The Experts below are selected from a list of 20280 Experts worldwide ranked by ideXlab platform
Kenta Umebayashi - One of the best experts on this subject based on the ideXlab platform.
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time and frequency varying noise floor estimation for Spectrum Usage measurement
Wireless Communications and Networking Conference, 2019Co-Authors: Hiroki Iwata, Kenta Umebayashi, Miguel Lopezbenitez, Ahmed Altahmeesschi, Janne LehtomakiAbstract:We investigate noise floor (NF) estimation for FFT-ED (Energy Detection based on FFT)-based Spectrum Usage measurement in the context of smart Spectrum access (SSA), in which Spectrum Usage information of primary users (PUs), such as channel occupancy rate (COR), will be exploited by secondary users (SUs). In FFT-ED, the NF has to be estimated to set a decision threshold for ED appropriately. In general, the NF is frequency-dependent and its level changes with time leading to the need of estimating the NF regularly while performing the Spectrum Usage measurement. In this paper, we propose an NF estimation method which exploits prior information regarding the shape of NF and forward consecutive mean excision (FCME) algorithm. Numerical and experimental evaluations show the proposed method enables an accurate NF estimation considering the time and frequency dependencies of the NF. Moreover, we show the proposed method can obtain the almost desired detection performance, but can not the comparative method (the original FCME method).
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WCNC Workshops - Time and Frequency Varying Noise Floor Estimation for Spectrum Usage Measurement
2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW), 2019Co-Authors: Hiroki Iwata, Kenta Umebayashi, Miguel Lopez-benitez, Ahmed Al-tahmeesschi, Janne LehtomakiAbstract:We investigate noise floor (NF) estimation for FFT-ED (Energy Detection based on FFT)-based Spectrum Usage measurement in the context of smart Spectrum access (SSA), in which Spectrum Usage information of primary users (PUs), such as channel occupancy rate (COR), will be exploited by secondary users (SUs). In FFT-ED, the NF has to be estimated to set a decision threshold for ED appropriately. In general, the NF is frequency-dependent and its level changes with time leading to the need of estimating the NF regularly while performing the Spectrum Usage measurement. In this paper, we propose an NF estimation method which exploits prior information regarding the shape of NF and forward consecutive mean excision (FCME) algorithm. Numerical and experimental evaluations show the proposed method enables an accurate NF estimation considering the time and frequency dependencies of the NF. Moreover, we show the proposed method can obtain the almost desired detection performance, but can not the comparative method (the original FCME method).
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Design of Spectrum Usage Detection in Wideband Spectrum Measurements
IEEE Access, 2019Co-Authors: Kenta Umebayashi, Miguel Lopez-benitez, Yoshitaka Tamaki, Janne J. LehtomakiAbstract:We investigate the design of signal processing in wideband Spectrum Usage (SPU) measurements for efficient and smart dynamic Spectrum access (DSA). In particular, we focus on Spectrum Usage detection (SPUD) in the experimental measurements. The detection results can be exploited to estimate statistics of the SPU. An appropriate design of the SPUD depends on the actual SPU in the target frequency band. There is a broad range of wireless systems in a considered broad measurement frequency band, such as from 60MHz to 6GHz, therefore a general design framework in the measurement frequency band is desired. In the proposed design framework, we at first define two models in terms of the SPU and the SPUD process. In addition, the proposed design procedure determines the adequate choice of parameters for the SPUD model based on given parameters of the SPU model in the target frequency band. Numerical evaluation based on computer simulations shows the validity of the design framework and design procedure. Moreover, a modified duty cycle (DC) estimation method is proposed, which can remove bias errors caused by low time resolution in the SPUD. Numerical evaluation based on experimental measurements demonstrates the practicality of the detection framework and procedure proposed in this work.
