The Optimization of PCI Interference in the 4G LTE Network in Padang

With the growth of the customers and the expansion of the 4G LTE network in the area of Padang City, a PCI (Physical cell identity) modulo interference spot has been detected. PCI modulo interference occurs when an area is covered by two or more cells, which have a strong signal, and these cells have the same PCI modulo value. Based on the measurement results by the driving test method, the network conditions were not optimal because the SINR percentage (Signal to Interference Noise Ratio) in the good category was still low, at 9.47%, and the download throughput in the good category was 18.94%. This indicated that the interference in the area was quite high. Thus, it was necessary to do optimization action. The optimization action was taken by rotating the PCI on the site by considering the modulo value of each site so that the PCI with the same modulo did not merely lead to one location. Besides, action was taken to change the azimuth direction of cells that were too dominant. Based on the optimization process that has been carried out and the driving test activities that have been carried out again, the performance in the existing conditions has increased. The SINR percentage in the good category increased by 10%, so it became 19.47%, and the download throughput in the good category increased by 44.74% and became 63.68%.

Representation and Denoising of ECG Signal Using Hybrid Filtering Approach

Electrocardiogram (ECG) signal plays an imperative role in monitoring and examining the health condition of the heart. ECG signal represents the electrical activity of the heat. The most consequential noises that degrade important features in ECG signal are powerline interference noise, external electromagnetic field interference noise, baseline wandering and electroencephalogram noise. The features of ECG signal obtained in time domain is not sufficient for analyzing the ECG signal. As the signal is non-stationary, the time-frequency representation can be used for feature extraction. The Short Time Fourier Transform can be used but its time frequency precision is not optimal. In this current project, we will be able to implement the ideology proposed to overcome the problem among various time frequency transformation. The discrete wavelet transform (DWT) is used which gives effective results for non-stationary signals like ECG signal which may be often contaminated. The combination of Savitzky-Golay filtering and DWT can be used for ECG denoising and feature extraction which has the advantage of preserving the important feature by elimination the noise components. The method is applied for the database which is taken from MIT- BIH arrhythmia and the algorithm is implemented in MATLAB platform.

Joint Optimization Framework For Maximization of Instantaneous Transmission Rate In Signal To Interference Noise Ratio Constrained UAVs-Supported Self-Organized Device-To-Device Network

Due to their high maneuverability, flexible deployment, and line of sight (LoS) transmission, unmanned aerial vehicles (UAVs) could be an alternative option for reliable device-to-device (D2D) communication when a direct link is not available between source and destination devices due to obstacles in the signal propagation path. Therefore, in this paper, we have proposed a UAVs-supported self-organized device-to-device (USSD2D) network where multiple UAVs are employed as aerial relays. We have developed a novel optimization framework that maximizes the total instantaneous transmission rate of the network by jointly optimizing the deployed location of UAVs, device association, and UAVs’ channel selection while ensuring that every device should achieve a given signal to interference noise ratio (SINR) constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt reinforcement learning (RL) based solution methodology that effectively decouples it into three individual optimization problems. The formulated problem is transformed into a Markov decision process (MDP) where UAVs learn the system parameters according to the current state and corresponding action aiming to maximize the generated reward under the current policy. Finally, we conceive SARSA, a low complexity iterative algorithm for updating the current policy in the case of randomly deployed device pairs which achieves a good computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on the optimal deployment of UAVs. The proposed methodology improves the total instantaneous transmission rate of the network by 75.37%, 52.08%, and 14.77% respectively as compared with RS-FORD, ES-FIRD, and AOIV schemes.