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Fairness in Wireless Networks

10 minute read

Date:

Wireless Networks (WNs) are characterized for sharing a medium with limited resources (i.e., the air), which may lead to coexistence issues that harm the overall performance of the overlapping devices. Due to the possibility of choosing different configurations (e.g., range of channels, carrier sense threshold, transmit power), we often find an unbalanced share of the resources between WNs. Such imbalance may be solved by the application of fairness policies. However, a fair solution does not always entail a maximization of the aggregate performance. In general, fairness is achieved by a central unit that decides the configuration of the wireless devices. However, we aim to extend this concept to collaborative approaches that share the same fairness goal that boosts the overall performance.

In this document, we aim to shed some light on the fairness problem in WNs, as well as on the main considerations to be done for a proper resource allocation. We also aim to bound the meaning of \emph{the cost of fairness}. Note, as well, that we focus on configurations in which the utilities of players are known. However, there are many other situations in which players show a selfish behavior and do not truthfully reveal their utilities. Such situations lead to what is commonly known as \emph{the price of anarchy}.

Disclaimer: most of the material in this document has been retrieved from Bertsimas, D., Farias, V. F., & Trichakis, N. (2011). The price of fairness. Operations research, 59(1), 17-31.

projects

Introducing Arduino and the IoT to Kids and Teenagers

Date:

This project aims to disseminate and motivate the use of new technologies among young people. To that purpose, several activities have been so far held in collaboration with the UPF and Ajuntament de Barcelona.

Komondor: an IEEE 802.11ax simulator

Date:

Komondor is an open-source simulation tool that aims to reproduce novel techniques to be included in next-generation WLANs. Particular emphasis is done to the IEEE 802.11ax amendment, and the inclusion of intelligent agents is one of the main novelties of the project.

UPF/ITU-T AI Challenge 2020

Date:

This challenge is promoted by Universitat Pompeu Fabra (UPF) and is part of the ITU Artificial Intelligence/Machine Learning in 5G Challenge. Further participation details can be found in ITU AI/ML 5G Challenge: Participation Guidelines. Participation is open to ITU members and any individual from an ITU Member State. “Participants” are individuals or companies that participate in the ITU AI/ML in 5G Challenge, providing solutions to problem sets of the Challenge. This challenge is open to the two categories of participants: student and professional.

Towards Spatial Reuse in Future WLANs: a Sequential Learning Approach

Date:

The Spatial Reuse (SR) operation is gaining momentum in the latest IEEE 802.11 family of standards due to the overwhelming requirements posed by next-generation wireless networks. In particular, the rising traffic requirements and the number of concurrent devices compromise the efficiency of increasingly crowded Wireless Local Area Networks (WLANs) and throw into question their decentralized nature. The SR operation, initially introduced by the IEEE 802.11ax-2021 amendment and further studied in IEEE 802.11be-2024, aims to increase the number of concurrent transmissions in an Overlapping Basic Service Set (OBSS) using sensitivity adjustment and transmit power control, thus improving spectral efficiency. Our analysis of the SR operation shows outstanding potential in improving the number of concurrent transmissions in crowded deployments, which contributed to enabling low-latency next-generation applications. However, the potential gains of SR are currently limited by the rigidity of the mechanism introduced for the 11ax, and the lack of coordination among BSSs implementing it. The SR operation is evolving towards coordinated schemes where different BSSs cooperate. Nevertheless, coordination entails communication and synchronization overhead, which impact on the performance of WLANs remains unknown. Moreover, the coordinated approach is incompatible with devices using previous IEEE 802.11 versions, potentially leading to degrading the performance of legacy networks. For those reasons, in this thesis, we start assessing the viability of decentralized SR, and thoroughly examine the main impediments and shortcomings that may result from it. We aim to shed light on the future shape of WLANs concerning SR optimization and whether their decentralized nature should be kept, or it is preferable to evolve towards coordinated and centralized deployments. To address the SR problem in a decentralized manner, we focus on Artificial Intelligence (AI) and propose using a class of sequential learning-based methods, referred to as Multi-Armed Bandits (MABs). The MAB framework suits the SR problem because it addresses the uncertainty caused by the concurrent operation of multiple devices (i.e., multi-player setting) and the lack of information in decentralized deployments. MABs can potentially overcome the complexity of the spatial interactions that result from devices modifying their sensitivity and transmit power. In this regard, our results indicate significant performance gains (up to 100 per cent throughput improvement) in highly dense WLAN deployments. Nevertheless, the multi-agent setting raises several concerns that may compromise network devices’ performance (definition of joint goals, time-horizon convergence, scalability aspects, or non-stationarity). Besides, our analysis of multi-agent SR encompasses an in-depth study of infrastructure aspects for next-generation AI-enabled networking.hallenge is open to the two categories of participants: student and professional.

