Software radio ideally provides the opportunity to communicate with any radio communication stand... more Software radio ideally provides the opportunity to communicate with any radio communication standard by modifying only the software, without any modification to hardware components. However, taking into account the static behavior of current communications protocols, the spectrum efficiency optimization, and flexibility, the radio domain has become an important factor. From this thinking appeared the cognitive radio paradigm. This evolution is today inescapable in the modern radio communication world. ...
This deliverable provides the final report with the summary of the activities carried out in NEWC... more This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.
Abstract In this paper, we consider the problem of exploiting spectrum resources for a secondary ... more Abstract In this paper, we consider the problem of exploiting spectrum resources for a secondary user (SU) of a wireless communication network. We suggest that Upper Confidence Bound (UCB) algorithms could be useful to design decision making strategies for SUs to exploit intelligently the spectrum resources based on their past observations. The algorithms use an index that provides an optimistic estimation of the availability of the resources to the SU.
Abstract—Within a context of Cognitive Radio, we explore the possibility to detect the presence o... more Abstract—Within a context of Cognitive Radio, we explore the possibility to detect the presence of users on a given band with no a priori knowledge on the environment. This paper ventures the analysis of a joint learning-detection framework where the detector improves its detecting abilities online relying on past gathered information on the probed frequency band. Simulations conducted on real data to support the suggested approach show interesting results.
This deliverable provides the final report with the summary of the activities carried out in NEWC... more This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.
Channel Selection in fading environments with no prior information
on the channels’ quality is a ... more Channel Selection in fading environments with no prior information on the channels’ quality is a challenging issue. In the case of ‘Rayleigh channels’ the measured Signal-To-Noise Ratio follows exponential distributions. Thus, we suggest in this paper a simple algorithm that deals with resource selection when the measured samples are drawn from exponential distributions. This strategy, referred to as Multiplicative Upper Confidence Bound Algorithm (MUCB), associates a utility index to every available arm, and then selects the arm with the highest index. For every arm, the associated index is equal to the product of a multiplicative factor by the sample mean of the rewards collected by this arm. We show that MUCB policies are order optimal. Moreover simulations illustrate and validate the stated theoretical results.
In this paper, we address the general case of a coordinated secondary network willing to exploit ... more In this paper, we address the general case of a coordinated secondary network willing to exploit communication opportunities left vacant by a licensed primary network. Since secondary users (SU) usually have no prior knowledge on the environment, they need to learn the availability of each channel through sensing techniques, which however can be prone to detection errors. We argue that cooperation among secondary users can enable efficient learning and coordination mechanisms in order to maximize the spectrum exploitation by SUs, while minimizing the impact on the primary network. To this goal, we provide three novel contributions in this paper. First, we formulate the spectrum selection in secondary networks as an instance of the Multi-Armed Bandit (MAB) problem, and we extend the analysis to the collaboration learning case, in which each SU learns the spectrum occupation, and shares this information with other SUs. We show that collaboration among SUs can mitigate the impact of sensing errors on system performance, and improve the convergence of the learning process to the optimal solution. Second, we integrate the learning algorithms with two collaboration techniques based on modified versions of the Hungarian algorithm and of the Round Robin algorithm that allows reducing the interference among SUs. Third, we derive fundamental limits to the performance of cooperative learning algorithms based on Upper Confidence Bound (UCB) policies in a symmetric scenario where all SU have the same perception of the quality of the resources. Extensive simulation results confirm the effectiveness of our joint learning-collaboration algorithm in protecting the operations of Primary Users (PUs), while maximizing the performance of SUs.
We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learn... more We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learning paradigm popular within Cognitive Network related topics (e.g., Spectrum Sensing and Allocation). We focus on the case where the rewards are exponentially distributed, which is common when dealing with Rayleigh fading channels. This strategy, named Multiplicative Upper Confidence Bound (MUCB), associates a utility index to every available arm, and then selects the arm with the highest index. For every arm, the associated index is equal to the product of a multiplicative factor by the sample mean of the rewards collected by this arm. We show that the MUCB policy has a low complexity and is order optimal.
