The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Michael Gastpar - One of the best experts on this subject based on the ideXlab platform.
-
uncoded transmission is exactly optimal for a simple gaussian sensor network
IEEE Transactions on Information Theory, 2008Co-Authors: Michael GastparAbstract:A single memoryless Gaussian source is observed by many terminals, subject to independent Gaussian observation noises. The terminals are linked to a fusion center via a standard Gaussian multiple-access Channel. The fusion center needs to recover the underlying Gaussian source with respect to mean-squared error. In this correspondence, a theorem of Witsenhausen is shown to imply that an optimal communication strategy is uncoded transmission, i.e., each terminal's Channel Input is merely a scaled version of its noisy observation.
-
on capacity under receive and spatial spectrum sharing constraints
Allerton Conference on Communication Control and Computing, 2007Co-Authors: Michael GastparAbstract:Capacity is often studied under constraints on the Channel Input signals. This paper investigates the behavior of capacity when constraints are placed on the Channel output signal (as well as generalizations thereof). While such a change in perspective leaves the point-to-point problem (essentially) unchanged, the main conclusion is that in certain network scenarios, including multiple-access and relay situations, both the structure of the problem and the conclusions change. For example, capacity results are found for the many-user Gaussian multiple-access Channel (MAC) with arbitrarily dependent sources, cooperation, or feedback, and for the nondegraded Gaussian relay network. The investigations are motivated by recent questions arising in spectrum sharing and dynamic spectrum allocation: Multiple independent networks share the same frequency band, but are spatially mostly disjoint. One approach to grant coexistence is via spatial interference power restrictions, imposed at the network level, rather than at the device level. The corresponding capacity question is posed and partially answered in this paper
-
uncoded transmission is exactly optimal for a simple gaussian sensor network
Information Theory and Applications, 2007Co-Authors: Michael GastparAbstract:One of the simplest sensor network models has one single underlying Gaussian source of interest, observed by many sensors, subject to independent Gaussian observation noise. The sensors communicate over a standard Gaussian multiple-access Channel to a fusion center whose goal is to estimate the underlying source with respect to mean-squared error. In this note, a theorem of Witsenhausen is shown to imply that an optimal communication strategy is uncoded transmission, i.e., each sensors' Channel Input is merely a scaled version of its noisy observation.
-
to code or not to code lossy source Channel communication revisited
IEEE Transactions on Information Theory, 2003Co-Authors: Michael Gastpar, Bixio Rimoldi, Martin VetterliAbstract:What makes a source-Channel communication system optimal? It is shown that in order to achieve an optimal cost-distortion tradeoff, the source and the Channel have to be matched in a probabilistic sense. The match (or lack of it) involves the source distribution, the distortion measure, the Channel conditional distribution, and the Channel Input cost function. Closed-form necessary and sufficient expressions relating the above entities are given. This generalizes both the separation-based approach as well as the two well-known examples of optimal uncoded communication. The condition of probabilistic matching is extended to certain nonergodic and multiuser scenarios. This leads to a result on optimal single-source broadcast communication.
Charalambos D Charalambous - One of the best experts on this subject based on the ideXlab platform.
-
sequential necessary and sufficient conditions for capacity achieving distributions of Channels with memory and feedback
IEEE Transactions on Information Theory, 2017Co-Authors: Photios A Stavrou, Charalambos D Charalambous, Christos K KourtellarisAbstract:We derive sequential necessary and sufficient conditions for any Channel Input conditional distribution ${\mathcal{ P}}_{0,n}\triangleq \{P_{X_{t}|X^{t-1},Y^{t-1}}:t=0,\ldots ,n\}$ to maximize the finite-time horizon directed information defined by $C^{\mathrm{ FB}}_{X^{n} \rightarrow Y^{n}} \triangleq \sup _{\cal P_{0,n}} I(X^{n}\rightarrow ~{Y^{n}})$ , where $I(X^{n} \rightarrow Y^{n}) =\sum _{t=0}^{n}{I}(X^{t};Y_{t}|Y^{t-1})$ , for Channel distributions $\{P_{Y_{t}|Y^{t-1},X_{t}}:t=0,\ldots ,n\}$ and $\{P_{Y_{t}|Y_{t-M}^{t-1},X_{t}}:~t=0,\ldots ,n\}$ , where $Y^{t}\triangleq \{Y^{-1}, Y_{0},\ldots ,Y_{t}\}$ and $X^{t}\triangleq \{X_{0},\ldots ,X_{t}\}$ are the Channel Input and output random processes, and $M$ is a finite non-negative integer. We apply the necessary and sufficient conditions to application examples of time-varying Channels with memory to derive recursive closed form expressions of the optimal distributions, which maximize the finite-time horizon directed information. Furthermore, we derive the feedback capacity from the asymptotic properties of the optimal distributions by investigating the limit $C_{X^\infty \rightarrow Y^\infty }^{\mathrm{ FB}} \triangleq \lim _{ n \longrightarrow \infty } ({1}/({n+1})) C_{X^{n} \rightarrow Y^{n}}^{\mathrm{ FB}}$ without any ´ a priori assumptions, such as stationarity, ergodicity, or irreducibility of the Channel distribution. The framework based on sequential necessary and sufficient conditions can be easily applied to a variety of Channels with memory, beyond the ones considered in this paper.
