Integration of the promising millimeter wave (mmWave) technology into the legacy cellular network is one of the main challenges toward a unified 5G cellular network. However, directly controlling the access of mmWave small cells (SCs) via the LTE eNB using the conventional LTE/WLANs interworking seems to be an inefficient solution. This is due to the short transmission range of the mmWave signal accompanied with the inevitable use of directional communications in these bands. In this paper, a novel radio access network-level hierarchical multi-band heterogeneous network (HetNet) is proposed to efficiently overlay mmWave SCs over LTE. In this architecture, the Wi-Fi medium coverage band is used as an intermediate level between the large coverage LTE band and the small coverage mmWave band. The Wi-Fi band via the proposed concept of Wi-Fi/mmWave sub-clouds enables precise control to the access over the mmWave band. Whereas, the LTE eNB only controls the access over the distributed Wi-Fi/mmWave sub-clouds. Accordingly, a novel concept of two-level control and user data (2C/U) planes splitting is introduced in this paper. In this 2C/U plane splitting, the LTE eNB controls the network access over the distributed sub-clouds using the first level of control (C1-plane). The second level of control (C2-plane), provided by the Wi-Fi band, controls the access over the distributed mmWave SCs inside the sub-clouds. Thanks to this distributed control, the mmWave band can be efficiently utilized and the complexity of the mmWave initial access can be highly reduced. Moreover, the associated signaling/processing load on both LTE eNB and core network can be highly relaxed. Analytical and numerical analysis assure the performance superiority of the proposed LTE/Wi-Fi/mmWave HetNet over the conventional LTE/mmWave HetNet.

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, reconfigurable intelligent surfaces (RISs) have been exhibited as a promising candidate for sixth generation (6G) wireless networks. Moreover, the concept of index modulation (IM) and its variants as spatial modulation (SM), has been adapted as another solution to improve the spectral efficiency (SE) and the energy efficiency (EE) for these wireless systems. Leveraging the inherent advantages of both SM and RIS, this paper proposes a dual hup RIS-based SM (DH RIS-SM) scheme. In addition, a novel RIS-based enhanced spatial modulation (RIS-ESM) scheme is proposed for further performance enhancement. In the proposed RIS-ESM scheme, information bits are conveyed by the indexes of the active antennas in addition to the constellations transmitted from each one of them with primary and secondary modulations, which in turns leads to significant improvements in both the SE and the EE. Moreover, for further EE enhancement, this paper proposes RIS-ESM- P ₈ , as a variant of the proposed ESM scheme, in which the primary modulation is subdivided into two subgroups and the lower-energy subgroup is used in a larger number of combinations. A mathematical framework is presented by using a greedy detector for the calculation of the bit error probability (BEP) for the DH RIS-SM scheme. Also, the simulation results show that the proposed RIS-ESM schemes outperforms the conventional RIS-SM schemes in terms of the SE and the EE.

Millimeter wave (mmWave) communication is considered as the most promising solution for future 5G cellular networks. However, it suffers from a severe propagation loss causing dramatic link quality degradation and limiting its coverage area. Antenna beamforming appears to be a suitable solution to overcome this problem and provides longer transmission outdoor mmWave links. Accordingly, highly complicated conventional beamforming training (BT) techniques based on exhaustively searching all beam settings in all possible directions are used for mmWave discovery and association. This results in a long-time delay and a high-power consumption mmWave small cell discovery and association due to the large BT overhead. In this paper, a paradigm of mmWave small cell BT, discovery and association based on WiFi localization is proposed especially for local outdoor urban environments, e.g., campus area. Due to the use of beamforming, mmWave becomes a location-driven wireless communication system, hence positioning can extremely relax the complexity of mmWave link establishment. Simulation results show the high potency of the proposed scheme in reducing the complexity of mmWave BT, discovery and association compared to the conventional scheme of exhaustively searching all beam directions.

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 clipping-compressive 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.