publications

Implications of decentralized Q-learning resource allocation in wireless networks

Published in IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017

Abstract: Reinforcement Learning is gaining attention by the wireless networking community due to its potential to learn good-performing configurations only from the observed results. In this work we propose a stateless variation of Q-learning, which we apply to exploit spatial reuse in a wireless network. In particular, we allow networks to modify both their transmission power and the channel used solely based on the experienced throughput. We concentrate in a completely decentralized scenario in which no information about neighbouring nodes is available to the learners. Our results show that although the algorithm is able to find the best-performing actions to enhance aggregate throughput, there is high variability in the throughput experienced by the individual networks. We identify the cause of this variability as the adversarial setting of our setup, in which the most played actions provide intermittent good/poor performance depending on the neighbouring decisions. We also evaluate the effect of the intrinsic learning parameters of the algorithm on this variability.

Recommended citation: Wilhelmi Roca, F., Bellalta, B., Cano Bastidas, C., & Jonsson, A. (2017). Implications of decentralized Q-learning resource allocation in wireless networks. In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC); 2017 Oct 8-13; Montreal, Canada. Piscataway (NJ): IEEE; 2017.[5 p.]. Institute of Electrical and Electronics Engineers (IEEE). http://ieeexplore.ieee.org/document/8292321/

Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits

Published in Ad-hoc Networks (Elsevier), 2017

Abstract: Next-generation wireless deployments are characterized by being dense and uncoordinated, which often leads to inefficient use of resources and poor performance. To solve this, we envision the utilization of completely decentralized mechanisms that enhance Spatial Reuse (SR). In particular, we concentrate in Reinforcement Learning (RL), and more specifically, in Multi-Armed Bandits (MABs), to allow networks to modify both their transmission power and channel based on their experienced throughput. In this work, we study the exploration-exploitation trade-off by means of the ε-greedy, EXP3, UCB and Thompson sampling action-selection strategies. Our results show that optimal proportional fairness can be achieved, even if no information about neighboring networks is available to the learners and WNs operate selfishly. However, there is high temporal variability in the throughput experienced by the individual networks, specially for ε-greedy and EXP3. We identify the cause of this variability to be the adversarial setting of our setup in which the set of most played actions provide intermittent good/poor performance depending on the neighboring decisions. We also show that this variability is reduced using UCB and Thompson sampling, which are parameter-free policies that perform exploration according to the reward distribution of each action.

Recommended citation: Wilhelmi, F., Cano, C., Neu, G., Bellalta, B., Jonsson, A., & Barrachina-Muñoz, S. (2019). Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits. Ad Hoc Networks 88 (2019): 129-141. https://www.sciencedirect.com/science/article/pii/S1570870518302646?casa_token=_3NaFYTPWgoAAAAA:7Z0DkV6IOJ3ITNi1uOoarip1kjU07-DKEdBMaYqoGHhDrqRoGLHQjHAOeaj9ETVoXNYrtXUx0w

Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs

Published in Journal of Network and Computer Applications (JNCA), 2018

Abstract: Spatial Reuse (SR) has recently gained attention for performance maximization in IEEE 802.11 Wireless Local Area Networks (WLANs). Decentralized mechanisms are expected to be key in the development of SR solutions for next-generation WLANs, since many deployments are characterized by being uncoordinated by nature. However, the potential of decentralized mechanisms is limited by the significant lack of knowledge with respect to the overall wireless environment. To shed some light on this subject, we show the main considerations and possibilities of applying online learning to address the SR problem in uncoordinated WLANs. In particular, we provide a solution based on Multi-Armed Bandits (MABs) whereby independent WLANs dynamically adjust their frequency channel, transmit power and sensitivity threshold. To that purpose, we provide two different strategies, which refer to selfish and environment-aware learning. While the former stands for pure individual behavior, the second one aims to consider the performance experienced by surrounding networks, thus taking into account the impact of individual actions on the environment. Through these two strategies we delve into practical issues of applying MABs in wireless networks, such as convergence guarantees or adversarial effects. Our simulation results illustrate the potential of the proposed solutions for enabling SR in future WLANs, showing that substantial improvements on network performance can be achieved regarding throughput and fairness.

