The fifth generation (5G) mobile networks are envisaged to enable a plethora of breakthrough advancements in wireless technologies, providing support of a diverse set of services over a single platform. While the deployment of 5G systems is scaling up globally, it is time to look ahead for beyond 5G systems. This is mainly driven by the emerging societal trends, calling for fully automated systems and intelligent services supported by extended reality and haptics communications. To accommodate the stringent requirements of their prospective applications, which are data-driven and defined by extremely low-latency, ultra-reliable, fast and seamless wireless connectivity, research initiatives are currently focusing on a progressive roadmap towards the sixth generation (6G) networks, which are expected to bring transformative changes to this premise. In this article, we shed light on some of the major enabling technologies for 6G, which are expected to revolutionize the fundamental architectures of cellular networks and provide multiple homogeneous artificial intelligence-empowered services, including distributed communications, control, computing, sensing, and energy, from its core to its end nodes. In particular, the present paper aims to answer several 6G framework related questions: What are the driving forces for the development of 6G? How will the enabling technologies of 6G differ from those in 5G? What kind of applications and interactions will they support which would not be supported by 5G? We address these questions by presenting a comprehensive study of the 6G vision and outlining seven of its disruptive technologies, i.e., mmWave communications, terahertz communications, optical wireless communications, programmable metasurfaces, drone-based communications, backscatter communications and tactile internet, as well as their potential applications. Then, by leveraging the state-of-the-art literature surveyed for each technology, we discuss the associated requirements, key challenges, and open research problems. These discussions are thereafter used to open up the horizon for future research directions.

New technological advancements in wireless networks have enlarged the number of connected devices. The unprecedented surge of data volume in wireless systems empowered by artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by centrally collecting datasets and training models. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost and (ii) threatened data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of federated learning (FL), a newly emerging technique coined to bring ML to the edge of wireless networks. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth-generation (B5G) and sixth-generation (6G) communication systems. This survey aims to provide an overview of the state-of-the-art FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

Unmanned aerial vehicles (UAVs) have recently established their capacity to provide cost-effective and credible solutions for various real-world scenarios. UAVs provide an immense variety of services due to their autonomy, mobility, adaptability, and communications interoperability. Despite the expansive use of UAVs to support ground communications, data exchanges in those networks are susceptible to security threats because most communication is through radio or Wi-Fi signals, which are easy to hack. While several techniques exist to protect against cyberattacks. Recently emerging technology blockchain could be one of promising ways to enhance data security and user privacy in peer-to-peer UAV networks. Borrowing the superiorities of blockchain, multiple entities can communicate securely, decentralized, and equitably. This article comprehensively overviews privacy and security integration in blockchain-assisted UAV communication. For this goal, we present a set of fundamental analyses and critical requirements that can help build privacy and security models for blockchain and help manage and support decentralized data storage systems. The UAV communication system's security requirements and objectives, including availability, authentication, authorization, confidentiality, integrity, privacy, and non-repudiation, are thoroughly examined to provide a deeper insight. We wrap up with a discussion of open research challenges, the constraints of current UAV standards, and potential future research directions.

Ultra-reliable and low-latency communications (URLLC) is one of the cornerstone services of fifth-generation and beyond (B5G) wireless networks. The URLLC use cases demand strict block error probability and very low-latency targets, and thus even a small 32 byte of payload corruption could be detrimental to the user experience and the overall system performance. Ideally, the current heterogeneous network model and innovative technologies have paved the way for the seamless optimization of B5G URLLC applications. Yet, unprecedented interference issues due to the uncoordinated nature of modern wireless networks, innovative radio access techniques, and frequency reuse methods can significantly affect the performance of the URLLC systems. Therefore, the understanding and mitigation of all types of interference associated with each URLLC technology, deployment scenario, and wireless transmission mode are prudent. In this regard, many authors have recently presented interpretative and analytical studies on the management of interference issues, however, the discussion was limited to application-centric approaches and earlier research trends. In this review article, we primarily discuss different types of interference challenges related to the URLLC systems using contemporary designs, frameworks, access modes, and enabling technologies for B5G and sixth-generation (6G) communication networks. We present state-of-the-art research work, in-depth analysis of interference problems, and guidance on the futuristic 6G URLLC technologies and communication networks. This study intends to provide a holistic vision of B5G/6G URLLC systems, their empirical aspects, limitations, essential techniques, and an outlook toward future research avenues.

The increasing demand for spectral- and energy-efficient communication networks has spurred great interest in energy harvesting cognitive radio networks. Such a revolutionary technology represents a paradigm shift in the development of wireless networks, as it can simultaneously enable the efficient use of the available spectrum and the exploitation of RF energy in order to reduce reliance on traditional energy sources. This is mainly triggered by the recent advancements in microelectronics that puts forward RF energy harvesting as a plausible technique in the near future. On the other hand, it has been suggested that the operation of a network relying on harvested energy needs to be redesigned to allow the network to reliably function in the long term. To this end, the aim of this survey article is to provide a comprehensive overview of recent development and the challenges regarding the operation of CRNs powered by RF energy. In addition, the potential open issues that might be considered for future research are also discussed in this article.

This letter investigates downlink beamforming for simultaneous wireless information and power transfer in multi-user multi-input single-output underlay cognitive radio networks. Under the assumption of the integrated power splitting (PS) receiver architecture at the secondary receivers, we aim to minimize the total transmission power from the secondary base station by jointly optimizing the beamforming vectors and the PS ratios. We first exploit the structure of the relaxed problem to provide a closed form expression for the optimal solution. Since the underlying optimization problem is non-convex, we resort to semidefinite programming relaxation and we analytically prove that the relaxation is in fact tight and yields optimal beamforming solution.