IMToolkit: An Open-Source Index Modulation Toolkit for Reproducible Research Based on Massively Parallel Algorithms

This paper presents a proposal of an open-source index modulation (IM) toolkit, which facilitates reproducible research and accelerates open innovation in IM studies. The proposed toolkit is implemented based on massively parallel algorithms that are designed for state-of-the-art graphics processing units (GPUs). Since high-performance GPUs are available at low cost, along with the intensive development in deep learning, this toolkit achieves large scale but significantly fast Monte Carlo simulations at low cost. Two large-tensor-based parallel algorithms are introduced for bit error ratio and average mutual information simulations. Additionally, the design of active indices is newly formulated into an integer linear programming problem that guarantees optimality, which is applicable to the generalized spatial modulation and subcarrier-index modulation schemes. Performance comparisons demonstrated that the proposed GPU-aided algorithms were up to 145 times faster than the conventional CPU-aided efficient counterparts. Furthermore, the designed active indices achieved the theoretical optimum performance in contrast to widely used conventional methods. A comprehensive database of these designed active indices is released online and is available to any researcher.