Artificial-Intelligence-Enabled Air Interface for 6G: Solutions, Challenges, and Standardization Impacts

Han, Shuangfeng; Xie, Tian; Chih‐Lin, I; Chai, Lu; Liu, Zhiming; Yuan, Yifei; Cui, Chunfeng

doi:10.1109/mcom.001.2000218

Cited by 70 publications

(39 citation statements)

References 10 publications

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“…This reward depends on the action made by the agent and the state of the environment. In addition, the environment evolves to a new state based on the current state and the action made by the agent [21].…”

Section: A Classification Of ML Techniquesmentioning

confidence: 99%

“…Here, any inconsistency between ML learning modules of different vendors can severely degrade the network performance [31]. Standardizing the interfaces between different modules is a key enabler for the application of ML techniques to wireless communications since it guarantees interoperability [21]. In this context, there is an initiative named open radio access network (O-RAN), that aims at standardizing most of the interfaces that have been kept as internal (i.e., vendor specific) in current 5G standard.…”

Section: B Emerging Applications Of ML To La and Main Challengesmentioning

confidence: 99%

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Emerging Tools for Link Adaptation on 5G NR and Beyond: Challenges and Opportunities

Martín-Vega¹,

Ruiz-Sicilia

Aguayo

et al. 2021

IEEE Access

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With the speeding up of the fifth generation (5G) new radio (NR) worldwide commercialization, one of the paramount questions for operators and vendors is how to optimize the radio links, considering the widely diverse scenarios envisioned. One of the key pillars of 5G has been an unprecedented flexibility on the configuration of the radio access network (RAN) on scenarios that include cellular, vehicular, and industrial networks among others. This flexibility has its main exponent on link adaptation (LA), which has evolved into a multi-domain technique where a plethora of parameters, like numerology, bandwidth part, radio frequency beam, power, modulation and coding scheme (MCS) or multiple antenna precoding can be adapted to the instantaneous link conditions. Although such enhancements open the door to a significant performance improvement, they also pose many challenges to LA optimization. In this article, we first present the signaling aspects of NR technology for multi-domain LA and the challenges that need to be faced. Then, we explore the latest advances on LA for wireless networks. We envision a combination of machine learning (ML) tools with multi-domain LA as a key enabler for 5G and beyond networks. Finally, we investigate emerging ML approaches for LA and present a promising application of ML for LA that is assessed with simulations. With this scheme, the training is performed at the network side to relieve the user equipment (UE) to do such a complex task. It is shown with simulations that our ML approach outperforms the well known outer loop link adaptation (OLLA) algorithm in terms of instantaneous block error rate (BLER), while reaching the same average spectral efficiency (SE). Interestingly, it is shown that the proposed scheme only requires 4 bits to represent the features used to train the model, which makes it suitable for implementation in real systems with limited feedback.INDEX TERMS 5G networks, link adaptation, machine learning, supervised learning.

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Section: A Classification Of ML Techniquesmentioning

confidence: 99%

Section: B Emerging Applications Of ML To La and Main Challengesmentioning

confidence: 99%

Emerging Tools for Link Adaptation on 5G NR and Beyond: Challenges and Opportunities

Martín-Vega¹,

Ruiz-Sicilia

Aguayo

et al. 2021

IEEE Access

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“…vironment from sensed data. A lexible communications system needs to bene it from the advantages of popular ML approaches such as reinforcement learning, deep learning, and edge computing [37,48,69,73]. Especially distributed intelligence (edge AI) with edge computing is a promising paradigm for 6G communications [36].…”

Section: Intelligent Communicationsmentioning

confidence: 99%

6G vision: An ultra-flexible perspective

Arslan¹,

Doğan-Tusha²,

Yazar³

2020

ITU J-FET

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The upcoming sixth generation (6G) communications systems are expected to support an unprecedented variety of applications, pervading every aspect of human life. It is clearly not possible to fulfill the service requirements without actualizing a plethora of flexible options pertaining to the key enabler technologies themselves. At that point, this work presents an overview of the potential 6G key enablers from the flexibility perspective, categorizes them, and provides a general framework to incorporate them in the future networks. Furthermore, the role of artificial intelligence and integrated sensing and communications as key enablers of the presented framework is also discussed.

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“…The so-called Cognitive Radio (CR) technology allows communication systems to make a more efficient use of the electromagnetic spectrum, by dynamically modifying the transceiver specifications according to the information sensed from the environment [4]. An efficient implementation of CRbased hand-held terminals will require embedding Artificial Intelligence (AI) in their main building blocks, which in turn will need new Circuits and Systems (CAS) strategies with a high degree of programmability and reconfigurability in order to dynamically select the optimum set of performance metrics and transmission band according to the information provided by the Digital Signal Processor (DSP) based on an interaction with the embedded AI engine [3], [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Radio Circuits and Systems - Application to Digitizers

Aboushady¹,

Sayed

Camuñas-Mesa

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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This paper gives an overview of Cognitive-Radio (CR) circuits and systems, that will enable the implementation of new technology paradigms such as software-defined electronics and Artificial Intelligence (AI) managed Internet-of-Things (IoT). A survey of the state of the art, trends and design challenges is presented from a top-down perspective -from system-level to circuit and chip implementation. As an application, special emphasis is put on analog/digital interfaces as one of the key building blocks in CR-based devices. Cutting-edge architectures -mostly based on ⌃ Modulators (⌃ Ms) -are discussed, as well as the best candidate circuit strategies to implement CRbased digitizers in deep nanometer CMOS. 1

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Artificial-Intelligence-Enabled Air Interface for 6G: Solutions, Challenges, and Standardization Impacts

Cited by 70 publications

References 10 publications

Emerging Tools for Link Adaptation on 5G NR and Beyond: Challenges and Opportunities

Emerging Tools for Link Adaptation on 5G NR and Beyond: Challenges and Opportunities

6G vision: An ultra-flexible perspective

Cognitive Radio Circuits and Systems - Application to Digitizers

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