@book {27852,
	title = {AI and ML {\textendash} Enablers for Beyond 5G Networks},
	year = {2021},
	month = {05/2021},
	publisher = {5G PPP Technology Board},
	organization = {5G PPP Technology Board},
	address = {Online},
	abstract = {This white paper on AI and ML as enablers of beyond 5G (B5G) networks is based on contributions from almost 20 5G PPP projects, coordinated through the 5G PPP Technology Board, that research, implement and validate 5G and B5G network systems. The paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for enhancing 5G and B5G networks.},
	doi = {10.5281/zenodo.429989},
	url = {https://5g-ppp.eu/wp-content/uploads/2021/05/AI-MLforNetworks-v1-0.pdf},
	author = {Mohammed A. {Abdulkadir} and Alay, {\"O}zg{\"u} and Alexiou, Angeliki and Anastasopoulos, Markos and Antevski, Kiril and Aumayr, Erik and Ayed, Dhouha and Baranda, Jordi and Behravesh, Rasoul and Benzaid, Chafika and Bisson, Pascal and Anne-Marie Cristina {Bosneag} and Alexandros-Apostolos A. {Boulogeorgos} and Jos{\'e} Alcaraz {Calero} and Carrozzo, Gino and Claudio Ettore {Casetti} and Caso, Giuseppe and Alberto Huertas {Celdr{\'a}n} and Chergui, Hatim and Carla Fabiana {Chiasserini} and Chinchilla-Romero, Lorena and Chollon, Geoffroy and Cogalan, Tezcan and Colman-Meixner, Carlos and Conti, Andrea and Cosmas, John and Costa-Requena, Jos{\'e} and Desai, Rachana and Devoti, Francesco and Diamanti, Maria and Dreibholz, Thomas and Elmokashfi, Ahmed and Feghhi, Saman and Garcia-Saavedra, Andres and Gavras, Anastasius and Ghoraishi, Mir and Giannopoulos, Anastasios and Goodarzi, Meysam and Gramaglia, Marco and Guimar{\~a}es, Carlos and G{\"u}rkan, G{\"u}r and He, Jiguang and Hrasnica, Halid and Jimeno, Elisa and Kakkavas, Grigorios and Kaloxylos, Alexandros and Karyotis, Vasileios and Katta, Saimanoj and Katzouris, Nikos and Takai Eddine {Kennouche} and Klinkenberg, Ralf and Kokkoniemi, Joonas and Kondepu, Koteswararao and Lazarakis, Fotis and Lekidis, Alexios and Li, Xi and Lopez, Diego and Magoula, Lina and Mahmood, Kashif and Majumdar, Sayantini and Malinverno, Marco and Mangues-Bafalluy, Josep and Mart{\'\i}nez, Ricardo and Mart{\'\i}n-P{\'e}rez, Jorge and Martrat, Josep and Meunier, Ben and Mohamed, Ramy and Morselli, Flavio and ‪Juan Jos{\'e} Ramo {Mu{\~n}oz‬} and Mu{\~n}oz-Luengo, Pablo and Daniel Camps {Muri} and Murphy, Chris and Edgardo Montes {de Oca} and Papagianni, Chrysa and Papavassiliou, Symeon and Antonio Agust{\'\i}n P {Perales} and Gregorio Mart{\'\i}nez {P{\'e}rez} and Manuel Gil {P{\'e}rez} and Wint Yi {Poe} and Prados-Garzon, Jonathan and Di Renzo, Marco and Joan Pujol {Roig} and Pedro Miguel {S{\'a}nchez} and De Schepper, Koen and Shi, Lina and Skarmeta, Antonio and Spantideas, Sotirios and Gokul Krishna {Srinivasan} and Stamou, Adamantia and Subramanya, Tejas and Trakadas, Panagiotis and Tzanakaki, Anna and Valcarenghi, Luca and Jos{\'e} Mar{\'\i}a Jor {Valero} and Videv, Stefan and Wang, Qi and Wang, Yue and Xie, Min and Xu, Zhao and Yan, Shuangyi and Yaqub, Edwin and Yesilkaya, Anil and Zanzi, Lanfranco and Zemouri, Sofiane and Zeydan, Engin and Zhang, Xun and Zhou, Xueqing}
}
@inproceedings { EuCNC2020,
	title = {AI-Driven Closed-Loop Service Assurance with Service Exposures},
	journal = {Proceedings of the 29th IEEE European Conference on Networks and Communications (EuCNC)},
	year = {2020},
	month = {06/2020},
	pages = {265-270},
	publisher = {IEEE Computer Society},
	address = {Dubrovnik, Dubrovnik-Neretva/Croatia},
