The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Miguel R D Rodrigues - One of the best experts on this subject based on the ideXlab platform.
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on multiple input multiple output gaussian channels with arbitrary inputs subject to jamming
International Symposium on Information Theory, 2009Co-Authors: Miguel R D Rodrigues, Gil RamosAbstract:This paper considers communication over channels subject to jamming. By capitalizing on the relationship between the mutual information and the minimum mean-squared error (MMSE), we investigate the interference covariance that minimizes the mutual information of a deterministic multiple-input multiple-output (MIMO) channel subject to Gaussian noise and Gaussian interference with arbitrary (not necessarily Gaussian) input distributions. We show that the worst interference covariance satisfies a fixed-Point Equation involving key system quantities, including the MMSE matrix. We also specialize the form of the worst interference covariance to the asymptotic regimes of low and high snr. We demonstrate that in the low-snr regime the worst interference covariance injects an appropriate amount of power directly into the channel eigenmodes. In contrast, in the high-snr regime the worst interference covariance minimizes the minimum distance between a modified version of the constellation vectors. Numerical results illustrate that optimization of the interference covariance has the potential to substantially decrease the reliable information transmission rate between a transmitterreceiver pair. The results are also applicable to scenarios where a jammer aims to impair the secrecy rate of wiretap channels.
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multiple input multiple output gaussian channels optimal covariance for non gaussian inputs
Information Theory Workshop, 2008Co-Authors: Miguel R D Rodrigues, Fernando Perezcruz, S. VerduyAbstract:We investigate the input covariance that maximizes the mutual information of deterministic multiple-input multipleo-utput (MIMO) Gaussian channels with arbitrary (not necessarily Gaussian) input distributions, by capitalizing on the relationship between the gradient of the mutual information and the minimum mean-squared error (MMSE) matrix. We show that the optimal input covariance satisfies a simple fixed-Point Equation involving key system quantities, including the MMSE matrix. We also specialize the form of the optimal input covariance to the asymptotic regimes of low and high snr. We demonstrate that in the low-snr regime the optimal covariance fully correlates the inputs to better combat noise. In contrast, in the high-snr regime the optimal covariance is diagonal with diagonal elements obeying the generalized mercury/waterfilling power allocation policy. Numerical results illustrate that covariance optimization may lead to significant gains with respect to conventional strategies based on channel diagonalization followed by mercury/waterfilling or waterfilling power allocation, particularly in the regimes of medium and high snr.
Achilleas Anastasopoulos - One of the best experts on this subject based on the ideXlab platform.
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a systematic process for evaluating structured perfect bayesian equilibria in dynamic games with asymmetric information
IEEE Transactions on Automatic Control, 2019Co-Authors: Deepanshu Vasal, Abhinav Sinha, Achilleas AnastasopoulosAbstract:We consider both finite-horizon and infinite-horizon versions of a dynamic game with $N$ selfish players who observe their types privately and take actions that are publicly observed. Players’ types evolve as conditionally independent Markov processes, conditioned on their current actions. Their actions and types jointly determine their instantaneous rewards. In dynamic games with asymmetric information, a widely used concept of equilibrium is perfect Bayesian equilibrium (PBE) which consists of a strategy and belief pair that simultaneously satisfy sequential rationality and belief consistency. In general, there does not exist a universal algorithm that decouples the interdependence of strategies and beliefs over time in calculating PBE. In this paper, for the finite-horizon game with independent types, we develop a two-step backward–forward recursive algorithm that sequentially decomposes the problem (w.r.t. time) to obtain a subset of PBEs, which we refer to as structured Bayesian perfect equilibria (SPBE). In such equilibria, a player's strategy depends on his/her history only through a common public belief and its current private type. The backward recursive part of this algorithm defines an equilibrium generating function. Each period in the backward recursion involves solving a fixed-Point Equation on the space of probability simplexes for every possible belief on types. Using this function, equilibrium strategies and beliefs are generated through a forward recursion. We then extend this methodology to the infinite-horizon model, where we propose a time-invariant single-shot fixed-Point Equation, which in conjunction with a forward recursive step, generates the SPBE. Sufficient conditions for the existence of SPBE are provided. With our proposed method, we find equilibria that exhibit signaling behavior. This is illustrated with the help of a concrete public goods example.
