The worst additive noise under a covariance constraint
نویسندگان
چکیده
منابع مشابه
The Worst Additive Noise Under a Covariance Constraint
The maximum entropy noise under a lag autocorrelation constraint is known by Burg’s theorem to be the th order Gauss–Markov process satisfying these constraints. The question is, what is the worst additive noise for a communication channel given these constraints? Is it the maximum entropy noise? The problem becomes one of extremizing the mutual information over all noise processes with covaria...
متن کاملCapacity Bounds and High-SNR Capacity of the Additive Exponential Noise Channel With Additive Exponential Interference
Communication in the presence of a priori known interference at the encoder has gained great interest because of its many practical applications. In this paper, additive exponential noise channel with additive exponential interference (AENC-AEI) known non-causally at the transmitter is introduced as a new variant of such communication scenarios. First, it is shown that the additive Gaussian ch...
متن کاملPerformance Analysis of Capacity of MIMO Systems under Multiuser Interference Based on Worst-Case Noise Behavior
The capacity of a cellular multiuser MIMO system depends on various parameters, for example, the system structure, the transmit and receive strategies, the channel state information at the transmitter and the receiver, and the channel properties. Recently, the main focus of research was on single-user MIMO systems, their channel capacity, and their error performance with spacetime coding. In ge...
متن کاملA New Adaptive Extended Kalman Filter for a Class of Nonlinear Systems
This paper proposes a new adaptive extended Kalman filter (AEKF) for a class of nonlinear systems perturbed by noise which is not necessarily additive. The proposed filter is adaptive against the uncertainty in the process and measurement noise covariances. This is accomplished by deriving two recursive updating rules for the noise covariances, these rules are easy to implement and reduce the n...
متن کاملRobust Linear Estimation with Covariance Uncertainties
We consider the problem of estimating a random vector x, with covariance uncertainties, that is observed through a known linear transformation H and corrupted by additive noise. We first develop the linear estimator that minimizes the worst-case meansquared error (MSE) across all possible covariance matrices. Although the minimax approach has enjoyed widespread use in the design of robust metho...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- IEEE Trans. Information Theory
دوره 47 شماره
صفحات -
تاریخ انتشار 2001