	abstract = {Artificial Intelligence (AI) is widely applied in mobile and wireless networks to enhance network operation and service management. Advanced AI mechanisms often require high level of network service exposure in order to access data from as many network elements as possible and execute the AI recommended outcomes into the networks. However, in practice, it is not always feasible to expose the network services to 3rd parties or customers with AI ambitions. Considering that service assurance (SA) is a major area to which AI is applied, this paper describes how a closed-loop SA architecture is associated with the service exposure model in the 5G networks with network slicing. Then we investigate the impact and implication of service exposure on SA. Finally, a set of experiment results are provided to demonstrate the trade-off relationship between the AI ambition and the exposure level in SA.},
	keywords = {AI, Service Assurance, Service Exposure},
	author = {Xie, Min and Pujol-Roig, Joan and Foivos Ioannis {Michelinakis} and Dreibholz, Thomas and Guerrero, Carmen and Sanchez, Adrian and Wint Yi {Poe} and Wang, Yue and Elmokashfi, Ahmed}
}
@inproceedings {26612,
	title = {Towards Closed Loop 5G Service Assurance Architecture for Network Slices as a Service},
	journal = {European Conference on Networks and Communications (EuCNC)},
	year = {2019},
	pages = {139-143},
	publisher = {IEEE},
	abstract = {5G intends to use network slicing to support multiple vertical industries. The dynamic resource sharing and diverse customer requirements bring new challenges towards service assurance (SA), such as automation and customer-centric. As a response to these challenges, this paper proposes a hierarchical, modular, distributed, and scalable SA architecture. This paper highlights an important key feature\&nbsp;SA coordination, which is facilitated by three new SA functions,\&nbsp;SA interpretation,\&nbsp;SA policy management, anddata fabric. Three closed-loops are introduced to coordinate and realize automation of service management. Challenges associated with realizing SA are briefly discussed and will be addressed by leveraging the 5G infrastructure developed within the H2020-ICT-17 project 5G-VINNI.},
	author = {Xie, Min and Wint Yi {Poe} and Wang, Yue and Andr{\'e}s Javier {Gonz{\'a}lez} and Elmokashfi, Ahmed and Joao Antonio Pe {Rodrigues} and Foivos Ioannis {Michelinakis}}
}
@article {Simula.mpg.12,
	title = {Audio-Visual Identification of Place of Articulation and Voicing in White and Babble Noise},
	journal = {Journal of the Acoustical Society of America},
	volume = {126},
	number = {1},
	year = {2009},
	month = {July},
	pages = {377-387},
	publisher = {Acoustical Society of America},
	abstract = {Research shows that noise and phonetic attributes influence the degree to which auditory and visual modalities are used in audio-visual speech perception (AVSP). Research has, however, mainly focused on white noise and single phonetic attributes, thus neglecting the more common babble noise and possible interactions between phonetic attributes. This study explores whether white and babble noise differentially influence AVSP and whether these differences depend on phonetic attributes. White and babble noise of 0 and {-}12 dB signal-to-noise ratio were added to congruent and incongruent audio-visual stop consonant-vowel stimuli. The audio (A) and video (V) of incongruent stimuli differed either in place of articulation (POA) or voicing. Responses from 15 young adults show that, compared to white noise, babble resulted in more audio responses for POA stimuli, and fewer for voicing stimuli. Voiced syllables received more audio responses than voiceless syllables. Results can be attributed to discrepancies in the acoustic spectra of both the noise and speech target. Voiced consonants may be more auditorily salient than voiceless consonants which are more spectrally similar to white noise. Visual cues contribute to identification of voicing, but only if the POA is visually salient and auditorily susceptible to the noise type.},
	doi = {10.1121/1.3129508},
	author = {Alm, Magnus and Dawn Marie {Behne} and Wang, Yue and Eg, Ragnhild}
}
