A Low-Complexity Detector for OTFS-Based Sensing

Orthogonal time frequency space (OTFS) modulation is gaining recognition for its potential to facilitate integrated sensing and communication (ISAC) within future mobile networks. However, computing the sensing channel matrix in orthogonal time frequency space (OTFS), a crucial step for accurate target parameter estimation, presents significant challenges due to its high dimensionality. Therefore, this study introduces an innovative method to reduce such computational complexity by combining two ingredients. First, through algebraic operations, we decompose the sensing channel matrix into four lower-dimensional matrices whose elements can be associated with a Dirichlet kernel. Second, we formulate an analytical criterion, independent of system parameters, that leverages the properties of the Dirichlet kernel and identifies the most informative elements of these matrices that deserve computation. To demonstrate the effectiveness of our approach, we assess the computational complexity of this distilled channel matrix in terms of the number of elementary operations required. Numerical results indicate that our technique markedly decreases receiver complexity by up to three orders of magnitude without compromising sensing performance.