The Experts below are selected from a list of 188760 Experts worldwide ranked by ideXlab platform
Frank R Kschischang - One of the best experts on this subject based on the ideXlab platform.
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a Signal Space distance measure for nondispersive optical fiber
IEEE Transactions on Information Theory, 2021Co-Authors: Reza Rafie Borujeny, Frank R KschischangAbstract:The nondispersive per-sample channel model for the optical fiber channel is considered. Under certain smoothness assumptions, the problem of finding the minimum amount of noise energy that can render two different input points indistinguishable is formulated. This minimum noise energy is then taken as a measure of distance between the points in the input alphabet. Using the machinery of optimal control theory, necessary conditions that describe the minimum-energy noise trajectories are stated as a system of nonlinear differential equations. It is shown how to find the distance between two input points by solving this system of differential equations. The problem of designing Signal constellations with the largest minimum distance subject to a peak power constraint is formulated as a clique-finding problem. As an example, a 16-point constellation is designed and compared with conventional quadrature amplitude modulation. A computationally efficient approximation for the proposed distance measure is provided. It is shown how to use this approximation to design large constellations with large minimum distances. Based on the control-theoretic viewpoint of this paper, a new decoding scheme for such nonlinear channels is proposed.
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a variational Signal Space distance measure for nondispersive optical fiber
International Symposium on Information Theory, 2019Co-Authors: Reza Rafie Borujeny, Frank R KschischangAbstract:The nondispersive per-sample channel model for the optical fiber channel is considered. Under some smoothness assumptions, the problem of finding the minimum amount of noise energy that can render two different input points indistinguishable is formulated. The necessary conditions for the noise trajectory that has the minimum energy are described as a system of nonlinear differential equations. It is suggested that this model can be generalized to consider dispersion and to design new communication schemes for fiber-optic communication systems.
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optical intensity modulated direct detection channels Signal Space and lattice codes
IEEE Transactions on Information Theory, 2003Co-Authors: Steve Hranilovic, Frank R KschischangAbstract:Traditional approaches to constructing constellations for electrical channels cannot be applied directly to the optical intensity channel. This work presents a structured Signal Space model for optical intensity channels where the nonnegativity and average amplitude constraints are represented geometrically. Lattice codes satisfying channel constraints are defined and coding and shaping gain relative to a baseline are computed. An effective Signal Space dimension is defined to represent the precise impact of coding and shaping on bandwidth. Average optical power minimizing shaping regions are derived in some special cases. Example lattice codes are constructed and their performance on an idealized point-to-point wireless optical link is computed. Bandwidth-efficient schemes are shown to have promise for high data-rate applications, but require greater average optical power.
Xiaojun Yuan - One of the best experts on this subject based on the ideXlab platform.
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mimo multiway relaying with clustered full data exchange Signal Space alignment and degrees of freedom
IEEE Transactions on Wireless Communications, 2014Co-Authors: Xiaojun YuanAbstract:Recently, much research interest has been focused on the design of efficient communication mechanisms for multiple-input-multiple-output (MIMO) multiway relay channels (mRCs). In this paper, we investigate achievable degrees of freedom (DoFs) of the MIMO mRC with L clusters and K users per cluster, where each user is equipped with M antennas and the relay with N antennas. Our analysis is focused on a new data exchange model, termed clustered full data exchange, i.e., each user in a cluster wants to learn the messages of all the other users in the same cluster. Novel Signal alignment techniques are developed to jointly and systematically construct the beamforming matrices at the users and the relay for efficient implementation of physical-layer network coding. Based on this, we derive an achievable DoF of the MIMO mRC with an arbitrary network configuration of L and K, as well as with an arbitrary antenna configuration of M and N. We show that our proposed scheme achieves the DoF capacity when M/N ≤ 1/(LK - 1) and M/N ≥ ((K - 1)L + 1)/KL. The DoF results derived in this paper can serve as fundamental benchmarks in evaluating the performance of practical communication systems over MIMO mRCs and provide guidance and insights into the design of wireless relay networks.
