Now showing items 1-6 of 6

• #### Adaptive Algorithms for Sparse Nonlinear Channel Estimation ﻿

(2009)
In this paper, we consider the estimation of sparse nonlinear communication channels. Transmission over the channels is represented by sparse Volterra models that incorporate the effect of Power Amplifiers. Channel estimation ...
• #### Adaptive Greedy Algorithm With Application to Nonlinear Communications ﻿

(Institute of Electrical and Electronics Engineers, 2010)
Greedy algorithms form an essential tool for compressed sensing. However, their inherent batch mode discourages their use in time-varying environments due to significant complexity and storage requirements. In this paper ...
• #### Distributed Dynamic Spectrum Allocation for Secondary Users in a Vertical Spectrum Sharing Scenario ﻿

(Japan Science and Technology Agency (JST), 2012)
In this paper, we study the problem of distributed spectrum allocation under a vertical spectrum sharing scenario in a cognitive radio network. The secondary users share the spectrum licensed to the primary user by observing ...
• #### GADIA: A Greedy Asynchronous Distributed Interference Avoidance Algorithm ﻿

(Institute of Electrical and Electronics Engineers, 2010)
In this paper, the problem of distributed dynamic frequency allocation is considered for a canonical communication network, which spans several networks such as cognitive radio networks and digital subscriber lines (DSLs). ...
• #### Regularized recursive least squares for anomaly detection in sparse channel tracking applications ﻿

(ACM Press, 2011)
In this paper, we study the problem of anomaly detection in sparse channel tracking applications via the $l_1$-regularized least squares adaptive filter (SPARLS). Anomalies arise due to unexpected adversarial changes in ...
• #### SPARLS: The Sparse RLS Algorithm ﻿

(Institute of Electrical and Electronics Engineers, 2010)
We develop a recursive ${cal L}_{1}$-regularized least squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the ...