Semi-tensor CS for distributed channel estimation in WSNs

Distributed channel estimation (DCE) is one of the core research topics in wireless sensor networks (WSNs). Under the hypothesis that channel parameters can be modeled as a sparse system, DCE based on compressed sensing (CS) is an effective approach to channel estimation. Among all the existing CS-DCE schemes, every node must store a sensing matrix whose size will increase with the number of channel parameters. Hence the workload of each node increases. To overcome this problem, in this paper, the CS-DCE scheme is developed with Semi-tensor product (STP) to relieve every sensor's storage space and reduce its computation complexity. Simulation results show the proposed framework releases the sensor's workload and ensures the reconstruction accuracy.