A Novel Unconstrained Precoding Technique For Channel Estimation Millimeter Wave Communication In 5g
Channel estimation is challenging for millimeter-wave massive MIMO with hybrid precoding, since the number of radio frequency chains is much smaller than that of antennas. Conventional compressive sensing based channel estimation schemes suffer from severe resolution loss due to the channel angle quantization. To improve the channel estimation accuracy, we propose an iterative reweight-based super resolution channel estimation scheme in this project. By optimizing an objective function through the gradient descent method, the proposed scheme can iteratively move the estimated angle of arrivals/departures towards the optimal solutions, and finally realize the super resolution channel estimation. In the optimization, a weight parameter is used to control the tradeoff between the sparsity and the data fitting error. In this proposal an optimized decomposition is used to reduce the computational complexity Simulation results shows that IL-SR channel estimation shows better performance than the SR-channel estimation.