Spectrum sensing performance characterization on ANRC's Hybrid Cognitive Radio Testbed

Ability to access the spectrum in an intelligent, smart and efficient way is the hallmark feature of a Cognitive Radio. It does this by sensing its ambience, spectrum usage, geo conditions and fuse these information to arrive at a spectrum decision which helps to access the network in a `smart', controlled manner. However, the incumbent licensed systems are not sure on the robustness and accuracy of the sensing systems of these Cognitive radios and have serious concern on the interference caused by `false' detection of spectrum holes. It is very much important to practically verify the advanced algorithms, theoretical frameworks for Cognitive radios both in terms of sensing and switching so as to ensure that the incumbent systems are fully convinced about its co-existence. Using ANRC's Hybrid Cognitive Radio Testbed, we have done the Performance benchmarking of the implementable algorithms. It is very important to characterize the same. On this testbed for Cognitive Radio, we have implemented Energy detector based Spectrum sensing algorithm and benchmarked their performance for real-world operations. The bit accurate implementation of algorithm was run on simulator part of testbed and the practical implementation using GNU Radiorunning on USRP. For realtime performance measurement it was run on emulation/hardware-in-loop. Both the results were compared, benchmarked and characterized to make the next blocks such as Spectrum decision systems to use this intelligence for further actions. This paper describes the process of implementation, verification, performance characterization and logging results for comparison of an Energy detector based spectrum sensing algorithm, which perhaps serves as a base for all future implementations on the testbed.