We address the problem of decentralized joint sparsity pattern recovery based on 1-bit compressive measurements in a distributed network. We assume that the distributed nodes observe sparse signals which share the same but unknown sparsity pattern. Each node obtains measurements via random projections and further quantizes
its measurement vector element-wise to 1-bit. We develop two decentralized variants of the binary iterative hard thresholding (BIHT) algorithm where each node communicates only with its one hop neighbors and exchanges its measurement information. This stage is followed by index fusion stage. For first and second algorithms, index fusion is performed at the end of and during BIHT iterations, respectively. The global estimate of the support set in both the algorithms is obtained by fusion of all the final local estimates. Experimental results show that the proposed collaborative algorithms have better (or at least similar) performance compared to the centralized
version.