Abstract
Abstract Cognitive radio (CR) is a novel methodology that facilitates unlicensed users to share a licensed spectrum without interfering with licensed users. This intriguing approach is exploited in the Long Term Evolution-Advanced (LTE-A) network for performance improvement. Although LTE-A is the foremost mobile communication standard, future underutilization of the spectrum needs to be addressed. Therefore, dynamic spectrum access is explored in this study. The performance of CR in LTE-A can significantly be enhanced by employing predictive modeling. The neural network-based channel state and idle time (CSIT) predictor is proposed in this article as a learning scheme for CR in LTE-A. This predictive-based learning is helpful in two ways: sensing only those channels that are predicted to be idle and selecting the channels for CR transmission that have the largest predicted idle time. The performance gains by exploiting CSIT prediction in CR LTE-A network are evaluated in terms of spectrum utilization, sensing energy, channel switching rate, packet loss ratio, and average instantaneous throughput. The results illustrate that significant performance is achieved by employing CSIT prediction in LTE-A network.
Highlights
Long Term Evolution-Advanced (LTE-A) is an evolving generation mobile network that guarantees high data rates up to 1 Gbps for low mobility and 100 Mbps for high mobility [1]
LTE-A is the foremost mobile communication technology aiming to meet the requirements in terms of IMT-Advanced, inculcating Cognitive radio (CR) concepts in it still needs to be addressed
The predictive modeling is inculcated by employing channel state and idle time (CSIT) prediction using multilayer perceptron (MLP) neural network (NN)
Summary
Long Term Evolution-Advanced (LTE-A) is an evolving generation mobile network that guarantees high data rates up to 1 Gbps for low mobility and 100 Mbps for high mobility [1]. Another approach towards reducing the sensing time and increasing the transmission rate is projected in [12] In their proposed model, the sensing task is supervised by the network, i.e., by giving the intelligent sensing matrix to the CR. The NN-based throughput learner for CR is proposed in [13] where authors exploited NN for predicting the appropriate transmission rate that can be used for a concerned channel. They presented the basic and extended throughput prediction model using NN. As far as the interference to the primary network is concerned, CUpredict applies sensing to the slot(s) that are predicted to be more idle, in terms of idle time slot (s), before using them for transmission. If the interference experienced by the PUs due to secondary users (SeU) exceeded beyond the specified threshold, eNB alerts the concerned CUpredict to change the slot(s) for transmission to avoid the interference
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