Abstract
Compressed sensing (CS) based channel estimation methods can effectively acquire channel state information for Massive MIMO wireless powered communication networks. In order to solve the problem that the existing sparsity-based adaptive matching pursuit (SAMP) channel estimation algorithm is unstable under low signal to noise ratio (SNR), an optimized adaptive matching pursuit (OAMP) algorithm is proposed in this paper. First, the channel is pre-estimated. Next, the energy entropy-based order determination is raised to optimize the reconstruction performance of the algorithm. Then, a staged adaptive variable step size method is put forward to further promote the accuracy of channel estimation. Finally, theoretical analysis and simulation results demonstrate that the proposed OAMP algorithm improves the accuracy at the expense of a small amount of time complexity, does not require a priori knowledge of sparsity and its comprehensive performance is superior to other existing channel estimation algorithms.
Highlights
The aim of wireless powered broadband communication system is to develop a new networking technology with broadband wireless access transmission as the core and wide area coverage [1]–[3]
The experimental results show that when the signal to noise ratio (SNR) of the system is 15 dB, the reconstructed signals obtained by the optimized adaptive matching pursuit (OAMP) algorithm can match the original channel very well, especially the number of tap coefficients
According to the sparsity characteristics of Massive MIMO wireless powered communication networks system in time domain, an optimized sparsity-based adaptive matching pursuit (OAMP) algorithm is raised in this paper
Summary
The aim of wireless powered broadband communication system is to develop a new networking technology with broadband wireless access transmission as the core and wide area coverage [1]–[3]. This kind of network deployment is flexible, rapid deployment, relatively low initial investment cost, and easy to achieve large-scale rapid coverage of distribution side, late expansion and network adjustment. Massive MIMO can be used as a solution to provide efficient connection services for terminal devices in the internet of things and wireless powered communication networks [6]–[10]. Accurate and stable channel state information is a necessary condition for high resolution signal transmission in wireless channel, which poses a huge challenge to channel estimation in complex environment [11]–[15]
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