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Efficient Time Domain Deterministic-Stochastic Model of Spectrum Usage
IEEE Transactions on Wireless Communications, 2018Co-Authors: Kenta Umebayashi, Masanao Kobayashi, Miguel Lopez-benitezAbstract:For achieving an efficient Spectrum sharing in a context of dynamic Spectrum access, understanding the Spectrum Usage by licensed users [primary users (PUs)], is important for secondary users (SUs). Duty cycle (DC) has been used to express the deterministic and stochastic aspects of Spectrum Usage. Specifically, a deterministic model for the mean of the duty cycle (M-DC) has been proposed in a previous work. The deterministic aspect of M-DC is affected by social behavior, and common habits of users, which can be confirmed in cellular systems. On the other hand, the observed DC (O-DC) during short time duration has randomness and a stochastic model is more suitable, e.g. distribution of O-DC. In this paper, we extend the conventional approach, in which only either the deterministic or stochastic aspect is considered, to a combined deterministic-stochastic (DS) model, which represents both the deterministic and stochastic aspects at once. For the distribution of the O-DC, the beta distribution has been used as stochastic model, but we employ a mixture of beta distributions. The mixture-beta distribution can achieve higher accuracy but requires more capacity for data storage in Spectrum Usage measurements since it has a higher number of parameters than the beta distribution. For this issue, we employ regression analysis in DS-model since this approach can reduce the number of parameters while retaining the accuracy. We show the validity of DS-model based on exhaustive Spectrum measurements in IEEE 802.11-based wireless local area networks and long-term evolution uplink.
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WCNC - A study on time domain deterministic-stochastic model of Spectrum Usage in WLAN
2018 IEEE Wireless Communications and Networking Conference (WCNC), 2018Co-Authors: Kenta Umebayashi, Miguel Lopez-benitezAbstract:Duty cycle (DC) has been used to express the deterministic and stochastic aspects of Spectrum Usage. Specifically, a deterministic model for the mean of the duty cycle (MDC) has been proposed in a previous work. On the other hand, the observed DC (O-DC) during short time duration has randomness and a stochastic model is used to express the randomness. In this paper, we extend the conventional approach to a combined deterministic-stochastic (DS) model which represents both the deterministic and stochastic aspects. For the distribution of the O-DC, the beta distribution has been used as stochastic model, but we employ a mixture of beta distributions. The mixture-beta distribution can achieve higher accuracy but requires more capacity for data storage in Spectrum Usage measurements since it has a higher number of parameters than the beta distribution. For this issue, we employ regression analysis in DS-model to reduce the number of parameters while retaining the accuracy. We show the validity of DS-model based on exhaustive Spectrum measurements in IEEE 802.11-based wireless local area networks.
Miguel Lopez-benitez - One of the best experts on this subject based on the ideXlab platform.
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WCNC Workshops - Time and Frequency Varying Noise Floor Estimation for Spectrum Usage Measurement
2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW), 2019Co-Authors: Hiroki Iwata, Kenta Umebayashi, Miguel Lopez-benitez, Ahmed Al-tahmeesschi, Janne LehtomakiAbstract:We investigate noise floor (NF) estimation for FFT-ED (Energy Detection based on FFT)-based Spectrum Usage measurement in the context of smart Spectrum access (SSA), in which Spectrum Usage information of primary users (PUs), such as channel occupancy rate (COR), will be exploited by secondary users (SUs). In FFT-ED, the NF has to be estimated to set a decision threshold for ED appropriately. In general, the NF is frequency-dependent and its level changes with time leading to the need of estimating the NF regularly while performing the Spectrum Usage measurement. In this paper, we propose an NF estimation method which exploits prior information regarding the shape of NF and forward consecutive mean excision (FCME) algorithm. Numerical and experimental evaluations show the proposed method enables an accurate NF estimation considering the time and frequency dependencies of the NF. Moreover, we show the proposed method can obtain the almost desired detection performance, but can not the comparative method (the original FCME method).
