Downloads

Software

• 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.
• PyScatter: A Python implementation of generalized scattering networks
  The PyScatter package provides a Python implementation of generalized scattering networks---a family of deep convolutional neural networks well-suited for feature extraction tasks (e.g., in classification, regression, or segmentation). The package allows to build generalized scattering networks with the following building blocks (i.e., filters, non-linearities, and pooling operations):
  • Filters: 1-D and 2-D Weyl-Heisenberg filters (with a Gaussian prototype window), 1-D and 2-D wavelet filters (with over 100 built-in wavelet filters from the PyWavelets package)
  • Non-linearities: Rectified linear unit, hyperbolic tangent, modulus, logistic sigmoid
  • Pooling: Sub-sampling, averaging, maximization
  Methods to visualize the scattering tree as well as the employed filters are included.
• NN2MV: extracting formulae in many-valued logic from ReLU networks
  The NN2MV package extracts formulae in many-valued logic and its extensions from ReLU networks with integer, rational, or real-valued weights. The extraction procedure is carried out in the following three steps.
  • Transform the given ReLU network into an equivalent CReLU network.
  • Extract logical formulae in each CReLU neuron layer by layer.
  • Compose the logical formulae corresponding to the individual CReLU neurons according to the layered structure of the CReLU network.
  Several one-dimensional and two-dimensional examples are included.

Measurement data

• 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.

Reproducible research

Here, you can find code to reproduce the numerical results in

• M. Tschannen and H. Bölcskei, "Noisy subspace clustering via matching pursuits," IEEE Transactions on Information Theory, Vol. 64, No. 6, pp. 4081-4104, June 2018.
• M. Tschannen and H. Bölcskei, "Robust nonparametric nearest neighbor random process clustering," IEEE Transactions on Signal Processing, Vol. 65, No. 22, pp. 6009-6023, Nov. 2017.
• R. Heckel, M. Tschannen and H. Bölcskei, "Dimensionality-reduced subspace clustering," Information and Inference: A Journal of the IMA, Vol. 6, No. 3, pp. 246-283, Sept. 2017.
• 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, pp. 2149-2158, June 2016.
• 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.