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Scalable machine learning algorithms to design massive MIMO systems
Get PDFMachine learning is a highly promising tool to design the physical layer of wireless communication systems, but its scaling properties for this purpose have not been widely studied. Machine learning algorithms are typically evaluated to learn SISO communications and low modulation orders, whereas current wireless standards use MIMO and high-order modulation schemes to increase capacity. The memory requirements of current machine learning algorithms for wireless communications increase exponentially with the number of antennas and thus they cannot be used for advanced physical layers and massive MIMO. In this paper, we study the requirements of end-to-end machine learning models for large-scale MIMO systems, determine the bottlenecks of the architecture, and design different solutions that vastly reduce overhead and allow training higher MIMO and modulation orders. We show that by training the autoencoder in a bit-wise manner, the memory requirements are reduced by several orders of magnitude, which is a critical step for machine learning-based physical layer design in practical scenarios. Besides the reduced memory requirements, our design also improves performance over the classical autoencoder for MIMO systems.TRUEinpres
What do Information Centric Networks, Trusted Execution Environments, and Digital Watermarking have to do with Privacy, the Data Economy, and their future?
Get PDFWhat if instead of having to implement controversial user tracking techniques, Internet advertising \& marketing companies asked explicitly to be granted access to user data by name and category, such as AliceMobility05-11-2020? The technology for implementing this already exists, and is none other than the Information Centric Networks (ICN), developed for over a decade in the framework of Next Generation Internet (NGI) initiatives. Beyond named access to personal data, ICN's in-network storage capability can be used as a substrate for retrieving aggregated, anonymized data, or even for executing complex analytics within the network, with no personal data leaking outside. In this opinion article we discuss how ICNs combined with trusted execution environments and digital watermarking, can be combined to build a personal data overlay inter-network in which users will be able to control who gets access to their personal data, know where each copy of said data is, negotiate payments in exchange for data, and even claim ownership, and establish accountability for data leakages due to malfunctions or malice. Of course, coming up with concrete designs about how to achieve all the above will require a huge effort from a dedicated community willing to change how personal data are handled on the Internet. Our hope is that this opinion article can plant some initial seeds towards this direction.pu
Breadth Analysis of Online Social Networks
Get PDFThis thesis is mainly motivated by the analysis, understanding, and prediction of human behavior by means of the study of their digital fingerprints. Unlike a classical PhD thesis, where you choose a topic and go further on a deep analysis on a research topic, we carried out a breadth analysis on the research topic of complex networks, such as those that humans create themselves with their relationships and interactions. These kinds of digital communities where humans interact and create relationships are commonly called Online Social Networks.
First, in 2013, we studied the media content people shared on these online social networks, such as Twitter. Our collection of tweets (text-messages database), namely corpus, was provided by the Spanish Society for Natural Language Processing (SEPLN in Spanish) for their workshop on NLP. We have basically applied the state-of-the-art techniques for Natural Language Processing, widely developed and tested on English texts, in a collection of Spanish Tweets and we compare the results for both Topic Detection and Sentiment Analysis tasks. The first conclusion of our study is that none of the techniques explored is the silver bullet for Spanish tweet topic classification, i.e., none made a clear difference when introduced in the algorithm. The second conclusion is that tweets are very hard to deal with, mostly due to their brevity and lack of context. The results of our experiments are encouraging though, since they show that it is possible to use classical methods for analyzing Spanish texts. Besides, the highest accuracy we reported (58% for topics and 42% for sentiment) is not far from the highest scores in the workshop we participated in. Thus, these conclusions reflect there is still room for improvement, justifying further efforts.
Next, in 2014, we focused on Topic Detection, creating our own classifier and applying it to the former tweets dataset. Our classifier builds graphs from the input texts and it relies on graph similarity techniques to classify short texts by topic. After preprocessing and filtering the texts, each word of the text represents a node, and two words are linked by an edge if they appear on the same tweet; not necessarily one next to the other. We use weighted graphs in the following way: Then, two nodes are connected with an edge whose weight is the product of their respective number of appearances in the text. The breakthroughs are two: our classifier relies on text-graphs from the input text and we achieved a figure of 70% accuracy, outperforming previous results.
