Beamforming Design for Anti-Jamming IoT Communication Based on Covariance Matrix Reconstruction With Little Prior Knowledge
Zihao Pan, Bangning Zhang, Heng Wang, Wenfeng Ma, Daoxing Guo · IEEE Transactions on Consumer Electronics · 2024
In this paper, a MVDR-beamformer-based anti-jamming scheme by using covariance matrix reconstruction (CMR) is proposed only with little prior knowledge. First, the lacking prior knowledge is divided into two parts, including steering vector and covariance matrix, where the former is acquired by adopting sliding window in Capon spatial spectrum and the latter is extracted by integrating over angular sectors complementary to sliding window. Then, we develop two efficient approximate methods to reduce the complexity. In proposed-1 methods, the Gauss-Legendre quadrature (GLQ) is used to simply the integral operation. Furthermore, the proposed-2 method employs the iterative conjugate gradient to avoid the explicit inversion of the matrix by updating the beam vectors with iterative techniques. Finally, simulation results show that the proposed beamformer can achieve good performance only with little prior knowledge, which can further attain a flexible trade-off between complexity and performance by using approximate methods.