-
sequential necessary and sufficient conditions for capacity achieving distributions of Channels with memory and feedback
arXiv: Information Theory, 2016Co-Authors: Photios A Stavrou, Charalambos D Charalambous, Christos K KourtellarisAbstract:We derive sequential necessary and sufficient conditions for any Channel Input conditional distribution ${\cal P}_{0,n}\triangleq\{P_{X_t|X^{t-1},Y^{t-1}}:~t=0,\ldots,n\}$ to maximize the finite-time horizon directed information defined by $$C^{FB}_{X^n \rightarrow Y^n} \triangleq \sup_{{\cal P}_{0,n}} I(X^n\rightarrow{Y^n}),~~~ I(X^n \rightarrow Y^n) =\sum_{t=0}^n{I}(X^t;Y_t|Y^{t-1})$$ for Channel distributions $\{P_{Y_t|Y^{t-1},X_t}:~t=0,\ldots,n\}$ and $\{P_{Y_t|Y_{t-M}^{t-1},X_t}:~t=0,\ldots,n\}$, where $Y^t\triangleq\{Y_0,\ldots,Y_t\}$ and $X^t\triangleq\{X_0,\ldots,X_t\}$ are the Channel Input and output random processes, and $M$ is a finite nonnegative integer. \noi We apply the necessary and sufficient conditions to application examples of time-varying Channels with memory and we derive recursive closed form expressions of the optimal distributions, which maximize the finite-time horizon directed information. Further, we derive the feedback capacity from the asymptotic properties of the optimal distributions by investigating the limit $$C_{X^\infty \rightarrow Y^\infty}^{FB} \triangleq \lim_{n \longrightarrow \infty} \frac{1}{n+1} C_{X^n \rightarrow Y^n}^{FB}$$ without any \'a priori assumptions, such as, stationarity, ergodicity or irreducibility of the Channel distribution. The necessary and sufficient conditions can be easily extended to a variety of Channels with memory, beyond the ones considered in this paper.
-
capacity of binary state symmetric Channel with and without feedback and transmission cost
Information Theory Workshop, 2015Co-Authors: Christos K Kourtellaris, Charalambos D CharalambousAbstract:We consider a unit memory Channel, called Binary State Symmetric Channel (BSSC), in which the Channel state is the modulo2 addition of the current Channel Input and the previous Channel output. We derive closed form expressions for the capacity and corresponding Channel Input distribution for the BSSC with and without feedback and transmission cost. We also show that the capacity of the BSSC, with or without feedback, is achieved by a first order symmetric Markov process.
-
capacity of the binary state symmetric Channel with cost constraint
2014Co-Authors: Christos K Kourtellaris, Charalambos D CharalambousAbstract:To be considered for an IEEE Jack Keil Wolf ISIT Student Paper Award. We consider a unit memory Channel, called Binary State Symmetric Channel, where we define the state of the Channel as the modulo2 addition of the current Channel Input and the previous Channel output, and impose a cost constraint related to the average use of the two states of the Channel. Then, we provide closed form expressions for its capacity and its optimal Input distribution, with and without the imposed cost constrain and with or without feedback. For the no feedback case we show that the optimal Input distribution that achieves the capacity is first order Markov and symmetric.
-
capacity of binary state symmetric Channel with and without feedback and transmission cost
arXiv: Information Theory, 2014Co-Authors: Christos K Kourtellaris, Charalambos D CharalambousAbstract:We consider a unit memory Channel, called Binary State Symmetric Channel (BSSC), in which the Channel state is the modulo2 addition of the current Channel Input and the previous Channel output. We derive closed form expressions for the capacity and corresponding Channel Input distribution, of this BSSC with and without feedback and transmission cost. We also show that the capacity of the BSSC is not increased by feedback, and it is achieved by a first order symmetric Markov process.
Christos K Kourtellaris - One of the best experts on this subject based on the ideXlab platform.