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Design of Spectrum Usage Detection in Wideband Spectrum Measurements
IEEE Access, 2019Co-Authors: Kenta Umebayashi, Miguel Lopez-benitez, Yoshitaka Tamaki, Janne J. LehtomakiAbstract:We investigate the design of signal processing in wideband Spectrum Usage (SPU) measurements for efficient and smart dynamic Spectrum access (DSA). In particular, we focus on Spectrum Usage detection (SPUD) in the experimental measurements. The detection results can be exploited to estimate statistics of the SPU. An appropriate design of the SPUD depends on the actual SPU in the target frequency band. There is a broad range of wireless systems in a considered broad measurement frequency band, such as from 60MHz to 6GHz, therefore a general design framework in the measurement frequency band is desired. In the proposed design framework, we at first define two models in terms of the SPU and the SPUD process. In addition, the proposed design procedure determines the adequate choice of parameters for the SPUD model based on given parameters of the SPU model in the target frequency band. Numerical evaluation based on computer simulations shows the validity of the design framework and design procedure. Moreover, a modified duty cycle (DC) estimation method is proposed, which can remove bias errors caused by low time resolution in the SPUD. Numerical evaluation based on experimental measurements demonstrates the practicality of the detection framework and procedure proposed in this work.
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Efficient Time Domain Deterministic-Stochastic Model of Spectrum Usage
IEEE Transactions on Wireless Communications, 2018Co-Authors: Kenta Umebayashi, Masanao Kobayashi, Miguel Lopez-benitezAbstract:For achieving an efficient Spectrum sharing in a context of dynamic Spectrum access, understanding the Spectrum Usage by licensed users [primary users (PUs)], is important for secondary users (SUs). Duty cycle (DC) has been used to express the deterministic and stochastic aspects of Spectrum Usage. Specifically, a deterministic model for the mean of the duty cycle (M-DC) has been proposed in a previous work. The deterministic aspect of M-DC is affected by social behavior, and common habits of users, which can be confirmed in cellular systems. On the other hand, the observed DC (O-DC) during short time duration has randomness and a stochastic model is more suitable, e.g. distribution of O-DC. In this paper, we extend the conventional approach, in which only either the deterministic or stochastic aspect is considered, to a combined deterministic-stochastic (DS) model, which represents both the deterministic and stochastic aspects at once. For the distribution of the O-DC, the beta distribution has been used as stochastic model, but we employ a mixture of beta distributions. The mixture-beta distribution can achieve higher accuracy but requires more capacity for data storage in Spectrum Usage measurements since it has a higher number of parameters than the beta distribution. For this issue, we employ regression analysis in DS-model since this approach can reduce the number of parameters while retaining the accuracy. We show the validity of DS-model based on exhaustive Spectrum measurements in IEEE 802.11-based wireless local area networks and long-term evolution uplink.
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WCNC - A study on time domain deterministic-stochastic model of Spectrum Usage in WLAN
2018 IEEE Wireless Communications and Networking Conference (WCNC), 2018Co-Authors: Kenta Umebayashi, Miguel Lopez-benitezAbstract:Duty cycle (DC) has been used to express the deterministic and stochastic aspects of Spectrum Usage. Specifically, a deterministic model for the mean of the duty cycle (MDC) has been proposed in a previous work. On the other hand, the observed DC (O-DC) during short time duration has randomness and a stochastic model is used to express the randomness. In this paper, we extend the conventional approach to a combined deterministic-stochastic (DS) model which represents both the deterministic and stochastic aspects. For the distribution of the O-DC, the beta distribution has been used as stochastic model, but we employ a mixture of beta distributions. The mixture-beta distribution can achieve higher accuracy but requires more capacity for data storage in Spectrum Usage measurements since it has a higher number of parameters than the beta distribution. For this issue, we employ regression analysis in DS-model to reduce the number of parameters while retaining the accuracy. We show the validity of DS-model based on exhaustive Spectrum measurements in IEEE 802.11-based wireless local area networks.
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Space-Dimension Models of Spectrum Usage for Cognitive Radio Networks
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Miguel Lopez-benitez, Fernando CasadevallAbstract:The dynamic Spectrum access (DSA) principle, relying on the cognitive radio (CR) paradigm, allows users to access Spectrum over time intervals or spatial areas where it remains unused. Due to the opportunistic nature of DSA/CR, the behavior and performance of DSA/CR networks depends on the perceived Spectrum Usage pattern. An accurate modeling of Spectrum occupancy therefore becomes essential in the context of DSA/CR. In this context, this paper addresses the problem of accurately modeling the Spectrum occupancy pattern perceived by DSA/CR users in the spatial domain. A novel spatial modeling approach is introduced to enable a simple yet practical and accurate characterization of Spectrum. First, a set of models is proposed to characterize and predict the average level of occupancy perceived by DSA/CR users at various locations based on the knowledge of some simple signal parameters. An extension is then proposed to characterize not only the average occupancy level but the instantaneous channel state perceived simultaneously by DSA/CR users observing the same transmitter from different locations as well. The validity and accuracy of the theoretical models are demonstrated with results from an extensive Spectrum measurement campaign. Some illustrative examples of their potential applicability are presented and discussed as well.
