Exploring deep learning for adaptive energy detection threshold determination: A multistage approach

The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system’s capability to adaptively determine a decision threshold for energy detection.European Union’s H2020 Framework Programm

A Speech Quality Classifier based on Tree-CNN Algorithm that Considers Network Degradations

Many factors can affect the users’ quality of experience (QoE) in speech communication services. The impairment factors appear due to physical phenomena that occur in the transmission channel of wireless and wired networks. The monitoring of users’ QoE is important for service providers. In this context, a non-intrusive speech quality classifier based on the Tree Convolutional Neural Network (Tree-CNN) is proposed. The Tree-CNN is an adaptive network structure composed of hierarchical CNNs models, and its main advantage is to decrease the training time that is very relevant on speech quality assessment methods. In the training phase of the proposed classifier model, impaired speech signals caused by wired and wireless network degradation are used as input. Also, in the network scenario, different modulation schemes and channel degradation intensities, such as packet loss rate, signal-to-noise ratio, and maximum Doppler shift frequencies are implemented. Experimental results demonstrated that the proposed model achieves significant reduction of training time, reaching 25% of reduction in relation to another implementation based on DRBM. The accuracy reached by the Tree-CNN model is almost 95% for each quality class. Performance assessment results show that the proposed classifier based on the Tree-CNN overcomes both the current standardized algorithm described in ITU-T Rec. P.563 and the speech quality assessment method called ViSQOL

Machine Learning for Multi-Layer Open and Disaggregated Optical Networks

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