-
sequential necessary and sufficient conditions for capacity achieving distributions of Channels with memory and feedback
IEEE Transactions on Information Theory, 2017Co-Authors: Photios A Stavrou, Charalambos D Charalambous, Christos K KourtellarisAbstract:We derive sequential necessary and sufficient conditions for any Channel Input conditional distribution ${\mathcal{ P}}_{0,n}\triangleq \{P_{X_{t}|X^{t-1},Y^{t-1}}:t=0,\ldots ,n\}$ to maximize the finite-time horizon directed information defined by $C^{\mathrm{ FB}}_{X^{n} \rightarrow Y^{n}} \triangleq \sup _{\cal P_{0,n}} I(X^{n}\rightarrow ~{Y^{n}})$ , where $I(X^{n} \rightarrow Y^{n}) =\sum _{t=0}^{n}{I}(X^{t};Y_{t}|Y^{t-1})$ , for Channel distributions $\{P_{Y_{t}|Y^{t-1},X_{t}}:t=0,\ldots ,n\}$ and $\{P_{Y_{t}|Y_{t-M}^{t-1},X_{t}}:~t=0,\ldots ,n\}$ , where $Y^{t}\triangleq \{Y^{-1}, Y_{0},\ldots ,Y_{t}\}$ and $X^{t}\triangleq \{X_{0},\ldots ,X_{t}\}$ are the Channel Input and output random processes, and $M$ is a finite non-negative integer. We apply the necessary and sufficient conditions to application examples of time-varying Channels with memory to derive recursive closed form expressions of the optimal distributions, which maximize the finite-time horizon directed information. Furthermore, we derive the feedback capacity from the asymptotic properties of the optimal distributions by investigating the limit $C_{X^\infty \rightarrow Y^\infty }^{\mathrm{ FB}} \triangleq \lim _{ n \longrightarrow \infty } ({1}/({n+1})) C_{X^{n} \rightarrow Y^{n}}^{\mathrm{ FB}}$ without any ´ a priori assumptions, such as stationarity, ergodicity, or irreducibility of the Channel distribution. The framework based on sequential necessary and sufficient conditions can be easily applied to a variety of Channels with memory, beyond the ones considered in this paper.
-
sequential necessary and sufficient conditions for capacity achieving distributions of Channels with memory and feedback
arXiv: Information Theory, 2016Co-Authors: Photios A Stavrou, Charalambos D Charalambous, Christos K KourtellarisAbstract:We derive sequential necessary and sufficient conditions for any Channel Input conditional distribution ${\cal P}_{0,n}\triangleq\{P_{X_t|X^{t-1},Y^{t-1}}:~t=0,\ldots,n\}$ to maximize the finite-time horizon directed information defined by $$C^{FB}_{X^n \rightarrow Y^n} \triangleq \sup_{{\cal P}_{0,n}} I(X^n\rightarrow{Y^n}),~~~ I(X^n \rightarrow Y^n) =\sum_{t=0}^n{I}(X^t;Y_t|Y^{t-1})$$ for Channel distributions $\{P_{Y_t|Y^{t-1},X_t}:~t=0,\ldots,n\}$ and $\{P_{Y_t|Y_{t-M}^{t-1},X_t}:~t=0,\ldots,n\}$, where $Y^t\triangleq\{Y_0,\ldots,Y_t\}$ and $X^t\triangleq\{X_0,\ldots,X_t\}$ are the Channel Input and output random processes, and $M$ is a finite nonnegative integer. \noi We apply the necessary and sufficient conditions to application examples of time-varying Channels with memory and we derive recursive closed form expressions of the optimal distributions, which maximize the finite-time horizon directed information. Further, we derive the feedback capacity from the asymptotic properties of the optimal distributions by investigating the limit $$C_{X^\infty \rightarrow Y^\infty}^{FB} \triangleq \lim_{n \longrightarrow \infty} \frac{1}{n+1} C_{X^n \rightarrow Y^n}^{FB}$$ without any \'a priori assumptions, such as, stationarity, ergodicity or irreducibility of the Channel distribution. The necessary and sufficient conditions can be easily extended to a variety of Channels with memory, beyond the ones considered in this paper.
-
capacity of binary state symmetric Channel with and without feedback and transmission cost
Information Theory Workshop, 2015Co-Authors: Christos K Kourtellaris, Charalambos D CharalambousAbstract:We consider a unit memory Channel, called Binary State Symmetric Channel (BSSC), in which the Channel state is the modulo2 addition of the current Channel Input and the previous Channel output. We derive closed form expressions for the capacity and corresponding Channel Input distribution for the BSSC with and without feedback and transmission cost. We also show that the capacity of the BSSC, with or without feedback, is achieved by a first order symmetric Markov process.
-
capacity of the binary state symmetric Channel with cost constraint
2014Co-Authors: Christos K Kourtellaris, Charalambos D CharalambousAbstract:To be considered for an IEEE Jack Keil Wolf ISIT Student Paper Award. We consider a unit memory Channel, called Binary State Symmetric Channel, where we define the state of the Channel as the modulo2 addition of the current Channel Input and the previous Channel output, and impose a cost constraint related to the average use of the two states of the Channel. Then, we provide closed form expressions for its capacity and its optimal Input distribution, with and without the imposed cost constrain and with or without feedback. For the no feedback case we show that the optimal Input distribution that achieves the capacity is first order Markov and symmetric.
