{'fraction_non_alphanumeric': 0.01779935275080906, 'fraction_numerical': 0.0008090614886731392, 'mean_word_length': 5.279187817258883, '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}}
Performance of cognitive radio networks under Resume and Restart retransmission strategies

In this paper, the performance of cognitive radio networks under Resume (RR) and Restart (RS) retransmission strategies for interrupted secondary calls is investigated. System performance is analyzed and evaluated in terms of secondary users' blocking probability and mean transmission delay. For the RR strategy, a trellis-based mathematical analysis is proposed and developed for the calculation of the secondary users' mean total transmission delay. Considering that service time for secondary users is Coxian distributed, numerical results are shown to quantitatively compare the performance of the RR and RS retransmission strategies. It is found that the value of the coefficient of variation (CoV) of service time of SUs plays a major role in the system performance of cognitive radio networks under RR or RS strategies. For scenarios where the CoV of service time for SUs has values around 1, the RS strategy is preferred over the more complex RR strategy. On the contrary, as the CoV of the service time of SUs increases, the system performance improvement becomes significantly greater for the RR strategy relative to the RS strategy.