Belief propagation‐based compressed video sensing for mobile Internet

Abstract

SUMMARYThe very high data rate applications such as streaming high-definition video have attracted much attention of researchers in 5G systems. However, the design of 5G communication terminals faces several challenges, including how to reduce the computational burden of signal acquisition at the terminals and how to improve both the reconstruction quality and robust performance in wireless channels. How simple and fast online video acquisition can be implemented and how the original images can be recovered effectively by an offline algorithm for mobile Internet are taken into consideration in this paper. Specially, we firstly propose a Markov chain model to characterize variations of the video sequences' temporal correlation, wherein we utilize the measurement residue that reflects the intensity changes to estimate the transition matrix. Then, a compressed video sensing framework for mobile Internet is presented with the belief propagation algorithm and the proposed video signal model. Numerical results show that our proposal could efficiently utilize the temporal correlation in video and then obtain both the improved reconstruction quality and robust performance in wireless communication systems.

A stationary wavelet transform approach to compressed spectrum sensing in cognitive radio

“…Recent advances include using compressive sensing to develop a signal separation algorithm in cognitive radio to combat interference by collecting compressed measurements from all the cognitive users in the network and using them to categorize the sub‐bands in the spectrum at the fusion center . Compressive sensing has also been used in ultra‐wide band channel estimation , video sensing for mobile internet , and wireless local area networks . In , a compressive sensing method based on wavelet packets is used in cognitive radio along with adaptive thresholding to reduce the complexity at the sensing side.…”