Abstract
In many communication systems the channel impulse response can be characterized with a parametric form, though the channel estimation is often performed using an equivalent discrete-time linear time-invariant system (usually modeled as a Moving Average (MA) system). When the number of parameters which describe the channel is less than the number of unknowns in the MA model, the ML estimate of the parameters describing the channel may lead to a better estimate of the channel response. However, this ML estimation procedure is highly complex. The objectives of this paper are 1) to cast the parameter estimation problem as a sparse estimation problem, 2) to compare the performance of this estimate with the CRB of the parameter estimation problem and the least squares estimate, and 3) to present novel guidelines on the amount of resources which one must devote to training for identification of the channel.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - IEEE Military Communications Conference MILCOM |
| DOIs | |
| State | Published - 2008 |
| Externally published | Yes |
| Event | 2008 IEEE Military Communications Conference, MILCOM 2008 - Assuring Mission Success - Washington, DC, United States Duration: Nov 17 2008 → Nov 19 2008 |
Other
| Other | 2008 IEEE Military Communications Conference, MILCOM 2008 - Assuring Mission Success |
|---|---|
| Country | United States |
| City | Washington, DC |
| Period | 11/17/08 → 11/19/08 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Estimation of sparse multipath channels. / Sharp, Matthew; Scaglione, Anna.
Proceedings - IEEE Military Communications Conference MILCOM. 2008. 4753291.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Estimation of sparse multipath channels
AU - Sharp, Matthew
AU - Scaglione, Anna
PY - 2008
Y1 - 2008
N2 - In many communication systems the channel impulse response can be characterized with a parametric form, though the channel estimation is often performed using an equivalent discrete-time linear time-invariant system (usually modeled as a Moving Average (MA) system). When the number of parameters which describe the channel is less than the number of unknowns in the MA model, the ML estimate of the parameters describing the channel may lead to a better estimate of the channel response. However, this ML estimation procedure is highly complex. The objectives of this paper are 1) to cast the parameter estimation problem as a sparse estimation problem, 2) to compare the performance of this estimate with the CRB of the parameter estimation problem and the least squares estimate, and 3) to present novel guidelines on the amount of resources which one must devote to training for identification of the channel.
AB - In many communication systems the channel impulse response can be characterized with a parametric form, though the channel estimation is often performed using an equivalent discrete-time linear time-invariant system (usually modeled as a Moving Average (MA) system). When the number of parameters which describe the channel is less than the number of unknowns in the MA model, the ML estimate of the parameters describing the channel may lead to a better estimate of the channel response. However, this ML estimation procedure is highly complex. The objectives of this paper are 1) to cast the parameter estimation problem as a sparse estimation problem, 2) to compare the performance of this estimate with the CRB of the parameter estimation problem and the least squares estimate, and 3) to present novel guidelines on the amount of resources which one must devote to training for identification of the channel.
UR - http://www.scopus.com/inward/record.url?scp=78650759308&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650759308&partnerID=8YFLogxK
U2 - 10.1109/MILCOM.2008.4753291
DO - 10.1109/MILCOM.2008.4753291
M3 - Conference contribution
AN - SCOPUS:78650759308
SN - 9781424426775
BT - Proceedings - IEEE Military Communications Conference MILCOM
ER -