Estimating the Spectrum in Computed Tomography Via Kullback–Leibler Divergence Constrained Optimization

Purpose We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x‐ray spectrum that can accurately model the x‐ray transmission curves and reflects a realistic shape of the typical energy spectra of the CT system. Methods Spectrum estimation is posed as an optimization problem with x‐ray spectrum as unknown variables, and a Kullback–Leibler (KL)‐divergence constraint is employed to incorporate prior knowledge of the spectrum and enhance numerical stability of the estimation process. The formulated constrained optimization problem is convex and can be solved efficiently by use of the exponentiated‐gradient (EG) algorithm. We demonstrate the effectiveness of the proposed approach on the simulated and experimental data. The comparison to the expectation–maximization (EM) method is also discussed. Results In simulations, the proposed algorithm is seen to yield x‐ray spectra that closely match the ground truth and represent the attenuation process of x‐ray photons in materials, both included and not included in the estimation process. In experiments, the calculated transmission curve is in good agreement with the measured transmission curve, and the estimated spectra exhibits physically realistic looking shapes. The results further show the comparable performance between the proposed optimization‐based approach and EM. Conclusions Our formulation of a constrained optimization provides an interpretable and flexible framework for spectrum estimation. Moreover, a KL‐divergence constraint can include a prior spectrum and appears to capture important features of x‐ray spectrum, allowing accurate and robust estimation of x‐ray spectrum in CT imaging

Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning

Accurate localization of brain regions responsible for language and cognitive functions in Epilepsy patients should be carefully determined prior to surgery. Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been shown to be a safer alternative to the electrical cortical stimulation mapping (ESM), which is currently the clinical/gold standard. Conventional methods for analyzing RTFM signals are based on statistical comparison of signal power at certain frequency bands. Compared to gold standard (ESM), they have limited accuracies when assessing channel responses. In this study, we address the accuracy limitation of the current RTFM signal estimation methods by analyzing the full frequency spectrum of the signal and replacing signal power estimation methods with machine learning algorithms, specifically random forest (RF), as a proof of concept. We train RF with power spectral density of the time-series RTFM signal in supervised learning framework where ground truth labels are obtained from the ESM. Results obtained from RTFM of six adult patients in a strictly controlled experimental setup reveal the state of the art detection accuracy of $\approx 78\%$ for the language comprehension task, an improvement of $23\%$ over the conventional RTFM estimation method. To the best of our knowledge, this is the first study exploring the use of machine learning approaches for determining RTFM signal characteristics, and using the whole-frequency band for better region localization. Our results demonstrate the feasibility of machine learning based RTFM signal analysis method over the full spectrum to be a clinical routine in the near future.Comment: This paper will appear in the Proceedings of IEEE International Conference on Systems, Man and Cybernetics (SMC) 201

Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple Antennas

In this paper, we propose a signal-selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple-antenna capability. This method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. The analytical threshold for achieving constant false alarm rate using this detection method is presented, verified through simulations, and shown to be independent of both the number of samples used and the noise variance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown, through numerical simulations, to outperform existing multiple-antenna cyclostationary-based spectrum sensing algorithms under a quasi-static Rayleigh fading channel, in both spatially correlated and uncorrelated noise environments. The algorithm also has significantly lower computational complexity than these other approaches.Comment: 6 pages, 6 figures, accepted to IEEE GLOBECOM 201

Wideband DOA Estimation with Frequency Decomposition via a Unified GS-WSpSF Framework

A unified group sparsity based framework for wideband sparse spectrum fitting (GS-WSpSF) is proposed for wideband direction-of-arrival (DOA) estimation, which is capable of handling both uncorrelated and correlated sources. Then, by making four different assumptions on a priori knowledge about the sources, four variants under the proposed framework are formulated as solutions to the underdetermined DOA estimation problem without the need of employing sparse arrays. As verified by simulations, improved estimation performance can be achieved by the wideband methods compared with narrowband ones, and adopting more a priori information leads to better performance in terms of resolution capacity and estimation accuracy

Cramer-Rao Bounds for Joint RSS/DoA-Based Primary-User Localization in Cognitive Radio Networks

Knowledge about the location of licensed primary-users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we consider the achievable accuracy of PU localization algorithms that jointly utilize received-signal-strength (RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only localization algorithms separately and assume DoA estimation error variance is a fixed constant or rather independent of RSS. We derive the CRB for joint RSS/DoA-based PU localization algorithms based on the mathematical model of DoA estimation error variance as a function of RSS, for a given CR placement. The bound is compared with practical localization algorithms and the impact of several key parameters, such as number of nodes, number of antennas and samples, channel shadowing variance and correlation distance, on the achievable accuracy are thoroughly analyzed and discussed. We also derive the closed-form asymptotic CRB for uniform random CR placement, and perform theoretical and numerical studies on the required number of CRs such that the asymptotic CRB tightly approximates the numerical integration of the CRB for a given placement.Comment: 20 pages, 11 figures, 1 table, submitted to IEEE Transactions on Wireless Communication

