arXiv subjects

Cristina Cano

Publications and source records attributed to Cristina Cano.

At least 19 recordsLinked to original sources

arXiv2022

5/6G: Networks of the Future or Defuturing Networks?

Energy efficiency is at the core of sustainability solutions for 5/6G networks. We argue this is a too narrow perspective on sustainability, as it ignores the effects of the increased traffic demand these networks stimulate and the need for additional equipment that this demand requires. The hope is that techniques to reduce the network's energy consumption in operation will be able to compensate for increases in traffic demand. However, there are more challenges than just reducing the energy that the network requires to function and it is not clear whether higher energy efficiency will be able to cope with increasing demand. The need for more equipment related to deployment of 5/6G networks may result in important environmental impacts: i) increased pressures on material extraction, which imply socio-environmental conflicts, ecosystem destruction and displacement, ii) more manufacturing and shipment, with effects on greenhouse gas emissions and pollution; iii) increased disposal complexities and challenges to recycle components of such equipments. By extending our view on sustainability to include the aforementioned often ignored implications, we are able to identify design requirements and technical pillars of 5/6G networks that need to be rethought. We also devise new paths forward to address these challenges. We argue that it is crucial to think of alternative applications and requirements that aim to serve user demands explicitly, instead of incessantly creating new needs. We also claim that acknowledging material limitations in the production of new hardware is critical, promoting retrofitting and modular design in future network development. The conclusions of this article show that it is time to start rethinking the course of mobile network development in order to align it to current environmental objectives to tackle the climate emergency the world is experiencing.

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arXiv2021

Spatial Reuse in IEEE 802.11ax WLANs

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.

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arXiv2021

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.

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arXiv2020

Debunking Wireless Sensor Networks Myths

In this article we revisit Wireless Sensor Networks from a contemporary perspective, after the surge of the Internet of Things. First, we analyze the evolution of distributed monitoring applications, which we consider inherited from the early idea of collaborative sensor networks. Second, we evaluate, within the current context of networked objects, the level of adoption of low-power multi-hop wireless, a technology pivotal to the Wireless Sensor Network paradigm. This article assesses the transformation of this technology in its integration into the Internet of Things, identifying outdated requirements and providing a critical view on future research directions.

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arXiv2020

A Flexible Machine Learning-Aware Architecture for Future WLANs

Lots of hopes have been placed on Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of large volumes of data, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networks are not yet prepared to support the ensuing requirements of ML-based applications in terms of 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. 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. In particular, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs. Finally, we showcase the superiority of the architecture through an ML-enabled use case for future networks.

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arXiv2019

A Channel Measurement Campaign for mmWave Communication in Industrial Settings

Industry 4.0 relies heavily on wireless technologies. Energy efficiency and device cost have played a significant role in the initial design of such wireless systems for industry automation. However, high reliability, high throughput, and low latency are also key for certain sectors such as the manufacturing industry. In this sense, existing wireless solutions for industrial settings are limited. Emerging technologies such as millimeter-wave (mmWave) communication are highly promising to address this bottleneck. Still, the propagation characteristics at such high frequencies in harsh industrial settings are not well understood. Related work in this area is limited to isolated measurements in specific scenarios. In this work, we carry out an extensive measurement campaign in highly representative industrial environments. Most importantly, we derive the statistical distributions of the channel parameters of widely accepted mmWave channel models that fit these environments. This is a highly valuable contribution, since researchers in this field can use our empirical model to understand the performance of their mmWave systems in typical industrial settings. Beyond analyzing and discussing our insights, with this paper we also shareoour extensive dataset with the research community.

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arXiv2019

A Square Peg in a Round Hole: The Complex Path for Wireless in the Manufacturing Industry

The manufacturing industry is at the edge of the 4th industrial revolution, a paradigm of integrated architectures in which the entire production chain (composed of machines, workers and products) is intrinsically connected. Wireless technologies can add further value in this manufacturing revolution. However, we identify some signs that indicate that wireless could be left out from the next generation of smart-factory equipment. This is particularly relevant considering that the heavy machinery characteristic of this sector can last for decades. We argue that at the core of this issue there is a mismatch between industrial needs and the interests of academic and partly-academic (such as standardization bodies) sectors. We base our claims on surveys from renowned advisory firms and interviews with industrial actors, which we contrast with results from content analysis of scientific articles. Finally we propose some convergence paths that, while still retaining the degree of novelty required for academic purposes, are more aligned with industrial concerns.

