Gridless 1-b DOA estimation exploiting SVM approach

We investigate the problem of direction of arrival (DOA) estimation with 1-b measurements in massive MIMO systems, as 1-b quantization offers low cost and low complexity in the implementation. We first establish the connection between 1-b DOA estimation and linear classification based on the sparsity of the incident signals. Then, we present an iterative refinement procedure based on Taylor expansion to obtain DOA estimation off the grid. This refinement procedure can be easily extended to other 1-b DOA estimation algorithms with minor changes. Finally, simulations are conducted for validation and the results illustrate the high performance of the proposed algorithm in spite of the extreme 1-b quantization.