Recommended citation: Wilhelmi, F., Barrachina-Muñoz, S., Bellalta, B., Cano, C., Jonsson, A., & Neu, G. (2019). Potential and pitfalls of multi-armed bandits for decentralized spatial reuse in wlans. Journal of Network and Computer Applications, 127, 26-42. https://www.sciencedirect.com/science/article/pii/S1084804518303655

Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs

Published in Wireless Days, 2019

Abstract: Komondor is a wireless network simulator for next-generation wireless local area networks (WLANs). The simulator has been conceived as an accessible (ready-to-use) open source tool for research on wireless networks and academia. An important advantage of Komondor over other well-known wireless simulators lies in its high event processing rate, which is furnished by the simplification of the core operation. This allows outperforming the execution time of other simulators like ns-3, thus supporting large-scale scenarios with a huge number of nodes. In this paper, we provide insights into the Komondor simulator and overview its main features, development stages and use cases. The operation of Komondor is validated in a variety of scenarios against different tools: the ns-3 simulator and two analytical tools based on Continuous Time Markov Networks (CTMNs) and the Bianchi’s DCF model. Results show that Komondor captures the IEEE 802.11 operation very similarly to ns-3. Finally, we discuss the potential of Komondor for simulating complex environments – even with machine learning support – in next-generation WLANs by easily developing new user-defined modules of code.

Recommended citation: Barrachina-Muñoz, S., Wilhelmi, F., Selinis, I., & Bellalta, B. (2019, April). Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs. In 2019 Wireless Days (WD) (pp. 1-8). IEEE. https://ieeexplore.ieee.org/abstract/document/8734225

Spatial Reuse in IEEE 802.11ax WLANs

Published in Elsevier Computer Communications, 2019

Abstract: Dealing with massively crowded scenarios is one of the most ambitious goals of next-generation wireless networks. With this goal in mind, the IEEE 802.11ax amendment includes, among other techniques, the Spatial Reuse (SR) operation. The SR operation encompasses a set of unprecedented techniques that are expected to significantly boost Wireless Local Area Networks (WLANs) performance in dense environments. In particular, the main objective of the SR operation is to maximize the utilization of the medium by increasing the number of parallel transmissions. Nevertheless, due to the novelty of the operation, its performance gains remain largely unknown. In this paper, we first provide a gentle tutorial of the SR operation included in the IEEE 802.11ax. Then, we analytically model SR and delve into the new kinds of MAC-level interactions among network devices. Finally, we provide a simulation-driven analysis to showcase the potential of SR in various deployments, comprising different network densities and traffic loads. Our results show that the SR operation can significantly improve the medium utilization, especially in scenarios under high interference conditions. Moreover, our results demonstrate the non-intrusive design characteristic of SR, which allows enhancing the number of simultaneous transmissions with a low impact on the environment. We conclude the paper by giving some thoughts on the main challenges and limitations of the IEEE 802.11ax SR operation, including research gaps and future directions.

Recommended citation: Wilhelmi, F., Barrachina-Muñoz, S., Cano, C., Selinis, I., & Bellalta, B. (2021). Spatial reuse in IEEE 802.11 ax WLANs. Computer Communications.

A Flexible Machine Learning-Aware Architecture for Future WLANs

Published in IEEE Communications Magazine, 2019

Abstract: Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on the ITU’s architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs.

Recommended citation: Wilhelmi, F., Barrachina-Munoz, S., Bellalta, B., Cano, C., Jonsson, A., & Ram, V. (2020). A Flexible Machine-Learning-Aware Architecture for Future WLANs. IEEE Communications Magazine, 58(3), 25-31.

On the Performance of the Spatial Reuse Operation in IEEE 802.11 ax WLANs

Published in IEEE Conference on Standards for Communications and Networking (CSCN), 2019

Abstract: The Spatial Reuse (SR) operation included in the IEEE 802.11ax-2020 (11ax) amendment aims at increasing the number of parallel transmissions in an Overlapping Basic Service Set (OBSS). However, many unknowns exist about the performance gains that can be achieved through SR. In this paper, we provide a brief introduction to the SR operation described in the IEEE 802.11ax (draft D4.0). Then, a simulation-based implementation is provided in order to explore the performance gains of the SR operation. Our results show the potential of using SR in different scenarios covering multiple network densities and traffic loads. In particular, we observe significant performance gains when a WLAN applies SR with respect to the default configuration. Interestingly, the highest improvements are observed in the most pessimistic situations in terms of network density and traffic load.