Janne Lehtomaki - One of the best experts on this subject based on the ideXlab platform.
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time and frequency varying noise floor estimation for Spectrum Usage measurement
Wireless Communications and Networking Conference, 2019Co-Authors: Hiroki Iwata, Kenta Umebayashi, Miguel Lopezbenitez, Ahmed Altahmeesschi, Janne LehtomakiAbstract:We investigate noise floor (NF) estimation for FFT-ED (Energy Detection based on FFT)-based Spectrum Usage measurement in the context of smart Spectrum access (SSA), in which Spectrum Usage information of primary users (PUs), such as channel occupancy rate (COR), will be exploited by secondary users (SUs). In FFT-ED, the NF has to be estimated to set a decision threshold for ED appropriately. In general, the NF is frequency-dependent and its level changes with time leading to the need of estimating the NF regularly while performing the Spectrum Usage measurement. In this paper, we propose an NF estimation method which exploits prior information regarding the shape of NF and forward consecutive mean excision (FCME) algorithm. Numerical and experimental evaluations show the proposed method enables an accurate NF estimation considering the time and frequency dependencies of the NF. Moreover, we show the proposed method can obtain the almost desired detection performance, but can not the comparative method (the original FCME method).
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WCNC Workshops - Time and Frequency Varying Noise Floor Estimation for Spectrum Usage Measurement
2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW), 2019Co-Authors: Hiroki Iwata, Kenta Umebayashi, Miguel Lopez-benitez, Ahmed Al-tahmeesschi, Janne LehtomakiAbstract:We investigate noise floor (NF) estimation for FFT-ED (Energy Detection based on FFT)-based Spectrum Usage measurement in the context of smart Spectrum access (SSA), in which Spectrum Usage information of primary users (PUs), such as channel occupancy rate (COR), will be exploited by secondary users (SUs). In FFT-ED, the NF has to be estimated to set a decision threshold for ED appropriately. In general, the NF is frequency-dependent and its level changes with time leading to the need of estimating the NF regularly while performing the Spectrum Usage measurement. In this paper, we propose an NF estimation method which exploits prior information regarding the shape of NF and forward consecutive mean excision (FCME) algorithm. Numerical and experimental evaluations show the proposed method enables an accurate NF estimation considering the time and frequency dependencies of the NF. Moreover, we show the proposed method can obtain the almost desired detection performance, but can not the comparative method (the original FCME method).
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a study on false alarm cancellation for Spectrum Usage measurements
Wireless Communications and Networking Conference, 2017Co-Authors: Riki Mizuchi, Kenta Umebayashi, Janne Lehtomaki, Miguel LopezbenitezAbstract:Two-layer smart Spectrum access (SSA) consists of Spectrum sharing based on dynamic Spectrum access (DSA: first layer) and Spectrum awareness system (SAS: second layer). A main role of SAS is providing useful statistical information in terms of Spectrum Usage by long term, wide-band and wide-area measurements. In this paper, we focus on signal area (SA) estimation which is a core signal processing in SAS to understand the Spectrum Usage. Specifically, SA estimation is used as post processing for energy detector outputs. It has been shown that Simple-SA (S-SA) estimation can enhance the Spectrum Usage measurement performance, but it inherently increases false alarms. For this issue, efficient false alarm cancellation technique, L-shaped false alarm cancellation (L-FC), is proposed in this paper. Numerical evaluations show that the proposed method can achieve proper detection performance while the computational cost is small compared to other methods.