実観測に基づく電波環境データベースを用いた空間的周波数共用に関する研究

The growth in demand for mobile communication systems has exponentially increased data traffic during the last decade. Because this exponential growth consumes finite spectrum resources, traditional spectrum utilization policies with exclusive resource allocation faces a limit. In order to develop novel spectrum resources, many researchers have shown an interest in spectrum sharing with cognitive radio (CR). This method allows secondary users (SUs) to share spectrum bands with primary users (PUs) under interference constraints for PUs. SUs are required to take into consideration the interference margin to the estimated interference temperature at PUs in order to protect communication quality of PUs. On the other hand, an excess interference margin decreases the spectrum sharing opportunity; therefore, it is important to manage the interference power properly. Spectrum estimation techniques in spectrum sharing can be categorized into two methods: spectrum sensing and spectrum database. Spectrum sensing uses the detection of PU signals to characterize radio environments. To provide good protection, signal detection must be performed under the (strict) condition that the PU signal strength be below the noise floor, even under low signal-to-noise ratios (SNRs) and fading conditions. These fluctuations make it difficult for the SUs to achieve stable detection; thus, it is very challenging to accurately estimate the actual activity of the PU. The second method is based on storing information about spectrum availabilities of each location in spectrum databases. In this method, afterSUs query the database before they utilize the spectrum, the database provides spectrum information to the SUs. Current databases usually evaluate white space (WS) based on empirical propagation models. However, it is well known that empirical propagation models cannot take into account all of the indeterminacies of radio environments, such as shadowing effects. Because SUs must not interfere toward PUs, the conventional database requires the SUs to set large margins to ensure no interference with PUs.In this dissertation, we propose and comprehensively study a measurement-based spectrum database for highly efficient spectrum management. The proposed database is a hybrid system, combining spectrum sensing and a spectrum database. The spectrum database consists of radio environment information measured by mobile terminals. After enough data are gathered, the database estimates the radio environment characteristics by statistical processing with the large datasets. Using the accurate knowledge of the received PU signal power, spectrum sharing based on PU signal quality metrics such as the signal-to-interference power ratio (SIR) can be implemented.We first introduce the proposed database architecture. After we briefly discuss a theoretical performance of the proposed database, we present experimental results for the database construction using actual TV broadcast signals. The experimental results show that the proposed database reduces the estimation error of the radio environment. Next, we propose a transmission power control method with a radio environment map (REM) for secondary networks. The REM stores the spatial distribution of the average received signal power. We can optimize the accuracy of the measurement-based REM using the Kriging interpolation. Although several researchers have maintained a continuous interest in improving the accuracy of the REM, sufficient study has not been done to actually explore the interference constraint considering the estimation error. The proposed method uses ordinary Kriging for the spectrum cartography. According to the predicted distribution of the estimation error, the allowable interference power to the PU is approximately formulated. Numerical results show that the proposed method can achieve the probabilistic interference constraint asymptotically, and an increase in the number of measurement datasets improves the spectrum sharing capability. After that, we extend the proposed database to the radio propagation estimation in distributed wireless links in order to accurately estimate interference characteristics from SUs to PUs. Although current wireless distributed networks have to rely on an empirical model to estimate the radio environment, in the spectrum sharing networks, such a path loss-based interference prediction decreases the spectrum sharing opportunity because of the requirement for the interference margin. The proposed method focuses on the spatial-correlation of radio propagation characteristics between different wireless links. Using Kriging-based shadowing estimation, the radio propagation of the wireless link that has arbitrary location relationship can be predicted. Numerical results show that the proposed method achieves higher estimation accuracy than path loss-based estimation methods. The methods discussed in this thesis can develop more spatial WSs in existing allocated bandwidth such as TVWS, and can provide these WSs to new wireless systems expected to appear in the future. Additionally, these results will contribute not only to such spectrum sharing but also to improvement of the spectrum management in existing systems. For example, in heterogeneous networks (HetNets), a suitable inter-cell interference management enables transmitters to reuse the frequency efficiently and the user equipment can select the optimum base station. We anticipate that this dissertation strongly contributes to improvingthe spectrum utilization efficiency of the whole wireless systems.電気通信大学201

Self-Adaptive Stochastic Rayleigh Flat Fading Channel Estimation

International audienceThis paper deals with channel estimation over flat fading Rayleigh channel with Jakes' Doppler Spectrum. Many estimation algorithms exploit the time-domain correlation of the channel by employing a Kalman filter based on a first-order (or sometimes second-order) approximation of the time-varying channel with a criterion based on correlation matching (CM), or on the Minimization of Asymptotic Variance (MAV). In this paper, we first consider a reduced complexity approach based on Least Mean Square (LMS) algorithm, for which we provide closed-form expressions of the optimal step-size coefficient versus the channel state statistic (additive noise power and Doppler frequency) and of corresponding asymptotic mean-squared-error (MSE). However, the optimal tuning of the step-size coefficient requires knowledge of the channel's statistic. This knowledge was also a requirement for the aforementioned Kalman-based methods. As a second contribution, we propose a self-adaptive estimation method based on a stochastic gradient which does not need a priori knowledge. We show that the asymptotic MSE of the self-adaptive algorithm is almost the same as the first order Kalman filter optimized with the MAV criterion and is better than the latter optimized with the conventional CM criterion. We finally improve the speed and reactivity of the algorithm by computing an adaptive speed process leading to a fast algorithm with very good asymptotic performance

Optimal resource allocation in femtocell networks based on Markov modeling of interferers' activity

Femtocell networks offer a series of advantages with respect to conventional cellular networks. However, a potential massive deployment of femto-access points (FAPs) poses a big challenge in terms of interference management, which requires proper radio resource allocation techniques. In this article, we propose alternative optimal power/bit allocation strategies over a time-frequency frame based on a statistical modeling of the interference activity. Given the lack of knowledge of the interference activity, we assume a Bayesian approach that provides the optimal allocation, conditioned to periodic spectrum sensing, and estimation of the interference activity statistical parameters. We consider first a single FAP accessing the radio channel in the presence of a dynamical interference environment. Then, we extend the formulation to a multi-FAP scenario, where nearby FAP's react to the strategies of the other FAP's, still within a dynamical interference scenario. The multi-user case is first approached using a strategic non-cooperative game formulation. Then, we propose a coordination game based on the introduction of a pricing mechanism that exploits the backhaul link to enable the exchange of parameters (prices) among FAP's