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mimo multiway relaying with clustered full data exchange Signal Space alignment and degrees of freedom
arXiv: Information Theory, 2014Co-Authors: Xiaojun YuanAbstract:We investigate achievable degrees of freedom (DoF) for a multiple-input multiple-output (MIMO) multiway relay channel (mRC) with $L$ clusters and $K$ users per cluster. Each user is equipped with $M$ antennas and the relay with $N$ antennas. We assume a new data exchange model, termed \emph{clustered full data exchange}, i.e., each user in a cluster wants to learn the messages of all the other users in the same cluster. Novel Signal alignment techniques are developed to systematically construct the beamforming matrices at the users and the relay for efficient physical-layer network coding. Based on that, we derive an achievable DoF of the MIMO mRC with an arbitrary network configuration of $L$ and $K$, as well as with an arbitrary antenna configuration of $M$ and $N$. We show that our proposed scheme achieves the DoF capacity when $\frac{M}{N} \leq \frac{1}{LK-1}$ and $\frac{M}{N} \geq \frac{(K-1)L+1}{KL}$.
Samu Taulu - One of the best experts on this subject based on the ideXlab platform.
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removal of magnetoencephalographic artifacts with temporal Signal Space separation demonstration with single trial auditory evoked responses
Human Brain Mapping, 2009Co-Authors: Samu Taulu, Riitta HariAbstract:Magnetic interference Signals often hamper analysis of magnetoencephalographic (MEG) measurements. Artifact sources in the proximity of the sensors cause strong and spatially complex Signals that are particularly challenging for the existing interference-suppression methods. Here we demonstrate the performance of the temporally extended Signal Space separation method (tSSS) in removing strong interference caused by external and nearby sources on auditory-evoked magnetic fields-the sources of which are well established. The MEG Signals were contaminated by normal environmental interference, by artificially produced additional external interference, and by nearby artifacts produced by a piece of magnetized wire in the subject's lip. After tSSS processing, even the single-trial auditory responses had a good-enough Signal-to-noise ratio for detailed waveform and source analysis. Waveforms and source locations of the tSSS-reconstructed data were in good agreement with the responses from the control condition without extra interference. Our results demonstrate that tSSS is a robust and efficient method for removing a wide range of different types of interference Signals in neuromagnetic multichannel measurements.
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spatiotemporal Signal Space separation method for rejecting nearby interference in meg measurements
Physics in Medicine and Biology, 2006Co-Authors: Samu Taulu, Juha SimolaAbstract:Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact Signals. Conventional interference rejection methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal Signal Space separation method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic separation into brain-related and external interference Signals is accomplished with Signal Space separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the method removes the artefacts without altering the field patterns of the brain Signals.
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applications of the Signal Space separation method
IEEE Transactions on Signal Processing, 2005Co-Authors: Samu Taulu, Juha Simola, Matti KajolaAbstract:The reliability of biomagnetic measurements is traditionally challenged by external interference Signals, movement artifacts, and comparison problems caused by different positions of the subjects or different sensor configurations. The Signal Space Separation method (SSS) idealizes magnetic multichannel Signals by transforming them into device-independent idealized channels representing the measured data in uncorrelated form. The transformation has separate components for the biomagnetic and external interference Signals, and thus, the biomagnetic Signals can be reconstructed simply by leaving out the contribution of the external interference. The foundation of SSS is a basis spanning all multichannel Signals of magnetic origin. It is based on Maxwell's equations and the geometry of the sensor array only, with the assumption that the sensors are located in a current free volume. SSS is demonstrated to provide suppression of external interference Signals, standardization of different positions of the subject, standardization of different sensor configurations, compensation for distortions caused by movement of the subject (even a subject containing magnetic impurities), suppression of sporadic sensor artifacts, a tool for fine calibration of the device, extraction of biomagnetic DC fields, and an aid for realizing an active compensation system. Thus, SSS removes many limitations of traditional biomagnetic measurements.
