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- 저자 : Rivero-Angeles, Mario E. (검색결과 2 건)
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Castellanos-Lopez, S. Lirio,Cruz-Perez, Felipe A.,Rivero-Angeles, Mario E.,Hernandez-Valdez, Genaro The Korea Institute of Information and Commucation 2014 Journal of communications and networks Vol.16 No.2
Due to the unpredictable nature of channel availability, carrying delay-sensitive traffic in cognitive radio networks (CRNs) is very challenging. Spectrum leasing of radio resources has been proposed in the so called coordinated CRNs to improve the quality of service (QoS) experienced by secondary users (SUs). In this paper, the performance of coordinated CRNs under fixed-rate with hard-delay-constraints traffic is analyzed. For the adequate and fair performance comparison, call admission control strategies with fractional channel reservation to prioritize ongoing secondary calls over new ones are considered. Maximum Erlang capacity is obtained by optimizing the number of reserved channels. Numerical results reveal that system performance strongly depends on the value of the mean secondary service time relative to the mean primary service time. Additionally, numerical results show that, in CRNs without spectrum leasing, there exists a critical utilization factor of the primary resources from which it is not longer possible to guarantee the required QoS of SUs and, therefore, services with hard delay constraints cannot be even supported in CRNs. Thus, spectrum leasing can be essential for CRN operators to provide the QoS demanded by fixed-rate applications with hard delay constraints. Finally, the cost per capacity Erlang as function of both the utilization factor of the primary resources and the maximum allowed number of simultaneously rented channels is evaluated.
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Improving communication protocols in smart cities with transformers
Edgar Romo-Montiel,Ricardo Menchaca-Mendez,Mario Eduardo Rivero-Angeles,Rolando Menchaca-Mendez 한국통신학회 2022 ICT Express Vol.8 No.1
Adaptive medium access control (MAC) protocols are essential in the context of Vehicular AdHoc Networks (Vanets) because of the rapid changes in topology caused by the high mobility of nodes. In this work, we propose an adaptive version of the Slotted-ALOHA (S-ALOHA) protocol, where the transmission probability is constantly adjusted based on estimates of the number of vehicles in the coverage area. These values are computed using deep learning models for time series prediction. One challenge for implementing this approach is that the inputs to the models are noisy since they are also estimates based on the protocol’s operation itself. To address this problem, we propose a new training scheme where we add noise, similar to that produced by the protocol’s operation, to the inputs of the training examples as a form of regularization. Our experiments show that the regularized models perform close to the theoretical optimal where the number of vehicles in the area is always known.
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