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es Ca 9 ee ce The model was optimized based on simulated data which enabled the fine-tuning of its parameters to improve its decision-making capability and enhance its spectrum allocation efficiency, adaptability, and scalability. After the optimization, the model was deployed and integrated into real-life cellular network infrastructure to monitor its performance metrics, and dynamic changes in both urban and rural conditions in comparison to existing process presented in the work of Dalla Pozza et al. (2022) shown in Figure 3. Figure 3 shows a typical scenario of devices demand of spectrum allocations, which formed the bases of validating the effectiveness of the model by comparing it to traditional static allocation techniques, with a focus on reducing interference, responding to changing network circumstances, and efficiency of spectrum allocation in both urban and rural locations.

Figure 3 es Ca 9 ee ce The model was optimized based on simulated data which enabled the fine-tuning of its parameters to improve its decision-making capability and enhance its spectrum allocation efficiency, adaptability, and scalability. After the optimization, the model was deployed and integrated into real-life cellular network infrastructure to monitor its performance metrics, and dynamic changes in both urban and rural conditions in comparison to existing process presented in the work of Dalla Pozza et al. (2022) shown in Figure 3. Figure 3 shows a typical scenario of devices demand of spectrum allocations, which formed the bases of validating the effectiveness of the model by comparing it to traditional static allocation techniques, with a focus on reducing interference, responding to changing network circumstances, and efficiency of spectrum allocation in both urban and rural locations.

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SEVEN SS ON laos ee Se te Ae ‘Using cutting- edge methods like cognitive radio sensing and reinforcement learning- based algorithmic spectrum analysis, the Perception Layer gathers real-time spectrum sensing data on network characteristics. Using this method, it continuously monitors the radio frequency in the environment, and passes this information to the decision layer. The decision layer analyses these data and make dynamic judgements with respect to available spectrum and the demands from several devices and pass the information to the execution layer. Using reinforcement learning algorithms, the execution layer allocates frequency in a dynamic and intelligent manner, maximizing the available frequency bands. With reward-based dynamic decision-making approach, the algorithm gradually learns and adjust to changing network circumstances. This process optimises frequency band allocation in response to demand in rea time, adapting to erratic network circumstances.
SEVEN SS ON laos ee Se te Ae ‘Using cutting- edge methods like cognitive radio sensing and reinforcement learning- based algorithmic spectrum analysis, the Perception Layer gathers real-time spectrum sensing data on network characteristics. Using this method, it continuously monitors the radio frequency in the environment, and passes this information to the decision layer. The decision layer analyses these data and make dynamic judgements with respect to available spectrum and the demands from several devices and pass the information to the execution layer. Using reinforcement learning algorithms, the execution layer allocates frequency in a dynamic and intelligent manner, maximizing the available frequency bands. With reward-based dynamic decision-making approach, the algorithm gradually learns and adjust to changing network circumstances. This process optimises frequency band allocation in response to demand in rea time, adapting to erratic network circumstances.
Figure 2. Deep-Q Learning Model for Dynamic Spectrum Allocation in a Cellular Network www.ejsit-journal.com Deployment and Integration
Figure 2. Deep-Q Learning Model for Dynamic Spectrum Allocation in a Cellular Network www.ejsit-journal.com Deployment and Integration
Figure 4. Graph of Model Allocated Spectrum using integrated Dataset
Figure 4. Graph of Model Allocated Spectrum using integrated Dataset
Figure 5. Virtual Network for Experimental Test-Bed
Figure 5. Virtual Network for Experimental Test-Bed
Figure 6. Visualization of Network Density and Allocation of Available Spectrum Using Gephi
Figure 6. Visualization of Network Density and Allocation of Available Spectrum Using Gephi
Figure 7. Graph of Model Allocated Spectrum using Simulated Dataset
Figure 7. Graph of Model Allocated Spectrum using Simulated Dataset
The model was validated using the two datasets: simulated and existing dataset, as wel as the integration of both. The model proved to effectively manage available spectrum up to 8: percent efficiency as shown in Figure 9. www.ejsit-journal.com
The model was validated using the two datasets: simulated and existing dataset, as wel as the integration of both. The model proved to effectively manage available spectrum up to 8: percent efficiency as shown in Figure 9. www.ejsit-journal.com
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Cellular NetworksArtificial Intelligence & Neural NetworkingPredictive Maintenance
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