Recommended citation: Wilhelmi, F., Barrachina-Muñoz, S., & Bellalta, B. (2019, October). On the Performance of the Spatial Reuse Operation in IEEE 802.11 ax WLANs. In 2019 IEEE Conference on Standards for Communications and Networking (CSCN) (pp. 1-6). IEEE.

Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

Published in Submitted to IEEE Wireless Communications Magazine, 2020

Abstract: Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance in front of complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. Network simulators can facilitate the adoption of ML-based solutions by means of training, testing, and validating ML models before being applied to an operative network. Finally, we showcase the potential benefits of integrating network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network.

Recommended citation: Wilhelmi, F., Carrascosa, M., Cano, C., Jonsson, A., Ram, V., & Bellalta, B. (2020). Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks. arXiv preprint arXiv:2005.08281.

talks

Improving Spatial Reuse in High-Density Wireless Networks through Learning

Date:

Abstract: Popularity of IEEE 802.11 Wireless Local Area Networks (WLANs) leads to massive deployments in which few frequency resources must be shared, resulting in inefficiency in dense environments. The behavior of the protocols that grant access to the medium, which are based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), is inefficient in dense deployments, and prone to generate issues such as the Hidden-Terminal and the Exposed-Terminal problems. Therefore, the overall throughput may be considerably reduced due to collisions and/or starvation. As the complexity of Wireless Networks in terms of variability prevents to computationally find the optimal configuration for a given network, we aim to use Reinforcement Learning (RL) to find close-to-optimal solutions adaptively.

Implications of Decentralized Learning in Dense WLANs - Submission to FG-ML5G First Meeting

Date:

Abstract: Understanding the consequences of applying Reinforcement Learning (RL) in dense and uncoordinated environments (e.g., Wi-Fi) is critical to optimize the performance of next-generation wireless networks. In this document we present a decentralized approach in which Wireless Networks (WNs) attempt to learn the best possible configuration in an adversarial environment according to their own performance. In particular, we provide a Multi-Armed Bandits (MABs) based model in which devices are allowed to tune their frequency channel, transmit power and Carrier Sense Threshold (CST). Our results show that, despite using only local information, a collaborative behavior can be obtained among independent devices that share the same resources. Furthermore, we study the effects of applying such method under different equilibrium situations with respect to the adversarial setting. Finally, some insights are provided regarding the consequences of applying learning in presence of legacy nodes.

A proposal to reuse Komondor, an IEEE 802.11ax-oriented simulator for future wireless networks in FG ML5G

Date:

Abstract: Komondor is an open-source IEEE 802.11ax-oriented simulator that includes intelligent agents as an integral part of the wireless operation. As part of this simulator, a common ML-based architecture is proposed to frame different types of learning, which range from decentralized to centralized systems, according to the available information. We propose to use Komondor to contribute to the FG-ML5G in a twofold manner: i) serve as a test environment for several purposes of the group (implementation of proposals, representation of data formats, etc.), and ii) generate synthetic data as a result of the IEEE 802.11 operation in a customized way. This document is intended as a starting point to showcase the potential of the simulator through specific use cases, so that interactions and potential collaboration with the FG can be found.

[ML5G-I-176] Adoption of the ITU-T’s Architecture in IEEE 802.11 WLANs

Date:

Abstract: A lot of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems, ranging from cloud-based (centralized) to edge-computing-like approaches (decentralized). Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This paper addresses the challenges of re-designing the network operation to accommodate the utilization of ML into future Wireless Local Area Networks (WLANs). For that, we propose to adopt the International Telecommunications Union (ITU) unified architecture for future networks. Based on the adoption of this architecture, insights are provided into the major challenges of introducing ML to WLANs. Moreover, we describe a set of use cases in which ML is applied to WLAN-related problems.

