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

Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to 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. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network

Throughput Analysis of IEEE 802.11bn Coordinated Spatial Reuse

Multi-Access Point Coordination (MAPC) is becoming the cornerstone of the IEEE 802.11bn amendment, alias Wi-Fi 8. Among the MAPC features, Coordinated Spatial Reuse (C-SR) stands as one of the most appealing due to its capability to orchestrate simultaneous access point transmissions at a low implementation complexity. In this paper, we contribute to the understanding of C-SR by introducing an analytical model based on Continuous Time Markov Chains (CTMCs) to characterize its throughput and spatial efficiency. Applying the proposed model to several network topologies, we show that C-SR opportunistically enables parallel high-quality transmissions and yields an average throughput gain of up to 59% in comparison to the legacy 802.11 Distributed Coordination Function (DCF) and up to 42% when compared to the 802.11ax Overlapping Basic Service Set Packet Detect (OBSS/PD) mechanism

Towards Energy-Aware Federated Traffic Prediction for Cellular Networks

Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine learning (ML) is a promising approach to effectively predict network traffic, the centralization of massive data in a single data center raises issues regarding confidentiality, privacy and data transfer demands. To address these challenges, federated learning (FL) emerges as an appealing ML training framework which offers high accurate predictions through parallel distributed computations. However, the environmental impact of these methods is often overlooked, which calls into question their sustainability. In this paper, we address the trade-off between accuracy and energy consumption in FL by proposing a novel sustainability indicator that allows assessing the feasibility of ML models. Then, we comprehensively evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint, which make them impractical for real-world applications.Comment: International Symposium on Federated Learning Technologies and Applications (FLTA), 202

Towards spatial reuse in future wireless local area networks: a sequential learning approach

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.L'operació de reutilització espacial (SR) està guanyant impuls per a la darrera família d'estàndards IEEE 802.11 a causa dels aclaparadors requisits que presenten les xarxes sense fils de nova generació. En particular, la creixent necessitat de tràfic i el nombre de dispositius concurrents comprometen l'eficiència de les xarxes d'àrea local sense fils (WLANs) cada cop més concorregudes i posen en dubte la seva naturalesa descentralitzada. L'operació SR, inicialment introduïda per l'estàndard IEEE 802.11ax-2021 i estudiada posteriorment a IEEE 802.11be-2024, pretén augmentar el nombre de transmissions concurrents en un conjunt bàsic de serveis superposats (OBSS) mitjançant l'ajustament de la sensibilitat i el control de potència de transmissió, millorant així l'eficiència espectral. El nostre estudi sobre el funcionament de SR mostra un potencial destacat per millorar el nombre de transmissions simultànies en desplegaments multitudinaris, contribuint així al desenvolupament d'aplicacions de nova generació de baixa latència. Tot i això, els beneficis potencials de SR són actualment limitats per la rigidesa del mecanisme introduït per a l'11ax, i la manca de coordinació entre els BSS que ho implementen. L'operació SR evoluciona cap a esquemes coordinats on cooperen diferents BSS. En canvi, la coordinació comporta una sobrecàrrega de comunicació i sincronització, el qual té un impacte en el rendiment de les WLAN. D'altra banda, l'esquema coordinat és incompatible amb els dispositius que utilitzen versions anteriors IEEE 802.11, la qual cosa podria deteriorar el rendiment de les xarxes ja existents. Per aquests motius, en aquesta tesi s'avalua la viabilitat de mecanismes descentralitzats per a SR i s'analitzen minuciosament els principals impediments i mancances que se'n poden derivar. El nostre objectiu és donar llum a la futura forma de les WLAN pel que fa a l?optimització de SR i si s'ha de mantenir el seu caràcter descentralitzat, o bé és preferible evolucionar cap a desplegaments coordinats i centralitzats. Per abordar SR de forma descentralitzada, ens centrem en la Intel·ligència Artificial (AI) i ens proposem utilitzar una classe de mètodes seqüencials basats en l'aprenentatge, anomenats Multi-Armed Bandits (MAB). L'esquema MAB s'adapta al problema descentralitzat de SR perquè aborda la incertesa causada pel funcionament simultani de diversos dispositius (és a dir, un entorn multi-jugador) i la falta d'informació que se'n deriva. Els MAB poden fer front a la complexitat darrera les interaccions espacials entre dispositius que resulten de modificar la seva sensibilitat i potència de transmissió. En aquest sentit, els nostres resultats indiquen guanys importants de rendiment (fins al 100 \%) en desplegaments altament densos. Tot i això, l'aplicació d'aprenentatge automàtic amb múltiples agents planteja diversos problemes que poden comprometre el rendiment dels dispositius d'una xarxa (definició d'objectius conjunts, horitzó de convergència, aspectes d'escalabilitat o manca d'estacionarietat). A més, el nostre estudi d'aprenentatge multi-agent per a SR multi-agent inclou aspectes d'infraestructura per a xarxes de nova generació que integrin AI de manera intrínseca

