A Deep Learning-Based Strategy to Predict Self-Interference in SFN DTT

This article belongs to the Proceedings of The 4th XoveTIC Conference[Abstract] A deep learning-based strategy for the analysis of the self-interference in single frequency networks (SFNs) for digital terrestrial television (DTT) broadcasting is considered. Several laboratory measurements were performed to create a dataset that relates the self-interference parameters and some quality metrics of the resulting received signal. The laboratory setup emulates an SFN scenario with two DTT transmitters. The strongest received signal and the relative values of attenuation and delay between the signals stand for the input parameters. The modulation error ratio (MER) of the strongest received signal, the MER of the resulting signal, and the SFN gain (SFNG) are the output parameters. This dataset is used to train four different multi-layer perceptron (MLP) models to predict accurate maps of interference and signal quality metrics. The considered models are suitable as complements for any multiple frequency network (MFN) coverage software with the capability to return the signal strength and the position data. This way, the SFN self-interference behavior can be predicted by considering only a proper description of the MFN coverage.This work has been funded by the Xunta de Galicia (by grant ED431C 2020/15, and grant ED431G2019/01 to support the CITIC, Centre for Information and Communications Technology Research, from the University System of Galicia), the Agencia Estatal de Investigación of Spain (by grants RED2018-102668-T and PID2019-104958RB-C42) and ERDF funds of the EU (FEDER Galicia 2014–2020 & AEI/FEDER Programs, UE), and the predoctoral grant BES-2017-081955.Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G2019/0

Deep Contextual Bandit and Reinforcement Learning for IRS-Assisted MU-MIMO Systems

© 2023 IEEE. This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/TVT.2023.3249353.[Abstract]: The combination of multiple-input multiple-output (MIMO) systems and intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both approaches aim to maximize the system sum-rate for every channel realization. The first proposed solution is a novel contextual bandit (CB) framework with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions, and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is appropriately met. Both proposals perform remarkably better than state-of-the-art heuristic methods in scenarios with high multi-user interference.This work has been supported by grants ED431C 2020/15 and ED431G 2019/01 (to support the Centro de Investigación de Galicia “CITIC”) funded by Xunta de Galicia and ERDF Galicia 2014-2020; and by grants PID2019-104958RB-C42 (ADELE) and BES-2017-081955 funded by MCIN/AEI/10.13039/501100011033.Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G 2019/0

Eavesdropping and Jamming via Pilot Attacks in 5G Massive MIMO

Cursos e Congresos, C-155[Abstract] In thiswork, we investigate pilot attacks for 5G single-cell multi-user massive multipleinput multiple-output (MaMIMO) systems with a single-antenna active eavesdropper and a single-antenna jammer operating in time-division duplex (TDD) schemes. Firstly, we describe the attacks when the base station (BS) estimates the channel state information (CSI) based on the uplink pilot transmissions. Finally, we propose a reinforcement learning (RL)-based framework for maximizing the system sum rate that proved robust to the eavesdropping and jamming attacksCITIC is funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS)

Diseño e Implementación de Algoritmos de Sincronismo para DTMB

In this paper, two frame synchronization schemes and one frequency synchronization scheme are proposed for TDS-OFDM, technique that DTMB uses. The first frame synchronization model is based on the value of the autocorrelation of the received signal. The second uses the result of the correlation of this signal with locally generated PN. This way, two solutions for the same problem are presented, each one with its own singularities on implementation method and performance characteristics. The fine frequency synchronization model uses the alternating PN autocorrelation algorithm. It enjoys the maximum length of identical PN between non-consecutive guard intervals. The tests are realized over different channel models with AWGN and multi-path fadings or Doppler Effect fadings. The validation of the schemes is gotten through the simulation results in MatLab/Simulink models and its comparison with the scientific literature. A brief introduction to DTMB standard is realized and a special attention is given to synchronization stages, signal frames structure and the use of PN sequences as frame headerEn este trabajo, se proponen las implementaciones de dos modelos completos de sincronismo de trama y un esquema de sincronismo de frecuencia para TDS-OFDM, técnica que emplea la norma DTMB. El primero de los esquemas de sincronismo de trama se basa en la autocorrelación de la propia señal recibida, mientras que el segundo está basado en la correlación de esta señal con una PN generada localmente. De esta forma, se presentan dos soluciones diferentes al mismo problema, con respectivas singularidades en cuanto a métodos de implementación y características de desempeño. El método de sincronismo fino de frecuencia presentado, emplea el algoritmo de autocorrelación de PN alternantes, el cual aprovecha la característica de máxima similitud existente entre ellas. Las comprobaciones se realizan sobre diferentes modelos de canal con presencia de AWGN y atenuaciones por multitrayecto o Efecto Doppler, lo cual evidencia el carácter práctico de estas implementaciones. Se consigue la validación de los esquemas anteriores a través de los resultados obtenidos en simulaciones en la plataforma MatLab/ Simulink y su comparación con la literatura científica. Se realiza una breve presentación al estándar DTMB, prestando especial atención a las etapas de sincronismo, la estructura de su trama de señal y el empleo de las secuencias PN como cabeceras de tram

Análisis del comportamiento de la ganancia de SFN para DTMB

En los últimos años se ha venido desplegando en Cuba el servicio de Televisión Digital Terrestre (TDT) de acuerdo con el estándar DTMB en su esquema para redes de múltiples frecuencias (MFN). Sin embargo, como parte de la evolución de esta tecnología, algunos países han migrado hacia el despliegue de redes de una sola frecuencia (SFN), pues este esquema provee un uso más eficiente del espectro radioeléctrico. Estudios sobre SFN muestran que es posible con este esquema conseguir una distribución más homogénea de la calidad de la señal recibida y, además, las señales provenientes de transmisores diferentes pueden ser combinadas de forma constructiva para obtener una ganancia en la recepción. No obstante, algunos autores consideran que un aumento de la intensidad total de la señal recibida, no siempre se corresponde con una mejor recepción. Es por esto que se han considerado parámetros propios de la recepción como: relación señal a ruido (SNR) y razón de modulación errónea (MER), en lugar de la intensidad de la señal recibida para evaluar la ganancia. En este artículo se presenta un análisis, a partir de los resultados de mediciones de laboratorio, que permite caracterizar la ganancia de SFN (SFNG) para DTMB, considerando el parámetro MER como medida de la calidad de la señal recibida. Además, se presentan los resultados obtenidos de evaluar la capacidad de recepción de un receptor comercial en SFN con presencia de multitrayectos con valores de retardo cercanos a la duración del intervalo de guarda