Challenges faced in network security have significantly steered the deployment timeline of Fifth Generation (5G) communication at a global level; therefore, research in Sixth Generation (6G) security analysis is profoundly necessitated. The prerogative of this paper is to present a survey on the emerging 6G cellular communication paradigm to highlight symmetry with legacy security concepts along with asymmetric innovative aspects such Artificial Intelligence (AI), Quantum Computing, Federated Learning, etc. We present a taxonomy of the threat model in 6G communication in five security legacy concepts, including Confidentiality, Integrity, Availability, Authentication and Access control (CIA³). We also suggest categorization of threat-countering techniques specific to 6G communication into three types: cryptographic methods, entity attributes and Intrusion Detection System (IDS). Thus, with this premise, we distributed the authentication techniques in eight types, including handover authentication, mutual authentication, physical layer authentication, deniable authentication, token-based authentication, certificate-based authentication, key agreement-based authentication and multi-factor authentication. We specifically suggested a series of future research directions at the conclusive edge of this survey.

Federated Learning is a promising paradigm for sharing Cyber Threat Intelligence (CTI) without privacy issues by leveraging the cross-silos data in Software Defined Networking (SDN). However, with the heterogenous nature of future sixth generation (6G) systems and continuously evolving threats spectrum, the training data at silos of individual SDN domains is expected to be non-independent and identically distributed (non-IID), which leads to substantial degradation in the performance of Federated Learning. To solve this problem, a framework, named, Federated Conditional Variational Auto Encoders (FCVAE) is proposed for Federated Learning based enhanced CTI with non-IID data. Firstly, based on the mathematical relation between expected weight divergence and the extent of non-IID statistics in data, a regularization expression is derived for mitigation of Non-IID effects. Secondly, threat-specific data is generated with CVAEs at each silo based on global parameters of latent space, to mitigate the performance degradation of CTI due to Non-IID data. Finally, the performance of the proposed scheme is evaluated with simulation results using the InSDN dataset, which depicts significant enhancement from 92% to 97% as potential solution to mitigate impact of Non-IID data on CTI in heterogenous networks.

The outright aim of Beyond Fifth Generation (B5G) communication is to bring a revolution in Quality of Service (QoS) through enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and massive Machine Type Communication (mMTC). Thereby, due to this massive expansion in the communication paradigm, interference is bound to surface unexpected challenges in the wireless domain. Thus, interference mitigation is a foundational aim in the research paraphernalia of B5G communication. Presently, Software Defined Networking (SDN) is emerging to empower wireless communication with centralized routing-based interference mitigation. With this premise, we aim to present a focused review on SDN-based interference mitigation in wireless communication. Initially, we discuss current research horizon in the subject domain and briefly explore literature for routing-based interference mitigation. Thereby, we analyze the potential of SDN through evaluation of existing solutions for interference mitigation in B5G communication. We conclude our survey by highlighting prominent future research directions in SDN-based interference mitigation.

The quantum leap in mobile data traffic and high density of wireless electronic devices, coupled with the advancements in industrial radio monitoring and autonomous systems, have created great challenges for smooth wireless network operations. The fifth-generation and beyond (B5G) (also being referred to as sixth-generation (6G)) wireless communication technologies, due to their compatibility with the previous generations, are expected to overcome these unparalleled challenges. Accompanied by traditional and new techniques, the massive multiple input multiple output (mMIMO) approach is one of the evolving technologies for B5G/6G systems used to control the ever-increasing user stipulations and the emergence of new cases efficiently. However, the major challenges in deploying mMIMO systems are their high computational intricacy and high computing time latencies, as well as difficulties in fully exploiting the multi-antenna multi-frequency channels. Therefore, to optimize the current and B5G/6G wireless network elements proficiently, the use of the mMIMO approach in a HetNet structure with artificial intelligence (AI) techniques, e.g., machine learning (ML), distributed learning, federated learning, deep learning, and neural networks, has been considered as the prospective efficient solution. This work analyzes the observed problems and their AI/ML-enabled mitigation techniques in different mMIMO deployment scenarios for 5G/B5G networks. To provide a complete insight into the mMIMO systems with emerging antenna and propagation precoding techniques, we address and identify various relevant topics in each section that may help to make the future wireless systems robust. Overall, this work is designed to guide all B5G/6G stakeholders, including researchers and operators, aiming to understand the functional behavior and associated techniques to make such systems more agile for future communication purposes.

This comprehensive article explores the massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, and security considerations, this article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights the challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of federated learning in M-MIMO systems. By examining these cutting-edge developments, this article aims to advance knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks.