arXiv subjects

Andrea P. Guevara

Publications and source records attributed to Andrea P. Guevara.

8 recordsLinked to original sources

arXiv2024

Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer

The number of sensors deployed in the world is expected to explode in the near future. At this moment, nearly 30 billion Internet of Things (IoT) devices are connected and this number is expected to double in the next four years. While not all of these are battery powered, as technology becomes smaller and mobility becomes more important to consumers, soon a larger portion will be. This forecast predicts that the number of machine to machine (M2M) devices will have the largest increase, representing nearly 50% of all devices in 2023.

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arXiv2024

Location-Based Load Balancing for Energy-Efficient Cell-Free Networks

Cell-Free Massive MIMO (CF mMIMO) has emerged as a potential enabler for future networks. It has been shown that these networks are much more energy-efficient than classical cellular systems when they are serving users at peak capacity. However, these CF mMIMO networks are designed for peak traffic loads, and when this is not the case, they are significantly over-dimensioned and not at all energy efficient. To this end, Adaptive Access Point (AP) ON/OFF Switching (ASO) strategies have been developed to save energy when the network is not at peak traffic loads by putting unnecessary APs to sleep. Unfortunately, the existing strategies rely on measuring channel state information between every user and every access point, resulting in significant measurement energy consumption overheads. Furthermore, the current state-of-art approach has a computational complexity that scales exponentially with the number of APs. In this work, we present a novel convex feasibility testing method that allows checking per-user Quality-of-Service (QoS) requirements without necessarily considering all possible access point activations. We then propose an iterative algorithm for activating access points until all users' requirements are fulfilled. We show that our method has comparable performance to the optimal solution whilst avoiding solving costly mixed-integer problems and measuring channel state information on only a limited subset of APs.

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arXiv2024

Electric Field Evaluation of Reconfigurable Intelligent Surface in Wireless Networks

Reconfigurable intelligent surface (RIS) used as infrastructure in wireless networks has been a trend, thanks to its low cost and high flexibility. Working in many ways including reflective mirrors and phase-shifted surfaces, RIS is able to enhance the coverage in communications and provide more degrees of freedom for sensing. However, the key issue lies in how to place RIS in accordance with the regulations for electromagnetic field (EMF) exposure, which requires refined evaluations. In this paper, we first investigate the regulations in terms of E-field. Then, relevant deployment characteristics are evaluated jointly: the minimum distance from the base station (BS) to the RIS, and the minimum height of the RIS are given for a given BS power limit and as function of the number of RIS elements. The ray-tracing simulations verify the correctness of our analysis. Besides, different frequency ranges (FRs) and radiation patterns of RIS elements are investigated. The results show that the EMF exposure risk is negligible when RIS works in the reflective-only (RO) mode. However, when it works in the beamforming (BO) mode, its placement should be well specified based on our analytical framework to comply with the regulations of E-field limit in general public scenarios. Finally, we provide an E-field measurement methodology and low-cost solutions in terms of general wireless networks and 5G standalone networks, which pave the way for real-world evaluation in future work.

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arXiv2023

Cell-Free Massive MIMO in the O-RAN Architecture: Cluster and Handover Strategies

Cell-free Massive MIMO has emerged as a promising solution for next-generation radio networks. Particularly the user-centric variant where users are served by a limited subset of access points, a so-called cluster, has garnered significant attention within the research community. Despite numerous proposed physical layer solutions, managing AP clusters in case of user mobility remains challenging. In this study, we first present a realistic method for modeling the temporal evolution of the channel in cell-free Massive MIMO. Next, we develop two clustering and handover strategies: 1) a fixed clustering strategy that computes the ideal cluster when a cluster handover threshold is exceeded and 2) an opportunistic strategy that opportunistically adds serving APs as the user moves. Moreover, we establish a connection between our findings and O-RAN, an emerging network architecture that offers open interfaces and network-wide control capabilities, thus, facilitating practical implementation of our research.