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arXiv2019

Implementation of the 3GPP LTE-WLAN Interworking Protocols in NS-3

The next generation wireless standard, called Fifth Generation (5G), is being designed to encompass Heterogeneous Networks (HetNets) architectures consisting of a single holistic network with Multiple Radio Access Technologies (Multi-RAT). Multiple connectivity protocols and spectrum would be managed from a common core (management system) handling both: i) traditional macro cellular systems (such as LTE), that can provide long-range, outdoor coverage, as well as ii) low-power wireless systems with high capacity (such as Wi-Fi), that can be deployed to cater indoor traffic needs. 5G HetNets are expected to achieve ubiquitous connectivity that would guarantee Quality of Service (QoS), Quality of Experience (QoE) along with efficient use of spectrum and energy at low cost. Tightly coupled LTE-Wi-Fi networks have emerged as one of the promising solutions in the 5G era to boost network capacity and improve end user's quality of experience. LTE/Wi-Fi Link Aggregation (LWA) and LTE WLAN Radio Level Integration with IPSec Tunnel (LWIP) are two approaches put forward by the 3rd Generation Partnership Project (3GPP) to enable flexible, general, and scalable LTE-WLAN inter-working. These techniques enable operator-controlled access of licensed and unlicensed spectrum and allow transparent access of operator's evolved core. The most important aspect of these techniques is that they could be enabled with straightforward software upgrades and can utilize the already existing Wi-Fi networks. This article presents and motivates the design details of LWA and LWIP protocols. We also present the first NS-3 LWA and LWIP implementations over Network Simulator 3 (NS-3). In particular, this work focuses on the adaptation and concurrent usage of different NS-3 modules and protocols of different technologies to enable the support of these interworking schemes.

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arXiv2018

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

Spatial Reuse (SR) has recently gained attention to maximize the performance of 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 considers 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. We show that substantial improvements on network performance can be achieved regarding throughput and fairness.

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arXiv2018

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

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 to enable Spatial Reuse (SR). In particular, we focus on dynamic channel selection and Transmission Power Control (TPC). We rely on Reinforcement Learning (RL), and more specifically on Multi-Armed Bandits (MABs), to allow networks to learn their best configuration. In this work, we study the exploration-exploitation trade-off by means of the $\varepsilon$-greedy, EXP3, UCB and Thompson sampling action-selection, and compare their performance. In addition, we study the implications of selecting actions simultaneously in an adversarial setting (i.e., concurrently), and compare it with a sequential approach. Our results show that optimal proportional fairness can be achieved, even when no information about neighboring networks is available to the learners and Wireless Networks (WNs) operate selfishly. However, there is high temporal variability in the throughput experienced by the individual networks, specially for $\varepsilon$-greedy and EXP3. These strategies, contrary to UCB and Thompson sampling, base their operation on the absolute experienced reward, rather than on its distribution. 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 learning sequentially, even if using a selfish strategy, contributes to minimize this variability. The sequential approach is therefore shown to effectively deal with the challenges posed by the adversarial settings that are typically found in decentralized WNs.

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arXiv2018

Wireless Optimisation via Convex Bandits: Unlicensed LTE/WiFi Coexistence

Bandit Convex Optimisation (BCO) is a powerful framework for sequential decision-making in non-stationary and partially observable environments. In a BCO problem, a decision-maker sequentially picks actions to minimize the cumulative cost associated with these decisions, all while receiving partial feedback about the state of the environment. This formulation is a very natural fit for wireless-network optimisation problems and has great application potential since: i) instead of assuming full observability of the network state, it only requires the metric to optimise as input, and ii) it provides strong performance guarantees while making only minimal assumptions about the network dynamics. Despite these advantages, BCO has not yet been explored in the context of wireless-network optimisation. In this paper, we make the first steps to demonstrate the potential of BCO techniques by formulating an unlicensed LTE/WiFi fair coexistence use case in the framework, and providing experimental results in a simulated environment. On the algorithmic front, we propose a simple and natural sequential multi-point BCO algorithm amenable to wireless networking optimisation, and provide its theoretical analysis. We expect the contributions of this paper to pave the way to further research on the application of online convex methods in the bandit setting.