We introduce in this paper the notion of "design space" as a conceptual object that defines a set... more We introduce in this paper the notion of "design space" as a conceptual object that defines a set of cognitive radio decision making problems by their constraints rather than by their degrees of freedom. We identified, in our analysis work, three dimensions of constrains: the environment's, the equipment's and the user's related constrains. Moreover , we define and use the notion of a priori knowledge, to show that the tackled challenges by the radio community to solve configuration adaptation decision making problems have often the same design space, however they differ by the a priori knowledge they assume available. Consequently, we suggest in this paper, the "a priori knowledge" as a classification criteria to discriminate the main proposed techniques in the literature to solve configuration adaptation decision making problems. In the rest of the paper we propose to further study a particular decision making framework where no a priori (or limited) information is provided to the cognitive radio equipment. An approach based on tools borrowed from the multi-armed bandit community is discussed. Finally, our simulation results highlight that by customizing algorithms developed for solving the multi-armed bandit, efficient engineering solutions to some problems met in cognitive radio can indeed be built.
This article draws a general retrospective view on the first 10 years of cognitive radio (CR). Mo... more This article draws a general retrospective view on the first 10 years of cognitive radio (CR). More specifically, we explore in this article decision making and learning for CR from an equipment perspective. Thus, this article depicts the main decision making problems addressed by the community as general dynamic configuration adaptation (DCA) problems and discuss the suggested solution proposed in the literature to tackle them. Within this framework dynamic spectrum management is briefly introduced as a specific instantiation of DCA problems. We identified, in our analysis study, three dimensions of constrains: the environment's, the equipment's and the user's related constrains. Moreover, we define and use the notion of a priori knowledge, to show that the tackled challenges by the radio community during first 10 years of CR to solve decision making problems have often the same design space, however they differ by the a priori knowledge they assume available. Consequently, we suggest in this article, the "a priori knowledge" as a classification criteria to discriminate the main proposed techniques in the literature to solve configuration adaptation decision making problems. We finally discuss the impact of sensing errors on the decision making process as a prospective analysis.
We revisit, in this letter, the impact of noise uncertainty on the performance of the well known ... more We revisit, in this letter, the impact of noise uncertainty on the performance of the well known energy detector. Mainly, we reconsider the case of a Log-Normal approximated noise uncertainty suggested in the work of Alexander Sonnenschein and Philip M. Fishman. We show that under a Log-Normal noise uncertainty, closed form expressions of the detector's performances and limits can be provided. Thus we show that, relying on mild approximations, we can design a detector with a fixed probability of false alarm function of the uncertainty, and present a new expression of the SNR-wall that depends on the desired performances of the detector as well as the introduced uncertainty parameter.
This paper describes the practical design issues and potential strategies for implementing cognit... more This paper describes the practical design issues and potential strategies for implementing cognitive radio (CR) equipments that support spectrum agility. We first discuss a simplified cognitive cycle that satisfies the requirements of a typical CR scenario. We give a brief overview of the various steps involved in the above cycle, namely, sensing, decision making and flexible signal processing. A management architecture called HDCRAM (Hierarchical and Distributed Cognitive Radio Architecture Management) is proposed as a solution for efficiently managing all the steps in the cognitive cycle. Finally we give an integrated proposal, which serves a starting point for designing future spectrum agile CR equipments.
Le contexte considéré est celui de l'Accès Opportuniste au Spectre. On suppose qu'il existe un ut... more Le contexte considéré est celui de l'Accès Opportuniste au Spectre. On suppose qu'il existe un utilisateur dit "secondaire" intéressé par une bande de fréquence dédiée à un réseau d'utilisateurs prioritaires dits "primaires". Les capacités d'écoute et d'exploitation du spectre de l'utilisateur secondaire sont supposées limitées à une sous-bande de la bande de fréquences sondée. Dans une précédente étude, les auteurs avaient suggéré une classe d'algorithme d'apprentissage par renforcement, connue sous le nom d'Upper Confidence Bound Algorithms, pour répondre à cette problématique. Le mécanisme d'apprentissage repose essentiellement sur l'observation de l'activité des utilisateurs primaires dans les bandes spectrales sollicitées par l'utilisateur secondaire. L'objectif de ce papier est d'évaluer l'impact des erreurs d'observations de la présence ou absence des utilisateurs primaires sur les performances d'apprentissage de l'utilisateur secondaire. Ces travaux montrent que la convergence vers le canal dit "optimal", i.e., le plus libre en probabilité, reste rapide malgré les erreurs d'observation liées à la détection des utilisateurs primaires. Ce résultat est mis en avant à travers un théorème qui montre que le temps passé par l'algorithme à observer un canal sous-optimal est borné par une fonction logarithmique du temps. Ainsi, les pertes de performances dues aux erreurs d'observations sont quantifiées en fonction des caractéristiques du capteur (probabilité de fausses alarmes notamment). Ces résultats sont illustrés à l'aide de simulations.
