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Massive multiple-input-multiple-output (M-MIMO) offers remarkable advantages in terms of spectral, energy, and hardware efficiency for future wireless systems. However, its performance relies on the accuracy of channel state information (CSI) available at the transceivers. This makes channel estimation pivotal in the context of M-MIMO systems. Prior research has focused on evaluating channel estimation methods under the assumption of spatially uncorrelated fading channel models. In this study, we evaluate the performance of the minimum-mean-square-error (MMSE) estimator in terms of the normalized mean square error (NMSE) in the uplink of M-MIMO systems over spatially correlated Rician fading. The NMSE allows for easy comparison of different M-MIMO configurations, serving as a relative performance indicator. Besides, it is an advantageous metric due to its normalization, scale invariance, and consistent performance indication across diverse scenarios. In the system model, we assume imperfections in channel estimation and that the random angles in the correlation model follow a Gaussian distribution. For this scenario, we derive an accurate closed-form expression for calculating the NMSE, which is validated via Monte-Carlo simulations. Our numerical results reveal that as the Rician K-factor decreases, approaching Rayleigh fading conditions, the NMSE improves. Additionally, spatial correlation and a reduction in the antenna array interelement spacing lead to a reduction in NMSE, further enhancing the overall system performance.
Massive multiple-input-multiple-output (M-MIMO) offers remarkable advantages in terms of spectral, energy, and hardware efficiency for future wireless systems. However, its performance relies on the accuracy of channel state information (CSI) available at the transceivers. This makes channel estimation pivotal in the context of M-MIMO systems. Prior research has focused on evaluating channel estimation methods under the assumption of spatially uncorrelated fading channel models. In this study, we evaluate the performance of the minimum-mean-square-error (MMSE) estimator in terms of the normalized mean square error (NMSE) in the uplink of M-MIMO systems over spatially correlated Rician fading. The NMSE allows for easy comparison of different M-MIMO configurations, serving as a relative performance indicator. Besides, it is an advantageous metric due to its normalization, scale invariance, and consistent performance indication across diverse scenarios. In the system model, we assume imperfections in channel estimation and that the random angles in the correlation model follow a Gaussian distribution. For this scenario, we derive an accurate closed-form expression for calculating the NMSE, which is validated via Monte-Carlo simulations. Our numerical results reveal that as the Rician K-factor decreases, approaching Rayleigh fading conditions, the NMSE improves. Additionally, spatial correlation and a reduction in the antenna array interelement spacing lead to a reduction in NMSE, further enhancing the overall system performance.
Rapid advancements in wireless communication technology have made it easier to transfer digital data globally. With the complete assistance of artificial intelligence, the sixth-generation (6G) system—a new paradigm in wireless communication—is anticipated to be put into use between 2027 and 2030. Faster system capacity, faster data rate, lower latency, higher security, and better quality of service (QoS) in comparison to the 5G system are some of the main concerns that need to be addressed beyond 5G. Combining the growing need for more network coverage, lower latency, and greater data rates is the aim of 6G. It is recommended that to meet these needs and enable new services and applications, intelligent communication be implemented. The main enablers and facilitators for implementing intelligent communication beyond 5G are outlined in this paper. The article provides the horizon for new adaptations and standardization for integrating 6G intelligent communication in future networks and outlines the requirements and use-case scenarios for 6G. It also highlights the potential of 6G and key enablers from the standpoint of flexibility. It examines key research gaps like spectrum efficiency, network parameters, infrastructure deployment, and security flaws in past transitions while contrasting 5G and 6G communication. To overcome these challenges, modernizing 6G research domains are essential. Therefore, this review article focuses on the importance of 6G wireless communication and its network architecture, which also provides the technological paradigm shift from 5G to 6G. Furthermore, it highlights popular domains such as Artificial Intelligence, Internet of Things, Managing Big Data, Wireless Mobile networks, and Massive MIMO (Multiple Input Multiple Output), Quantum communication, Block chain Technology, Terahertz Communications (THz), Cell-free Communications and Intelligent Reflecting Surface as research objectives.
The field of wireless network and communication technology is evolving from generation to generation from 1G to 6G as of now till expected to be deployed and used by 2030. It is to succeed in 5G and bring significant improvements in terms of connectivity, speed, and size in next-generation communication technology. 6G aims to deal with the rising need for more rapid information speed, low latency, and wider network coverage. This intelligent communication is proposed to meet these demands and enable new services and applications. This review paper highlights the key enablers and challenges involved in implementing intelligent communication beyond 5G. The paper identifies the research gaps for incorporating beyond 5G communication networks and outlines the possible 6G key objectives from a flexibility standpoint. It reviews infrastructure deployment, network densification, spectrum capacity and network energy efficiency in predecessors to 6G. This paper emphasizes the need for standardization and adaptation of research areas to revolutionize 6G wireless communication, focusing on areas like, ultra massive MIMO, Terahertz Communications, Cell-Free Communications, Intelligent Reflecting Surface, Visible Light Communication, Internet of Things, Big Data management, Artificial Intelligence, and network connectivity techniques.
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