2 research outputs found
Adaptive Reduced-Rank Constrained Constant Modulus Beamforming Algorithms Based on Joint Iterative Optimization of Filters
This paper proposes a robust reduced-rank scheme for adaptive beamforming
based on joint iterative optimization (JIO) of adaptive filters. The novel
scheme is designed according to the constant modulus (CM) criterion subject to
different constraints, and consists of a bank of full-rank adaptive filters
that forms the transformation matrix, and an adaptive reduced-rank filter that
operates at the output of the bank of filters to estimate the desired signal.
We describe the proposed scheme for both the direct-form processor (DFP) and
the generalized sidelobe canceller (GSC) structures. For each structure, we
derive stochastic gradient (SG) and recursive least squares (RLS) algorithms
for its adaptive implementation. The Gram-Schmidt (GS) technique is applied to
the adaptive algorithms for reformulating the transformation matrix and
improving performance. An automatic rank selection technique is developed and
employed to determine the most adequate rank for the derived algorithms. The
complexity and convexity analyses are carried out. Simulation results show that
the proposed algorithms outperform the existing full-rank and reduced-rank
methods in convergence and tracking performance.Comment: 10 figures; IEEE Transactions on Signal Processing, 201
A biologically plausible neural network for multi-channel Canonical Correlation Analysis
Cortical pyramidal neurons receive inputs from multiple distinct neural
populations and integrate these inputs in separate dendritic compartments. We
explore the possibility that cortical microcircuits implement Canonical
Correlation Analysis (CCA), an unsupervised learning method that projects the
inputs onto a common subspace so as to maximize the correlations between the
projections. To this end, we seek a multi-channel CCA algorithm that can be
implemented in a biologically plausible neural network. For biological
plausibility, we require that the network operates in the online setting and
its synaptic update rules are local. Starting from a novel CCA objective
function, we derive an online optimization algorithm whose optimization steps
can be implemented in a single-layer neural network with multi-compartmental
neurons and local non-Hebbian learning rules. We also derive an extension of
our online CCA algorithm with adaptive output rank and output whitening.
Interestingly, the extension maps onto a neural network whose neural
architecture and synaptic updates resemble neural circuitry and synaptic
plasticity observed experimentally in cortical pyramidal neurons.Comment: 46 pages, 14 figure