A simplified spatial Modulation MISO-OFDM scheme

Index modulation is one of the promising techniques for future communications systems due to many improvement over the classical Orthogonal frequency division multiplexing systems such as single RF chain, increased throughput for the same modulation order, achieved tradeoff between the efficiencies of the power and the spectral, and elimination of inter-channel interference. Many forms of index modulation researches exist where symbols are conveyed in antennas, subcarriers, time slots, and the space-time matrix.  Spatial modulation is one member of index modulation family where symbols are conveyed in activating transmit/receive antennas. In this paper, a modification to a standard multiple input single output scheme by integrating spatial modulation using simplified mathematical procedure is achieved. In the transmitter side, data and activation symbols are distributed simultaneously using mathematical module and floor functions. At the receiver, a simplified maximum likelihood detector is used to obtain transmitted pair of symbols. To verify this, MATLAB SIMULINK is used to simulate a downlink system where spatial modulation is applied to a base station. Results for different transmit antenna number and modulation order are obtained in the form of bit error rate versus signal to noise ratio

A machine learning approach for risk factors analysis and survival prediction of Heart Failure patients

In this study, we propose machine learning (ML) for risk factors analysis and survival prediction of Heart Failure (HF) patients using a survival dataset. Five supervised ML methods are applied to the dataset: Decision Tree (DT), Decision Tree Regressor (DTR), Random Forest (RF), XGBoost, and Gradient Boosting (GB) algorithms. We compare the applied algorithms’ performances based on accuracy, precision, recall, F-measure, and log loss value and show RF provides the highest accuracy of 97.78%. The analysis of the risk factors shows the most predictive features based on coefficients and feature importance. The top six risk factors for HF patients are serum creatinine (SC), age, ejection fraction (EF), platelets, creatinine phosphokinase (CPK), and SS (SS). Further analysis of these factors shows significant clustering of the features. The survival analysis finds that the increment of SC, age, and SS and the decrement of EF are the most significant risk factors for HF patients. Our results suggest that HF survival prediction is possible with higher accuracy using the proposed model. Our ML models are useful in clinical settings for screening patients with HF probability