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a systematic process for evaluating structured perfect bayesian equilibria in dynamic games with asymmetric information
Social Science Research Network, 2016Co-Authors: Deepanshu Vasal, Abhinav Sinha, Achilleas AnastasopoulosAbstract:We consider both finite-horizon and infinite-horizon versions of a dynamic game with N selfish players who observe their types privately and take actions that are publicly observed. Players’ types evolve as conditionally independent Markov processes, conditioned on their current actions. Their actions and types jointly determine their instantaneous rewards. In dynamic games with asymmetric information, a widely used concept of equilibrium is perfect Bayesian equilibrium (PBE), which consists of a strategy and belief pair that simultaneously satisfy sequential rationality and belief consistency. In general, there does not exist a universal algorithm that decouples the interdependence of strategies and beliefs over time in calculating PBE. In this paper, for the finite-horizon game with independent types, we develop a two-step backward-forward recursive algorithm that sequentially decomposes the problem (w.r.t. time) to obtain a subset of PBEs, which we refer to as structured Bayesian perfect equilibria (SPBE). In such equilibria, a player’s strategy depends on its history only through a common public belief and its current private type. The backward recursive part of this algorithm defines an equilibrium generating function. Each period in the backward recursion involves solving a fixed-Point Equation on the space of probability simplexes for every possible belief on types. Using this function, equilibrium strategies and beliefs are generated through a forward recursion. We then extend this methodology to the infinite-horizon model, where we propose a time-invariant single-shot fixed-Point Equation, which in conjunction with a forward recursive step, generates the SPBE. Sufficient conditions for the existence of SPBE are provided. With our proposed method, we find equilibria that exhibit signaling behavior. This is illustrated with the help of a concrete public goods example.
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a systematic process for evaluating structured perfect bayesian equilibria in dynamic games with asymmetric information
Advances in Computing and Communications, 2016Co-Authors: Deepanshu Vasal, Achilleas AnastasopoulosAbstract:We consider a finite horizon dynamic game with N selfish players who observe their types privately and take actions, which are publicly observed. Players' types evolve as conditionally independent Markov processes, conditioned on their current actions. Their actions and types jointly determine their instantaneous rewards. Since each player has a different information set, this forms a dynamic game with asymmetric information and there is no known methodology to find perfect Bayesian equilibria (PBE) for such games in general. In this paper, we provide a two-step backward-forward recursive algorithm to find a class of PBE using a belief state based on common information of the players. We refer to such equilibria as structured Bayesian perfect equilibria (SPBE). The backward recursive part of this algorithm defines an equilibrium generating function. Each period in the backward recursion involves solving a fixed Point Equation on the space of probability simplexes for every possible belief on types. Using this function, equilibrium strategies and beliefs are defined through a forward recursion. We provide a public goods example to demonstrate the methodology.
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Structured perfect Bayesian equilibrium in infinite horizon dynamic games with asymmetric information
2016 54th Annual Allerton Conference on Communication Control and Computing (Allerton), 2016Co-Authors: Abhinav Sinha, Achilleas AnastasopoulosAbstract:In dynamic games with asymmetric information structure, the widely used concept of equilibrium is perfect Bayesian equilibrium (PBE). This is expressed as a strategy and belief pair that simultaneously satisfy sequential rationality and belief consistency. Unlike symmetric information dynamic games, where subgame perfect equilibrium (SPE) is the natural equilibrium concept, to date there does not exist a universal algorithm that decouples the interdependence of strategies and beliefs over time in calculating PBE. In this paper we find a subset of PBE for an infinite horizon discounted reward asymmetric information dynamic game. We refer to it as Structured PBE or SPBE; in SPBE, any agents' strategy depends on the public history only through a common public belief and on private history only through the respective agents' latest private information (his private type). The public belief acts as a summary of all the relevant past information and it's dimension does not increase with time. The motivation for this comes the common information approach proposed in Nayyar et al. (2013) for solving decentralized team (non-strategic) resource allocation problems with asymmetric information. We calculate SPBE by solving a single-shot fixed-Point Equation and a corresponding forward recursive algorithm. We demonstrate our methodology by means of a public goods example.