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presentation of electromagnetic multichannel data the Signal Space separation method
Journal of Applied Physics, 2005Co-Authors: Samu Taulu, Matti KajolaAbstract:Measurement of external magnetic fields provides information on electric current distribution inside an object. For example, in magnetoencephalography modern measurement devices sample the magnetic field produced by the brain in several hundred distinct locations around the head. The Signal Space separation (SSS) method creates a fundamental linear basis for all measurable multichannel Signal vectors of magnetic origin. The SSS basis is based on the fact that the magnetic field can be expressed as a combination of two separate and rapidly converging expansions of harmonic functions with one expansion for Signals arising from inside of the measurement volume of the sensor array and another for Signals arising from outside of this volume. The separation is based on the different convergence volumes of the two expansions and on the fact that the sensors are located in a source current-free volume between the interesting and interfering sources. Individual terms of the expansions are shown to contain uncorrelated information of the underlying source distribution. SSS provides a stable decomposition of the measurement into a fundamental device-independent form when used with an accurately calibrated multichannel device. The external interference Signals are elegantly suppressed by leaving the interference components out from the reconstruction based on the decomposition. Representation of multichannel data with the SSS basis is shown to provide a large variety of applications for improved analysis of multichannel data.
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suppression of interference and artifacts by the Signal Space separation method
Brain Topography, 2003Co-Authors: Samu Taulu, Matti Kajola, Juha SimolaAbstract:Multichannel measurement with hundreds of channels oversamples a curl-free vector field, like the magnetic field in a volume free of sources. This is based on the constraint caused by the Laplace's equation for the magnetic scalar potential; outside of the source volume the Signals are spatially band limited. A functional solution of Laplace's equation enables one to separate the Signals arising from the sphere enclosing the interesting sources, e.g. the currents in the brain, from the magnetic interference. Signal Space separation (SSS) is accomplished by calculating individual basis vectors for each term of the functional expansion to create a Signal basis covering all measurable Signal vectors. Because the SSS basis is linearly independent for all practical sensor arrangements, any Signal vector has a unique SSS decomposition with separate coefficients for the interesting Signals and Signals coming from outside the interesting volume. Thus, SSS basis provides an elegant method to remove external disturbances. The device-independent SSS coefficients can be used in transforming the interesting Signals to virtual sensor configurations. This can also be used in compensating for distortions caused by movement of the object by modeling it as movement of the sensor array around a static object. The device-independence of the decomposition also enables physiological DC phenomena to be recorded using voluntary head movements. When used with properly designed sensor array, SSS does not affect the morphology or the Signal-to-noise ratio of the interesting Signals.
Reza Rafie Borujeny - One of the best experts on this subject based on the ideXlab platform.
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a Signal Space distance measure for nondispersive optical fiber
IEEE Transactions on Information Theory, 2021Co-Authors: Reza Rafie Borujeny, Frank R KschischangAbstract:The nondispersive per-sample channel model for the optical fiber channel is considered. Under certain smoothness assumptions, the problem of finding the minimum amount of noise energy that can render two different input points indistinguishable is formulated. This minimum noise energy is then taken as a measure of distance between the points in the input alphabet. Using the machinery of optimal control theory, necessary conditions that describe the minimum-energy noise trajectories are stated as a system of nonlinear differential equations. It is shown how to find the distance between two input points by solving this system of differential equations. The problem of designing Signal constellations with the largest minimum distance subject to a peak power constraint is formulated as a clique-finding problem. As an example, a 16-point constellation is designed and compared with conventional quadrature amplitude modulation. A computationally efficient approximation for the proposed distance measure is provided. It is shown how to use this approximation to design large constellations with large minimum distances. Based on the control-theoretic viewpoint of this paper, a new decoding scheme for such nonlinear channels is proposed.
