# Advanced Physical-Layer Technologies for Beyond 5G Wireless Communication Networks
## 3 Brief Review of Technical Papers
D2D communication is a promising relay strategy for 5G networks. In the paper titled Device-to-Device Aided Cooperative Relaying Scheme Exploiting Spatial Modulation: An Interference-Free Strategy [22], the authors proposed a novel interference-free dual-hop D2D-aided cooperative relaying strategy based on spatial modulation (termed D2D-CRS-SM), and demonstrated the interference-free information reception with multiple cellular and D2D users operating in the same frequency band. The symbol and antenna indices of a transmitter were the desired pieces of information of a single receiver in a conventional SM [23]. In contrast, the presented D2D-CRS-SM scheme considered symbol and antenna indices as the desired pieces of information of two different receivers; thus, it facilitated efficient multiuser communication. The performance of the proposed D2D-CRS-SM was studied in terms of bit error rate and SE considering M-ary phase-shift keying and quadrature amplitude modulation over independent Rayleigh fading channels. Lastly, the efficiency of D2D-CRS-SM was demonstrated via Monte Carlo simulations. Results demonstrated the usefulness of the proposed scheme in 5G cellular-D2D wireless communication networks. The deployment of 5G technologies with home automation systems (HASs) has created a new ecosystem with significant potential. Challenges in HAS include the availability of infrastructure (e.g., Internet or electrical energy), human-appliance interactions, and the implementation of ML models in real time. To address the aforementioned automation challenges and control energy wastage owing to user lifestyles in smart homes, the paper titled Towards Energy Efficient Home Automation: A Deep-Learning Approach [24] proposed an autonomous smart-home system to automatically control energy consumption by employing machine- and deep-learning techniques. In detail, the proposed scheme was operated in three phases: (i) feature extraction and classification based on a 1-dimensional deep convolutional neural network, (ii) LSTM to forecast the load based on the extracted features in Phase 1, and (iii) a scheduling algorithm based on forecasted data to schedule the operational time of smart-home appliances. The proposed scheme efficiently automated the smart-home appliances with lower energy consumption while adapting to the lifestyle of the users. The validation of the proposed scheme was tested using several simulation scenarios incorporating datasets from authentic data sources. The simulation results indicated the advantage of the proposed HAS scheme in terms of fulfilling the energy demands of the users. The increasing number of wirelessly connected devices in 5G IoT networks has increased the demand for MIMO technology to efficiently utilize the limited bandwidth. The large space at the base station enables the easy exploitation of MIMO technology. In contrast, complex procedures are required for mobile handsets because of limited space to increase the number of active antenna elements. The paper titled Dual Band and Dual Diversity Four-Element MIMO Dipole for 5G Handsets [25] proposed the design of a four-element MIMO dual-band, dual-diversity, dipole antenna for 5G-enabled handsets, and focused the space and pattern diversity to provide acceptable MIMO performance. The usefulness of the proposed 4 \(\times\) 4 MIMO dipole antenna was verified by comparing simulated and measured results using a fabricated version of the proposed antenna. Specific absorption-rate analysis was performed using the CST Voxel model. The proposed antenna was tested to be operational in dual sub-6 GHz bands, as the envelope correlation coefficient, isolation, and capacity loss were demonstrated to be below the standard margin; thus, the antenna structure was appropriate for 5G MIMO applications. The 5G mmWave communication enables the high-capacity delivery and better handling of peak data rates.