- Soft-input soft-output single tree-search sphere decoding (SISO STS-SD)
The SISO STS-SD algorithm is an efficient method for iterative detection and decoding in multiple-input multiple-output (MIMO) wireless communication systems. The low-complexity algorithm is based on sphere decoding (SD) and the single-tree search (STS) paradigm. The SISO STS-SD algorithm incorporates clipping of the extrinsic log-likelihood ratios (LLRs) into the tree-search, which results in significant complexity savings. In addition, LLR clipping allows to cover a large performance/complexity tradeoff by adjusting only a single parameter. The zip-package provided here contains Matlab and MEX code of the SISO STS-SD algorithm and a modular MIMO-OFDM simulator.
- UWB measurement campaign
We conducted two ultrawideband (UWB) channel measurement campaigns to obtain data for statistical UWB channel modeling. Both measurement campaigns are described in detail in a paper that was published in the IEEE Transactions on Wireless Communications, Vol. 6, No. 7, pp. 2464-2475, July 2007. On this website, we provide the raw measurement data from these measurement campaigns for download.
Here, you can find code to reproduce the numerical results in
- M. Tschannen and H. Bölcskei, "Noisy subspace clustering via matching pursuits," submitted to IEEE Transactions on Information Theory, Dec. 2016.
- M. Tschannen and H. Bölcskei, "Robust nonparametric nearest neighbor random process clustering," submitted to IEEE Transactions on Signal Processing, Dec. 2016.
- T. Wiatowski, M. Tschannen, A. Stanić, P. Grohs, and H. Bölcskei, "Discrete deep feature extraction: A theory and new architectures," Proc. of International Conference on Machine Learning (ICML), New York, USA, June 2016.
- R. Heckel, M. Tschannen and H. Bölcskei, "Dimensionality-reduced subspace clustering," Information and Inference, 2017, to appear.
- M. Tschannen and H. Bölcskei, "Nonparametric nearest neighbor random process clustering," Proc. of IEEE International Symposium on Information Theory (ISIT), Hong Kong, China, pp. 1207-1211, June 2015.
- R. Heckel and H. Bölcskei, "Robust subspace clustering via thresholding," IEEE Transactions on Information Theory, Vol. 61, No. 11, pp. 6320-6342, 2015.
- R. Heckel, M. Tschannen, and H. Bölcskei, "Subspace clustering of dimensionality-reduced data," Proc. of IEEE International Symposium on Information Theory (ISIT), Honolulu, HI, pp. 2997-3001, July 2014.