The intrusion detection system (IDS) is considered an essential sector in maintaining communication network security and has been desirably adopted by all network administrators. Several existing methods have been proposed for early intrusion detection systems. However, they experience drawbacks that make them subsequently inefficient against new/distinct attacks. To overcome these drawbacks, this paper proposes the enhanced long-short term memory (ELSTM) technique with recurrent neural network (RNN) (ELSTM-RNN) to enhance security in IDS. Intrusion detection technology has been associated with various problems, such as gradient vanishing, generalization, and overfitting issues. The proposed system solves the gradient-clipping issue using the likely point particle swarm optimization (LPPSO) and enhanced LSTM classification. The proposed method was evaluated using the NSL-KDD dataset (KDD TEST PLUS and KDD TEST21) for validation and testing. Many efficient features were selected using an enhanced technique, namely, the particle swarm optimization. The selected features serve for effective classification using an enhanced LSTM framework, where it is used to efficiently classify and detect the attack data from the normal data. The proposed system has been applied to the UNSW-NB15, CICIDS2017, CSE-CIC-IDS2018, and BOT _DATASET datasets for further verification. Results show that the training time of the proposed system is much less than that of other methods for different classes. Finally, the performance of the proposed ELSTM-RNN framework is analyzed using various metrics, such as accuracy, precision, recall, and error rate. Our proposed method outperformed LPBoost and DNNs methods.

Given that, the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell M-MIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is determined according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process.

In this study, we introduce a unique geometrical design of a rectangular patch antenna for sub-6 GHz 5G applications. The proposed antenna is designed at 5.2 GHz, and design criteria and performance characteristics have been investigated through numerical simulations and verified using measured data. A composite structure is suggested to overcome the restrictive parameters of conventional antennas. Two different materials, Rogers RT5880, and RT5870, are utilized, achieving a maximum gain of 6.210 dB, and a 90.2% radiation efficiency was attained. Four layers of superstrates are placed above the antenna to enhance the antenna's radiation performance further. The fabricated antenna is evaluated by measuring its radiation characteristics and return loss. The final antenna prototype offers a maximum bandwidth of about 896 MHz at a −10 dB reflection coefficient with a maximum gain of 9 dB. The proposed model achieved ultimate efficiency as high as 89% by employing four superstrates. The results showed good impedance matching (S11, VSWR), high efficiency, and appropriate radiation patterns. The outcomes from simulation and measurement are well aligned, making this antenna suitable for high-performance 5G applications.

Millimeter-wave (mmWave) communication systems utilize narrow beamforming to ensure adequate signal power. However, beam alignment requires significant training overhead, especially in high-mobility scenarios. Previous research has utilized synthetic data for position-aided beam prediction, which does not fully capture real-world complexities. In this work, an Enhanced Convolutional Neural Network model (E-CNN) is proposed for optimal prediction of beam indices with the aid of real-world GPS position data. The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. For 64-beams Scenario 1, the E-CNN model showed an increase in average top-1 accuracy from 55.57% to 63.92%, and in case of 32-beams, the accuracy increased from 71.34 % to 82.06%. For 16-beams, the accuracy increased from 86.17% to 94.64 %, while for 8-beams, the accuracy increased from 90.24% to 97.11%. In addition, besides showing significant power loss reduction in various scenarios, the proposed E-CNN model has demonstrated robustness regarding real-word conditions and adaptability for various beam setups. The model realized as high as a 50% power loss reduction in arguably the most challenging graphs, which is an exercise in reliability. This research fills the existing gap between the simulated aid beam alignment and real-world position beam aided alignment, which can be useful in improving beamforming in the upcoming wireless networks.