Design of cost-effective wireless networks in high-density enclosures

Treball de fi de grau en TelemàticaTutor: Carles BockThe design of high-density wireless networks is a challenge due to the compromise among several design factors. Thus, this study has two major purposes: (1) To provide guidelines for performing high-density wireless deployments in a costeffective way and by using innovative technologies (2) To apply the guidelines provided in a real scenario. In addition, the project will contribute to obtain an in depth understanding on network designing and business models related to wireless deployments. For fulfilling the first purpose, we will present different innovative backbone and wireless technologies that can be used in several types of high-density environment deployments. Then, we will propose network design blueprint to fulfill typical network requirements of this kind of networks. In the second part of the project we will present a use case in order to apply the presented technologies and methods for a cost-effective high-density wireless deployment. The use case consists in designing a wireless network for the RCDE “Power8” stadium, which will allow us to analyze the challenges that we can face during a deployment of such magnitude on an already built infrastructure

Towards spatial reuse in future wireless local area networks: a sequential learning approach

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.L'operació de reutilització espacial (SR) està guanyant impuls per a la darrera família d'estàndards IEEE 802.11 a causa dels aclaparadors requisits que presenten les xarxes sense fils de nova generació. En particular, la creixent necessitat de tràfic i el nombre de dispositius concurrents comprometen l'eficiència de les xarxes d'àrea local sense fils (WLANs) cada cop més concorregudes i posen en dubte la seva naturalesa descentralitzada. L'operació SR, inicialment introduïda per l'estàndard IEEE 802.11ax-2021 i estudiada posteriorment a IEEE 802.11be-2024, pretén augmentar el nombre de transmissions concurrents en un conjunt bàsic de serveis superposats (OBSS) mitjançant l'ajustament de la sensibilitat i el control de potència de transmissió, millorant així l'eficiència espectral. El nostre estudi sobre el funcionament de SR mostra un potencial destacat per millorar el nombre de transmissions simultànies en desplegaments multitudinaris, contribuint així al desenvolupament d'aplicacions de nova generació de baixa latència. Tot i això, els beneficis potencials de SR són actualment limitats per la rigidesa del mecanisme introduït per a l'11ax, i la manca de coordinació entre els BSS que ho implementen. L'operació SR evoluciona cap a esquemes coordinats on cooperen diferents BSS. En canvi, la coordinació comporta una sobrecàrrega de comunicació i sincronització, el qual té un impacte en el rendiment de les WLAN. D'altra banda, l'esquema coordinat és incompatible amb els dispositius que utilitzen versions anteriors IEEE 802.11, la qual cosa podria deteriorar el rendiment de les xarxes ja existents. Per aquests motius, en aquesta tesi s'avalua la viabilitat de mecanismes descentralitzats per a SR i s'analitzen minuciosament els principals impediments i mancances que se'n poden derivar. El nostre objectiu és donar llum a la futura forma de les WLAN pel que fa a l?optimització de SR i si s'ha de mantenir el seu caràcter descentralitzat, o bé és preferible evolucionar cap a desplegaments coordinats i centralitzats. Per abordar SR de forma descentralitzada, ens centrem en la Intel·ligència Artificial (AI) i ens proposem utilitzar una classe de mètodes seqüencials basats en l'aprenentatge, anomenats Multi-Armed Bandits (MAB). L'esquema MAB s'adapta al problema descentralitzat de SR perquè aborda la incertesa causada pel funcionament simultani de diversos dispositius (és a dir, un entorn multi-jugador) i la falta d'informació que se'n deriva. Els MAB poden fer front a la complexitat darrera les interaccions espacials entre dispositius que resulten de modificar la seva sensibilitat i potència de transmissió. En aquest sentit, els nostres resultats indiquen guanys importants de rendiment (fins al 100 \%) en desplegaments altament densos. Tot i això, l'aplicació d'aprenentatge automàtic amb múltiples agents planteja diversos problemes que poden comprometre el rendiment dels dispositius d'una xarxa (definició d'objectius conjunts, horitzó de convergència, aspectes d'escalabilitat o manca d'estacionarietat). A més, el nostre estudi d'aprenentatge multi-agent per a SR multi-agent inclou aspectes d'infraestructura per a xarxes de nova generació que integrin AI de manera intrínseca