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Erfan Soltanmohammadi

Bio: Erfan Soltanmohammadi is an academic researcher from Louisiana State University. The author has contributed to research in topics: Cognitive radio & Expectation–maximization algorithm. The author has an hindex of 8, co-authored 32 publications receiving 387 citations. Previous affiliations of Erfan Soltanmohammadi include Amirkabir University of Technology & Marvell Technology Group.

Papers
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Journal ArticleDOI
TL;DR: The traffic issues of M2M communications and the challenges they impose on both access channel and traffic channel of a radio access network and the congestion problems they create in the CN are investigated.
Abstract: Machine-to-machine (M2M) communication, also referred to as Internet of Things (IoT), is a global network of devices such as sensors, actuators, and smart appliances which collect information, and can be controlled and managed in real time over the Internet. Due to their universal coverage, cellular networks and the Internet together offer the most promising foundation for the implementation of M2M communication. With the worldwide deployment of the fourth generation (4G) of cellular networks, the long-term evolution (LTE) and LTE-advanced standards have defined several quality-of-service classes to accommodate the M2M traffic. However, cellular networks are mainly optimized for human-to-human (H2H) communication. The characteristics of M2M traffic are different from the human-generated traffic and consequently create sever problems in both radio access and the core networks (CNs). This survey on M2M communication in LTE/LTE-A explores the issues, solutions, and the remaining challenges to enable and improve M2M communication over cellular networks. We first present an overview of the LTE networks and discuss the issues related to M2M applications on LTE. We investigate the traffic issues of M2M communications and the challenges they impose on both access channel and traffic channel of a radio access network and the congestion problems they create in the CN. We present a comprehensive review of the solutions for these problems which have been proposed in the literature in recent years and discuss the advantages and disadvantages of each method. The remaining challenges are also discussed in detail.

142 citations

Journal ArticleDOI
TL;DR: Numerical results compared with those from the reputation-based schemes show a significant improvement in both classification of the nodes and hypothesis testing results.
Abstract: Wireless sensor networks are prone to node misbehavior arising from tampering by an adversary (Byzantine attack), or due to other factors such as node failure resulting from hardware or software degradation. In this paper, we consider the problem of decentralized detection in wireless sensor networks in the presence of one or more classes of misbehaving nodes. Binary hypothesis testing is considered where the honest nodes transmit their binary decisions to the fusion center (FC), while the misbehaving nodes transmit fictitious messages. The goal of the FC is to identify the misbehaving nodes and to detect the state of nature. We identify each class of nodes with an operating point (false alarm and detection probabilities) on the receiver operating characteristic (ROC) curve. Maximum likelihood estimation of the nodes' operating points is then formulated and solved using the expectation maximization (EM) algorithm with the nodes' identities as latent variables. The solution from the EM algorithm is then used to classify the nodes and to solve the decentralized hypothesis testing problem. Numerical results compared with those from the reputation-based schemes show a significant improvement in both classification of the nodes and hypothesis testing results. We also discuss an inherent ambiguity in the node classification problem which can be resolved if the honest nodes are in majority.

66 citations

Journal ArticleDOI
TL;DR: This work proposes a blind modulation classification algorithm when the channel coefficient, the noise power and the energy of the transmitted signal are unknown at the receiver and evaluates the unknown parameters using the iterative expectation maximization algorithm.
Abstract: We propose a blind modulation classification algorithm when the channel coefficient, the noise power and the energy of the transmitted signal are unknown at the receiver. First, under each candidate modulation scheme, we evaluate the unknown parameters using the iterative expectation maximization algorithm. Modulation classification is then accomplished by minimizing the distance between the log-likelihood of the received data and the expected log-likelihood under each candidate modulation scheme. Results are presented from simulations in terms of detection probability vs. SNR for the class of BPSK, QPSK, 16QAM and 64QAM modulation schemes. The results show a significant improvement over QHLRT and are very close to the upper bound ALRT-UB [1].

51 citations

Journal ArticleDOI
TL;DR: A novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities in cognitive radio networks (CRN).
Abstract: In this paper we consider the problem of cooperative spectrum sensing in cognitive radio networks (CRN) in the presence of misbehaving nodes. We propose a novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities. In contrast to previous work we assume that the FC has no prior information about the radios in the network except that the honest radios are in majority. As shown in the paper this is required for any algorithm to uniquely identify the CRs. Another distinguishing feature is that our approach can classify the radios into more than just two classes of honest and malicious CRs. This applies in cases where the honest CRs have different detection and false alarm probabilities, which may arise when they employ different spectrum sensing techniques or encounter dissimilar channel and noise conditions. Another case is when the CRN includes more than one type of misbehaving CRs. Our numerical results show significant improvements over the widely popular reputation-based classifier (RBC). In particular, with only a few decisions from the CRs, the proposed algorithm can quickly and efficiently classify the CRs whereas the RBC method fails even for networks with a large number of CRs. In all of our numerical results the EM algorithm converged in five or fewer iterations resulting in fast convergence of the proposed method. This makes the proposed method a good candidate for implementation in CRNs. The numerical results are also compared with the Cramer-Rao lower bound and show a close match. Simulation results are also presented to demonstrate the efficacy of the proposed algorithm in the presence of correlated observations among the radios.

33 citations

Journal ArticleDOI
TL;DR: This paper formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas.
Abstract: Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. Many approaches have been proposed when both primary user and secondary user employ a single antenna. Recently several techniques have also been proposed assuming that the the secondary user employs multiple antennas. In this paper, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary user's signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. Through analysis we exhibit the role of the eigenvalues in characterizing the signal ${+}$ noise and noise subspaces in the received data. Simulation results are presented in terms of the receiver operating characteristics and detection probabilities for several cases of interest.

24 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Book ChapterDOI
E.R. Davies1
01 Jan 1990
TL;DR: This chapter introduces the subject of statistical pattern recognition (SPR) by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier.
Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

1,189 citations

Journal ArticleDOI
TL;DR: This exhaustive survey provides insights into the state-of-the-art of IoT enabling and emerging technologies and brings order in the existing literature by classifying contributions according to different research topics.

510 citations

Journal ArticleDOI
TL;DR: This survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service.
Abstract: Due to the under-utilization problem of the allocated radio spectrum, cognitive radio (CR) communications have recently emerged as a reliable and effective solution. Among various network models, this survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service. Spectrum sensing provides the essential information to enable this interweave communications in which primary and secondary users are not allowed to access the medium concurrently. Several researchers have already considered various aspects to realize efficient techniques for spectrum sensing. In this direction, this survey paper provides a detailed review of the state-of-the-art related to the application of spectrum sensing in CR communications. Starting with the basic principles and the main features of interweave communications, this paper provides a classification of the main approaches based on the radio parameters. Subsequently, we review the existing spectrum sensing works applied to different categories such as narrowband sensing, narrowband spectrum monitoring, wideband sensing, cooperative sensing, practical implementation considerations for various techniques, and the recent standards that rely on the interweave network model. Furthermore, we present the latest advances related to the implementation of the legacy spectrum sensing approaches. Finally, we conclude this survey paper with some suggested open research challenges and future directions for the CR networks in next generation Internet-of-Things applications.

483 citations