Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart

Liang, Ying‐Chang; Long, Ruizhe; Zhang, Qianqian; Chen, Jie; Cheng, Hei Victor; Guo, Huayan

doi:10.23919/jcin.2019.8917871

Cited by 264 publications

(58 citation statements)

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“…The authors in [155] study the RISs-based cognitive radio network for enhancing the SE through dynamic resource management techniques and derives the expressions for wrong alarm and detection probabilities as well as signal transmission probability and throughput. The authors in [159] study the RISs-enabled IoT network, and presents the average data rate, bit error rate (BER), and outage probability in analyzing the impacts of fading and shadowing in the cellular network by adopting the Fisher-Snedecor model. The authors in [152] analyzed the physical layer security of a V2V network which investigated the average secrecy capacity with a RISs-based relay system.…”

Section: ) Smart Radio Environmentsmentioning

confidence: 99%

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Das¹,

Mudi²,

Rahman³

et al. 2022

Preprint

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<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

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Section: ) Smart Radio Environmentsmentioning

confidence: 99%

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Das¹,

Mudi²,

Rahman³

et al. 2022

Preprint

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“…[26, Eq. (8.354.1)], the lower incomplete Gamma function in (5) can be expanded into series. With some mathematical simplifications, we can obtain the CDF of γ * as in (10).…”

Section: B Ris Selection Strategymentioning

confidence: 99%

“…fully. In the literature, single-RIS-aided systems have been extensively studied [5]- [13]. In [5], an overview of the basic characteristics of the RIS/antenna technology and its potential applications has been provided.…”

mentioning

confidence: 99%

“…In the literature, single-RIS-aided systems have been extensively studied [5]- [13]. In [5], an overview of the basic characteristics of the RIS/antenna technology and its potential applications has been provided. A detailed overview on the state-of-the-art solutions, fundamental differences of RIS with other technologies, and the most important open research issues in this research area has been provided in [6].…”

mentioning

confidence: 99%

“…It is worth mentioning that the characterization of distributions that be utilized to statistically model the magnitude of the e2e channel coefficient of distributed multi-RIS-aided systems is still a challenging problem. One can observe that some previous works, [5] and [19], adopted the assumption that the channel distribution of RIS networks can be approximated by the NCCS where the CLT analysis approach could be used. However, results based on CLT are valid only for a very high number of reflecting or meta-surface elements.…”

mentioning

confidence: 99%

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Multiple RISs-Aided Networks: Performance Analysis and Optimization

Aldababsa¹,

Salhab²,

Nasir³

et al. 2022

Preprint

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This paper analyzes the performance of multiple reconfigurable intelligent surfaces (RISs)-aided networks. The paper also provides some optimization results on the number of reflecting elements on RISs and the optimal placement of RISs. We first derive accurate closed-form approximations for RIS channels' distributions assuming independent non-identically distributed (i.ni.d.) Nakagami-m fading environment. Then, the approximate expressions for outage probability (OP) and average symbol error probability are derived in closed-form. Furthermore, to get more insights into the system performance, we derive the asymptotic OP at the high signal-to-noise ratio regime and provide closed-form expressions for the system diversity order and coding gain. Finally, the accuracy of our theoretical analysis is validated through Monte-Carlo simulations. The obtained results show that the considered RIS scenario can provide a diversity order of a 2 K, where a is a function of the Nakagami fading parameter m and the number of meta-surface elements N , and K is the number of RISs.

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Federated Learning‐Based Beam Management in Dense Millimeter Wave Communication Systems

Xue,

Yang

2023

Federated Learning for Future Intelligent Wireless Networks

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Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart

Cited by 264 publications

References 0 publications

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Multiple RISs-Aided Networks: Performance Analysis and Optimization

Federated Learning‐Based Beam Management in Dense Millimeter Wave Communication Systems

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