Tootfinder

Intelligent Reflecting Surface Aided AirComp: Multi-Timescale Design and Performance Analysis
Guangji Chen, Jun Li, Qingqing Wu, Meng Hua, Kaitao Meng, Zhonghao Lyu
https://arxiv.org/abs/2405.05549

Intelligent Reflecting Surface Aided AirComp: Multi-Timescale Design and Performance Analysis
The integration of intelligent reflecting surface (IRS) into over-the-air computation (AirComp) is an effective solution for reducing the computational mean squared error (MSE) via its high passive beamforming gain. Prior works on IRS aided AirComp generally rely on the full instantaneous channel state information (I-CSI), which is not applicable to large-scale systems due to its heavy signalling overhead. To address this issue, we propose a novel multi-timescale transmission protocol. In parti…

An Efficient Finite Difference Approximation via a Double Sample-Recycling Approach
Guo Liang, Guangwu Liu, Kun Zhang
https://arxiv.org/abs/2405.05638 http…

An Efficient Finite Difference Approximation via a Double Sample-Recycling Approach
Estimating stochastic gradients is pivotal in fields like service systems within operations research. The classical method for this estimation is the finite difference approximation, which entails generating samples at perturbed inputs. Nonetheless, practical challenges persist in determining the perturbation and obtaining an optimal finite difference estimator in the sense of possessing the smallest mean squared error (MSE). To tackle this problem, we propose a double sample-recycling approach…

This https://arxiv.org/abs/2402.10043 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

How to validate average calibration for machine learning regression tasks ?
Average calibration of the uncertainties of machine learning regression tasks can be tested in two ways. One way is to estimate the calibration error (CE) as the difference between the mean absolute error (MSE) and the mean variance (MV) or mean squared uncertainty. The alternative is to compare the mean squared z-scores or scaled errors (ZMS) to 1. Both approaches might lead to different conclusion, as illustrated on an ensemble of datasets from the recent machine learning uncertainty quantifi…

This https://arxiv.org/abs/2110.01928 has been replaced.
link: https://scholar.google.com/scholar?q=a

Time Encoding Quantization of Bandlimited and Finite-Rate-of-Innovation Signals
This paper studies the impact of quantization in integrate-and-fire time encoding machine (IF-TEM) sampler used for bandlimited (BL) and finite-rate-of-innovation (FRI) signals. An upper bound is derived for the mean squared error (MSE) of IF-TEM sampler and is compared against that of classical analog-to-digital converters (ADCs) with uniform sampling and quantization. The interplay between a signal's energy, bandwidth, and peak amplitude is used to identify how the MSE of IF-TEM sampler with …

This https://arxiv.org/abs/2402.10043 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Negative impact of heavy-tailed uncertainty and error distributions on the reliability of calibration statistics for machine learning regression tasks
Average calibration of the prediction uncertainties of machine learning regression tasks can be tested in two ways: one is to estimate the calibration error (CE) as the difference between the mean absolute error (MSE) and the mean variance (MV) or mean squared uncertainty; the alternative is to compare the mean squared z-scores (ZMS) or scaled errors to 1. The problem is that both approaches might lead to different conclusions, as illustrated in this study for an ensemble of datasets from the r…

Optimal Pilot Design for OTFS in Linear Time-Varying Channels
Ids van der Werf, Richard Heusdens, Richard C. Hendriks, Geert Leus
https://arxiv.org/abs/2403.19379

Optimal Pilot Design for OTFS in Linear Time-Varying Channels
This paper investigates the positioning of the pilot symbols, as well as the power distribution between the pilot and the communication symbols in the OTFS modulation scheme. We analyze the pilot placements that minimize the mean squared error (MSE) in estimating the channel taps. In addition, we optimize the average channel capacity by adjusting the power balance. We show that this leads to a significant increase in average capacity. The results provide valuable guidance for designing the OTFS…

This https://arxiv.org/abs/2402.10043 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Negative impact of heavy-tailed uncertainty and error distributions on the reliability of calibration statistics for machine learning regression tasks
Average calibration of the prediction uncertainties of machine learning regression tasks can be tested in two ways: one is to estimate the calibration error (CE) as the difference between the mean absolute error (MSE) and the mean variance (MV) or mean squared uncertainty; the alternative is to compare the mean squared z-scores (ZMS) or scaled errors to 1. The problem is that both approaches might lead to different conclusions, as illustrated in this study for an ensemble of datasets from the r…

Joint Training and Reflection Pattern Optimization for Non-Ideal RIS-Aided Multiuser Systems
Zhenyao He, Jindan Xu, Hong Shen, Wei Xu, Chau Yuen, Marco Di Renzo
https://arxiv.org/abs/2403.19955

Joint Training and Reflection Pattern Optimization for Non-Ideal RIS-Aided Multiuser Systems
Reconfigurable intelligent surface (RIS) is a promising technique to improve the performance of future wireless communication systems at low energy consumption. To reap the potential benefits of RIS-aided beamforming, it is vital to enhance the accuracy of channel estimation. In this paper, we consider an RIS-aided multiuser system with non-ideal reflecting elements, each of which has a phase-dependent reflecting amplitude, and we aim to minimize the mean-squared error (MSE) of the channel esti…

How to validate average calibration for machine learning regression tasks ?
Pascal Pernot
https://arxiv.org/abs/2402.10043 https://ar…

How to validate average calibration for machine learning regression tasks ?
Average calibration of the uncertainties of machine learning regression tasks can be tested in two ways. One way is to estimate the calibration error (CE) as the difference between the mean absolute error (MSE) and the mean variance (MV) or mean squared uncertainty. The alternative is to compare the mean squared z-scores or scaled errors (ZMS) to 1. Both approaches might lead to different conclusion, as illustrated on an ensemble of datasets from the recent machine learning uncertainty quantifi…

This https://arxiv.org/abs/2401.16751 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Simultaneous Computation and Communication over MAC
We study communication over a Gaussian multiple-access channel (MAC) with two types of transmitters: Digital transmitters hold a message from a discrete set that needs to be communicated to the receiver with vanishing error probability. Analog transmitters hold sequences of analog values, and some functions of these distributed values (but not the values themselves) need to be conveyed to the receiver, subject to a fidelity criterion such as mean squared error (MSE) or a certain maximum error w…

Optimal Structure of Receive Beamforming for Over-the-Air Computation
Hongbin Zhu, Hua Qian
https://arxiv.org/abs/2404.14036 https://…

Optimal Structure of Receive Beamforming for Over-the-Air Computation
We investigate fast data aggregation via over-the-air computation (AirComp) over wireless networks. In this scenario, an access point (AP) with multiple antennas aims to recover the arithmetic mean of sensory data from multiple wireless devices. To minimize estimation distortion, we formulate a mean-squared-error (MSE) minimization problem that considers joint optimization of transmit scalars at wireless devices, denoising factor, and receive beamforming vector at the AP. We derive closed-form …

The Pairwise Matching Design is Optimal under Extreme Noise and Assignments
David Azriel, Abba M. Krieger, Adam Kapelner
https://arxiv.org/abs/2402.07247 h…

The Pairwise Matching Design is Optimal under Extreme Noise and Assignments
We consider the general performance of the difference-in-means estimator in an equally-allocated two-arm randomized experiment under common experimental endpoints such as continuous (regression), incidence, proportion, count and uncensored survival. We consider two sources of randomness: the subject-specific assignments and the contribution of unobserved subject-specific measurements. We then examine mean squared error (MSE) performance under a new, more realistic "simultaneous tail criterion".…