RiskNet: neural risk assessment in networks of unreliable resources

We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated on the basis of the Barabási–Albert model. However, the results obtained show that we can accurately model the penalties in a wide range of existing topologies. We show that GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study—in practice, the entire time of path placement evaluation based on the prediction is no longer than 4 ms on modern hardware. In this way, we gain up to 12 000 times in speed improvement compared to calculations based on simulations.This work was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University of Science and Technology (P.B., P.C.) and by the PL-Grid Infrastructure (K.R.).Peer ReviewedPostprint (published version

Modulation and Coding Techniques in Wireless Communications

This is a timely book on wireless communications, with twelve chapters covering theoretical results and material of standards. The first nine chapters, some 380 pages, are devoted to basic concepts on channel models, modulation, coding, equalization, MIMO techniques, and multiple access methods. The last three chapters extend up to 274 pages and cover the modern wireless communication standardsCasares Giner, V.; Martínez Zaldívar, FJ. (2012). Modulation and Coding Techniques in Wireless Communications. IEEE Communications Magazine. 50(6):13-14. doi:10.1109/MCOM.2012.6211478S131450

Fast traffic engineering by gradient descent with learned differentiable routing

Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewise, the ever-faster traffic dynamics will demand network control mechanisms that can operate at short timescales (e.g., sub-minute). In this context, Traffic Engineering (TE) is a key component to efficiently control network traffic according to some performance goals (e.g., minimize network congestion).This paper presents Routing By Backprop (RBB), a novel TE method based on Graph Neural Networks (GNN) and differentiable programming. Thanks to its internal GNN model, RBB builds an end-to-end differentiable function of the target TE problem (MinMaxLoad). This enables fast TE optimization via gradient descent. In our evaluation, we show the potential of RBB to optimize OSPF-based routing (˜25% of improvement with respect to default OSPF configurations). Moreover, we test the potential of RBB as an initializer of computationally-intensive TE solvers. The experimental results show promising prospects for accelerating this type of solvers and achieving efficient online TE optimization.This work was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University and by the PL-Grid Infrastructure. Also, this publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund.Peer ReviewedPostprint (author's final draft

Considerations about service differentiation using a combined QoS/QoR approach

The paper proposes a novel approach to service differentiation using both 'classical' QoS parameters and novel resilience-oriented QoR (Quality of Resilience) parameters. In this concept, two network states are defined, 'fully operational' and 'after failure', enabling the operators to define precisely the QoS guarantees before and after a failure occurrence, and to distribute Information about failure severity to a customer/user. As such, a combined QoS/QoR approach can be seen as a kind of an 'insurance' for the customer, i.e. sharing the risk of a failure occurrence and enabling selective treatment of Individual services. In the 'after failure' state, the failure is unverifiable for the user, and a service with a lower QoS guarantee Is provided at possibly reduced costs