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stochastic control of relay channels with cooperative and strategic users
IEEE Transactions on Communications, 2014Co-Authors: Deepanshu Vasal, Achilleas AnastasopoulosAbstract:This paper studies node cooperation in a wireless network from the MAC layer perspective. A simple relay channel with a source, a relay, and a destination node is considered where the source can transmit a packet directly to the destination or transmit through the relay. The tradeoff between average energy and delay is studied by posing the problem as a stochastic dynamical optimization problem. The following two cases are considered: 1) nodes are cooperative and information is decentralized, and 2) nodes are strategic and information is centralized. With decentralized information and cooperative nodes, a structural result is proven that the optimal policy is the solution of a Bellman-type fixed-Point Equation over a time invariant state space. For specific cost functions reflecting transmission energy consumption and average delay, numerical results are presented showing that a policy found by solving this fixed-Point Equation outperforms conventionally used time-division multiple access (TDMA) and random access (RA) policies. When nodes are strategic and information is common knowledge, it is shown that cooperation can be induced by exchange of payments between the nodes, imposed by the network designer such that the socially optimal Markov policy corresponding to the centralized solution is the unique subgame perfect equilibrium of the resulting dynamic game.
Gil Ramos - One of the best experts on this subject based on the ideXlab platform.
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on multiple input multiple output gaussian channels with arbitrary inputs subject to jamming
International Symposium on Information Theory, 2009Co-Authors: Miguel R D Rodrigues, Gil RamosAbstract:This paper considers communication over channels subject to jamming. By capitalizing on the relationship between the mutual information and the minimum mean-squared error (MMSE), we investigate the interference covariance that minimizes the mutual information of a deterministic multiple-input multiple-output (MIMO) channel subject to Gaussian noise and Gaussian interference with arbitrary (not necessarily Gaussian) input distributions. We show that the worst interference covariance satisfies a fixed-Point Equation involving key system quantities, including the MMSE matrix. We also specialize the form of the worst interference covariance to the asymptotic regimes of low and high snr. We demonstrate that in the low-snr regime the worst interference covariance injects an appropriate amount of power directly into the channel eigenmodes. In contrast, in the high-snr regime the worst interference covariance minimizes the minimum distance between a modified version of the constellation vectors. Numerical results illustrate that optimization of the interference covariance has the potential to substantially decrease the reliable information transmission rate between a transmitterreceiver pair. The results are also applicable to scenarios where a jammer aims to impair the secrecy rate of wiretap channels.
S. Verduy - One of the best experts on this subject based on the ideXlab platform.
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multiple input multiple output gaussian channels optimal covariance for non gaussian inputs
Information Theory Workshop, 2008Co-Authors: Miguel R D Rodrigues, Fernando Perezcruz, S. VerduyAbstract:We investigate the input covariance that maximizes the mutual information of deterministic multiple-input multipleo-utput (MIMO) Gaussian channels with arbitrary (not necessarily Gaussian) input distributions, by capitalizing on the relationship between the gradient of the mutual information and the minimum mean-squared error (MMSE) matrix. We show that the optimal input covariance satisfies a simple fixed-Point Equation involving key system quantities, including the MMSE matrix. We also specialize the form of the optimal input covariance to the asymptotic regimes of low and high snr. We demonstrate that in the low-snr regime the optimal covariance fully correlates the inputs to better combat noise. In contrast, in the high-snr regime the optimal covariance is diagonal with diagonal elements obeying the generalized mercury/waterfilling power allocation policy. Numerical results illustrate that covariance optimization may lead to significant gains with respect to conventional strategies based on channel diagonalization followed by mercury/waterfilling or waterfilling power allocation, particularly in the regimes of medium and high snr.
Biao Luo - One of the best experts on this subject based on the ideXlab platform.
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simultaneous policy update algorithms for learning the solution of linear continuous time h state feedback control
Information Sciences, 2013Co-Authors: Biao LuoAbstract:It is well known that the H"~ state feedback control problem can be viewed as a two-player zero-sum game and reduced to find a solution of the algebra Riccati Equation (ARE). In this paper, we propose a simultaneous policy update algorithm (SPUA) for solving the ARE, and develop offline and online versions. The offline SPUA is a model-based approach, which obtains the solution of the ARE by solving a sequence of Lyapunov Equations (LEs). Its convergence is established rigorously by constructing a Newton's sequence for the fixed Point Equation. The online SPUA is a partially model-free approach, which takes advantage of the thought of reinforcement learning (RL) to learn the solution of the ARE online without requiring the internal system dynamics, wherein both players update their action policies simultaneously. The convergence of the online SPUA is proved by demonstrating that it is mathematically equivalent to the offline SPUA. Finally, by conducting comparative simulation studies on an F-16 aircraft plant and a power system, the results show that both the offline SPUA and the online SPUA can find the solution of the ARE, and achieve much better convergence than the existing methods.