A Stieltjes transform approach for analyzing the RLS adaptive Filter

Although the RLS filter is well-known and various algorithms have been developed for its implementation, analyzing its performance when the regressors are random, as is often the case, has proven to be a formidable task. The reason is that the Riccati recursion, which propagates the error covariance matrix, becomes a random recursion. The existing results are approximations based on assumptions that are often not very realistic. In this paper we use ideas from the theory of large random matrices to find the asymptotic (in time) eigendistribution of the error covariance matrix of the RLS filter. Under the assumption of a large dimensional state vector (in most cases n = 10-20 is large enough to get quite accurate predictions) we find the asymptotic eigendistribution of the error covariance for temporally white regressors, shift structured regressors, and for the RLS filter with intermittent observations

A Stieltjes transform approach for studying the steady-state behavior of random Lyapunov and Riccati recursions

In this paper we study the asymptotic eigenvalue distribution of certain random Lyapunov and Riccati recursions that arise in signal processing and control. The analysis of such recursions has remained elusive when the system and/or covariance matrices are random. Here we use transform techniques (such as the Stieltjes transform and free probability) that have gained popularity in the study of large random matrices. While we have not yet developed a full theory, we do obtain explicit formula for the asymptotic eigendistribution of certain classes of Lyapunov and Riccati recursions, which well match simulation results. Generalizing the results to arbitrary classes of such recursions is currently under investigation

On the steady-state performance of Kalman filtering with intermittent observations for stable systems

Many recent problems in distributed estimation and control reduce to estimating the state of a dynamical system using sensor measurements that are transmitted across a lossy network. A framework for analyzing such systems was proposed in and called Kalman filtering with intermittent observations. The performance of such a system, i.e., the error covariance matrix, is governed by the solution of a matrix-valued random Riccati recursion. Unfortunately, to date, the tools for analyzing such recursions are woefully lacking, ostensibly because the recursions are both nonlinear and random, and hence intractable if one wants to analyze them exactly. In this paper, we extend some of the large random matrix techniques first introduced in to Kalman filtering with intermittent observations. For systems with a stable system matrix and i.i.d. time-varying measurement matrices, we obtain explicit equations that allow one to compute the asymptotic eigendistribution of the error covariance matrix. Simulations show excellent agreement between the theoretical and empirical results for systems with as low as n = 10, 20 states. Extending the results to unstable system matrices and time-invariant measurement matrices is currently under investigation

On the throughput of opportunistic beamforming with imperfect CSI

The throughput of a multiple-antenna broadcast channel highly depends on the channel state information (CSI) at the transmitter side. However, due to the time variant nature of wireless channels, having perfect knowledge of the under- lying links appears to be a questionable assumption, especially when the number of users and/or antennas increases. Although it can become computationally prohibitive in practice, theoretically any point on the capacity region of a Gaussian broadcast channel is achievable using dirty paper coding (DPC) if full CSI is available. The aforementioned drawbacks of DPC have motivated the development of simpler transmission strategies that re- quire little CSI and yet can deliver a large portion of the capacity. One such scheme is opportunistic beam-forming that is shown to be able to achieve the same throughput scaling as that of DPC for the regime of large number of users. In this paper we investigate the performance of opportunistic beamforming when the perfect channel state information is not available; i.e., the channel estimation is erroneous. We will show that in order to maximize the throughput (sum rate capacity), the transmitter needs to back off the rate than what is suggested by the estimated channel state. We obtain the optimal back off and show that by using this modified opportunistic scheme, the same multiuser gain can be achieved

The Role of Learning Organizations in Improving Human Resources Management

The rapid pace of changes in internal and external business environment affects the organizations to develop a new strategy such as learning ; in this change process, what is more important is that how organizations struggle to survive and succeed in business environment . The proposed strategy is that organizations develop into learning organizations , in other words , innovations in different dimensions make the organizations to learn as much as or faster than outside environment , in case the organizations do not embrace change and not learn as much as or even more than environmental changes , doubtlessly, they shall be destroyed. Organizational learning in terms of methods, structures within organization encourages the human resources in the organizations. Keywords: Learning, learning Organizations, Human Resource