A Collaborative Learning Based Approach for Parameter Configuration of Cellular Networks

Chuai, Jie; Chen, Zhitang; Liu, Guochen; Guo, Xueying; Wang, Xiaoxiao; Liu, Xin; Zhu, Chongming; Shen, Feiyi

doi:10.1109/infocom.2019.8737657

Cited by 22 publications

(12 citation statements)

References 22 publications

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“…Similarly to RL, contextual bandits have been recently employed to adjust video streaming rates [27]; configure BS parameters (e.g., handover thresholds) [29], [30]; assign CPU time to virtualized BSs [15]; and control mmWave networks [31], [32]. Here, instead, we combine Gaussian Processes [9] and contextual bandit algorithms [26] to build a data-efficient Bayesian optimization framework [8] with convergence guarantees.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs

Ayala‐Romero

García‐Saavedra

Costa‐Pérez

et al. 2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

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Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We perform an in-depth experimental analysis of the energy consumption of virtualized Base Stations (vBSs) and render two conclusions: (i) characterizing performance and power consumption is intricate as it depends on human behavior such as network load or user mobility; and (ii) there are many control policies and some of them have non-linear and monotonic relations with power and throughput. Driven by our experimental insights, we argue that machine learning holds the key for vBS control. We formulate two problems and two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient and have provably performance, which is paramount for carriergrade vRANs. We demonstrate the convergence and flexibility of our approach and assess its performance using an experimental prototype.

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Section: Related Workmentioning

confidence: 99%

Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs

Ayala‐Romero

García‐Saavedra

Costa‐Pérez

et al. 2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

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“…For example, telecommunication networks usually have a number of parameters affecting the performances like throughput. However, obtaining a reliable evaluation of a set of parameters may require one or two weeks in practice, and it becomes time-consuming to find the optimal parameters [9]. The proposed data generation framework can therefore be used, together with some historical data, to support the policy evaluation.…”

Section: Discussionmentioning

confidence: 99%

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

Liu¹,

Dai²,

Wu³

et al. 2022

Preprint

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Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item ratings while omitting the underlying causes behind the ratings. For example, the widely-used Yahoo!R3 dataset contains little information on the causes of the user-movie ratings. A solution could be to conduct surveys and require the users to provide such information. In practice, the user surveys can hardly avoid compliance issues and sparse user responses, which greatly hinders the exploration of causality-based recommendation. To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios. To illustrate the use of our framework, we construct a semi-synthetic dataset with Causal Tags And Ratings (CTAR), based on the movies as well as their descriptive tags and rating information collected from a famous movie rating website. Using the collected data and the causal graph, the user-item-ratings and their corresponding user-item-tags are automatically generated, which provides the reasons (selected tags) why the user rates the items. Descriptive statistics and baseline results regarding the CTAR dataset are also reported. The proposed data generation framework is not limited to recommendation, and * Both authors contributed equally to this research.

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“…These papers, however, do not close the loop through the experimental evaluation of the control action or classification accuracy on real testbeds and networks. Chuai et al describe a largescale, experimental evaluation on a production network, but the evaluation is limited to a single performance metric [44].…”

Section: Related Workmentioning

confidence: 99%

ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms

Polese¹,

Bonati²,

Basagni³

et al. 2021

Preprint

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Cellular networks are undergoing a radical transformation toward disaggregated, fully virtualized, and programmable architectures with increasingly heterogeneous devices and applications. In this context, the open architecture standardized by the O-RAN Alliance enables algorithmic and hardware-independent Radio Access Network (RAN) adaptation through closed-loop control. O-RAN introduces Machine Learning (ML)-based network control and automation algorithms as socalled xApps running on RAN Intelligent Controllers. However, in spite of the new opportunities brought about by the Open RAN, advances in ML-based network automation have been slow, mainly because of the unavailability of large-scale datasets and experimental testing infrastructure. This slows down the development and widespread adoption of Deep Reinforcement Learning (DRL) agents on real networks, delaying progress in intelligent and autonomous RAN control. In this paper, we address these challenges by proposing practical solutions and software pipelines for the design, training, testing, and experimental evaluation of DRL-based closed-loop control in the Open RAN. We introduce ColO-RAN, the first publicly-available large-scale O-RAN testing framework with software-defined radios-in-the-loop. Building on the scale and computational capabilities of the Colosseum wireless network emulator, ColO-RAN enables ML research at scale using O-RAN components, programmable base stations, and a "wireless data factory". Specifically, we design and develop three exemplary xApps for DRL-based control of RAN slicing, scheduling and online model training, and evaluate their performance on a cellular network with 7 softwarized base stations and 42 users. Finally, we showcase the portability of ColO-RAN to different platforms by deploying it on Arena, an indoor programmable testbed. Extensive results from our first-of-its-kind large-scale evaluation highlight the benefits and challenges of DRL-based adaptive control. They also provide insights on the development of wireless DRL pipelines, from data analysis to the design of DRL agents, and on the tradeoffs associated to training on a live RAN. ColO-RAN and the collected largescale dataset will be made publicly available to the research community.

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A Collaborative Learning Based Approach for Parameter Configuration of Cellular Networks

Cited by 22 publications

References 22 publications

Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs

Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms

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