Uncoded transmission is exactly optimal for a simple Gaussian "sensor" network

By Michael C. Gastpar. Information Theory and Applications Workshop, 2007

320 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/5754411d269aaf827647555f1399ad10f0c06066

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kaibin Huang
2. Bobak Anthony Nazer
3. Yong Wang
4. Michael C. Gastpar
5. Guangxu Zhu
6. Deniz Gündüz
7. Yuanming Shi
8. Mohammad Mohammadi Amiri

In Computer Networks and Communications

1. Kaibin Huang
2. Guangxu Zhu
3. Yong Wang
4. Bobak Anthony Nazer
5. Michael C. Gastpar
6. Syed Ali Jafar
7. Rui Zhang
8. Shuguang Cui

Citations in the corpus

• Broadband Analog Aggregation for Low-Latency Federated Edge Learning (2018, background): “It was subsequently discovered in [15] that simple analog transmission without coding but with channel pre-equalization can achieve the minimum distortion if the data sources are independent and identically distributed (i.”
• Computation Over Multiple-Access Channels (2007, background): “In [4], [5], an uncoded joint source–channel scheme is shown to be optimal (and significantly better than separation) for estimating a remote source from multiple observations.”