One of the greatest challenges facing the physical layer design of the internet of things (IoT) resides in the imposed constraint of very low power consumption. Recently, new modulation scheme termed OFDM with sparse index modulation (OFDM-SIM) has been introduced as an energy efficient multicarrier scheme (MCS). Although of its high energy efficiency (EE) and spectral efficiency (SE), OFDM-SIM cannot fulfill the IoT energy requirements owing to its high PAPR. In this regard, an enhanced OFDM-SIM is proposed in this paper as an energy efficient MCS for IoT communications. In particular, a novel clippingcompressive sensing (CS) based PAPR reduction technique for OFDM-SIM is proposed. In the transmitter (TX) side, considering the complexity constraints for IoT devices, the simple and low complex clipping method is exploited to deal with the PAPR issue. On the receiver (RX) side, a robust CS signal recovery scheme is proposed to deal with tough resulting clipping noise. Unlike high complex conventional CS-based schemes, the proposed scheme exploits the inherent sparsity of the received enhanced OFDM-SIM signal rather than clipping noise sparsity to achieve a low complex CS signal detection. Moreover, in this paper, the information-theoretic limits on sparsity recovery are exploited to derive an upper bound measure of the bit error rate (BER). The simulation results demonstrate the superiority of the proposed scheme, as it significantly enhances the overall system performance in terms of EE and PAPR reduction compared to the conventional clipped coded-OFDM. INDEX TERMS IoT; OFDM-Sparse index modulation (OFDM-SIM); PAPR; Clipping; Compressive sensing (CS).
In this paper, a beat-based autoencoder is proposed for mapping photoplethysmography (PPG) to a single-lead electrocardiogram (single-lead ECG) signal. The main limiting factors represented in uncleaned data, subject dependency, and erroneous beat segmentation are regarded. The dataset is cleaned by a two-stage clustering approach. Rather than complete single–lead ECG signal reconstruction, a beat-based PPG-to-single-lead-ECG (PPG2ECG) conversion is introduced for providing a simple lightweight model that meets the computational capabilities of wearable devices. In addition, peak-to-peak segmentation is employed for alleviating errors in PPG onset detection. Furthermore, subject-dependent training is highlighted as a critical factor in training procedures because most existing work includes different beats/signals from the same subject’s record in both training and testing sets. So, we provide a completely subject-independent model where the testing subjects’ records are hidden in the training stage entirely, i.e., a subject record appears once either in the training or testing set, but testing beats/signals belong to records that never appear in the training set. The proposed deep learning model is designed for providing efficient feature extraction that attains high reconstruction quality over subject-independent scenarios. The achieved performance is about 0.92 for the correlation coefficient and 0.0086 for the mean square error for the dataset extracted/cleaned from the MIMIC II dataset.
Recently, 60 GHz wireless networks have drawn much attention due to availability of huge bandwidth around 60 GHz frequency band which has ability to support very high data rate, i.e., up to 6.7 Gbps. This is also called millimetre wave (mmWave) networking which paves the way for realization of Gigabit WLANs. However, heavy attenuation at 60 GHz frequency band limits the range of transmitted signal up to few meters. In order to extend the range of 60 GHz signal, use of directional antennas have been suggested as the most viable solution. Directional antennas use array of antennas to beamform in a particular direction. The difficulty arises when a user is moving. It is a challenging task to track the movement of a user and thus direct the beam in intended direction. According to the IEEE802.11 ad standard, each deployed mmWave AP in the target environment performs an exhaustive beam forming training (BT) to track the moving user to maintain the link. In this paper, we propose a mmWave user tracking scheme in outdoor environment based on the previous user equipment (UE) context information's. Where, the preceding beam ID directions used to predict a group of mmWave Tx beams to search on them to find out the best beam ID for the next UE location. Simulations show that the proposed scheme highly reduces BT complexity comparable to conventional scheme with conversing almost same performance.
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