High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it better predict channel fluctuations and modify beam patterns accordingly. For multi-user massive multiple-input multiple-output (mMIMO) systems, hybrid precoding requires sophisticated real-time low-complexity power allocation (PA) approaches to achieve near-optimal capacity. This study presents a unique angular-based hybrid precoding (AB-HP) framework that minimizes radio frequency (RF) chain and channel estimation while optimizing energy efficiency (EE) and spectral efficiency (SE). DRL is essential for mm-wave technology to make adaptive and intelligent decision-making possible, which effectively transforms wireless communication systems. DRL optimizes RF chain usage to maintain excellent SE while drastically lowering hardware complexity and energy consumption in an AB-HP architecture by dynamically learning optimal precoding methods using environmental angular information. This article proposes enabling dual optimization of EE and SE while drastically lowering beam training overhead by incorporating maximum reward beam training driven (RBT) in the DRL. The proposed RBT-DRL improves system performance and flexibility by dynamically modifying the number of active RF chains in dynamic network situations. The simulation results show that RBT-DRL-driven beam training guarantees good EE performance for mobile users while increasing SE in mm-wave structures. Even though total power consumption rises by 45%, the SE improves by 39%, increasing from 14 dB to 20 dB, suggesting that this strategy could successfully achieve a balance between performance and EE in upcoming B5G networks.

With the rapid adoption of the Internet of Things, it is necessary to go beyond fifth-generation applications and apply stringent high reliability and low latency requirements, closely related to strict delay demands. These requirements support massive network connectivity for multiple Internet of Things devices. Hence, in this paper, we optimize energy efficiency and achieve quality-of-service requirements by mitigating co-channel interference, performing efficient power control of transmitters, and harvesting energy using time-slot exchanges. Due to a nonconvex optimization problem, we propose an iterative algorithm for power allocation and time slot interchange to reduce the computational complexity. To achieve a high degree of ultra-reliability and low latency with quality-of-service-aware instantaneous reward under massive connectivity, we efficiently employ multiagent reinforcement learning by addressing the intelligent resource management problem via a novel Double Deep Q Network. The network prioritizes experience replay to exploit the best policy and maximize accumulative rewards. It also learns the optimal policy and enhances learning efficiency by maximizing its reward function to make decisions with high intelligence and guarantee strict ultra-reliability and low latency. The simulation result shows that the Double Deep Q Network with prioritized experience replay can guarantee stringent ultra-reliability and low latency. As a result, the co-channel interference between transmission links and the high-power consumption density associated with the massive connectivity of the Internet of Things devices are mitigated.

Due to its large peak to average power ratio (PAPR) and high out of band emission (OOBE), OFDM doesn't meet the requirements of 5G services. Additionally, it supports only one type of waveform parameters in entire bandwidth. In contrast, f-OFDM is dividing the system's bandwidth into a number of subbands to support several waveform parameters based on various service scenarios. So, Filtered-OFDM (f-OFDM) is considered as a modern enabler of the flexible waveform to overcome the OFDM drawbacks while remaining its advantages as well as, to encounter the new challenges that faced 5G. Nonetheless, there is a trade-off among OOBE, PAPR and SNR performance. Meanwhile, channel coding technology is one of the most important issue in physical layer which is playing an essential role in order to achieve the reliability and latency. So, BCH code has been suggested in this paper for f-OFDM system to achieve the reliability of transmission information and thus improving BER performance over multipath fading channel. Whilst, BCH-LTE system is introduced as a baseline in this paper that using for comparison purpose with proposed system. Simulation results showed that the proposed BCH-f-OFDM system was significantly better than BCH-LTE system in terms of decreasing OOBE and achieving improving in BER performance. Although, PAPR levels was stilling high in proposed system due to the trade-off among OOBE, PAPR and SNR performance. However, the proposed system is considered a promising candidate to meet the requirements of 5G services because of its ability to solve two important issues in between three trade-offs'.

RS Codes for Downlink LTE System over LTE-MIMO Channel