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Quality of service (QoS) is a crucial requirement in distributed applications. Internet of Things architectures have become a widely used approach in many application domains, from Industry 4.0 to smart agriculture; thus, it is crucial to develop appropriate methodologies for managing QoS in such contexts. In an overcrowded spectrum scenario, cogni...
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... physical channel is one burst period per TDMA frame. Figure 1 shows the frame structure adopted in GSM900. The organization of the frames in multiframe and superframe levels is out of the scope of our work. ...
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... expected, the distributions are affected by the primary loads for both circuit switching and packet switching. For the lighter packet switching and circuit switching loads (Figures 7a,b and 8a,b, N d = 1), the distributions appears to be almost uniform, regardless of the queue size. When setting the primary packet switching load to N d = 1, the distribution of the enqueued IUs in the secondary user appears to be almost linearly increasing for QS = 5 (Figure 7c,e, N d = 1), while it appears to be almost exponentially increasing for the others in terms of the considered queue sizes (Figures 7d,f and 8c-f, N d = 1). ...
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... conditions means an overload of the SU buffer with a high loss probability as the outcome. (Figure 10a,c,e, N d > 1), and at 1.8 s for QS = 20 (Figure 10b,d,f, N d > 1), regardless of the primary switching circuit load W l . For the lighter primary packet switching load (N d = 1), the knee of the curves moves slightly to the left; the moving appears to be higher for higher queue sizes. ...
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... conditions means an overload of the SU buffer with a high loss probability as the outcome. (Figure 10a,c,e, N d > 1), and at 1.8 s for QS = 20 (Figure 10b,d,f, N d > 1), regardless of the primary switching circuit load W l . For the lighter primary packet switching load (N d = 1), the knee of the curves moves slightly to the left; the moving appears to be higher for higher queue sizes. ...
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... point out that the obtained CDFs are defective because the size of the queue is finite and a loss is experienced in almost all cases. The loss probability P l is computed by taking into account the probability of having a full queue (i.e., P s (5), P s (10) of Figure 9 and P s (15), P s (20) of Figure 10); we can note that the loss probability corresponds to the value of the complement to 1 of each CDF at infinity (i.e., 1 − F r (∞)). Table 5 summarizes the value of loss probabilities in the different workload conditions that we analyzed when the SU application uses different buffer sizes, as modeled by resorting to different queue sizes QS. ...
Citations
... Moreover, statistics from the Federal Communications Commission (FCC) indicate that the existing fixed spectrum allocation policy has led to the inefficient utilization of licensed spectrum bands [4]. Therefore, cognitive radio (CR) [5] has emerged as a promising technology that incorporates intelligent spectrum management techniques to effectively utilize frequency bands in specific times and locations (known as spectrum gaps or, more generically, white spaces) when not in use by licensed users [6,7]. In this situation, unlicensed users can transmit their signals with the understanding that the transmission of licensed users is adequately safeguarded. ...
Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning techniques to spectrum sensing has attracted considerable interest in the literature. In this contribution, we study cooperative spectrum sensing in a cognitive radio network where multiple secondary users cooperate to detect a primary user. We introduce multiple cooperative spectrum sensing schemes based on a deep neural network, which incorporate a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity patterns of the primary user. The scenario of an imperfect transmission channel is considered for service messages to demonstrate the robustness of the proposed model. The performance of the proposed methods is evaluated with the receiver operating characteristic curve, the probability of detection for various SNR levels and the computational time. The simulation results confirm the effectiveness of the bidirectional long short-term memory-based method, surpassing the performance of the other proposed schemes and the current state-of-the-art methods in terms of detection probability, while ensuring a reasonable online detection time.
... Moreover, statistics from the Federal Communications Commission (FCC) indicate that the existing fixed spectrum allocation policy has led to inefficient utilization of licensed spectrum bands [1]. Therefore, cognitive radio (CR) [2] has emerged as a promising technology that incorporates intelligent spectrum management techniques to effectively utilize frequency bands in specific times and locations (known as spectrum gaps or, more generically, white spaces) when not in use by licensed users [3], [4], [5]. In this situation, unlicensed users can transmit their signals with the understanding that the transmission of licensed users is adequately safeguarded. ...
p>Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning techniques to spectrum sensing has attracted consid?erable interest in the literature. In this contribution, we study cooperative spectrum sensing in a cognitive radio network where multiple secondary users cooperate to detect a primary user. We introduced multiple cooperative spectrum sensing schemes based on a tree deep neural network architecture, which incorporate a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity pattern of the primary user. The scenario of an imperfect transmission channel is considered to demonstrate the robustness of the proposed model. The performance of the proposed methods are evaluated with the receiver operating characteristic curves, the probability of detection for various SNR levels and the computational time. The simulation results confirm the effectiveness of the bidirectional long short-term memory based method, surpassing the performance of the other proposed schemes and the current state of the art in terms of detection probability while ensuring a reasonable online detection time.</p
... Moreover, statistics from the Federal Communications Commission (FCC) indicate that the existing fixed spectrum allocation policy has led to inefficient utilization of licensed spectrum bands [1]. Therefore, cognitive radio (CR) [2] has emerged as a promising technology that incorporates intelligent spectrum management techniques to effectively utilize frequency bands in specific times and locations (known as spectrum gaps or, more generically, white spaces) when not in use by licensed users [3], [4], [5]. In this situation, unlicensed users can transmit their signals with the understanding that the transmission of licensed users is adequately safeguarded. ...
p>Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning techniques to spectrum sensing has attracted consid?erable interest in the literature. In this contribution, we study cooperative spectrum sensing in a cognitive radio network where multiple secondary users cooperate to detect a primary user. We introduced multiple cooperative spectrum sensing schemes based on a tree deep neural network architecture, which incorporate a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity pattern of the primary user. The scenario of an imperfect transmission channel is considered to demonstrate the robustness of the proposed model. The performance of the proposed methods are evaluated with the receiver operating characteristic curves, the probability of detection for various SNR levels and the computational time. The simulation results confirm the effectiveness of the bidirectional long short-term memory based method, surpassing the performance of the other proposed schemes and the current state of the art in terms of detection probability while ensuring a reasonable online detection time.</p
