Learning to make decisions in an uncertain and dynamic environment is a task of fundamental performance in a number of domains. This paper concerns the problem of learning control policies for an unknown linear dynamical system so as to minimize a quadratic cost function. We present a method, based on convex optimization, that accomplishes this task ‘robustly’, i.e., the worst-case cost, accounting for system uncertainty given the observed data, is minimized. The method balances exploitation and exploration, exciting the system in such a way so as to reduce uncertainty in the model parameters to which the worst-case cost is most sensitive. Numerical simulations and application to a hardware-in-the-loop servo-mechanism are used to demonstrate the approach, with appreciable performance and robustness gains over alternative methods observed in both.

In this paper, we consider the recovery of piecewise sparse signals from incomplete noisy measurements via a greedy algorithm. Here piecewise sparse means that the signal can be approximated in certain domain with known number of nonzero entries in each piece/segment. This paper makes a two-fold contribution to this problem: 1) formulating a piecewise sparse model in the framework of compressed sensing and providing the theoretical analysis of corresponding sensing matrices; 2) developing a greedy algorithm called piecewise orthogonal matching pursuit (POMP) for the recovery of piecewise sparse signals. Experimental simulations verify the effectiveness of the proposed algorithms.

We derive a modular fluid-flow network congestion control model based on a law of fundamental nature in networks: the conservation of information. Network elements such as queues, users, and transmission channels and network performance indicators like sending/acknowledgment rates and delays are mathematically modeled by applying this law locally. Our contributions are twofold. First, we introduce a modular metamodel that is sufficiently generic to represent any network topology. The proposed model is composed of building blocks that implement mechanisms ignored by the existing ones, which can be recovered from exact reduction or approximation of this new model. Second, we provide a novel classification of previously proposed models in the literature and show that they are often not capable of capturing the transient behavior of the network precisely. Numerical results obtained from packet-level simulations demonstrate the accuracy of the proposed model.

A major constraint in deployments of resource-limited networks is the energy consumption related to the battery lifetime of the network nodes. To this end, power efficient digital modulation techniques such as On-Off keying (OOK) are highly attractive. In this paper, a novel complete probabilistic description of the Direct Sequence - Coded Division Multiple Access (DS-CDMA) system with random signatures employing OOK modulation is presented. The system scenario considers simultaneously transmitting nodes in Rayleigh fading conditions. Numerical simulations are provided to support the derived results.

Despite the tremendous research effort spent on MIMO systems - very few papers address the impact of hardware imperfections. In this paper we analyze the distortion noise of the transmitter branches of a MIMO transmitter. In particular we analyze the cross-correlation of the distortion noises for the case that the transmitted signals are correlated. This case arises when there is just a single stream being transmitted with beamforming or in the case of multiple streams and linear precoding. We analyze the problem in two ways: using analytical derivations and using measurements on our testbed. In the analytical case we assume a transmitter impaired by a 3rd order non-linearity. For this scenario we find that the absolute correlation coefficient of the distortion noises is given by the absolute value of the correlation coefficients of the input signals to the power of three, while the phase is the same as that of the input signal. Our measurements also show good agreement between measurements and analytical results. It seems to suggest that the distortion noises can be regarded as practically independent whenever there are two or more modulation streams being transmitted (spatial multiplexing). In a single-stream scenario, the distortion noise will tend to have the same spatial distribution as the desired signal.

This paper develops a new model that describes the queueing process of a communication network when data sources use window flow control. The model takes into account the burstiness in sub-round-trip time (RTT) timescales and the instantaneous rate differences of a flow at different links. It is generic and independent of actual source flow control algorithms. Basic properties of the model and its relation to existing work are discussed. In particular, for a general network with multiple links, it is demonstrated that spatial interaction of oscillations allows queue instability to occur even when all flows have the same RTTs and maintain constant windows. The model is used to study the dynamics of delay-based congestion control algorithms. It is found that the ratios of RTTs are critical to the stability of such systems, and previously unknown modes of instability are identified. Packet-level simulations and testbed measurements are provided to verify the model and its predictions.

A novel continuous time fluid flow model of the dynamics of the interaction between ACK-clocking and the link buffer is presented. A fundamental integral equation relating the instantaneous flow rate and the window dynamics is derived. Properties of the model, such as well-posedness and stability, are investigated. Packet level experiments verify that this new model is more accurate than existing models, correctly predicting qualitatively different behaviors, for example when round trip delays are heterogeneous.

This paper studies window flow control focusing on bridging the gap between microscopic factors such as burstiness in sub-RTT timescales, and observable macroscopic properties such as steady state bandwidth sharing and flow level stability. Using new models, we analytically capture notable effects of microscopic behavior on macroscopic quantities. For loss-based protocols, we calculate the loss synchronization rate for different flows and use it to quantitatively explain the unfair bandwidth sharing between paced and unpaced TCP flows. For delay-based protocols, we show that the ratios of round trip delays are critical to the stability of the system. These results deepen the fundamental understanding of congestion control systems. Packet level simulations are used to verify our theoretical claims.

The solution of many important signal processing problems depends on the estimation of the parameters of a hidden Markov model (HMM). Unfortunately, to date the only known methods for performing this estimation have been iterative, and therefore computationally demanding. By way of contrast, this paper presents a new fast and non-iterative method that utilizes certain 'state spaced subspace system identification' (4SID) ideas from the control theory literature. A short simulation example presented here indicates this new technique to be almost as accurate as maximum-likelihood estimation, but an order of magnitude less computationally demanding than the Baum-Welch (EM) algorithm.