Multi-Step-Ahead Spectrum Prediction for Cognitive Radio in Fading Scenarios

Elias, Felipe Gabriel de Mello; Fernández, Evelio; Reguera, Vitalio Alfonso

doi:10.1590/2179-10742020v19i41069

Cited by 5 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All those works aim to predict the occupancy status for the immediately next time slot, meaning the forecast of a single‐slot transmission opportunity. Predictions of multiple idle slots have been explored in 21,23,24 . Multiple idle slots' predictions using HMM have been explored by 23,24 .…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Predicting consecutive spectrum opportunities with hidden Markov models

Bezerra

Bordim

2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary The increasing demands for wireless channels to accommodate the surge of internet of things devices and the associated services exacerbated the need for flexible channel allocation strategies. Opportunistic spectrum sharing is expected to provide a more reasonable use of the limited radio frequencies by allowing the coexistence of licensed users and unlicensed users in the same frequency. This arrangement is called opportunistic channel allocation, where unlicensed users explore the channel when the licensed user is not transmitting. The challenge in opportunistic spectrum allocation is to find transmission opportunities. Statistical and machine learning techniques have been used to forecast spectrum opportunities on time slotted channels based on acquired transmission data from licensed users. However, opportunity forecast is usually limited to one slot ahead of time. This work explores the use of Hidden Markov Model (HMM) training and predicting procedures to forecast a consecutive sequence of idle slots that can be explored for opportunistic transmission. To improve prediction accuracy and speedup the training process, the proposed training procedures reduce the classification alphabet, which is achieved by balancing the number of training sequences to limit the influence of outliers and provide opportunity forecast even when the training process is executed over a limited number of observed sequences. While an aggressive predictor may explore more opportunities as compared to a conservative predictor, the former may incur in higher collision rates while the latter may explore fewer opportunities. Hence, this work proposes a metric to assist in the process of selecting the best predictor's parameters to balance collision and opportunity rates. The proposed HMM predictors are evaluated on both synthetic and real traffic traces. The results on synthetic traffic shows that, for channel load from 30%$$ 30\% $$ up to 70%$$ 70\% $$, the proposed predictors identified over 80%$$ 80\% $$ of channel opportunities with the collision rates below 10%$$ 10\% $$. On real traffic traces, where the licensed users' transmissions have deterministic inter‐packet delays, the HMM predictors identified over 95%$$ 95\% $$ of the channel opportunities with collision rates below 1%$$ 1\% $$.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Predictions of multiple idle slots have been explored in. 21,23,24 Multiple idle slots' predictions using HMM have been explored by. 23,24 Compared to more elaborated machine learning strategies, the HMMs provide reasonable results with reduced training costs.…”

Section: Related Workmentioning

confidence: 99%

Predicting consecutive spectrum opportunities with hidden Markov models

Bezerra

Bordim

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…A time series [25] is used to study the PU channel state (i.e., "idle" or "occupied"). Then Hidden Markov Model (HMM) model is used to capture PU channel status [26,27].…”

Section: Introductionmentioning

confidence: 99%

Spectral Vacancy Prediction Using Time Series Forecasting for Cognitive Radio Applications

Mathai¹,

Indumathi²

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings.From the simulation, it can be inferred that Weibull fading outperforms all the other fading models considered. To accomplish missed detection probability of 1% in the Rayleigh channel, the false alarm probability obtained is almost 0.8 however to obtain the same missed detection probability in the Weibull channel, false alarm probability is less than 0.1 which is very favorable for both indoor and outdoor scenarios. Numerical analyses are carried out here to predict Primary User (PU) channel condition using Hidden Markov Model with the help of Time series forecasting learning method. It is evident that the prediction performance has reached 100% as the result of using the Weibull Fading Model for a period of 200 ms when compared to the Rayleigh model which is achieving only 84.5% accuracy in prediction.

show abstract