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WCNC Workshops - A Study on False Alarm Cancellation for Spectrum Usage Measurements
2017 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2017Co-Authors: Riki Mizuchi, Kenta Umebayashi, Janne Lehtomaki, Miguel Lopez-benitezAbstract:Two-layer smart Spectrum access (SSA) consists of Spectrum sharing based on dynamic Spectrum access (DSA: first layer) and Spectrum awareness system (SAS: second layer). A main role of SAS is providing useful statistical information in terms of Spectrum Usage by long term, wide-band and wide-area measurements. In this paper, we focus on signal area (SA) estimation which is a core signal processing in SAS to understand the Spectrum Usage. Specifically, SA estimation is used as post processing for energy detector outputs. It has been shown that Simple-SA (S-SA) estimation can enhance the Spectrum Usage measurement performance, but it inherently increases false alarms. For this issue, efficient false alarm cancellation technique, L-shaped false alarm cancellation (L-FC), is proposed in this paper. Numerical evaluations show that the proposed method can achieve proper detection performance while the computational cost is small compared to other methods.
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duty cycle and noise floor estimation with welch fft for Spectrum Usage measurements
International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2014Co-Authors: Kenta Umebayashi, Ryota Takagi, Naoki Ioroi, Yasuo Suzuki, Janne LehtomakiAbstract:In dynamic Spectrum access, a new wireless system (secondary user: SU) can utilize the Spectrum licensed to an existing wireless system (primary user: PU) while the Spectrum is vacant. For accomplishing effective and reliable Spectrum utilization, statistics of the Spectrum Usage by the PU, such as duty cycle (DC), are useful for the SU. In this paper, we investigate accurate Spectrum measurement technique with energy detector for obtaining the accurate duty cycle (DC) estimation with a noise floor (NF) estimation which is used for proper threshold setting. In one conventional method, median filtered FCME (Forward Consecutive Mean Excision), frequency selectivity due to interference among spectra of independent symbols degrades the NF and DC estimation performances. In this paper, we propose Welch FFT based NF estimation and DC estimation since Welch FFT has a potential to suppress the effect of the frequency selectivity. We analytically obtain probability density function (PDF) and right tail probability of output of the Welch FFT process and this analysis enables to set proper threshold. Numerical results will show that the proposed method can achieve better NF estimation performance. In addition, the NF estimation improvement and the suppression of frequency selectivity by the Welch FFT can provide around 15 dB SNR gain in the DC estimation performance.
Miguel Lopezbenitez - One of the best experts on this subject based on the ideXlab platform.
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time and frequency varying noise floor estimation for Spectrum Usage measurement
Wireless Communications and Networking Conference, 2019Co-Authors: Hiroki Iwata, Kenta Umebayashi, Miguel Lopezbenitez, Ahmed Altahmeesschi, Janne LehtomakiAbstract:We investigate noise floor (NF) estimation for FFT-ED (Energy Detection based on FFT)-based Spectrum Usage measurement in the context of smart Spectrum access (SSA), in which Spectrum Usage information of primary users (PUs), such as channel occupancy rate (COR), will be exploited by secondary users (SUs). In FFT-ED, the NF has to be estimated to set a decision threshold for ED appropriately. In general, the NF is frequency-dependent and its level changes with time leading to the need of estimating the NF regularly while performing the Spectrum Usage measurement. In this paper, we propose an NF estimation method which exploits prior information regarding the shape of NF and forward consecutive mean excision (FCME) algorithm. Numerical and experimental evaluations show the proposed method enables an accurate NF estimation considering the time and frequency dependencies of the NF. Moreover, we show the proposed method can obtain the almost desired detection performance, but can not the comparative method (the original FCME method).
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a study on false alarm cancellation for Spectrum Usage measurements
Wireless Communications and Networking Conference, 2017Co-Authors: Riki Mizuchi, Kenta Umebayashi, Janne Lehtomaki, Miguel LopezbenitezAbstract:Two-layer smart Spectrum access (SSA) consists of Spectrum sharing based on dynamic Spectrum access (DSA: first layer) and Spectrum awareness system (SAS: second layer). A main role of SAS is providing useful statistical information in terms of Spectrum Usage by long term, wide-band and wide-area measurements. In this paper, we focus on signal area (SA) estimation which is a core signal processing in SAS to understand the Spectrum Usage. Specifically, SA estimation is used as post processing for energy detector outputs. It has been shown that Simple-SA (S-SA) estimation can enhance the Spectrum Usage measurement performance, but it inherently increases false alarms. For this issue, efficient false alarm cancellation technique, L-shaped false alarm cancellation (L-FC), is proposed in this paper. Numerical evaluations show that the proposed method can achieve proper detection performance while the computational cost is small compared to other methods.
