{'fraction_non_alphanumeric': 0.03025064822817632, 'fraction_numerical': 0.011235955056179775, 'mean_word_length': 5.327868852459017, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Improvement the Performing of Spectrum Distinguishing in Cognitive Radio using MIMO-Alamouti Scheme

This paper exploits the efficient performing of the Multiple Input Multiple Output (MIMO)-Alamouti scheme for spectrum sensing in cognitive radio (CR). Consequently, enhancement in the overall performance and the detection probability by using the MIMO-Alamouti scheme is achieved. Moreover, at low signal-to-noise ratio (SNR), the cooperative spectrum distinguishing algorithm among the different spectrum distinguishing techniques is employed to raise the probability of detection and also solving the hidden node problem. Matlab software is used to simulate the detection probability versus SNR for different schemes. Up to 50% enhancement in detection probability (Pd) as compared with the conventional technique under signal to noise ratio (SNR) equals −15 dB and false alarm probability (Pf) equals 0.1. As compared with the common spectrum sensing approach in case of the majority rule, at least 10% advance in the probability of detection at false alarm probability equals 0.1 under SNR equals −10 dB and the number of secondary user (SU) equals 5.