Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems

Hamidouche, Kenza; Kasgari, Ali Taleb Zadeh; Saad, Walid; Bennis, Mehdi; Debbah, Mérouane

doi:10.1109/iccw.2018.8403664

Cited by 24 publications

(14 citation statements)

References 14 publications

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“…Therefore, the estimation of the parameters σ and ξ with high accuracy using QSI samples gathered at each VUE is crucial. In this regard, modeling the excess queue distribution requires a central controller (e.g., the RSU) to compute and communicate with all VUEs at each time t. However, this RSU-centric approach is impractical due to the fact that: i) The overhead needed for frequent communications with all the VUEs in a highly dynamic network will degrade the networkwide performance, and ii) VUEs may prefer not to share their QSI with other vehicles, in which warrants collaborative learning techniques [16], [19]. Therefore, next, we propose a distributed solution based on FL that allows each VUE to learn the GPD parameters (local model) individually using local QSI observations and minimal communication with the RSU.…”

Section: Learning the Parameters Of The Maximum Queue Distributionmentioning

confidence: 99%

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

Samarakoon

Bennis

Saad

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilistic queuing delays. In particular, a reliability measure is defined to characterize extreme events (i.e., when vehicles' queue lengths exceed a predefined threshold with non-negligible probability) using extreme value theory (EVT). Leveraging principles from federated learning (FL), the distribution of these extreme events corresponding to the tail distribution of queues is estimated by VUEs in a decentralized manner. Finally, Lyapunov optimization is used to find the joint transmit power and resource allocation policies for each VUE in a distributed manner. The proposed solution is validated via extensive simulations using a Manhattan mobility model. It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79% reductions in the amount of data that need to be exchanged. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, without an additional power consumption, compared to an average queue-based baseline. Compared to systems with fixed power consumption and focusing on queue stability while minimizing average power consumption, the reduction in extreme events of the proposed method is about two orders of magnitude.

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Section: Learning the Parameters Of The Maximum Queue Distributionmentioning

confidence: 99%

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

Samarakoon

Bennis

Saad

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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221

140

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“…The emergence of IoT has given rise to a significant amount of data, collected from sensors, user devices, and BSs, that must be processed by the next-generation wireless system. The problem of cell association when the density of users increases has been extensively addressed in the past [133], [134], but recently as ML techniques have emerged, Q-learning algorithms have been proposed to enable users to select their serving BS faster by exploiting local data and the learning outcomes of neighboring users, instead of exchanging all the local data among users [82].…”

Section: ) Connection Densitymentioning

confidence: 99%

Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

2019

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Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the ever-increasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning. We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications.INDEX TERMS Machine learning, 5G mobile communication, B5G, wireless communication, mobile communication, artificial intelligence. II. THE THREE TYPES OF LEARNING AND ITS APPLICATION IN WIRELESS COMMUNICATIONSThe article is divided according to the level of supervision that the ML procedure requires on the training stage. The major categories discussed in the following sections are supervised, unsupervised, and reinforcement learning

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“… Maximizes the data rate through local data in cell associations between a small number of base stations and users. Q-learning enables users to predict their return function using a neural network [196].…”

Section: Reinforcement Learningmentioning

confidence: 99%

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

et al. 2021

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The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-toend behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.INDEX TERMS Artificial neural networks, artificial intelligence, Internet of Things, sixth-generation wireless communication and network architecture, URLLC.

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Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems

Cited by 24 publications

References 14 publications

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

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