### Abstract

This paper presents a new adaptive algorithm for interference suppression in DS/CDMA communication systems. A constrained SGD algorithm is proposed to adaptively adjust the filter parameter by minimizing a properly chosen cost function while satisfying a set of constraints. The algorithm incorporates some a priori knowledge of the desired signal for filter adaptation instead of a training sequence. The algorithm improves the steady state performance by jointly estimating the reference parameter as well as the filter parameters. An iterative maximum likelihood absolute mean estimation algorithm is devised to estimate the reference parameter. The sequential EM algorithm is used to monotonically decrease the cost function. For each input, the algorithm updates the parameters by iterating between estimating the cost function (E step), and minimizing the cost function with respect to each unknown parameter (M step). In the M step of the filter update algorithm, a set of constraints is applied to guarantee convergence. Thus, the algorithm asymptotically converges to the constrained optimum solution. The proposed algorithm is tested by computer simulations and is shown to significantly improve the steady state performance compared to the existing adaptive algorithm.

Original language | English (US) |
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Pages | 592-596 |

Number of pages | 5 |

State | Published - Dec 1 1996 |

Event | Proceedings of the 1996 15th Annual Military Communications Conference, MILCOM 96. Part 3 (of 3) - Washington, DC, USA Duration: Oct 21 1996 → Oct 24 1996 |

### Other

Other | Proceedings of the 1996 15th Annual Military Communications Conference, MILCOM 96. Part 3 (of 3) |
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City | Washington, DC, USA |

Period | 10/21/96 → 10/24/96 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Electrical and Electronic Engineering

### Cite this

*Constrained adaptive algorithm for multiple access interference suppression in DS/CDMA communication systems*. 592-596. Paper presented at Proceedings of the 1996 15th Annual Military Communications Conference, MILCOM 96. Part 3 (of 3), Washington, DC, USA, .

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**Constrained adaptive algorithm for multiple access interference suppression in DS/CDMA communication systems.** / Park, Sang C.; Doherty, John F.

Research output: Contribution to conference › Paper

TY - CONF

T1 - Constrained adaptive algorithm for multiple access interference suppression in DS/CDMA communication systems

AU - Park, Sang C.

AU - Doherty, John F.

PY - 1996/12/1

Y1 - 1996/12/1

N2 - This paper presents a new adaptive algorithm for interference suppression in DS/CDMA communication systems. A constrained SGD algorithm is proposed to adaptively adjust the filter parameter by minimizing a properly chosen cost function while satisfying a set of constraints. The algorithm incorporates some a priori knowledge of the desired signal for filter adaptation instead of a training sequence. The algorithm improves the steady state performance by jointly estimating the reference parameter as well as the filter parameters. An iterative maximum likelihood absolute mean estimation algorithm is devised to estimate the reference parameter. The sequential EM algorithm is used to monotonically decrease the cost function. For each input, the algorithm updates the parameters by iterating between estimating the cost function (E step), and minimizing the cost function with respect to each unknown parameter (M step). In the M step of the filter update algorithm, a set of constraints is applied to guarantee convergence. Thus, the algorithm asymptotically converges to the constrained optimum solution. The proposed algorithm is tested by computer simulations and is shown to significantly improve the steady state performance compared to the existing adaptive algorithm.

AB - This paper presents a new adaptive algorithm for interference suppression in DS/CDMA communication systems. A constrained SGD algorithm is proposed to adaptively adjust the filter parameter by minimizing a properly chosen cost function while satisfying a set of constraints. The algorithm incorporates some a priori knowledge of the desired signal for filter adaptation instead of a training sequence. The algorithm improves the steady state performance by jointly estimating the reference parameter as well as the filter parameters. An iterative maximum likelihood absolute mean estimation algorithm is devised to estimate the reference parameter. The sequential EM algorithm is used to monotonically decrease the cost function. For each input, the algorithm updates the parameters by iterating between estimating the cost function (E step), and minimizing the cost function with respect to each unknown parameter (M step). In the M step of the filter update algorithm, a set of constraints is applied to guarantee convergence. Thus, the algorithm asymptotically converges to the constrained optimum solution. The proposed algorithm is tested by computer simulations and is shown to significantly improve the steady state performance compared to the existing adaptive algorithm.

UR - http://www.scopus.com/inward/record.url?scp=0030401193&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030401193&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0030401193

SP - 592

EP - 596

ER -