Search

In this work we present the results of a measurement campaign carried out in the Mediterranean sea aimed at characterizing the underwater acoustic channel in a wideband at ultrasonic frequencies centered at 80 kHz with a width of 96 kHz, covering two octaves from 32 to 128 kHz. So far, these type of wideband measurements are not found in the literature. Periodic orthogonal frequency division multiplexing (OFMD) sounding signals using Zadoff-Chu sequences have been specially designed for this purpose. The collected data has been post-processed to estimate the time-variant impulse and frequency responses and relevant parameters for system design like the time coherence, bandwidth coherence, delay spread and Doppler bandwidth. The statistical behavior of the channel gain random fluctuation has also been analyzed. This information has been extracted for both the global channel and each path separately. The wide bandwidth of the measurements have allowed the characterization of the channel in a scarcely explored ultrasonic band with an accuracy that is far beyond what is reported in previous works., Comment: 13 pages, 15 figures

As global cellular networks converge to 5G, one question lingers: Are we ready for the 5G challenge? A growing concern surrounds how well do existing congestion control algorithms perform in diverse 5G networks. Given that 5G networks are not yet widely deployed, assessing the performance of existing congestion control algorithms in realistic 5G settings presents several challenges. Moreover, existing network simulation and emulation environments are also not ideally suited to address the unique challenges of 5G network environments. Therefore, building a simple and easily accessible platform becomes crucial to allow testing and comparison of congestion control algorithms under different testing conditions. This paper makes two main contributions. First, we present Zeus, an open-source testbed that emulates 5G channels to evaluate congestion control algorithms in a repeatable and reproducible manner. Second, we assess and characterize ten of today's state-of-the-art congestion control algorithms under different 5G environments and show the difficulties of these solutions to achieve high performance under highly varying 5G channel conditions. In addition, we also utilize the recently proposed harm metric to characterize the detriment done by such algorithms to TCP Cubic cross traffic in 5G environments., Comment: 21 pages, 18 figures

Although we have reached new levels in smart city installations and systems, efforts so far have focused on providing diverse sources of data to smart city services consumers while neglecting to provide ways to simplify making good use of them. In this context, one first step that will bring added value to smart cities is knowledge creation in smart cities through anomaly detection and data annotation, supported in both an automated and a crowdsourced manner. We present here LearningCity, our solution that has been validated over an existing smart city deployment in Santander, and the OrganiCity experimentation-as-a-service ecosystem. We discuss key challenges along with characteristic use cases, and report on our design and implementation, together with some preliminary results derived from combining large smart city datasets with machine learning., Comment: Preprint of chapter submitted to "Smart Cities Performability, Cognition, & Security". EAI/Springer Innovations in Communication and Computing. Springer, Cham. arXiv admin note: text overlap with arXiv:2103.16998

Smart cities solutions are often monolithically implemented, from sensors data handling through to the provided services. The same challenges are regularly faced by different developers, for every new solution in a new city. Expertise and know-how can be re-used and the effort shared. In this article we present the methodologies to minimize the efforts of implementing new smart city solutions and maximizing the sharing of components. The final target is to have a live technical community of smart city application developers. The results of this activity comes from the implementation of 35 city services in 27 cities between Europe and South Korea. To share efforts, we encourage developers to devise applications using a modular approach. Single-function components that are re-usable by other city services are packaged and published as standalone components, named Atomic Services. We identify 15 atomic services addressing smart city challenges in data analytics, data evaluation, data integration, data validation, and visualization. 38 instances of the atomic services are already operational in several smart city services. We detail in this article, as atomic service examples, some data predictor components. Furthermore, we describe real-world atomic services usage in the scenarios of Santander and three Danish cities. The resulting atomic services also generate a side market for smart city solutions, allowing expertise and know-how to be re-used by different stakeholders., Comment: 8 pages, 9 figures, 4 Tables, to be published in IEEE IoT Journal

