# Advanced Physical-Layer Technologies for Beyond 5G Wireless Communication Networks
## 3 Brief Review of Technical Papers
The paper titled Interbeam Cochannel Downlink and Uplink Interference for 5G New Radio in mmWave Bands' [26] presented a methodology of assessing cochannel interference for multibeam antennas in 5G MIMO systems. The developed methodology maximized the SE by using the same frequency bands for angularly separated antenna beams of a 5G base station (gNodeB). The presented multiellipsoidal propagation model (MPM) provided mapping of multipath propagation, and considered the directivity of antenna beams. Then, 5G microcell base stations operating in mmWave bands were analyzed. Similar approaches based on the 3 GPP statistical model require the knowledge of the spatial parameters. In contrast, the proposed MPM approach considered signal-to-interference ratio assessments only; therefore, it was more suitable for any propagation scenarios. Results indicated that the proposed MPM approach provided faster determination of the same optimal directions for adjacent gNodeB beams. MIMO is envisaged as a key enabling technology for 5G networks [27]. In the paper titled A Two-Hop mmWave MIMO NR-Relay Nodes to Enhance the Average System Throughput and BER in Outdoor-to-Indoor Environments [28], the authors presented the architecture of two-hop out-of-band relay nodes (RNs) with MIMO capabilities to enable 5G communications in the mmWave band. Two relaying strategies, i.e., amplify-and-forward and decode-and-forward, were implemented to assist communications in outdoor-to-indoor environments between 5G radio node and NR user equipment. The proposed scheme considered two different channel-estimation approaches: (i) the complete knowledge of a channel, (ii) and channel estimation through least-squares estimator. Furthermore, simulation results implemented in MATLAB and Simulink verified the effectiveness of the proposed two-hop mmWave MIMO NR-RNs architecture. Simulation results implemented with 5G features such as downlink shared-channel transport channel coding, synchronization-signal burst generation, and physical downlink shared-channel generation showed improvements for the proposed scheme in terms of bit error rate and throughput. The design of modulation-detection techniques is critical in order to satisfy the diverse requirements of 5G services, as it determines the throughput, complexity, and reliability of the system from the perspective of the physical layer. The paper titled Machine Learning for 5G MIMO Modulation Detection [29] proposed a low-complexity modulation-detection algorithm for 5G multirelay cooperative MIMO systems over imperfectly estimated correlated channels. In detail, higher-order statistics of the received signal were extracted as discriminating features at the destination node. A comparative study between the random committee and AdaBoost ML techniques was established at a low SNR after applying principal-component analysis. The performance of the proposed modulation-detection scheme was analyzed in terms of various matrices, including recall, false-positive and true-positive rates, F measure, and time consumed to build the model. The superiority of the proposed scheme in terms of computational complexity and modulation detection was verified through simulation results. The paper titled 6G-Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap [30] presented details on the evolution of wireless-communication standards from 1G to 5G, and insights into the panoramic vision of 6G networks. The comparative review with the limitations of related survey papers and contributions of the paper to fill the knowledge gap was extensively discussed. Then, potential enabling technologies and emerging applications of 6G networks were reviewed. In contrast to previous papers, this paper presented extensive analysis of the associated challenges with the actualization of 6G networks in terms of commercialization, social, and health, and new use cases in terms of education, transport, media, and logistics.