Optimized GOMP-Based OTFS Channel Estimation Algorithm for V2X Communications

Vehicle-to-everything (V2X) communication, a current key area of research, has a large impact on traffic safety, traffic efficiency, autonomous driving technology development, and intelligent transport. In order to achieve the low-latency performance and high transmission efficiency required for V2X communication, channel estimation for transmission channels is particularly important. In this regard, this paper proposes an improved general orthogonal match pursuit (GOMP) channel estimation algorithm based on the base extension model for an orthogonal time frequency space (OTFS) system. Firstly, the channel matrix is decomposed using the basis expansion model. Then, the strong sparsity of the basis function is exploited for channel estimation using the GOMP algorithm, while the ordinal difference restriction method and the weak selectivity principle are introduced to improve the system. The obtained improved GOMP algorithm not only shows a greater improvement in terms of normalized mean square error (NMSE) and bit error rate (BER) performance but also greatly reduces computational complexity, enabling it to better satisfy the needs of V2X communication.