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arXiv2018

The Wireless Technology Landscape in the Manufacturing Industry: A Reality Check

An upcoming industrial IoT revolution, supposedly led by the introduction of embedded sensing and computing, seamless communication and massive data analytics within industrial processes [1], seems unquestionable today. Multiple technologies are being developed, and huge marketing efforts are being made to position solutions in this industrial landscape. However, we have observed that industrial wireless technologies are hardly being adopted by the manufacturing industry. In this article, we try to understand the reasons behind this current lack of wireless technologies adoption by means of conducting visits to the manufacturing industry and interviews with the maintenance and engineering teams in these industries. The manufacturing industry is very diverse and specialized, so we have tried to cover some of the most representative cases: the automotive sector, the pharmaceutical sector (blistering), machine-tool industries (both consumer and aerospace sectors) and robotics. We have analyzed the technology of their machinery, their application requirements and restrictions, and identified a list of obstacles for wireless technology adoption. The most immediate obstacles we have found are the need to strictly follow standards and certifications processes, as well as their prudence. But the less obvious and perhaps even more limiting obstacles are their apparent lack of concern regarding low energy consumption or cost which, in contrast, are believed to be of utmost importance by wireless researchers and practitioners. In this reality-check article, we analyze the causes of this different perception, we identify these obstacles and devise complementary paths to make wireless adoption by the industrial manufacturing sector a reality in the coming years.

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arXiv2017

Implications of Decentralized Q-learning Resource Allocation in Wireless Networks

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.

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arXiv2016

Fair Coexistence of Scheduled and Random Access Wireless Networks: Unlicensed LTE/WiFi

We study the fair coexistence of scheduled and random access transmitters sharing the same frequency channel. Interest in coexistence is topical due to the need for emerging unlicensed LTE technologies to coexist fairly with WiFi. However, this interest is not confined to LTE/WiFi as coexistence is likely to become increasingly commonplace in IoT networks and beyond 5G. In this article we show that mixing scheduled and random access incurs and inherent throughput/delay cost, the cost of heterogeneity. We derive the joint proportional fair rate allocation, which casts useful light on current LTE/WiFi discussions. We present experimental results on inter-technology detection and consider the impact of imperfect carrier sensing.

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arXiv2016

State-of-the-art in Power Line Communications: from the Applications to the Medium

In recent decades, power line communication has attracted considerable attention from the research community and industry, as well as from regulatory and standardization bodies. In this article we provide an overview of both narrowband and broadband systems, covering potential applications, regulatory and standardization efforts and recent research advancements in channel characterization, physical layer performance, medium access and higher layer specifications and evaluations. We also identify areas of current and further study that will enable the continued success of power line communication technology.

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arXiv2016

srsLTE: An Open-Source Platform for LTE Evolution and Experimentation

Testbeds are essential for experimental evaluation as well as for product development. In the context of LTE networks, existing testbed platforms are limited either in functionality and/or extensibility or are too complex to modify and customise. In this work we present srsLTE, an open-source platform for LTE experimentation designed for maximum modularity and code reuse and fully compliant with LTE Release 8. We show the potential of the srsLTE library by extending the baseline code to allow LTE transmissions in the unlicensed bands and coexistence with WiFi. We also expand previous results on this emerging research area by showing how different vendor-specific mechanisms in WiFi cards might affect coexistence.

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arXiv2015

Unlicensed LTE/WiFi Coexistence: Is LBT Inherently Fairer Than CSAT?

Ensuring fair co-existence between unlicensed LTE and WiFi networks is currently of major concern to both cellular operators and WiFi providers. Two main unlicensed LTE approaches currently being discussed, namely Carrier Sense Adaptive Transmission (CSAT) and Listen Before Talk (LBT). While these mechanisms differ in their compatibility with existing LTE specifications and regulatory compliance in different countries, they also use fundamentally different approaches to access the channel. Nevertheless, we show in this article that when optimally configured both approaches are capable of providing the same level of fairness to WiFi and that the choice between CSAT and LBT is solely driven by the LTE operator's interests.

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arXiv2014

On Efficiency and Validity of Previous Homeplug MAC Performance Analysis

The Medium Access Control protocol of Power Line Communication networks (defined in Homeplug and IEEE 1901 standards) has received relatively modest attention from the research community. As a consequence, there is only one analytic model that complies with the standardised MAC procedures and considers unsaturated conditions. We identify two important limitations of the existing analytic model: high computational expense and predicted results just prior to the predicted saturation point do not correspond to long-term network performance. In this work, we present a simplification of the previously defined analytic model of Homeplug MAC able to substantially reduce its complexity and demonstrate that the previous performance results just before predicted saturation correspond to a transitory phase. We determine that the causes of previous misprediction are common analytical assumptions and the potential occurrence of a transitory phase, that we show to be of extremely long duration under certain circumstances. We also provide techniques, both analytical and experimental, to correctly predict long-term behaviour and analyse the effect of specific Homeplug/IEEE 1901 features on the magnitude of misprediction errors.

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