Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques

Alghamdi, Rawan; Alhadrami, Reem; Alhothali, Dalia; Almorad, Heba; Faisal, Alice; Helal, Sara; Shalabi, Rahaf; Asfour, Rawan; Hammad, Noofa; Shams, Asmaa; Saeed, Nasir; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed‐Slim

doi:10.1109/access.2020.3031959

Cited by 156 publications

(90 citation statements)

References 76 publications

(214 reference statements)

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“…Intelligent reflecting surfaces, also known as smart surfaces (SS) have emerged as promising 6G wireless communication technology. These smart surfaces consist of flexible metamaterials that allow them to passively/actively reflect the received signals improving the communication channel's quality [172]. Considering numerous smart surfaces' opportunities, it is wellsuited for the spatial platforms, including satellites, HAPs, and UAVs [173].…”

Section: Smart Platformsmentioning

confidence: 99%

Point-to-Point Communication in Integrated Satellite-Aerial 6G Networks: State-of-the-Art and Future Challenges

Saeed

Dahrouj

Al-Naffouri

et al. 2021

IEEE Open J. Commun. Soc.

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This paper surveys the literature on point-to-point (P2P) links for integrated satellite-aerial networks, which are envisioned to be among the key enablers of the sixth-generation (6G) of wireless networks vision. The paper first outlines the unique characteristics of such integrated large-scale complex networks, often denoted by spatial networks, and focuses on two particular space-air infrastructures, namely, satellites networks and high-altitude platforms (HAPs). The paper then classifies the connecting P2P communications links as satellite-to-satellite links at the same layer (SSLL), satellite-to-satellite links at different layers (SSLD), and HAP-to-HAP links (HHL). The paper surveys each layer of such spatial networks separately, and highlights the possible natures of the connecting links (i.e., radiofrequency or free-space optics) with a dedicated survey of the existing link-budget results. The paper, afterwards, presents the prospective merit of realizing such an integrated satellite-HAP network towards providing broadband services in under-served and remote areas. Finally, the paper sheds light on several future research directions in the context of spatial networks, namely large-scale network optimization, intelligent offloading, smart platforms, energy efficiency, multiple access schemes, distributed spatial networks, and routing. Index Terms-Integrated satellite-aerial networks, spatial networks, satellites, high-altitude platforms, broadband services.

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Section: Smart Platformsmentioning

confidence: 99%

Point-to-Point Communication in Integrated Satellite-Aerial 6G Networks: State-of-the-Art and Future Challenges

Saeed

Dahrouj

Al-Naffouri

et al. 2021

IEEE Open J. Commun. Soc.

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“…Reconfigurable Intelligent Surfaces (RIS) have emerged recently as an innovative technology to enhance the QoS of future wireless networks [196], [197]. RIS can be deployed in cellular networks as passive reflecting elements to provide near line-of-sight communication links to users, hence enhancing communication reliability and reducing latency [198], [199].…”

Section: G) Drl For Rram In Ris-assisted Wireless Networkmentioning

confidence: 99%

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Alwarafy¹,

Abdallah²,

Çiftler³

et al. 2021

Preprint

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<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

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“…where w i,j are the elements of w j , i.e. w i,j " }h i,j`ni,j } 2 , m MAX " 255 and m MIN " 0, and 16) are the maximum and minimum received power value from a point p j along the surface. The input structure supported by VGG19 is a RGB image of n c " 3 channels.…”

Section: Dataset Formatmentioning

confidence: 99%

“…The performance analysis of LIS and RIS assisted systems has attracted considerable attention in the recent years, and many works have studied the applicability of these technologies. For instance, the use of RIS to control the signals propagation has been analyzed in the context of communications through the so-called passive beamforming [7,[12][13][14], in location and positioning systems [15][16][17], and in physical layer This work was submitted for review on 18 th February 2021. A preliminary version of this work has been accepted to 15-th EAI International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM 2020) [1].…”

Section: Introductionmentioning

confidence: 99%

Assessing Wireless Sensing Potential With Large Intelligent Surfaces

Vaca-Rubio

Ramírez-Espinosa

Kansanen

et al. 2021

IEEE Open J. Commun. Soc.

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Sensing capability is one of the most highlighted new feature of future 6G wireless networks. This paper addresses the sensing potential of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the attention received by LIS in terms of communication aspects, it can offer a high-resolution rendering of the propagation environment. This is because, in an indoor setting, it can be placed in proximity to the sensed phenomena, while the high resolution is offered by densely spaced tiny antennas deployed over a large area. By treating an LIS as a radio image of the environment relying on the received signal power, we develop techniques to sense the environment, by leveraging the tools of image processing and machine learning. Once a radio image is obtained, a Denoising Autoencoder (DAE) network can be used for constructing a super-resolution image leading to sensing advantages not available in traditional sensing systems. Also, we derive a statistical test based on the Generalized Likelihood Ratio (GLRT) as a benchmark for the machine learning solution. We test these methods for a scenario where we need to detect whether an industrial robot deviates from a predefined route. The results show that the LIS-based sensing offers high precision and has a high application potential in indoor industrial environments.

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Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques

Cited by 156 publications

References 76 publications

Point-to-Point Communication in Integrated Satellite-Aerial 6G Networks: State-of-the-Art and Future Challenges

Point-to-Point Communication in Integrated Satellite-Aerial 6G Networks: State-of-the-Art and Future Challenges

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Assessing Wireless Sensing Potential With Large Intelligent Surfaces

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