Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient’s characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.

Automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural network (CNN). The basic building convolutional blocks of the proposed model include asymmetric kernels organized in parallel combinations to extract more meaningful and powerful features from the raw I/Q sequences of the received signals. These blocks are connected via skip connection to avoid vanishing gradient problems. The experimental results reveal that the proposed model performs well in classifying nine different modulation schemes simulated with different real wireless channel impairments, including AWGN, Rician multipath fading, and clock offset. The performance of the proposed system systems shows that it outperforms its best rivals from the literature in recognizing the modulation type. The proposed CNN architecture remarkably improves classification accuracy at low SNRs, which is appropriate in realistic scenarios. It achieves 86.1% accuracy at −2 dB SNR. Furthermore, it reaches an accuracy of 96.5% at 0 dB SNR and 99.8% at 10 dB SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family, with an overall average accuracy of 81.02%.

Renewable energy sources play a critical role in all governments’ and organizations’ energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids.

The present work proposes three MIMO antennas with different configurations for the future applications of wireless communications in the Q-band of the frequency to realize both spatial and polarization diversities. A circularly polarized (CP) printed antenna operating over two frequency bands at 37.8 and 50 GHz is utilized as a single element to construct the proposed MIMO antennas. Two-element MIMO antenna systems arranged in two configurations, side-by-side and face-to-face, are proposed to achieve spatial diversity. Also, a four-element MIMO antenna system is designed to achieve polarization diversity in addition to spatial diversity. The proposed MIMO antenna systems are designed with the aid of the CST simulator. The three MIMO antennas are fabricated and their performance is experimentally evaluated regarding the circular polarization, impedance matching, antenna gain, envelope correlation coefficient (ECC), and diversity gain (DG). The experimental results for the single-element as well as the MIMO antennas come in good agreement with simulation results showing high performance. Both the numerical and experimental investigations reveal that the mutual coupling between any two ports of the proposed MIMO antennas is below $$-25 {\text{dB}}$$- 25 dB . Also, for any two ports it is shown that the ECC is below $$1\times {10}^{-7}$$1 × 10 - 7 and the diversity gain is higher than $$9.99$$9.99 . The impedance matching bandwidths (for $$\left|{S}_{11}\right|<-10 {\text{dB}}$$S 11 < - 10 dB ) are shown to be $$1.53$$1.53 and $$1.88$$1.88 GHz at $$37.8$$37.8 and $$50\mathrm{ GHz}$$50 GHz , respectively, and the corresponding 3-dB axial ratio bandwidths are $$700$$700 and $$130\mathrm{ MHz}$$130 MHz , respectively.

Ensuring the security and privacy of confidential data during transmission is a critical challenge, necessitating advanced techniques to protect against unwarranted disclosures. Steganography, a concealment technique, enables secret information to be embedded in seemingly harmless carriers such as images, audio, and video. This work proposes two secure audio steganography models based on the least significant bit (LSB) and discrete wavelet transform (DWT) techniques for concealing different types of multimedia data (i.e., text, image, and audio) in audio files, representing an enhancement of current research that tends to focus on embedding a single type of multimedia data. The first model (secured model (1)) focuses on high embedding capacity, while the second model (secured model (2)) focuses on improved security. The performance of the two proposed secure models was tested under various conditions. The models’ robustness was greatly enhanced using convolutional encoding with binary phase shift keying (BPSK). Experimental results indicated that the correlation coefficient (Cr) of the extracted secret audio in secured model (1) increased by 18.88% and by 16.18% in secured model (2) compared to existing methods. In addition, the Cr of the extracted secret image in secured model (1) was improved by 0.1% compared to existing methods. The peak signal-to-noise ratio (PSNR) of the steganography audio of secured model (1) was improved by 49.95% and 14.44% compared to secured model (2) and previous work, respectively. Furthermore, both models were evaluated in an orthogonal frequency division multiplexing (OFDM) system over various wireless channels, i.e., Additive White Gaussian Noise (AWGN), fading, and SUI-6 channels. In order to enhance the system performance, OFDM was combined with differential phase shift keying (DPSK) modulation and convolutional coding. The results demonstrate that secured model (1) is highly immune to noise generated by wireless channels and is the optimum technique for secure audio steganography on noisy communication channels.

This paper presents a resource allocation scheme that fulfills the maximum possible aggregate rate of the capacity region by targeting the corner points of the multiple-input multiple-output multiple access channel. This corner points of the channel’s capacity region are attainable whenever each user’s transmission has minimum possible interference among other users. This work aims to investigate the non-singularity of such situations by the exploitation of users’ geographic location seeking the opportunity of getting users’ transmission spatially multiplexed. The developed model demonstrates that similar results can be achieved with partial channel state information knowledge under certain conditions throughout the operational signal to noise ratio range. The proposed resource allocation scheme is designed for a shotgun cellular system with a random distribution of users over a circular coverage area. The proposed model uses stochastic geometry to prove that when number of users grows up within the coverage area, the probability of achieving the corner points sum rate increases rapidly. The developed model was evaluated, and the results show that for a circular coverage area with a radius of 10 km, the probability of having users whose transmissions can be spatially multiplexed with minimum interference increases as the number of users grows to 300 users.