Andres Ortega

This paper presents a novel and computationally efficient approach for dimensionality reduction in channel matrices in gigantic MIMO (GMIMO) systems, designed to address the extreme complexity of large-scale antenna arrays in future 6 G wireless networks. A high-fidelity unreal engine-based deterministic ray-tracing simulator is used to generate synthetic channel matrices under both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions in realistic indoor and outdoor environments. The proposed method integrates principal component analysis (PCA) with k-means++ clustering to compress channel matrices by capturing dominant spatial features and grouping antenna elements with similar propagation characteristics. These hybrid techniques enable high compression ratios while preserving critical channel properties. To evaluate its effectiveness, the method is benchmarked against a baseline approach that combines singular value decomposition (SVD) with the same k-means++ clustering framework. The results demonstrate that the PCA-based method consistently outperforms SVD-based reduction in complex NLoS scenarios, achieving a matrix size reduction of up to 95% with significantly better retention of spatial and phase fidelity. In LoS environments, both methods perform comparably in terms of compression, although PCA maintains slightly higher accuracy under stronger dimensionality constraints. A clear trade-off is observed between achievable MIMO capacity and compression efficiency, whereby higher retained channel energy leads to increased capacity but requires a larger number of spatial components to be preserved. Additionally, the impact of the clustering factor on error convergence and spatial fidelity has been analysed, as determined by the number of clusters generated by the k-means algorithm. This dimensionality reduction strategy offers a scalable, interpretable, and lightweight solution for high-resolution 6G simulation platforms and Digital Twin environments, enabling real-time processing and efficient system-level evaluations of ultra-dense antenna systems.

This paper analyzes the performance of a generalized frequency division multiplexing scheme for multiple-input multiple-output configuration (MIMO-GFDM) in the presence of nonlinear power amplification with pilot insertion at the transmitter side to evaluate the saturation effects. For this purpose, two non-ideal transfer functions of high power amplifiers (HPA) are modeled: a Traveling Wave Tube Amplifier (TWTA) and a Solid State Power Amplifier (SSPA). In addition, for nonlinear distortion cancellation, we explore some peak-to-average power ratio (PAPR) reduction techniques, such as selective mapping (SLM), clipping, and clipping and filtering (C&F), to evaluate the best response and adaptability for the MIMO-GFDM scheme. The results show that an optimal power transmitter in the amplifier saturation region with large-scale input back-off (IBO) can mitigate out-of-band (OOB) radiation effects and consequently reduce the error floor in terms of bit error rate (BER) caused by amplifier saturation effects. In addition, we can control some trade-offs between PAPR, OOB emissions, BER performance, and power when the large-scale IBO reaches 4 dB. Finally, multicast effects in the Rayleigh channel can further saturate the amplifier when the transmit power increases, causing a proportional increase of the background noise with the channel variance. Thus, we can determine the maximum transmit power to avoid distortion and error-floor noise in poor channel conditions.

The deployment of heterogeneous networks (HetNets) is a way to increase the network capacity and release part of the traffic generated by users inside a cell to small-scale wireless networks for service. In this context, the main problem is managing the interference due to the coexistence of small cells and macro cells. In this paper, a QoS-aware Resource Allocation (RA) algorithm jointly working with admission control (AC) over a two-tier HetNet scenario is investigated in the presence of both the pilot-symbols for channel estimation and the channel estimation error. The RA algorithm allows two users, the macro cell user (CU) and small cell user (SU), to simultaneously share the same resource block. Moreover, system performance and fairness are improved by including adaptive power allocation to users over resource blocks. In the framework of RA with proportional rate constraints, a novel algorithm is designed by including the effects of pilot-aided channel estimation. The algorithm is able to distribute the same proportional rate to all CUs and SUs, even in the presence of channel estimation error. Relevant numerical results for the downlink of a two-tier HetNet with pilot-aided channel estimation show that the rate dispersion is driven to zero while the sum-rate is maximized, and the average user rate penalty with respect to a perfect-CSI scenario may rise to 20%.