After that, we moved to analyze the network structure (or topology) and their data values to detect outliers. We hypothesize that in social networks there is a large mass of users that behaves similarly, while a reduced set of them behave in a different way. However, especially among this last group, we try to separate those with high activity, or low activity, or any other parameter/feature that make them belong to different kinds of outliers. We aim to detect influential users in one of these outliers set, and in an unsupervised way, since we do not define influencers in advance on our method. We propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), labeling the outliers detected as of shape, magnitude, amplitude or combination of those. Our method relies on FDA theory (Functional Data Analysis) and we applied it to a subset of roughly 400 million Google+ users, identifying and discriminating automatically sets of outlier users. The results are promising. Our method is highly scalable and parallelizable for multicore machines. Based on the preliminary tests performed on synthetic datasets with controlled outliers, the performance is similar (usually better) to those methods on the literature but only one of such state-of-the-art methods scales for millions of users along with ours. Besides, our method yields different groups of outliers by nature, because not all outliers are necessarily influencers. Actually, the result reveals that the different outlier classes identified by MUOD include users that respond to different definitions of influence previously used in the literature. Hence, the results show strong evidences of the utility of MUOD as an algorithm to support the unsupervised identification of influencers when a predefined type of influential user does not exist. MUOD algorithm can be applied to a myriad of problems, in which the nodes/users/entities can be defined by a set of properties mapped into a signal.
Finally, we find interesting to address the monitorization of real complex networks. We leverage the characterization of complex networks by two cost-effective metrics to monitorize abrupt changes in a network stream by simply detecting abrupt changes on those metrics. We aim to economize resources (computation and time) while having a low loss accuracy on the target monitorization metric. We created a framework to dynamically adapt the temporality of large-scale dynamic networks, reducing compute overhead by at least 76%, data volume by 60% and overall cloud costs by at least 54%, while always maintaining accuracy above 88%.MathematicsUniversidad Carlos III de Madrid, Spai
TeleNoise: A Network-Noise Module for In-Band Real-Time Telemetry
Get PDFIn-band real-time telemetry is a promising direction for management of modern programmable networks. While network noise in the form of packet reordering and loss affects in-band collection of distributed state, there is a need to compute telemetry functions on the collected state correctly despite the network noise. To address this common need, we propose TeleNoise that equips each packet with few sync bits and offers primitives of group affiliation and group completion to support noise-resilient computation of per-group telemetry functions. This paper gives real-world examples of such functions, elaborates on the role of TeleNoise in a modular in-band telemetry architecture, and presents algorithms for the two TeleNoise primitives. We derive analytical guarantees on correctness and performance of the algorithms and report a trace-driven evaluation that corroborates the effective low-overhead profile of TeleNoise, e.g., the assuredly correct operation and at most 1.6 packets of the average measurement lag for 12-packet groups and 3 sync bits.TRUEpu
News or Social Media? Socioeconomic divide of mobile service consumption
Get PDFReliable and timely information on socioeconomic status and divides is critical to social and economic research and policing. Novel data sources from mobile communication platforms have enabled new cost-effective approaches and models to investigate social dis- parity, but their lack of interpretability, accuracy or scale has limited their relevance to date. We investigate the divide in digital mobile service usage with a large dataset of 3.7 billion time-stamped and geo-referenced mobile traffic records in a major European country, and find profound geographical unevenness in mobile service usage – especially on news, e-mail, social media consumption, and audio/video streaming. We relate such diversity with income, educational attainment, and inequality, and reveal how low income or low education areas are more likely to engage in video streaming or social media, and less in news consumption, information searching, e-mail, or audio streaming. The digital usage gap is so large that we can accurately infer socioeconomic status of a small area or even its Gini coefficient only from aggregated data traffic. Our results make the case for a cheap, privacy-preserving, real-time, and scalable way to understand the digital usage divide and, in turn, poverty, unemployment, or economic growth in our societies through mobile phone data.Comunidad de MadridAgence Nationale de la RechercheTRUEpu
Closer than Close: MEC-Assisted Platooning with Intelligent Controller Migration