In a world with an ever increasing urbanization, governance is investigating innovative solutions to sustain the society evolution. Internet-of-Things promises huge benefits for cities and the proliferation of smart city deployments demonstrates the common acceptance of IoT as basis for many solutions. The city pilots developments occurred in parallel and with different designs thus creating fragmentation of IoT. The European project SynchroniCity aims to synchronize 8 smart cities to establish a shared environment fostering a self-sustained business growth. In this article we present the collaborative methodology and shared efforts spent towards the creation of a common ecosystem for the development of smart city services. Our design evolves around the concept of "atomic services" that implements a single functional block to be composed for full-fledged smart city services. This creates opportunities for diverse stakeholders to participate to a global smart cities market. The methodology and outcome of our efforts will be followed by 10 new cities globally, thus expanding the market range for IoT stakeholders, Comment: 2019 IEEE Global Internet-of-Things Summit (GIoTS)

Orthogonal frequency division multiplexing (OFDM) signals with rectangularly windowed pulses exhibit low spectral confinement. Two approaches usually referred to as pulse-shaping and active interference cancellation (AIC) are classically employed to reduce the out-of-band emission (OOBE) without affecting the receiver. This paper proposes a spectral shaping method that generalizes and unifies these two strategies. To this end, the OFDM carriers are shaped with novel pulses, referred to as generalized pulses, that consist of the ones used in conventional OFDM systems plus a series of cancellation terms aimed at reducing the OOBE of the former. Hence, each generalized pulse embeds all the terms required to reduce its spectrum in the desired bands. This leads to a data-independent optimization problem that notably simplifies the implementation complexity and allows the analytical calculation of the resulting power spectral density (PSD), which in most methods found in the literature can only be estimated by means of simulations. As an example of its performance, the proposed technique allows complying with the stringent PSD mask imposed by the EN 50561-1 with a data carrier loss lower than 4%. By contrasts, 28% of the data carriers have to be nulled when pulse-shaping is employed in this scenario.

We expose some concepts concerning the channel impulse response (CIR) of linear time-varying (LTV) channels to give a proper characterization of the mobile-to-mobile underwater channel. We find different connections between the linear time-invariant (LTI) CIR of the static channel and two definitions of LTV CIRs of the dynamic mobile-to-mobile channel. These connections are useful to design a dynamic channel simulator from the static channel models available in the literature. Such feature is particularly interesting for overspread channels, which are hard to characterize by a measuring campaign. Specifically, the shallow water acoustic (SWA) channel is potentially overspread due the signal low velocity of propagation which prompt long delay spread responses and great Doppler effect. Furthermore, from these connections between the LTI static CIRs and the LTV dynamic CIRS, we find that the SWA mobile-to-mobile CIR does not only depend on the relative velocity between transceivers, but also on the absolute velocity of each of them referred to the velocity of propagation. Nevertheless, publications about this topic do not consider it and formulate their equations in terms of the relative velocity between transceivers. We illustrate our find using two couples of examples where, even though the relative velocity between the mobiles is the same, their CIRs are not.

We analyze the level crossing rate (LCR) and the average fade duration of the output signal-to-noise-ratio (SNR) in generalized switched diversity systems. By using a common approach, we study these higher order statistics for two different kinds of configurations: (1) Colocated diversity, i.e. receiver equipped with multiple antennas, and (2) Distributed diversity, i.e. relaying link with multiple single-antenna threshold-based decode-and-forward (DF) relays. In both cases, we consider the switched diversity combining strategies Selection Combining and Switch \& Stay Combining (SSC). Whenever using threshold-based techniques such as DF or SSC, the output SNR is a discontinuous random process and hence classic Rice approach to calculate the LCR is not applicable. Thus, we use an alternative formulation in terms of the one and two-dimensional cumulative distribution functions of the output SNR. Our results are general, and hold for any arbitrary distribution of fading at the different diversity branches. Moreover, we develop a general asymptotic framework to calculate these higher order statistics in high mean SNR environments which only needs of the univariate probability density function.