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arXiv2022

Massive MIMO Channel Measurement Data Set for Localization and Communication

Channel state information (CSI) needs to be estimated for reliable and efficient communication, however, location information is hidden inside and can be further exploited. This article presents a detailed description of a Massive Multi-Input Multi-Output (MaMIMO) testbed and provides a set of experimental location-labelled CSI data. In this article, we focus on the design of the hardware and software of a MaMIMO testbed for gathering multiple CSI data sets. We also show this data can be used for learning-based localization and enhanced communication research. The data set presented in this work is made fully available to the research community. We show a CSI-based joint communication and sensing processing pipeline can be evaluated and designed based on the collected data set. Specifically, the localization output obtained by a convolutional neural network (CNN) trained on the data sets is used to schedule users for improving the spectral efficiency (SE) of the communication system. Finally, we pose promising directions on further exploiting this data set and creating future data sets.

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arXiv2021

Experimental Exploration of Unlicensed Sub-GHz Massive MIMO for Massive Internet-of-Things

Due to the increase of Internet-of-Things (IoT) devices, IoT networks are getting overcrowded. Networks can be extended with more gateways, increasing the number of supported devices. However, as investigated in this work, massive MIMO has the potential to increase the number of simultaneous connections, while also lowering the energy expenditure of these devices. We present a study of the channel characteristics of massive MIMO in the unlicensed sub-GHz band. The goal is to support IoT applications with strict requirements in terms of number of devices, power consumption, and reliability. The assessment is based on experimental measurements using both a uniform linear and a rectangular array. Our study demonstrates and validates the advantages of deploying massive MIMO gateways to serve IoT nodes. While the results are general, here we specifically focus on static nodes. The array gain and channel hardening effect yield opportunities to lower the transmit-power of IoT nodes while also increasing reliability. The exploration confirms that exploiting large arrays brings great opportunities to connect a massive number of IoT devices by separating the nodes in the spatial domain. In addition, we give an outlook on how static IoT nodes could be scheduled based on partial channel state information.

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arXiv2020

Massive MIMO goes Sub-GHz: Implementation and Experimental Exploration for LPWANs

Low-Power Wide-Area Networks operating in the unlicensed bands are being deployed to connect a rapidly growing number of Internet-of-Things devices. While the unlicensed sub-GHz band offers favorable propagation for long-range connections, measurements show that the energy consumption of the nodes is still mostly dominated by the wireless transmission affecting their autonomy. We investigate the potential benefits of deploying massive MIMO technology to increase system reliability and at the same time support low-energy devices with good coverage at sub-GHz frequencies. The impact of different antenna configurations and propagation conditions is analyzed. Both actual average experienced array gain and channel hardening are examined. The assessment demonstrates the effect of channel hardening as well as the potential benefits of the experienced array gain. These measurements serve as a first assessment of the channel conditions of massive MIMO at sub-GHz frequencies and are, to the best of our knowledge, the first of its kind.

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arXiv2020

Weave and Conquer: A Measurement-based Analysis of Dense Antenna Deployments

Massive MIMO is bringing significant performance improvements in the context of outdoor macrocells, such as favourable propagation conditions, spatially confined communication, high antenna gains to overcome pathloss, and good angular localisation. In this paper we explore how these benefits scale to indoor scattering-rich deployments based on a dense indoor measured Massive MIMO dataset. First, we design and implement three different and relevant topologies to position our 64 antennas in the environment: \emph{Massive MIMO, RadioStripes} and \emph{RadioWeaves} topologies. Second, we measure 252004 indoor channels for a $3x3m^2$ area for each topology, using an automated user-positioning and measurement system. Using this dense dataset, we provide a unique analysis of system level properties such as pathloss, favourable propagation, spatial focusing and localisation performance. Our measurement-based analyses verify and quantify that distributing the antennas throughout the environment results in an improved propagation fairness, better favourable propagation conditions, higher spatial confinement and finally a high localisation performance. The dataset is publicly available and can serve as a reference database for benchmarking of future indoor communication systems and communication models. We outline the implementation challenges we observed, and also list diverse R\&D challenges that can benefit from using this dataset as a benchmark.

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