We investigate in this paper a Log-Normal approximation of Chi-Square distributions. Our study is... more We investigate in this paper a Log-Normal approximation of Chi-Square distributions. Our study is motivated by the analysis of ratios of random variables where both, Log-Normal and Chi-Square distributions are involved. Such ratios appear, for instance, when dealing with an energy detector while only an imperfect knowledge of the noise level is available. In order to characterize the distribution of the ratio, an accurate approximation of the Chi-Square distribution by a Log-Normal distribution would highly simplify this problem known to be analytically intractable otherwise.
Abstract In this paper, focusing on identifying standards blindly, we propose a bandwidth shape s... more Abstract In this paper, focusing on identifying standards blindly, we propose a bandwidth shape sensor and a GI (guard interval) sensor using USRP (Universal Software Radio Peripheral) platforms and SDR4all tools. These sensors are fundamental parts of the so-called Blind Standard Recognition Sensor. The blind standard bandwidth sensor is based on a Radial Basis Function Neuronal Network designed in Matlab. We have presented first experience of using blind standard bandwidth sensor in a previous work. We will provide ...
Software radio ideally provides the opportunity to communicate with any radio communication stand... more Software radio ideally provides the opportunity to communicate with any radio communication standard by modifying only the software, without any modification to hardware components. However, taking into account the static behavior of current communications protocols, the spectrum efficiency optimization, and flexibility, the radio domain has become an important factor. From this thinking appeared the cognitive radio paradigm. This evolution is today inescapable in the modern radio communication world. ...
Software radio ideally provides the opportunity to communicate with any radio communication stand... more Software radio ideally provides the opportunity to communicate with any radio communication standard by modifying only the software, without any modification to hardware components. However, taking into account the static behavior of current communications protocols, the spectrum efficiency optimization, and flexibility, the radio domain has become an important factor. From this thinking appeared the cognitive radio paradigm. This evolution is today inescapable in the modern radio communication world. ...
This deliverable provides the final report with the summary of the activities carried out in NEWC... more This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.
Abstract In this paper, we consider the problem of exploiting spectrum resources for a secondary ... more Abstract In this paper, we consider the problem of exploiting spectrum resources for a secondary user (SU) of a wireless communication network. We suggest that Upper Confidence Bound (UCB) algorithms could be useful to design decision making strategies for SUs to exploit intelligently the spectrum resources based on their past observations. The algorithms use an index that provides an optimistic estimation of the availability of the resources to the SU.
Abstract—Within a context of Cognitive Radio, we explore the possibility to detect the presence o... more Abstract—Within a context of Cognitive Radio, we explore the possibility to detect the presence of users on a given band with no a priori knowledge on the environment. This paper ventures the analysis of a joint learning-detection framework where the detector improves its detecting abilities online relying on past gathered information on the probed frequency band. Simulations conducted on real data to support the suggested approach show interesting results.
This deliverable provides the final report with the summary of the activities carried out in NEWC... more This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.
Channel Selection in fading environments with no prior information
on the channels’ quality is a ... more Channel Selection in fading environments with no prior information on the channels’ quality is a challenging issue. In the case of ‘Rayleigh channels’ the measured Signal-To-Noise Ratio follows exponential distributions. Thus, we suggest in this paper a simple algorithm that deals with resource selection when the measured samples are drawn from exponential distributions. This strategy, referred to as Multiplicative Upper Confidence Bound Algorithm (MUCB), associates a utility index to every available arm, and then selects the arm with the highest index. For every arm, the associated index is equal to the product of a multiplicative factor by the sample mean of the rewards collected by this arm. We show that MUCB policies are order optimal. Moreover simulations illustrate and validate the stated theoretical results.
In this paper, we address the general case of a coordinated secondary network willing to exploit ... more In this paper, we address the general case of a coordinated secondary network willing to exploit communication opportunities left vacant by a licensed primary network. Since secondary users (SU) usually have no prior knowledge on the environment, they need to learn the availability of each channel through sensing techniques, which however can be prone to detection errors. We argue that cooperation among secondary users can enable efficient learning and coordination mechanisms in order to maximize the spectrum exploitation by SUs, while minimizing the impact on the primary network. To this goal, we provide three novel contributions in this paper. First, we formulate the spectrum selection in secondary networks as an instance of the Multi-Armed Bandit (MAB) problem, and we extend the analysis to the collaboration learning case, in which each SU learns the spectrum occupation, and shares this information with other SUs. We show that collaboration among SUs can mitigate the impact of sensing errors on system performance, and improve the convergence of the learning process to the optimal solution. Second, we integrate the learning algorithms with two collaboration techniques based on modified versions of the Hungarian algorithm and of the Round Robin algorithm that allows reducing the interference among SUs. Third, we derive fundamental limits to the performance of cooperative learning algorithms based on Upper Confidence Bound (UCB) policies in a symmetric scenario where all SU have the same perception of the quality of the resources. Extensive simulation results confirm the effectiveness of our joint learning-collaboration algorithm in protecting the operations of Primary Users (PUs), while maximizing the performance of SUs.
