Below the list of projects cofunded by the María de Maeztu program (selected via internal calls, in this link the first one launched at the beginning of the program, and in this link the second one, launched in September 2016).
In addition, the program supported:
- joint calls for cooperation between DTIC and the UPF Department of Experimental and Health Sciences (CEXS), also recognised as a María de Maeztu Unit of Excellence. Here the link to the second call (November 2017). The first call took place in January 2017.
- its own Open Science and Innovation program
- a pilot program to promote educational research collaborations with industry
The detail of the internal procedures for the distribution of funds associated to the program can be found here
Wireless Networking through Learning: Searching for Optimality in Highly-dynamic and Decentralized scenarios
Wireless Networking through Learning: Searching for Optimality in Highly-dynamic and Decentralized scenarios
Wireless Networking through Learning: Searching for Optimality in Highly-dynamic and Decentralized scenarios
Wireless Networks operating in ISM bands, with Wireless Local Area Networks (WLANs) as their most representative technology, are nowadays the most common wireless Internet access technology. A new generation of WLANs, the one that will be part of the 5G ecosystem, is already under development and will make its appearance in the forthcoming years. However, although this new generation of WLANs will be technologically very advanced, it will still be based on a traditional conception, where the WLAN is a simple standalone wireless extension of the fixed network, and operate following a rigid approach, with all functionalities pre-configured. Clearly, that situation will not be able to satisfy the demanding users’ requirements in the expected dense, highly dynamic, heterogeneous and complex wireless network future scenarios, where bandwidth-hungry tactile-like real- time multimedia interaction with people and things, real and virtual, will require a high degree of flexibility from the network to dynamically adapt to many different situations. In addition, most of the time those networks are operating in a never ending transient state, in which all expected performance figures computed using traditional optimization models are completely useless.
The challenge is to provide a feasible solution to maximize the wireless networks performance in such complex and dynamic scenarios.
To solve that challenge, a possible solution is that each individual network makes decisions regarding the use of the channel resources based on its own observations of the spectrum occupancy, and the gathered experience from the result of its previous actions. Those decisions may be simple, such as switching to a different channel, increase / decrease the transmission power, the channel bandwidth, or the energy detection level. In case assistance from the cloud is provided, more complex decisions can be made, including those that affect one or more networks such as distributing the users efficiently between different access points. To do that, we need to provide to the group of wireless networks with the means to extract knowledge from the environment, and make decisions based on it that result in a better performance. However, there is always an associated overhead and complexity when adding those new functionalities which requires to be carefully considered.
Therefore, the main goal of this MdM project is to show that combining machine learning techniques to allow networks learn what are the best action to take in every situation, and data analytic techniques to study large data-sets containing miscellaneous information to make global recommendations, wireless networks can be able to adapt to many different situations, achieve an efficient use of the resources, and operate close to the optimal point. Namely, we want to provide answers to the following questions: Where, how and when can we use machine learning techniques to optimize the operation of wireless networks? Can we extract useful information from analyzing collected data from the networks and users in order to recommend new network configurations? Can we enforce collaboration strategies to share information between networks over the radio interface or through the cloud? What is the overhead (or downside) of using those techniques? Overall, is it possible to evaluate how much close those techniques are able to make the networks operate close to the desired point?
Principal researchers
Boris Bellalta Anders JonssonResearchers
Gergely Neu Maddalena Nurchis Sergio BarrachinaRelated Assets:
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Martins Dias G, Borges Margi C, de Oliveira FCP, Bellalta B. Cloud-Empowered, Self-Managing Wireless Sensor Networks: Interconnecting Management Operations at the Application Layer. IEEE Consumer Electronics Magazine
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Martins Dias G, Nurchis M, Bellalta B. Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning. IEEE World Forum on Internet of Things 2016
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Martins Dias G, Bellalta B, Oechsner S. Using data prediction techniques to reduce data transmissions in the IoT. 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT)
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First International Workshop on Data Science for Internet of Things at IEEE MASS 2016
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First Workshop on Data Science for Internet of Things
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Barrachina-Muñoz S, Bellalta B. Learning Optimal Routing for the Uplink in LPWANs Using Similarity-enhanced epsilon-greedy. 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
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Wilhelmi F, Cano C, Neu G, Bellalta B, Jonsson A, Barrachina-Muñoz S. Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits. Ad Hoc Networks
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New CISCO Research Grant to Boris Bellalta for work in high-density WLANs
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2nd International Workshop on Data Science for Internet of Things
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Dias, GM, Bellalta B, Oechsner S. The impact of dual prediction schemes on the reduction of the number of transmissions in sensor networks. Computer Communications
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Wilhelmi F, Bellalta B, Cano C, Jonsson A. Implications of Decentralized Q-learning Resource Allocation in Wireless Networks. arXiv pre-print
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Spatial Flexible Continuous Time Markov Network (SFCTMN)
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López-Raventós A, Wilhelmi F, Barrachina-Muñoz S, Bellalta B. Machine Learning and Software Defined Networks for High-Density WLANs. arXiv pre-print.
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Barrachina-Muñoz S, Wilhelmi Roca FJ, Bellalta B. Dynamic Channel Bonding in Spatially Distributed High-Density WLANs. IEEE Transactions on Mobile Computing
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Komondor: An IEEE 802.11ax Simulator
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Barrachina-Munoz S, Wilhelmi F, Bellalta B. To overlap or not to overlap: Enabling Channel Bonding in High Density WLANs
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Wilhelmi F, Barrachina-Muñoz S, Bellalta B, Cano C, Jonsson A, Neu G. Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs. arXiv pre-print
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Nurchis M, Bellalta B. Target Wake Time: Scheduled access in IEEE 802.11ax WLANs. arXiv preprint.
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Barrachina-Muñoz S, Wilhelmi F, Selinis I, Bellalta B. Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs. arXiv pre-print.
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Carrascosa M, Bellalta B. Decentralized AP selection using Multi-Armed Bandits: Opportunistic ε-Greedy with Stickiness. Symposium on Computers and Communications IEEE ISCC 2019
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Barrachina-Muñoz S, Adame T, Bel A, Bellalta B. Towards Energy Efficient LPWANs Through Learning-based Multi-hop Routing. 2019 IEEE World Forum on Internet of Things (WF-IoT 2019)
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Barrachina-Muñoz S, Wilhelmi F, Bellalta B. Online Primary Channel Selection for Dynamic Channel Bonding in High-Density WLANs. arXiv preprint
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Sergio Barrachina's stay at Rice University
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Wilhelmi F, Barrachina-Muñoz S, Bellalta B. On the Performance of the Spatial Reuse Operation in IEEE 802.11ax WLANs. arXiv pre-print
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Adame T, Bel A, Bellalta B. Increasing LPWAN Scalability by Means of Concurrent Multiband IoT Technologies: An Industry 4.0 Use Case. IEEE Access
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Two RIS3CAT projects aimed at measuring the impact of mobility on pollution and on the validation of the IoT in industrial environments
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[PhD thesis] Responsive spectrum management for wireless local area networks: from heuristic-based policies to model-free reinforcement learning
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[PhD thesis] Towards spatial reuse in future wireless local area networks: a sequential learning approach
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A pioneering study into the description of the architecture of a new standard for telecommunications networks of the future
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[PhD Thesis] Responsive spectrum management for wireless local area networks: from heuristic-based policies to model-free reinforcement learning