Este artículo describe la implementación de la técnica basada en multiplexación espacial D–BLAST sobre equipos de radio definido por software (SDR) específicamente usando USRP Ettus Research x310; con el objetivo de afrontar el problema de la diversidad espacial que posee el esquema de MIMO Alamouti, al no poder incrementar el número de antenas del transmisor respecto al del receptor. El escenario de simulación fue en un ambiente indoor usando las herramientas de programación gráfica con el software Labview Communications, logrando un diseño más robusto de codificación basado en la no linealidad de ecuaciones matriciales, mitigando, de este modo, a través de la redundancia de información los efectos de la interferencia que genera el incremento propio de las antenas en el transmisor. Los resultados experimentales evaluados fueron la tasa de error de bit (BER) y la tasa de error de símbolo (SER) para determinar la efectividad de la diversidad espacial. La ganancia lograda fue alrededor de 10 dB y 7 dB en MIMO 2×2 y MIMO 3×2 respectivamente, usando la técnica D–BLAST simétrica.

Citizens are expected to require the growth of multiple Internet of Things (IoT) -based applications to improve public and private services. According to their concept, smart cities seek to improve the efficiency, reliability, and resilience of these services. Consequently, this paper searches for a new vision for resolving problems related to the quick deployment of a wireless sensor network (WSN) by using a sizing model and considering the capacity and coverage of the concentrators. Additionally, three different routing models of these technology resources are presented as alternatives for each WSN deployment to ensure connectivity between smart meters and hubs required for smart metering. On the other hand, these solutions must reduce costs when this type of wireless communication network is deployed. The present work proposes various optimization models that consider the physical and network layers in order to integrate different wireless communication technologies, thus reducing costs in terms of the minimum number of data aggregation points. Using a heterogeneous wireless network can reduce resource costs and energy consumption in comparison to a single cellular technology, as proposed in previous works. This work proposes a sizing model and three different models for routing wireless networks. In each case, constraints are evaluated and can be associated with different real-world scenarios. This document provides an optimization model that encompasses all of the proposed constraints; due to the combinatorial nature of the problem, this would require a heuristic technique.

Este trabajo analiza métodos y algoritmos de predicción del comportamiento de consumo eléctrico basados en redes neuronales, usando datos obtenidos de la infraestructura de medición avanzada (AMI) de una institución educativa. También, se ha realizado un contraste entre el uso de redes neuronales convencionales (ANN), redes neuronales basadas en wavelets (WNN) y los polinomios potenciales de grado uno (P1P). Se analiza la correlación de cada método de predicción, así como el comportamiento del error cuadrático medio (MSE) para finalmente establecer si existe un desbalance en el coste computacional a través del análisis de Big-O y el tiempo de ejecución. Los resultados cuantitativos del error MSE están por debajo del 0,05% para predicciones con ANN y usan un alto costo computacional. Para P1P se presentan errores alrededor del 1,2% mostrando como método de predicción de bajo consumo computacional pero aplicable de forma principal para un análisis a corto plazo. Este trabajo se da en respuesta a la necesidad de establecer una plataforma que permita aprovechar la estructura de medición inteligente, a través de la predicción de perfil de consumo eléctrico con el objetivo de elaborar una planificación de mantenimiento y gestión de la demanda eléctrica para reducir costos de operación desde el consumidor final hasta el gestor de la distribución de energía eléctrica. Para el análisis de las proyecciones sobre el perfil de carga eléctrica se consideran las características estadísticas del consumo para seleccionar los algoritmos de predicción según la cantidad de días a proyectar, usando los datos de cualquiera de los medidores inteligentes, que pueden ser monitoreados en una red eléctrica orientada a las Smart Grids.

Robust Multidimensional Optical Modulation Based on Hybrid Subcarrier/Amplitude/Phase/Dual Polarization for Wavelength-Division Multiplexing Systems

The deployment of heterogeneous networks (Het-Nets) is a way to increase the capacity of future mobile networks. However, the use of pilot symbols for channel estimation, which is needed to perform demodulation at the receiver, reduces spectral and power efficiency, and the presence of channel estimation error degrades the capacity of each link. In this paper we study the effects of pilot-aided channel estimation on the capacity of a two-tier HetNet with QoS-aware centralized admission control (AC) and radio resource allocation (RRA) algorithms that control the effects of cross-interference and optimize a service provider revenue. With this aim, we design and implement an AC algorithm that takes into account link-rate degradation due to channel estimation error and pilot-symbols overhead. The results for both uplink and downlink show that the impact of channel estimation error is not negligible and is especially relevant for downlink cellular users. However, the results also show that this degradation can be partially mitigated by allocating more power over pilot symbols.