We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learn... more We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learning paradigm popular within Cognitive Network related topics (e.g., Spectrum Sensing and Allocation). We focus on the case where the rewards are exponentially distributed, which is common when dealing with Rayleigh fading channels. This strategy, named Multiplicative Upper Confidence Bound (MUCB), associates a utility index to every available arm, and then selects the arm with the highest index. For every arm, the associated index is equal to the product of a multiplicative factor by the sample mean of the rewards collected by this arm. We show that the MUCB policy has a low complexity and is order optimal.
We introduce in this paper the notion of "design space" as a conceptual object that defines a set... more We introduce in this paper the notion of "design space" as a conceptual object that defines a set of cognitive radio decision making problems by their constraints rather than by their degrees of freedom. We identified, in our analysis work, three dimensions of constrains: the environment's, the equipment's and the user's related constrains. Moreover , we define and use the notion of a priori knowledge, to show that the tackled challenges by the radio community to solve configuration adaptation decision making problems have often the same design space, however they differ by the a priori knowledge they assume available. Consequently, we suggest in this paper, the "a priori knowledge" as a classification criteria to discriminate the main proposed techniques in the literature to solve configuration adaptation decision making problems. In the rest of the paper we propose to further study a particular decision making framework where no a priori (or limited) information is provided to the cognitive radio equipment. An approach based on tools borrowed from the multi-armed bandit community is discussed. Finally, our simulation results highlight that by customizing algorithms developed for solving the multi-armed bandit, efficient engineering solutions to some problems met in cognitive radio can indeed be built.
This article draws a general retrospective view on the first 10 years of cognitive radio (CR). Mo... more This article draws a general retrospective view on the first 10 years of cognitive radio (CR). More specifically, we explore in this article decision making and learning for CR from an equipment perspective. Thus, this article depicts the main decision making problems addressed by the community as general dynamic configuration adaptation (DCA) problems and discuss the suggested solution proposed in the literature to tackle them. Within this framework dynamic spectrum management is briefly introduced as a specific instantiation of DCA problems. We identified, in our analysis study, three dimensions of constrains: the environment's, the equipment's and the user's related constrains. Moreover, we define and use the notion of a priori knowledge, to show that the tackled challenges by the radio community during first 10 years of CR to solve decision making problems have often the same design space, however they differ by the a priori knowledge they assume available. Consequently, we suggest in this article, the "a priori knowledge" as a classification criteria to discriminate the main proposed techniques in the literature to solve configuration adaptation decision making problems. We finally discuss the impact of sensing errors on the decision making process as a prospective analysis.
We revisit, in this letter, the impact of noise uncertainty on the performance of the well known ... more We revisit, in this letter, the impact of noise uncertainty on the performance of the well known energy detector. Mainly, we reconsider the case of a Log-Normal approximated noise uncertainty suggested in the work of Alexander Sonnenschein and Philip M. Fishman. We show that under a Log-Normal noise uncertainty, closed form expressions of the detector's performances and limits can be provided. Thus we show that, relying on mild approximations, we can design a detector with a fixed probability of false alarm function of the uncertainty, and present a new expression of the SNR-wall that depends on the desired performances of the detector as well as the introduced uncertainty parameter.
This paper describes the practical design issues and potential strategies for implementing cognit... more This paper describes the practical design issues and potential strategies for implementing cognitive radio (CR) equipments that support spectrum agility. We first discuss a simplified cognitive cycle that satisfies the requirements of a typical CR scenario. We give a brief overview of the various steps involved in the above cycle, namely, sensing, decision making and flexible signal processing. A management architecture called HDCRAM (Hierarchical and Distributed Cognitive Radio Architecture Management) is proposed as a solution for efficiently managing all the steps in the cognitive cycle. Finally we give an integrated proposal, which serves a starting point for designing future spectrum agile CR equipments.
