Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities

ElMossallamy, Mohamed A.; Zhang, Hongliang; Song, Lingyang; Seddik, Karim G.; Han, Zhu; Li, Geoffrey Ye

doi:10.1109/tccn.2020.2992604

Cited by 586 publications

(330 citation statements)

References 42 publications

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“…Over the years, there has been relentless pursuit to expand and increase the capacity of the cellular networks to support higher data rates. Optimizing the aspect of the communication system has been the focus of designers, the process was initiated with multiplexing techniques such as orthogonal frequency-division multiplexing (OFDM), adaptive coding techniques, beamforming (MIMO), frequency reuse, advanced adaptive modulation and inter-cell coordination techniques [174]. Recently, the initiation of reconfigurable intelligent surfaces (RIS), as well as the advent of smart radio environment concept [173], has given the opportunity to control the ambient environment thereby given us the partial control of the wireless channel to deliver more satisfactory propagation characteristics.…”

Section: Emerging Technologymentioning

confidence: 99%

Cellular Communications Coverage Prediction Techniques: A Survey and Comparison

et al. 2020

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A lot of effort and time is utilized in the planning and building of the cellular wireless networks to use minimum infrastructural components to provide the best network coverage as well as delivery of quality of service. Generally, path loss models are used for the prediction of wireless network coverage. Therefore, detailed knowledge of the appropriate path loss model suitable for the proposed geographical area is needed to determine the coverage quality of any wireless network design. However, to the best of our knowledge, despite the importance of path loss models, as used for the prediction of wireless network coverage, there doesn't exist any comprehensive survey in this field. Therefore, the purpose of this paper is to survey the existing techniques and mechanisms which can be addressed in this domain. Briefly, the contributions of this paper are: (1) providing a comprehensive and up to date survey of the various network coverage prediction techniques, indicating the different frequency ranges the models were developed, (2) the different suitable terrains for each of the model and the best suit mobile generation were presented, and lastly, (3) providing comparative analysis to aid the planning and implementation of the cellular networks. INDEX TERMS Path loss model, prediction, wireless, propagation scenarios, mobile generations, signal. I. INTRODUCTION The remarkable changes experienced by the development of mobile communication system over the last few years has led to severe challenges to the planning of mobile wireless networks. This fruition journey of the first-Generation (1G) network started back in the year 1979 and has progressed to the presently explored Fifth Generation (5G) network. Each new generation is usually built upon the present generation's needs, which led to research and development for a better technology that will accommodate the needs, capacities, proper availability to the end-user. With this exponential increase in the use of mobile-connected devices as well as the constant expansion of mobile communication networks, the effective provision of the coverage of the mobile networks is imperative for the delivery of quality of service (QoS) [1]. Radio propagation can be defined as the behaviour of radio waves experienced while signals are transmitted from one point to another [2]. Such phenomena like absorptions, reflections, scattering, refractions, among others, affect the radio wave [3]. Therefore, mobile network coverage prediction is a vital and essential task in the planning and deployment of cellular technology.

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Section: Emerging Technologymentioning

confidence: 99%

Cellular Communications Coverage Prediction Techniques: A Survey and Comparison

et al. 2020

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“…At the same time, these characteristics also pose new challenges in designing RIS-assisted networks/systems (e.g., communication, sensing, wireless charging, etc. ), such as information transfer within the RIS-embedded environment, RIS configuration optimization with limited information, resource allocation and network optimization in such communication systems as discussed in [6] [7].…”

Section: Introductionmentioning

confidence: 99%

“…Traditional or analytics-based solutions build upon communication theories, mathematical models, and optimization algorithms. While solid and very successful, they face challenges in supporting RIS-assisted systems as discussed in [6]. Firstly, most analytics-based recent works on the optimization of RIS parameters assume relevant channels are available and develop algorithms to configure the RIS given the channel knowledge [8] [9].…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, most analytics-based recent works on the optimization of RIS parameters assume relevant channels are available and develop algorithms to configure the RIS given the channel knowledge [8] [9]. However, given the nearly-passive nature of the RIS (i.e., elements on the RIS are not equipped with on-board sensing or signal processing capability while only passively reflecting the incidents according to the configuration) and the massive number of parameters to be estimated, these approaches would encounter more challenges in RIS-assisted wireless systems as discussed in [6] [7]. On top of that, the channel model of an RIS-assisted MIMO system has not yet been well understood.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the challenges are illustrations of the current wireless network design choice and assumptions that the improvements only operate on the end-points of the communication environment while regarding the wireless propagation environment between the communicating devices as uncontrollable as pointed out by [2] [6]. It leaves the job of tuning and adapting to the environment to field engineers or optimization tools.…”

Section: Introductionmentioning

confidence: 99%

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