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Register a new userAI-enabled Future Wireless Networks: Challenges, Opportunities and Open Issues
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Added by
Medhat Elsayed
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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A plethora of demanding services and use cases mandate a revolutionary shift in the management of future wireless network resources. Indeed, when tight quality of service demands of applications are combined with increased complexity of the network, legacy network management routines will become unfeasible in 6G. Artificial Intelligence (AI) is emerging as a fundamental enabler to orchestrate the network resources from bottom to top. AI-enabled radio access and AI-enabled core will open up new opportunities for automated configuration of 6G. On the other hand, there are many challenges in AI-enabled networks that need to be addressed. Long convergence time, memory complexity, and complex behaviour of machine learning algorithms under uncertainty as well as highly dynamic channel, traffic and mobility conditions of the network contribute to the challenges. In this paper, we survey the state-of-art research in utilizing machine learning techniques in improving the performance of wireless networks. In addition, we identify challenges and open issues to provide a roadmap for the researchers.
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Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integrated large arrays of inexpensive antennas. Existing research demonstrates that RISs significantly improve the physical layer performance, including the wireless coverage, achievable data rate and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaborate upon centralized, distributed and hybrid artificial-intelligence (AI)-assisted MAC architectures in RIS-aided multi-user communications systems. Finally, we discuss some challenges, perspectives and potential applications of RISs as they are related to MAC design.
Driven by the emerging use cases in massive access future networks, there is a need for technological advancements and evolutions for wireless communications beyond the fifth-generation (5G) networks. In particular, we envisage the upcoming sixth-generation (6G) networks to consist of numerous devices demanding extremely high-performance interconnections even under strenuous scenarios such as diverse mobility, extreme density, and dynamic environment. To cater for such a demand, investigation on flexible and sustainable radio access network (RAN) techniques capable of supporting highly diverse requirements and massive connectivity is of utmost importance. To this end, this paper first outlines the key driving applications for 6G, including smart city and factory, which trigger the transformation of existing RAN techniques. We then examine and provide in-depth discussions on several critical performance requirements (i.e., the level of flexibility, the support for massive interconnectivity, and energy efficiency), issues, enabling technologies, and challenges in designing 6G massive RANs. We conclude the article by providing several artificial-intelligence-based approaches to overcome future challenges.
Broadband access is key to ensuring robust economic development and improving quality of life. Unfortunately, the communication infrastructure deployed in rural areas throughout the world lags behind its urban counterparts due to low population density and economics. This article examines the motivations and challenges of providing broadband access over vast rural regions, with an emphasis on the wireless aspect in view of its irreplaceable role in closing the digital gap. Applications and opportunities for future rural wireless communications are discussed for a variety of areas, including residential welfare, digital agriculture, and transportation. This article also comprehensively investigates current and emerging wireless technologies that could facilitate rural deployment. Although there is no simple solution, there is an urgent need for researchers to work on coverage, cost, and reliability of rural wireless access.
Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area.
5G wireless communications technology is being launched, with many smart applications being integrated. However, 5G specifications merge the requirements of new emerging technologies forcefully. These include data rate, capacity, latency, reliability, resources sharing, and energy/bit. To meet these challenging demands, research is focusing on 6G wireless communications enabling different technologies and emerging new applications. In this report, the latest research work on 6G technologies and applications is summarized, and the associated research challenges are discussed.
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