Get PDFThe advent of multi access edge computing (MEC) will enable latency-critical applications such as cooperative adaptive cruise control (also known as platooning) to be hosted at the edge of the network. MEC-based platooning will leverage the coverage of the cellular infrastructure to enable inter-vehicular communications, potentially overcoming crucial problems of vehicular ad-hoc networks (VANETs) such as non-trivial packet loss rates. However, MEC-based platooning will require the controller to be migrated to the most suitable positions at the network edge, in order to maintain low-latency connections as the platoon moves. In this paper, we propose a context-aware -learning algorithm that carries out such migrations only as often as is necessary, and thereby reduces the additional delays implicit in application migration across MEC hosts. When compared to the state-of-the-art approach named FollowME, our scheme exhibits better compliance of vehicle speed and spacing values to preset targets, as well as a reduced statistical dispersion.Italian Ministry for University and Research (MIUR) under the initiative “Departments of Excellence” (Law 232/2016)Free University of Bolzano–Bozen under the SECEDA project, RTD Call 2021Spanish State Research Agency (AEI) PID2019-109805RB-I00/AEI/10.13039/501100011033TRUEinpres
Alviu: An Intent-Based SD-WAN Orchestrator of Network Slices for Enterprise Networks
Get PDFThe introduction of the Software-Defined Networking paradigm for the operation, administration and management of WAN environments, known as SD-WAN, is becoming increasingly popular due to the clear advantages that this technology provides, such as the reduction of CAPEX and OPEX related to the networking infrastructure or the flexibility provided by the development and deployment of network applications regardless of the underlying infrastructure. However, there is still a lack of solutions for enterprise networks, without carrying a substantial increase in cost, adapted to their daily reality, in which the WAN can be split in several domains, each of them possibly managed by different operators and based on different technologies and protocols. As a result, today it is not possible to have a complete solution for SD-WAN that covers all domains, reducing its scope to a single set of domains which may interact with other external domains. To address this issue, this paper presents Alviu, a SD-WAN network orchestrator based on open-source technologies that assures end-to-end network slicing to managed enterprise and academic networks thanks to a dynamic intentbased configuration of the different elements of the managed network. In addition to positioning Alviu in the current state of the art and detailing its architecture, we also evaluate Alviu’s operation in a testbed that emulates the interconnection between a set of SD-WAN domains with are also connected to other external domains based on legacy routing protocols, assessing the deployment time depending on the domains present in the complete network.TRUEpu
High-Speed Millimeter-Wave Mobile Experimentation on Software-Defined Radios
Get PDFMillimeter-wave (mm-wave) communications have become an integral part of WLAN standards and 5G mobile networks and, as application data rate requirements increase, more and more traffic will move to these very high frequency bands. While for sub-6 GHz research there is an ample choice of powerful experimental platforms, building mm-wave systems is much more difficult due to the very high hardware requirements. To address the lack of suitable experimentation platforms, we propose mm-FLEX, a flexible and modular open platform with real-time signal processing capabilities that supports a bandwidth of 2 GHz and is compatible with current mm-wave standards. The platform is built around a fast FPGA processor and a 60 GHz phased antenna array front-end that can be reconfigured at nanosecond timescales. Together with its ease of use, this turns the platform into a unique tool for research on beam training in highly mobile scenarios and full-bandwidth mm-wave signal processing.pu
Mobility-Driven and Energy-Efficient Deployment of Edge Data Centers in Urban Environments
No full textMulti-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better support low-latency applications. In this paper, we tackle the problem of EDC deployment in urban environments. Previous research on mobile phone data has exposed a strong correlation between the demand for mobile communications and the urban tissue. For example, joint analysis of mobile data and vehicle traffic can be extrapolated to estimate demand for transportation and human activities, thereby inferring the land use of the area where such activities take place. Our work takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments, in order to minimize outages while guaranteeing energy-efficiency. This is achieved by modeling both the energy consumption attributed to network components (e.g., base stations) and computing components (e.g., servers). We propose and compare three heuristics and show that mobility-aware deployments achieve superior performance. The results are obtained with a custom-designed simulator able to operate over large-scale realistic urban environments.pu
A Real-Time Experimentation Platform for sub-6 GHz and Millimeter-Wave MIMO Systems
No full textThe performance of wireless communication systems is evolving rapidly, making it difficult to build experimentation platforms that meet the hardware requirements of new standards. The bandwidth of current systems ranges from 160 MHz forIEEE 802.11ac/ax to 2 GHz for Millimeter-Wave (mm-wave) IEEE 802.11ad/ay, and they support up to 8 spatial MIMO streams. Mobile 5G and beyond systems have a similarly diverse set of requirements.
To address this, we propose a highly configurable wireless platform that meets such requirements and is both affordable and scalable. It is implemented on a single state-of-the-art FPGA board that can be configured from 4x4 mm-wave MIMO with 2 GHz channels to 8x8 MIMO with 160 MHz channels in sub-6 GHz bands. In addition, multi-band operation will play an important role in future wireless networks and our platform supports mixed configurations with simultaneous use of mm-wave and sub-6 GHz. Finally, the platform supports real-time operation, e.g., for closed-loop MIMO beam training with low-latency, by implementing suitable hardware/software accelerators. We demonstrate the platform’s performance in a wide range of experiments. The platform is provided as open-source to build a community to use and extend it.TRUEpu