2 research outputs found
Geometric Sequence Decomposition with -simplexes Transform
This paper presents a computationally efficient technique for decomposing
non-orthogonally superposed geometric sequences. The method, which is named
as geometric sequence decomposition with -simplexes transform (GSD-ST), is
based on the concept of transforming an observed sequence to multiple
-simplexes in a virtual -dimensional space and correlating the volumes of
the transformed simplexes. Hence, GSD-ST turns the problem of decomposing
geometric sequences into one of solving a -th order polynomial equation. Our
technique has significance for wireless communications because sampled points
of a radio wave comprise a geometric sequence. This implies that GSD-ST is
capable of demodulating randomly combined radio waves, thereby eliminating the
effect of interference. To exemplify the potential of GSD-ST, we propose a new
radio access scheme, namely non-orthogonal interference-free radio access
(No-INFRA). Herein, GSD-ST enables the collision-free reception of
uncoordinated access requests. Numerical results show that No-INFRA effectively
resolves the colliding access requests when the interference is dominant
Sparse Channel Estimation in Wideband Systems with Geometric Sequence Decomposition
The sparsity of multipaths in the wideband channel has motivated the use of
compressed sensing for channel estimation. In this letter, we propose a
different approach to sparse channel estimation. We exploit the fact that
taps of channel impulse response in time domain constitute a non-orthogonal
superposition of geometric sequences in frequency domain. This converts the
channel estimation problem into the extraction of the parameters of geometric
sequences. Numerical results show that the proposed scheme is superior to
existing algorithms in high signal-to-noise ratio (SNR) and large bandwidth
conditions