**Steven Van Vaerenbergh, Emilio Estébanez, Ignacio Santamaría
Dept. of Communications Engineering, University of Cantabria, Spain
E-mail: {steven,emilioea,nacho}@gtas.dicom.unican.es
**

Clustering techniques for equalization have been proposed by a number of authors in the last decade. However, most of these approaches focus only on time-invariant single-input single-output (SISO) channels. In this paper we consider the case of fast time-varying multiple-input multiple-output (MIMO) channels. The varying nature of the mixing matrix poses new problems that cannot be solved by conventional clustering techniques. By introducing the time scale into the clustering process we are able to untangle the clusters, which in this way behave like intertwined threads. Then, a spectral clustering algorithm is applied. Finally, the identified clusters are assigned to the transmitted symbols using only a few pilots. The geometry of the transmitted constellation is exploited within the spectral clustering algorithm in order to reduce the number of clusters. As shown in the paper, the proposed procedure saves a considerable amount of pilot symbols in comparison to other recently proposed techniques.

- Introduction
- Problem formulation
- Clustering for time-varying channels
- Spectral clustering
- Self-tuning Spectral Clustering
- Incorporating the temporal dimension into the clustering problem

- Exploiting the constellation geometry

- Symbol decoding
- Test results and comparison

- Conclusions
- Bibliography

Last modified: 2007-10-17