Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This work presents generalized likelihood ratio test (GLRT) based spectrum sensing schemes for multiple-input multiple-output (MIMO) single carrier frequency division multiple access (SC-FDMA) cognitive radio networks. The finite record GLRT is initially derived for the MIMO SC-FDMA cognitive radio scenario. Subsequently, the GLRT is extended to a scenario with unknown noise variance. Closed form expressions are derived for the probabilities of false alarm and detection to characterize the performance of the GLRT for finite sample as ll as asymptotic scenarios. Further, the computational complexity of the proposed GLRT-based detectors is analyzed. Simulation results are presented to illustrate the spectrum sensing performance of the proposed tests in comparison with existing detectors in literature such as the conventional energy detector, maximum eigenvalue-based detector and also to verify the analytical expressions derived for the detection performance.
['Ayush Kumar', 'Saumya Dwivedi', 'Aditya K. Jagannatham']
I am writing a research paper on a idea GLRT Based Spectrum Sensing for MIMO SC-FDMA Cognitive Radio Networks
[{"from": "human", "value": "I am writing a research paper on a idea GLRT Based Spectrum Sensing for MIMO SC-FDMA Cognitive Radio Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This work presents generalized likelihood ratio test (GLRT) based spectrum sensing schemes for multiple-input multiple-output (MIMO) single carrier frequency division multiple access (SC-FDMA) cognitive radio networks. The finite record GLRT is initially derived for the MIMO SC-FDMA cognitive radio scenario. Subsequently, the GLRT is extended to a scenario with unknown noise variance. Closed form expressions are derived for the probabilities of false alarm and detection to characterize the performance of the GLRT for finite sample as ll as asymptotic scenarios. Further, the computational complexity of the proposed GLRT-based detectors is analyzed. Simulation results are presented to illustrate the spectrum sensing performance of the proposed tests in comparison with existing detectors in literature such as the conventional energy detector, maximum eigenvalue-based detector and also to verify the analytical expressions derived for the detection performance."}]