Ananth Subramanian

Joint rate and power control algorithms for wireless networks

There is a fundamental tradeoff between power consumption, data transmission rates, and congestion levels in a wireless network. These three elements influence the performance of rate and power control strategies, and they need to be coordinated judiciously. This paper proposes dynamic rate and power control algorithms for distributed wireless networks that also account for the congestion levels in a network. The design is pursued by formulating state-space models with and without uncertain dynamics and by determining control signals that help meet certain performance criteria (such as robustness and desired levels of signal-to-interference ratio). Simulation results illustrate the performance of the proposed control schemes.

Multiobjective filter design for uncertain stochastic time-delay systems

This note addresses the problem of robust multiobjective filtering for discrete time-delay systems with mixed stochastic and deterministic uncertainties, in addition to unmodeled nonlinearities. A procedure is developed for the design of linear and exponentially stable filters with a bounded error variance, exponential rate of decay, and robust performance for the error system.

REACA: An Efficient Protocol Architecture for Large Scale Sensor Networks

The emergence of wireless sensor networks has imposed many challenges on network design such as severe energy constraints, limited bandwidth and computing capabilities. This kind of networks necessitates network protocol architectures that are robust, energy-efficient, scalable, and easy for deployment. This paper proposes a robust energy-aware clustering architecture (REACA) for large-scale wireless sensor networks. We analyze the performance of the REACA network in terms of quality-of-service, asymptotic throughput capacity, and power consumption. In particular, we study how the throughput capacity scales with the number of nodes and the number of clusters. We show that by exploiting traffic locality, clustering can achieve performance improvement both in capacity and in power consumption over general-purpose ad hoc networks. We also explore the fundamental trade-off between throughput capacity and power consumption for single-hop and multi-hop routing schemes in cluster-based networks. The protocol architecture and performance analysis developed in this paper provide useful insights for practical design and deployment of large-scale wireless sensor network.

Regularized robust filters for time-varying uncertain discrete-time systems

This note develops robust filters for time-varying uncertain discrete-time systems. The developed filters are based on a data regularization solution and they enforce a minimum state-error variance property. Simulation results confirm their superior performance over other robust filter designs.

A robust power and rate control method for state-delayed wireless networks

A robust power and rate control algorithm is proposed for distributed wireless networks where the network dynamics is modelled as an uncertain discrete-time state-delayed system.

REACA: An Efficient Protocol Architecture for Large Scale Sensor Networks (Corrected)*

The emergence of wireless sensor networks has imposed many challenges on network design such as severe energy constraints, limited bandwidth and computing capabilities. This kind of networks necessitates network protocol architectures that are robust, energy-efficient, scalable, and easy for deployment. This paper proposes a robust energy-aware clustering architecture (REACA) for large-scale wireless sensor networks. We analyze the performance of the REACA network in terms of quality-of-service, asymptotic throughput capacity, and power consumption. In particular, we study how the throughput capacity scales with the number of nodes and the number of clusters. We show that by exploiting traffic locality, clustering can achieve performance improvement both in capacity and in power consumption over general-purpose ad hoc networks. We also explore the fundamental trade-off between throughput capacity and power consumption for single-hop and multi-hop routing schemes in cluster-based networks. The protocol architecture and performance analysis developed in this paper provide useful insights for practical design and deployment of large-scale wireless sensor network.

UWB Linear Quadratic Frequency Domain Frequency Invariant Beamforming and Angle of Arrival Estimation

In this paper, we present Ultra Wide Band (UWB) frequency invariant beamforming and angle of arrival (AoA) estimation techniques. We propose a new linear quadratic (LQ) frequency domain frequency invariant beamforming strategy. Based on the proposed beamforming strategy, we give a Kalman filter based AoA estimation technique. Simulation results illustrate the performance of the proposed beamforming and AoA estimation strategies.

A probabilistic power-aware routing algorithm for wireless ad-hoc networks

We propose a probabilistic routing algorithm for wireless ad-hoc networks. The focus is on applications that can tolerate end-to-end delays and those for which the final destination for the packets is a small region. The nodes are assumed stationary and establish connections with a master node according to a priority scheme that relates to their distances from the master node. Simulation results illustrate the performance of the proposed algorithm.

Robust exponential filtering for uncertain systems with stochastic and polytopic uncertainties

This paper addresses the problem of robust exponential filtering for discrete uncertain systems with mixed stochastic and deterministic uncertainties, in addition to unmodelled nonlinearities and measurement and process noises with bounded variances.

An uplink DS-CDMA receiver using a robust post-correlation Kalman structure

Kalman filtering has been proposed in the literature for wireless channel estimation, however, it is not sufficiently robust to uncertainties in the channel auto-correlation model as well as to multiple access interference (MAI). This paper presents a receiver structure for direct-sequence code-division multiple-access (DS-CDMA) systems by using robust Kalman estimation and post-correlation (i.e., symbol rate) processing for channel estimation. The proposed structure is also generalized to incorporate multiple-antenna combining and interference cancellation techniques. The resulting receiver outperforms earlier structures in the presence of channel modeling uncertainties, MAI, and low-received signal-to-noise ratio. The enhancement in performance is achieved at the same order of complexity as a standard Kalman-based receiver.