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time dimension models of Spectrum Usage for the analysis design and simulation of cognitive radio networks
IEEE Transactions on Vehicular Technology, 2013Co-Authors: Miguel Lopezbenitez, Fernando CasadevallAbstract:This paper addresses the problem of accurately modeling the Spectrum occupancy patterns of real radio communication systems, which is an essential aspect in the study of cognitive radio (CR) networks. The main drawbacks and limitations of previous works are identified, and the methodological procedures on which they rely are improved and extended. Two sophisticated measurement platforms, providing low and high time resolutions, are used to obtain extensive real-world data from a multiband Spectrum measurement campaign, embracing a wide variety of Spectrum bands of practical interest for CR applications. A comprehensive, systematical, and rigorous analysis of the statistical properties observed in the measurement data is then performed to find accurate models capable of capturing and reproducing, within reasonable complexity limits, the statistical properties of temporal patterns, at both short and long timescales, in real wireless systems. Innovative modeling approaches capable of simultaneously describing statistical properties at both timescales are developed as well. In summary, this paper contributes realistic and accurate time-dimension Spectrum Usage models for their application to the study and development of CR.
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Spectrum Usage models for the analysis design and simulation of cognitive radio networks
TDX (Tesis Doctorals en Xarxa), 2012Co-Authors: Miguel Lopezbenitez, Fernando CasadevallAbstract:This chapter presents a comprehensive set of Spectrum occupancy models specifically envisaged for the analysis, design and simulation of cognitive radio systems. The presented models have been proven to accurately capture and reproduce the statistical properties of Spectrum occupancy patterns in real systems. The chapter begins with the description of various time-dimension modeling approaches (in discrete and continuous time) along with models for time-correlation properties. Subsequently, joint time-frequency models as well as space-dimension models are explained in detail. Finally, the chapter concludes with a discussion on the combination and integration of the presented models into a unified modeling approach where the time, frequency and space dimensions of Spectrum Usage can be modeled simultaneously.
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modeling and simulation of joint time frequency properties of Spectrum Usage in cognitive radio
Cognitive Radio and Advanced Spectrum Management, 2011Co-Authors: Miguel Lopezbenitez, Fernando Casadevall, David Lopezperez, Athanasios V VasilakosAbstract:The development of the Dynamic Spectrum Access/Cognitive Radio (DSA/CR) technology can significantly benefit from the availability of realistic models able to accurately capture and reproduce the statistical properties of Spectrum Usage in real wireless communication systems. Relying on field measurements of real systems, this paper analyzes the joint time-frequency statistical properties of Spectrum Usage and develops adequate models describing the observed characteristics. Based on such models, a sophisticated method is also proposed to generate artificial Spectrum data for simulation or other purposes. The proposed method is able to accurately reproduce the statistical time-frequency characteristics of Spectrum Usage in real systems.
Fernando Casadevall - One of the best experts on this subject based on the ideXlab platform.
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Space-Dimension Models of Spectrum Usage for Cognitive Radio Networks
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Miguel Lopez-benitez, Fernando CasadevallAbstract:The dynamic Spectrum access (DSA) principle, relying on the cognitive radio (CR) paradigm, allows users to access Spectrum over time intervals or spatial areas where it remains unused. Due to the opportunistic nature of DSA/CR, the behavior and performance of DSA/CR networks depends on the perceived Spectrum Usage pattern. An accurate modeling of Spectrum occupancy therefore becomes essential in the context of DSA/CR. In this context, this paper addresses the problem of accurately modeling the Spectrum occupancy pattern perceived by DSA/CR users in the spatial domain. A novel spatial modeling approach is introduced to enable a simple yet practical and accurate characterization of Spectrum. First, a set of models is proposed to characterize and predict the average level of occupancy perceived by DSA/CR users at various locations based on the knowledge of some simple signal parameters. An extension is then proposed to characterize not only the average occupancy level but the instantaneous channel state perceived simultaneously by DSA/CR users observing the same transmitter from different locations as well. The validity and accuracy of the theoretical models are demonstrated with results from an extensive Spectrum measurement campaign. Some illustrative examples of their potential applicability are presented and discussed as well.
