arXiv subjects

Marian Temprana Alonso

Publications and source records attributed to Marian Temprana Alonso.

4 recordsLinked to original sources

arXiv2025

Deep Reinforcement Learning for MIMO Communication with Low-Resolution ADCs

Multiple-input multiple-output (MIMO) wireless systems conventionally use high-resolution analog-to-digital converters (ADCs) at the receiver side to faithfully digitize received signals prior to digital signal processing. However, the power consumption of ADCs increases significantly as the bandwidth is increased, particularly in millimeter wave communications systems. A combination of two mitigating approaches has been considered in the literature: i) to use hybrid beamforming to reduce the number of ADCs, and ii) to use low-resolution ADCs to reduce per ADC power consumption. Lowering the number and resolution of the ADCs naturally reduces the communication rate of the system, leading to a tradeoff between ADC power consumption and communication rate. Prior works have shown that optimizing over the hybrid beamforming matrix and ADC thresholds may reduce the aforementioned rate-loss significantly. A key challenge is the complexity of optimization over all choices of beamforming matrices and threshold vectors. This work proposes a reinforcement learning (RL) architecture to perform the optimization. The proposed approach integrates deep neural network-based mutual information estimators for reward calculation with policy gradient methods for reinforcement learning. The approach is robust to dynamic channel statistics and noisy CSI estimates. It is shown theoretically that greedy RL methods converge to the globally optimal policy. Extensive empirical evaluations are provided demonstrating that the performance of the RL-based approach closely matches exhaustive search optimization across the solution space.

cs.IT↗

arXiv2025

Non-Linear Analog Processing in MIMO Systems with Coarse Quantization

Analog to digital converters (ADCs) are a major contributor to the power consumption of multiple-input multiple-output (MIMO) receivers in large bandwidth millimeter-wave systems. Prior works have considered two mitigating solutions to reduce the ADC power consumption: i) decreasing the number of ADCs via analog and hybrid beamforming, and ii) decreasing the ADC resolution, i.e., utilizing one-bit and few-bit ADCs. These mitigating solutions lead to performance loss in terms of achievable rates due to increased quantization error. In this work, the use of nonlinear analog operators such as envelope detectors and polynomial operators, prior to sampling and quantization is considered, as a way to reduce the aforementioned rate-loss. The receiver architecture consists of linear combiners, nonlinear analog operators, and few-bit ADCs. The fundamental performance limits of the resulting communication system, in terms of achievable rates, are investigated under various assumptions on the set of implementable analog operators. Extensive numerical evaluations are provided to evaluate the set of achievable rates and the power consumption of the proposed receiver architectures. Circuit simulations and measurement results, based on both 22 nm FDSOI CMOS technology and 65 nm Bulk CMOS transistor technologies, are provided to justify the power efficiency of the proposed receiver architectures.

cs.IT↗

arXiv2024

Non-Linear Analog Processing Gains in Task-Based Quantization

In task-based quantization, a multivariate analog signal is transformed into a digital signal using a limited number of low-resolution analog-to-digital converters (ADCs). This process aims to minimize a fidelity criterion, which is assessed against an unobserved task variable that is correlated with the analog signal. The scenario models various applications of interest such as channel estimation, medical imaging applications, and object localization. This work explores the integration of analog processing components -- such as analog delay elements, polynomial operators, and envelope detectors -- prior to ADC quantization. Specifically, four scenarios, involving different collections of analog processing operators are considered: (i) arbitrary polynomial operators with analog delay elements, (ii) limited-degree polynomial operators, excluding delay elements, (iii) sequences of envelope detectors, and (iv) a combination of analog delay elements and linear combiners. For each scenario, the minimum achievable distortion is quantified through derivation of computable expressions in various statistical settings. It is shown that analog processing can significantly reduce the distortion in task reconstruction. Numerical simulations in a Gaussian example are provided to give further insights into the aforementioned analog processing gains.

cs.IT↗

arXiv2022

Optimal Fault-Tolerant Data Fusion in Sensor Networks: Fundamental Limits and Efficient Algorithms

Distributed estimation in the context of sensor networks is considered, where distributed agents are given a set of sensor measurements, and are tasked with estimating a target variable. A subset of sensors are assumed to be faulty. The objective is to minimize i) the mean square estimation error at each node (accuracy objective), and ii) the mean square distance between the estimates at each pair of nodes (consensus objective). It is shown that there is an inherent tradeoff between the former and latter objectives. Assuming a general stochastic model, the sensor fusion algorithm optimizing this tradeoff is characterized through a computable optimization problem, and a Cramer-Rao type lower bound for the achievable accuracy-consensus loss is obtained. Finding the optimal sensor fusion algorithm is computationally complex. To address this, a general class of low-complexity Brooks-Iyengar Algorithms are introduced, and their performance, in terms of accuracy and consensus objectives, is compared to that of optimal linear estimators through case study simulations of various scenarios.

eess.SP↗