Le contexte considéré est celui de l'Accès Opportuniste au Spectre. On suppose qu'il existe un ut... more Le contexte considéré est celui de l'Accès Opportuniste au Spectre. On suppose qu'il existe un utilisateur dit "secondaire" intéressé par une bande de fréquence dédiée à un réseau d'utilisateurs prioritaires dits "primaires". Les capacités d'écoute et d'exploitation du spectre de l'utilisateur secondaire sont supposées limitées à une sous-bande de la bande de fréquences sondée. Dans une précédente étude, les auteurs avaient suggéré une classe d'algorithme d'apprentissage par renforcement, connue sous le nom d'Upper Confidence Bound Algorithms, pour répondre à cette problématique. Le mécanisme d'apprentissage repose essentiellement sur l'observation de l'activité des utilisateurs primaires dans les bandes spectrales sollicitées par l'utilisateur secondaire. L'objectif de ce papier est d'évaluer l'impact des erreurs d'observations de la présence ou absence des utilisateurs primaires sur les performances d'apprentissage de l'utilisateur secondaire. Ces travaux montrent que la convergence vers le canal dit "optimal", i.e., le plus libre en probabilité, reste rapide malgré les erreurs d'observation liées à la détection des utilisateurs primaires. Ce résultat est mis en avant à travers un théorème qui montre que le temps passé par l'algorithme à observer un canal sous-optimal est borné par une fonction logarithmique du temps. Ainsi, les pertes de performances dues aux erreurs d'observations sont quantifiées en fonction des caractéristiques du capteur (probabilité de fausses alarmes notamment). Ces résultats sont illustrés à l'aide de simulations.
We investigate in this paper a Log-Normal approximation of Chi-Square distributions. Our study is... more We investigate in this paper a Log-Normal approximation of Chi-Square distributions. Our study is motivated by the analysis of ratios of random variables where both, Log-Normal and Chi-Square distributions are involved. Such ratios appear, for instance, when dealing with an energy detector while only an imperfect knowledge of the noise level is available. In order to characterize the distribution of the ratio, an accurate approximation of the Chi-Square distribution by a Log-Normal distribution would highly simplify this problem known to be analytically intractable otherwise.
Abstract In this paper, focusing on identifying standards blindly, we propose a bandwidth shape s... more Abstract In this paper, focusing on identifying standards blindly, we propose a bandwidth shape sensor and a GI (guard interval) sensor using USRP (Universal Software Radio Peripheral) platforms and SDR4all tools. These sensors are fundamental parts of the so-called Blind Standard Recognition Sensor. The blind standard bandwidth sensor is based on a Radial Basis Function Neuronal Network designed in Matlab. We have presented first experience of using blind standard bandwidth sensor in a previous work. We will provide ...
Software radio ideally provides the opportunity to communicate with any radio communication stand... more Software radio ideally provides the opportunity to communicate with any radio communication standard by modifying only the software, without any modification to hardware components. However, taking into account the static behavior of current communications protocols, the spectrum efficiency optimization, and flexibility, the radio domain has become an important factor. From this thinking appeared the cognitive radio paradigm. This evolution is today inescapable in the modern radio communication world. ...
Uploads
on the channels’ quality is a challenging issue. In the
case of ‘Rayleigh channels’ the measured Signal-To-Noise
Ratio follows exponential distributions. Thus, we suggest in
this paper a simple algorithm that deals with resource selection
when the measured samples are drawn from exponential
distributions. This strategy, referred to as Multiplicative Upper
Confidence Bound Algorithm (MUCB), associates a utility
index to every available arm, and then selects the arm with
the highest index. For every arm, the associated index is equal
to the product of a multiplicative factor by the sample mean of
the rewards collected by this arm. We show that MUCB policies
are order optimal. Moreover simulations illustrate and
validate the stated theoretical results.
on the channels’ quality is a challenging issue. In the
case of ‘Rayleigh channels’ the measured Signal-To-Noise
Ratio follows exponential distributions. Thus, we suggest in
this paper a simple algorithm that deals with resource selection
when the measured samples are drawn from exponential
distributions. This strategy, referred to as Multiplicative Upper
Confidence Bound Algorithm (MUCB), associates a utility
index to every available arm, and then selects the arm with
the highest index. For every arm, the associated index is equal
to the product of a multiplicative factor by the sample mean of
the rewards collected by this arm. We show that MUCB policies
are order optimal. Moreover simulations illustrate and
validate the stated theoretical results.