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a variational Signal Space distance measure for nondispersive optical fiber
International Symposium on Information Theory, 2019Co-Authors: Reza Rafie Borujeny, Frank R KschischangAbstract:The nondispersive per-sample channel model for the optical fiber channel is considered. Under some smoothness assumptions, the problem of finding the minimum amount of noise energy that can render two different input points indistinguishable is formulated. The necessary conditions for the noise trajectory that has the minimum energy are described as a system of nonlinear differential equations. It is suggested that this model can be generalized to consider dispersion and to design new communication schemes for fiber-optic communication systems.
Joohwan Chun - One of the best experts on this subject based on the ideXlab platform.
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Signal Space alignment for an encryption message and successive network code decoding on the mimo k way relay channel
International Conference on Communications, 2011Co-Authors: Joohwan ChunAbstract:This paper investigates a network information flow problem for a multiple-input multiple-output (MIMO) Gaussian wireless network with K-users and a single intermediate relay having M antennas. In this network, each user intends to convey a multicast message to all other users while receiving K-1 independent messages from the other users via an intermediate relay. This network information flow is termed a MIMO Gaussian K-way relay channel. For this channel, we show that K/2 degrees of freedom is achievable if M=K-1. To demonstrate this, we come up with an encoding and decoding strategy inspired from cryptography theory. The proposed encoding and decoding strategy involves a Signal Space alignment for an encryption message for the multiple access phase (MAC) and zero forcing with successive network code decoding for the broadcast (BC) phase. The idea of the Signal Space alignment for an encryption message is that all users cooperatively choose the precoding vectors to transmit the message so that the relay can receive a proper encryption message with a special structure, network code chain structure. During the BC phase, zero forcing combined with successive network code decoding enables all users to decipher the encryption message from the relay despite the fact that they all have different self-information which they use as a key.
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Signal Space alignment for an encryption message and successive network code decoding on the mimo k way relay channel
arXiv: Information Theory, 2010Co-Authors: Joohwan ChunAbstract:This paper investigates a network information flow problem for a multiple-input multiple-output (MIMO) Gaussian wireless network with $K$-users and a single intermediate relay having $M$ antennas. In this network, each user intends to convey a multicast message to all other users while receiving $K-1$ independent messages from the other users via an intermediate relay. This network information flow is termed a MIMO Gaussian $K$-way relay channel. For this channel, we show that $\frac{K}{2}$ degrees of freedom is achievable if $M=K-1$. To demonstrate this, we come up with an encoding and decoding strategy inspired from cryptography theory. The proposed encoding and decoding strategy involves a \textit{Signal Space alignment for an encryption message} for the multiple access phase (MAC) and \textit{zero forcing with successive network code decoding} for the broadcast (BC) phase. The idea of the \emph{Signal Space alignment for an encryption message} is that all users cooperatively choose the precoding vectors to transmit the message so that the relay can receive a proper encryption message with a special structure, \textit{network code chain structure}. During the BC phase, \emph{zero forcing combined with successive network code decoding} enables all users to decipher the encryption message from the relay despite the fact that they all have different self-information which they use as a key.
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degrees of freedom of the mimo y channel Signal Space alignment for network coding
IEEE Transactions on Information Theory, 2010Co-Authors: Joohwan ChunAbstract:In this paper, we study a network information flow problem for a multiple-input-multiple-output (MIMO) Gaussian wireless network with three users each equipped with M antennas and a single intermediate relay equipped with N antennas. In this network, each user intends to convey independent messages for two different users via the intermediate relay while receiving two independent messages from the other two users. This is a generalized version of the two-way relay channel for the three-user case. We will call it a "MIMO Y channel." For this MIMO Y channel, we show that the capacity is 3M log(SNR) + o(log(SNR)) if N ≥ ⌈3M/2⌉ by using two novel Signaling techniques, which are Signal Space alignment for network coding, and network-coding-aware interference nulling beamforming.
