Abstract
This paper presents a novel training-based adaptive linear receiver to suppress the multiple access interference, which has been one of the central issues in DS/CDMA wireless communication systems. The proposed receiver is derived by extending the Krylov-proportionate normalized least-mean-square (KPNLMS) algorithm to complex-valued signals. The key idea of KPNLMS is (a) sparse representation of the minimum mean square error (MMSE) receiver based on a certain Krylov subspace, (b) use of the 'sparsity' for fast convergence, and (c) simplification to keep linear complexity per iteration. To clarify the convergence properties of the proposed receiver, its derivation from the variable-metric version of the adaptive projected subgradient method (V-APSM) is presented. The simulation results demonstrate the efficacy of the proposed receiver.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |
| Pages | 169-174 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: 2008 Oct 16 → 2008 Oct 19 |
Other
| Other | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |
|---|---|
| Country | Mexico |
| City | Cancun |
| Period | 08/10/16 → 08/10/19 |
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Keywords
- Adaptive filter
- CDMA
- Krylov subspace
- Linear receiver
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Electrical and Electronic Engineering
Cite this
DS/CDMA linear receiver based on Krylov-proportionate adaptive filtering technique - An extension to complex-valued signals. / Yukawa, Masahiro.
Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008. 2008. p. 169-174 4685474.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - DS/CDMA linear receiver based on Krylov-proportionate adaptive filtering technique - An extension to complex-valued signals
AU - Yukawa, Masahiro
PY - 2008
Y1 - 2008
N2 - This paper presents a novel training-based adaptive linear receiver to suppress the multiple access interference, which has been one of the central issues in DS/CDMA wireless communication systems. The proposed receiver is derived by extending the Krylov-proportionate normalized least-mean-square (KPNLMS) algorithm to complex-valued signals. The key idea of KPNLMS is (a) sparse representation of the minimum mean square error (MMSE) receiver based on a certain Krylov subspace, (b) use of the 'sparsity' for fast convergence, and (c) simplification to keep linear complexity per iteration. To clarify the convergence properties of the proposed receiver, its derivation from the variable-metric version of the adaptive projected subgradient method (V-APSM) is presented. The simulation results demonstrate the efficacy of the proposed receiver.
AB - This paper presents a novel training-based adaptive linear receiver to suppress the multiple access interference, which has been one of the central issues in DS/CDMA wireless communication systems. The proposed receiver is derived by extending the Krylov-proportionate normalized least-mean-square (KPNLMS) algorithm to complex-valued signals. The key idea of KPNLMS is (a) sparse representation of the minimum mean square error (MMSE) receiver based on a certain Krylov subspace, (b) use of the 'sparsity' for fast convergence, and (c) simplification to keep linear complexity per iteration. To clarify the convergence properties of the proposed receiver, its derivation from the variable-metric version of the adaptive projected subgradient method (V-APSM) is presented. The simulation results demonstrate the efficacy of the proposed receiver.
KW - Adaptive filter
KW - CDMA
KW - Krylov subspace
KW - Linear receiver
UR - http://www.scopus.com/inward/record.url?scp=58049149857&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58049149857&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2008.4685474
DO - 10.1109/MLSP.2008.4685474
M3 - Conference contribution
SN - 9781424423767
SP - 169
EP - 174
BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
ER -