Linear Combination of Exponential Moving Averages for Wireless Channel
Prediction
- URL: http://arxiv.org/abs/2312.07945v1
- Date: Wed, 13 Dec 2023 07:44:05 GMT
- Title: Linear Combination of Exponential Moving Averages for Wireless Channel
Prediction
- Authors: Gabriele Formis, Stefano Scanzio, Gianluca Cena, Adriano Valenzano
- Abstract summary: In this work, prediction models based on the exponential moving average (EMA) are investigated in depth.
A new model that we called EMA linear combination (ELC) is introduced, explained, and evaluated experimentally.
- Score: 2.34863357088666
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The ability to predict the behavior of a wireless channel in terms of the
frame delivery ratio is quite valuable, and permits, e.g., to optimize the
operating parameters of a wireless network at runtime, or to proactively react
to the degradation of the channel quality, in order to meet the stringent
requirements about dependability and end-to-end latency that typically
characterize industrial applications.
In this work, prediction models based on the exponential moving average (EMA)
are investigated in depth, which are proven to outperform other simple
statistical methods and whose performance is nearly as good as artificial
neural networks, but with dramatically lower computational requirements.
Regarding the innovation and motivation of this work, a new model that we
called EMA linear combination (ELC), is introduced, explained, and evaluated
experimentally.
Its prediction accuracy, tested on some databases acquired from a real setup
based on Wi-Fi devices, showed that ELC brings tangible improvements over EMA
in any experimental conditions, the only drawback being a slight increase in
computational complexity.
Related papers
- Deep Neural Networks Tend To Extrapolate Predictably [51.303814412294514]
neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs.
We observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD.
We show how one can leverage our insights in practice to enable risk-sensitive decision-making in the presence of OOD inputs.
arXiv Detail & Related papers (2023-10-02T03:25:32Z) - Predicting Wireless Channel Quality by means of Moving Averages and
Regression Models [4.626261940793027]
Knowing in advance how much channel behavior may change can speed up procedures for adaptively selecting the best channel.
A simple technique based on a linear combination of outcomes from different techniques was presented and analyzed.
We found that the best model is the exponential moving average, which managed to predict the frame delivery ratio with a 2.10% average error.
arXiv Detail & Related papers (2023-06-14T16:55:24Z) - Learning to Precode for Integrated Sensing and Communications Systems [11.689567114100514]
We present an unsupervised learning neural model to design transmit precoders for ISAC systems.
We show that the proposed method outperforms traditional optimization-based methods in presence of channel estimation errors.
arXiv Detail & Related papers (2023-03-11T11:24:18Z) - Model-based Deep Learning Receiver Design for Rate-Splitting Multiple
Access [65.21117658030235]
This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods.
The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead.
Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
arXiv Detail & Related papers (2022-05-02T12:23:55Z) - Interpretable AI-based Large-scale 3D Pathloss Prediction Model for
enabling Emerging Self-Driving Networks [3.710841042000923]
We propose a Machine Learning-based model that leverages novel key predictors for estimating pathloss.
By quantitatively evaluating the ability of various ML algorithms in terms of predictive, generalization and computational performance, our results show that Light Gradient Boosting Machine (LightGBM) algorithm overall outperforms others.
arXiv Detail & Related papers (2022-01-30T19:50:16Z) - An Adaptive Device-Edge Co-Inference Framework Based on Soft
Actor-Critic [72.35307086274912]
High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices.
We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations.
Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
arXiv Detail & Related papers (2022-01-09T09:31:50Z) - Convolutional generative adversarial imputation networks for
spatio-temporal missing data in storm surge simulations [86.5302150777089]
Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods.
We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
arXiv Detail & Related papers (2021-11-03T03:50:48Z) - Wireless Sensing With Deep Spectrogram Network and Primitive Based
Autoregressive Hybrid Channel Model [20.670058030653458]
Human motion recognition (HMR) based on wireless sensing is a low-cost technique for scene understanding.
Current HMR systems adopt support vector machines (SVMs) and convolutional neural networks (CNNs) to classify radar signals.
This paper proposes a deep spectrogram network (DSN) by leveraging the residual mapping technique to enhance the HMR performance.
arXiv Detail & Related papers (2021-04-21T06:33:01Z) - Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural
Networks [52.32646357164739]
We propose a deep neural network (DNN) to solve the solutions of the optimal power flow (ACOPF)
The proposed SIDNN is compatible with a broad range of OPF schemes.
It can be seamlessly integrated in other learning-to-OPF schemes.
arXiv Detail & Related papers (2021-03-27T00:45:23Z) - DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator
Search [55.164053971213576]
convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead.
Structured (channel) pruning is usually applied to reduce the model redundancy while preserving the network structure.
Existing structured pruning methods require hand-crafted rules which may lead to tremendous pruning space.
arXiv Detail & Related papers (2020-11-04T07:43:01Z) - Industrial Forecasting with Exponentially Smoothed Recurrent Neural
Networks [0.0]
We present a class of exponential smoothed recurrent neural networks (RNNs) which are well suited to modeling non-stationary dynamical systems arising in industrial applications.
Application of exponentially smoothed RNNs to forecasting electricity load, weather data, and stock prices highlight the efficacy of exponential smoothing of the hidden state for multi-step time series forecasting.
arXiv Detail & Related papers (2020-04-09T17:53:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.