## Abstract

This paper considers the problem of high-dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the fusion center (FC). We design optimal collaboration strategies which are universal for a class of deterministic signals. By establishing the equivalence between the collaboration strategy design problem and sparse principal component analysis (PCA), we solve the problem efficiently and evaluate the impact of collaboration on detection performance.

Original language | English (US) |
---|---|

Article number | 7548304 |

Pages (from-to) | 1484-1488 |

Number of pages | 5 |

Journal | IEEE Signal Processing Letters |

Volume | 23 |

Issue number | 10 |

DOIs | |

State | Published - Oct 2016 |

## Keywords

- Dimensionality reduction
- multitask detection
- sparse learning
- universal collaboration

## ASJC Scopus subject areas

- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics