A compact dual-frequency ( $38/60 \mathrm{GHz}$ ) microstrip patch antenna with novel design is proposed for 5G mobile handsets to combine complicated radiation mechanisms for dual-band operation. The proposed antenna is composed of two electromagnetically coupled patches. The first patch is directly fed by a microstrip line and is mainly responsible for radiation in the lower band ( $38 \mathrm{GHz}$ ). The second patch is fed through both capacitive and inductive coupling to the first patch and is mainly responsible for radiation in the upper frequency band ( $60 \mathrm{GHz}$ ). Numerical and experimental results show good performance regarding return loss, bandwidth, radiation patterns, radiation efficiency, and gain. The impedance matching bandwidths achieved in the $38 \mathrm{GHz}$ and $60 \mathrm{GHz}$ bands are about $2 \mathrm{GHz}$ and $3.2 \mathrm{GHz}$ , respectively. The minimum value of the return loss is $-42$ dB for the $38 \mathrm{GHz}$ band and $-47$ for the $60 \mathrm{GHz}$ band. Radiation patterns are omnidirectional with a balloon-like shape for both bands, which makes the proposed single antenna an excellent candidate for a multiple-input multiple-output (MIMO) system constructed from a number of properly allocated elements for 5G mobile communications with excellent diversity schemes. Numerical comparisons show that the proposed antenna is superior to other published designs.

This paper presented the design and implementation of a 60 GHz single element monopole antenna as well as a two-element array made of two 60 GHz monopole antennas. The proposed antenna array was used for 5G applications with radiation characteristics that conformed to the requirements of wireless communication systems. The proposed single element was designed and optimized to work at 60 GHz with a bandwidth of 6.6 GHz (57.2–63.8 GHz) and a maximum gain of 11.6 dB. The design was optimized by double T-shaped structures that were added in the rectangular slots, as well as two external stubs in order to achieve a highly directed radiation pattern. Moreover, ring and circular slots were made in the partial ground plane at an optimized distance as a defected ground structure (DGS) to improve the impedance bandwidth in the desired band. The two-element array was fed by a feed network, thus improving both the impedance bandwidth and gain. The single element and array were fabricated, and the measured and simulated results mimicked each other in both return loss and antenna gain.

Vehicle-to-vehicle communication is a promising paradigm that enables all vehicles in the traffic road to communicate with each other to enhance traffic performance and increase road safety. Through vehicle-to-vehicle (V2V) communication, vehicles can understand the traffic conditions based on the information sent among vehicles on the road. Due to the potential delay caused by traffic jams, emergency vehicles may not be able to reach their destination in the required time, leading to severe losses. The case is more severe especially in developing countries where no emergency-vehicle-dedicated lanes are allocated. In this study, a new emergency vehicle route-clarifying strategy is proposed. The new clarifying strategy is based on vehicular traffic management in different interference medium scenarios. The proposed model aims, through V2V communication, to find the nearest vehicle with which to communicate. This vehicle plays an important role in reducing the travel time: as the emergency message is received, this vehicle will immediately communicate with all the neighboring vehicles on the road. Based on V2V communications, all the vehicles in the road will clear from the lane in the road for the emergency vehicle can safely reach its destination with the minimum possible travel time. The maximum distance between the emergency vehicle and the nearest vehicle was determined under different channel conditions. The proposed strategy applied an optimization technique to find the varied road traffic parameters. The proposed traffic management strategy was evaluated and examined through different assumptions and several simulation scenarios. The obtained results validated the effectiveness and the accuracy of the proposed model, and also indicated significant improvement in the network’s performance in terms of packet delivery ratio (PDR) and average end-to-end delay (E2E).

The classification of wireless communication channel scenarios is vital for modern wireless technologies. Efficient data preprocessing for identification, especially starting from 5G and beyond, where multiple scenario transitions occur, is crucial. Machine Learning (ML) is employed for scenario identification. Moreover, accurate ML classification is required to enhance the decision-making process in each communication layer. The proposed model in this study utilizes an enhanced preprocessing phase. The proposed model proves that adding the variance inflation factor (VIF) elimination layer has a significant effect in eliminating the residual noise after regularization. By evaluating the VIF, the high multi-collinear features are removed after adding a regularization penalty. Consequently, the total explained variance (TEV) was enhanced by 5% and reached 76%. Thus, the classification accuracy of the identification processes of different rural and urban scenarios was increased by 3%, on average, compared with previous work for each algorithm: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Gaussian Mixture model (GMM).

Wireless communication channel scenario classification is crucial for new modern wireless technologies. Reducing the time consumed by the data preprocessing phase for such identification is also essential, especially for multiple-scenario transitions in 6G. Machine learning (ML) has been used for scenario identification tasks. In this paper, the least absolute shrinkage and selection operator (LASSO) is used instead of ElasticNet in order to reduce the computational time of data preprocessing for ML. Moreover, the computational time and performance of different ML models are evaluated based on a regularization technique. The obtained results reveal that the LASSO operator achieves the same feature selection performance as ElasticNet; however, the LASSO operator consumes less computational time. The achieved run time of LASSO is 0.33 s, while the ElasticNet corresponding value is 0.67 s. The identification for each specific class for K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and k-Means and Gaussian Mixture Model (GMM) is evaluated using Receiver Operating Characteristics (ROC) curves and Area Under the Curve (AUC) scores. The KNN algorithm has the highest class-average AUC score at 0.998, compared to SVM, k-Means, and GMM with values of 0.994, 0.983, and 0.989, respectively. The GMM is the fastest algorithm among others, having the lowest classification time at 0.087 s, compared to SVM, k-Means, and GMM with values of 0.155, 0.26, and 0.087, respectively.