractical due to high power consumption,high-cost,and hardware com-plexity of high-speed analogue to digital converters (ADCs).To overcome the sampling rate bottleneck,several sub-Nyquist sampling methods[1-9],recovery algorithms[10-18]and channel detection methods[19-23]have been proposed.More-over,the recent advancements in machine-learning-based spec-trum sensing have been characterised,which has provided fur-ther intelligence to CR devices with better adaptivity and higher flexibility under complex radio environments[24-30].
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