-
capacity of binary state symmetric Channel with and without feedback and transmission cost
arXiv: Information Theory, 2014Co-Authors: Christos K Kourtellaris, Charalambos D CharalambousAbstract:We consider a unit memory Channel, called Binary State Symmetric Channel (BSSC), in which the Channel state is the modulo2 addition of the current Channel Input and the previous Channel output. We derive closed form expressions for the capacity and corresponding Channel Input distribution, of this BSSC with and without feedback and transmission cost. We also show that the capacity of the BSSC is not increased by feedback, and it is achieved by a first order symmetric Markov process.
Tho Lengoc - One of the best experts on this subject based on the ideXlab platform.
-
vector perturbation precoding under quantized csi
IEEE Transactions on Vehicular Technology, 2016Co-Authors: Sanjeewa P Herath, Duy H N Nguyen, Tho LengocAbstract:This paper focuses on the design of vector perturbation (VP) precoding for multiuser multiple-Input–single-output (MU-MISO) downlink transmission under quantized Channel side information (CSI). In particular, each receiver decomposes its downlink Channel vector in forms of Channel direction information (CDI) and Channel magnitude information (CMI) for feedback to the transmitter. The CMI contribution is studied in two scenarios: i) perfect CMI available to the transmitter and ii) only CMI statistics known at the transmitter. Under these two scenarios, using the quantized CDI and quantization error statistics, the authors propose a unified approach to design the VP precoders that minimize the mean square error (MSE) between Channel Input and output. Closed-form expressions to the precoders are then derived. Bit-error-rate (BER) results indicate that the proposed VP precoder designs are less sensitive to quantization errors, and CMI availability offers significant performance improvements.
-
non linear vector perturbation precoding for multi user downlink under quantized csi
Wireless Communications and Networking Conference, 2015Co-Authors: Sanjeewa P Herath, Duy H N Nguyen, Tho LengocAbstract:This paper focuses on the design of vector perturbation (VP) precoding for multi-user multiple-Input singleoutput downlink transmission under quantized Channel state information. Each receiver decomposes its downlink Channel vector in forms of Channel direction information (CDI) and Channel magnitude information (CMI) for feedback to the transmitter. Under quantized CDI and quantization error statistics, closed-form expressions to the mean-squared-error (MSE) between Channel Input and output when (i) perfect CMI available to the transmitter and (ii) only CMI statistics known at the transmitter, are derived. We then propose a unified approach to design the MSE minimization based VP precoders. Bit error rate simulation results indicate that the proposed VP precoder designs are less sensitive to quantization errors and CMI availability helps to improve the performance.
Sanjeewa P Herath - One of the best experts on this subject based on the ideXlab platform.
-
vector perturbation precoding under quantized csi
IEEE Transactions on Vehicular Technology, 2016Co-Authors: Sanjeewa P Herath, Duy H N Nguyen, Tho LengocAbstract:This paper focuses on the design of vector perturbation (VP) precoding for multiuser multiple-Input–single-output (MU-MISO) downlink transmission under quantized Channel side information (CSI). In particular, each receiver decomposes its downlink Channel vector in forms of Channel direction information (CDI) and Channel magnitude information (CMI) for feedback to the transmitter. The CMI contribution is studied in two scenarios: i) perfect CMI available to the transmitter and ii) only CMI statistics known at the transmitter. Under these two scenarios, using the quantized CDI and quantization error statistics, the authors propose a unified approach to design the VP precoders that minimize the mean square error (MSE) between Channel Input and output. Closed-form expressions to the precoders are then derived. Bit-error-rate (BER) results indicate that the proposed VP precoder designs are less sensitive to quantization errors, and CMI availability offers significant performance improvements.
-
non linear vector perturbation precoding for multi user downlink under quantized csi
Wireless Communications and Networking Conference, 2015Co-Authors: Sanjeewa P Herath, Duy H N Nguyen, Tho LengocAbstract:This paper focuses on the design of vector perturbation (VP) precoding for multi-user multiple-Input singleoutput downlink transmission under quantized Channel state information. Each receiver decomposes its downlink Channel vector in forms of Channel direction information (CDI) and Channel magnitude information (CMI) for feedback to the transmitter. Under quantized CDI and quantization error statistics, closed-form expressions to the mean-squared-error (MSE) between Channel Input and output when (i) perfect CMI available to the transmitter and (ii) only CMI statistics known at the transmitter, are derived. We then propose a unified approach to design the MSE minimization based VP precoders. Bit error rate simulation results indicate that the proposed VP precoder designs are less sensitive to quantization errors and CMI availability helps to improve the performance.