Block-Based Optimization for Frequency-Selective One-Bit Quantization

Block-Based Optimization for Frequency-Selective One-Bit Quantization — Code and Test Data

Reference implementation and reproducible test framework accompanying the
publication Block-Based Optimization for Frequency-Selective One-Bit
Quantization (F. Mayer, C. Vogel, ISCAS 2025).

One-bit quantization converts real-valued signals into binary sequences.
Sigma-Delta quantization shapes the resulting noise out of the signal band,
but is essentially limited to lowpass signals. OBBQ instead formulates the
task as a constrained optimization problem and solves it block by block: the
signal is partitioned into blocks of length M, each block is optimized against
the accumulated error of its predecessors, and the noise is shaped by the
choice of the FIR filter matrix — for lowpass, bandpass or multiband targets
alike. This reduces the complexity of the NP-hard full problem from
O(2^(M·P)) to O(2^M · P) while retaining the frequency selectivity.

The archive contains the quantization algorithms, the parametrized generator
for the multitone test scenarios used in the paper, the evaluation and
plotting tools, and the corresponding parameter sets.

Getting started: unpack the archive and open `README.md` for an overview
of the repository structure and the individual modules. `HOWTO.md` is the
step-by-step guide: required Python packages, the Gurobi academic license
needed for the solver, and the three-stage pipeline from signal generation
through quantization to evaluation, including how to define your own test
scenarios.

Requires Python 3 with NumPy, SciPy, Matplotlib and PyQt5, plus a Gurobi
license (free for academic use).

Funded by the Austrian Science Fund (FWF) [10.55776/DFH 5] within the DENISE
project and the province of Styria.