[ML5G-I-238] Machine Learning Sandbox for future networks including IMT-2020: requirements and architecture framework

Date:

Abstract: Use cases for integrating machine learning (ML) to future networks including IMT-2020 has been documented in Supplement 55 and an architecture framework for this integration was specified in ITU-T Y.3172. However, network stakeholders are apprehensive about using ML-driven approaches directly in live networking systems because it can lead to unexpected situations that can degrade KPIs. This is mostly due to the apparent complexity of ML mechanisms (e.g., deep learning), the incompleteness of the available training data, the uncertainty produced by exploration-exploitation approaches (e.g., reinforcement learning), etc. In the face of such impediments, the ML Sandbox emerges as a potential solution that allows mobile network operators (MNOs) for improving the degree of confidence in ML solutions before their application to the network infrastructure. This contribution discusses the requirements, architecture, and implementation examples for ML Sandbox in future networks including IMT-2020.

The ITU-T AI Challenge at the UPF: Improving the capacity of IEEE 802.11 WLANs through Machine Learning

Date:

Abstract: The talk is intended to present, describe and solve doubts concerning the problem statement “Improving the capacity of IEEE 802.11 WLANs through Machine Learning” (https://www.upf.edu/web/wnrg/ai_challenge) of the ITU AI/ML in 5G Challenge. The first part of the talk will be devoted to introducing IEEE 802.11 WLANs and the Channel Bonding (CB) problem. Then, the dataset will be described together with the goals of the challenge.

Towards Spatial Reuse in Future WLANs: a Sequential Learning Approach

Date:

Abstract: The Spatial Reuse (SR) operation is gaining momentum in the latest IEEE 802.11 family of standards due to the overwhelming requirements posed by next-generation wireless networks. In particular, the rising traffic requirements and the number of concurrent devices compromise the efficiency of increasingly crowded Wireless Local Area Networks (WLANs) and throw into question their decentralized nature. The SR operation, initially introduced by the IEEE 802.11ax-2021 amendment and further studied in IEEE 802.11be-2024, aims to increase the number of concurrent transmissions in an Overlapping Basic Service Set (OBSS) using sensitivity adjustment and transmit power control, thus improving spectral efficiency. Our analysis of the SR operation shows outstanding potential in improving the number of concurrent transmissions in crowded deployments, which contributed to enabling low-latency next-generation applications. However, the potential gains of SR are currently limited by the rigidity of the mechanism introduced for the 11ax, and the lack of coordination among BSSs implementing it. The SR operation is evolving towards coordinated schemes where different BSSs cooperate. Nevertheless, coordination entails communication and synchronization overhead, which impact on the performance of WLANs remains unknown. Moreover, the coordinated approach is incompatible with devices using previous IEEE 802.11 versions, potentially leading to degrading the performance of legacy networks. For those reasons, in this thesis, we start assessing the viability of decentralized SR, and thoroughly examine the main impediments and shortcomings that may result from it. We aim to shed light on the future shape of WLANs concerning SR optimization and whether their decentralized nature should be kept, or it is preferable to evolve towards coordinated and centralized deployments. To address the SR problem in a decentralized manner, we focus on Artificial Intelligence (AI) and propose using a class of sequential learning-based methods, referred to as Multi-Armed Bandits (MABs). The MAB framework suits the SR problem because it addresses the uncertainty caused by the concurrent operation of multiple devices (i.e., multi-player setting) and the lack of information in decentralized deployments. MABs can potentially overcome the complexity of the spatial interactions that result from devices modifying their sensitivity and transmit power. In this regard, our results indicate significant performance gains (up to 100% throughput improvement) in highly dense WLAN deployments. Nevertheless, the multi-agent setting raises several concerns that may compromise network devices’ performance (definition of joint goals, time-horizon convergence, scalability aspects, or non-stationarity). Besides, our analysis of multi-agent SR encompasses an in-depth study of infrastructure aspects for next-generation AI-enabled networking.

teaching

Teaching Assistant: Networks

Undergraduate course, Universitat Pompeu Fabra, 2016

Introductory course on basic networks concepts, based on James F. Kurose and Keith W. Ross, “Computer Networking. A Top-down Approach”, Pearson/Addison Wesley. More information here.

Teaching Assistant: Networks Laboratory

Undergraduate course, Universitat Pompeu Fabra, 2016

Course on practical networks concepts (routing, switching, etc.). Cisco devices are mostly used along the entire subject, which is practical in nature. More information here.

Course instructor: Fonaments de xarxa i arquitectures

Applied Data Science, Universitat Oberta de Catalunya, 2020

Introductory course on networks and their role in the data lifecycle. The content of the subject is based on James F. Kurose and Keith W. Ross, “Computer Networking. A Top-down Approach”, Pearson/Addison Wesley. More information here.