Deep learning (DL) methods have been proved effective in improving the performance of channel estimation and signal detection. In this work, we propose three DL algorithms: fully connected deep neural network (FCDNN), convolutional neural networks (CNN), and long short-term memory (LSTM) neural networks for signal processing in multiuser orthogonal frequency-division multiplexing (OFDM) communications systems. The bit error rates (BERs) of these DL methods are compared with the conventional linear minimum mean squared error (LMMSE) detector. Additionally, the relationships between the BER and signal-to-interference ratio (SIR), signal-to-noise ratio (SNR), the number of interfering users (NoI) and modulation type are investigated. Numerical results show that all DL methods outperform LMMSE under different multiuser interference conditions, and FCDNN and LSTM give the best and robust anti-multiuser performance. This work shows that FCDNN and LSTM network have strong anti-interference ability and are useful in multiuser OFDM systems.
Summary Machine learning (ML) theories and methods are mainly based on probability theory and statistics. It is a very powerful tool for data modelling. On the other hand, energy harvesting has been regarded as a viable solution to extending battery lifetime of wireless sensor network. Motivated by these, modelling of the radio frequency (RF) energy available to the wireless nodes is required for efficient operation of wireless networks. In this work, we will use different ML algorithms to model the RF energy data for efficient operation of energy harvesting communication systems. Four ML algorithms are studied and compared in terms of the accuracy for RF energy modelling using the energy data in the band between 1805 and 1880 MHz. The results show that linear regression (LR) has the highest accuracy and the most stable performance, while decision tree is the worst model. Also, in terms of the operation efficiency of the system, LR has the best performance, followed by support vector machine and random forest algorithm.
In this paper, machine learning (ML) algorithms are used for channel prediction in wireless communications. The performances of five ML algorithms are compared in terms of the prediction accuracy and the symbol error rate (SER) of different modulation schemes based on the prediction. The result shows that, for channel prediction, support vector machine (SVM) has the best performance in terms of accuracy and stability. For signal detection, SVM and linear regression (LR) have their own advantages in different ranges of signal to noise ratio (SNR). At high constellation size, ML methods give similar performances to existing scheme. From the numerical examples, the SERs based on SVM and LR can both reach lower than 10 −3 in binary phase shift keying and 16-ary quadrature amplitude modulation signalling, and can reach 1.13×10 −2 and 4.28×10 −3 in 16-ary phase shift keying signalling respectively. In terms of prediction time, SVM is more efficient.
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