Abstract
Traditionally, the dictionary matrices used in sparse
wireless channel estimation have been based on the discrete
Fourier transform, following the assumption that the channel
frequency response (CFR) can be approximated as a linear
combination of a small number of multipath components, each
one being contributed by a specific propagation path. In practical
communication systems, however, the channel response experienced
by the receiver includes additional effects to those induced
by the propagation channel. This composite channel embodies,
in particular, the impact of the transmit (shaping) and receive
(demodulation) filters. Hence, the assumption of the CFR being
sparse in the canonical Fourier dictionary may no longer hold.
In this work, we derive a signal model and subsequently a novel
dictionary matrix for sparse estimation that account for the
impact of transceiver filters. Numerical results obtained in an
OFDM transmission scenario demonstrate the superior accuracy
of a sparse estimator that uses our proposed dictionary rather
than the classical Fourier dictionary, and its robustness against
a mismatch in the assumed transmit filter characteristics.
wireless channel estimation have been based on the discrete
Fourier transform, following the assumption that the channel
frequency response (CFR) can be approximated as a linear
combination of a small number of multipath components, each
one being contributed by a specific propagation path. In practical
communication systems, however, the channel response experienced
by the receiver includes additional effects to those induced
by the propagation channel. This composite channel embodies,
in particular, the impact of the transmit (shaping) and receive
(demodulation) filters. Hence, the assumption of the CFR being
sparse in the canonical Fourier dictionary may no longer hold.
In this work, we derive a signal model and subsequently a novel
dictionary matrix for sparse estimation that account for the
impact of transceiver filters. Numerical results obtained in an
OFDM transmission scenario demonstrate the superior accuracy
of a sparse estimator that uses our proposed dictionary rather
than the classical Fourier dictionary, and its robustness against
a mismatch in the assumed transmit filter characteristics.
| Original language | English |
|---|---|
| Title of host publication | Signal Processing Advances in Wireless Communications (SPAWC), 2014 IEEE 15th International Workshop on |
| Number of pages | 5 |
| Publisher | IEEE Press |
| Publication date | 22 Jun 2014 |
| Pages | 424 - 428 |
| ISBN (Print) | 978-1-4799-4903-8 |
| DOIs | |
| Publication status | Published - 22 Jun 2014 |
| Event | 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications - Toronto, ON, Canada Duration: 22 Jun 2014 → 25 Jun 2014 Conference number: 32691 |
Conference
| Conference | 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications |
|---|---|
| Number | 32691 |
| Country/Territory | Canada |
| City | Toronto, ON |
| Period | 22/06/2014 → 25/06/2014 |
| Series | I E E E Workshop on Signal Processing Advances in Wireless Communications. Proceedings |
|---|---|
| ISSN | 1242-5125 |