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Spectrum Usage in Cognitive Radio Networks: From Field Measurements to Empirical Models
IEICE Transactions on Communications, 2014Co-Authors: Miguel Lopez-benitez, Fernando CasadevallAbstract:Cognitive Radio (CR) is aimed at increasing the e ffi ciency of Spectrum utilization by allowing unlicensed users to access, in an op- portunistic and non-interfering manner, some licensed bands temporarily and / or spatially unoccupied by the licensed users. The analysis of CR sys- tems usually requires the spectral activity of the licensed system to be rep- resented and characterized in a simple and tractable, yet accurate manner, which is accomplished by means of Spectrum models. In order to guarantee the realism and accuracy of such models, the use of empirical Spectrum oc- cupancy data is essential. In this context, this paper explains the complete process of Spectrum modeling, from the realization of field measurements to the obtainment of the final validated model, and highlights the main rel- evant aspects to be taken into account when developing Spectrum Usage models for their application in the context of the CR technology
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time dimension models of Spectrum Usage for the analysis design and simulation of cognitive radio networks
IEEE Transactions on Vehicular Technology, 2013Co-Authors: Miguel Lopezbenitez, Fernando CasadevallAbstract:This paper addresses the problem of accurately modeling the Spectrum occupancy patterns of real radio communication systems, which is an essential aspect in the study of cognitive radio (CR) networks. The main drawbacks and limitations of previous works are identified, and the methodological procedures on which they rely are improved and extended. Two sophisticated measurement platforms, providing low and high time resolutions, are used to obtain extensive real-world data from a multiband Spectrum measurement campaign, embracing a wide variety of Spectrum bands of practical interest for CR applications. A comprehensive, systematical, and rigorous analysis of the statistical properties observed in the measurement data is then performed to find accurate models capable of capturing and reproducing, within reasonable complexity limits, the statistical properties of temporal patterns, at both short and long timescales, in real wireless systems. Innovative modeling approaches capable of simultaneously describing statistical properties at both timescales are developed as well. In summary, this paper contributes realistic and accurate time-dimension Spectrum Usage models for their application to the study and development of CR.
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Spectrum Usage models for the analysis design and simulation of cognitive radio networks
TDX (Tesis Doctorals en Xarxa), 2012Co-Authors: Miguel Lopezbenitez, Fernando CasadevallAbstract:This chapter presents a comprehensive set of Spectrum occupancy models specifically envisaged for the analysis, design and simulation of cognitive radio systems. The presented models have been proven to accurately capture and reproduce the statistical properties of Spectrum occupancy patterns in real systems. The chapter begins with the description of various time-dimension modeling approaches (in discrete and continuous time) along with models for time-correlation properties. Subsequently, joint time-frequency models as well as space-dimension models are explained in detail. Finally, the chapter concludes with a discussion on the combination and integration of the presented models into a unified modeling approach where the time, frequency and space dimensions of Spectrum Usage can be modeled simultaneously.
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modeling and simulation of joint time frequency properties of Spectrum Usage in cognitive radio
Cognitive Radio and Advanced Spectrum Management, 2011Co-Authors: Miguel Lopezbenitez, Fernando Casadevall, David Lopezperez, Athanasios V VasilakosAbstract:The development of the Dynamic Spectrum Access/Cognitive Radio (DSA/CR) technology can significantly benefit from the availability of realistic models able to accurately capture and reproduce the statistical properties of Spectrum Usage in real wireless communication systems. Relying on field measurements of real systems, this paper analyzes the joint time-frequency statistical properties of Spectrum Usage and develops adequate models describing the observed characteristics. Based on such models, a sophisticated method is also proposed to generate artificial Spectrum data for simulation or other purposes. The proposed method is able to accurately reproduce the statistical time-frequency characteristics of Spectrum Usage in real systems.
