Research On C-RAN Network Forward Compression Based On Compressed Sensing

With the exponential growth of data communication business,people's requirements for bandwidth and quality of high-speed wireless technology are constantly improving.The capacity limitation of Cloud Radio Access Network(C-RAN)forward link is difficult to ignore.To reduce the demand for bandwidth for data storage,in this paper,the Compressed Sensing(CS)technology is applied to C-RAN forward limited link,and the function distribution of remote radio unit pull remote end and baseband processing pool is carried out,which reduces the load of C-RAN forward link and improves the recovery accuracy of transmitted signal.Based on this,a forward compression model combining compressed sensing and CRAN structure is proposed,and the signal compression of the compressed sensing CRAN uplink model is carried out.The simulation experiment set the load compression rate of the forward link for analysis,and the results show that the compressed sensing technology can effectively reduce the load of the forward link of C-RAN network,and the load size is closely related to the sparsity rate of the conversion coefficient corresponding to the sampling signal to be compressed.The smaller the sparsity,the better the load compression effect.In view of C-RAN's limited forward transmission capacity,the function allocation of C-RAN network transmission terminal and signal processing terminal was carried out in this paper,and a model combining compressed sensing technology and C-RAN Physical Layer Split(PLS)architecture was proposed.Combining the advantages of PLS pretransfer architecture network in C-RAN,the mathematical model of PLS-CS and the bandwidth expression of the pretransfer capacity were designed.Experimental results show that the proposed PLS-CS architecture can effectively reduce the actual bandwidth required by C-RAN,and achieve efficient bandwidth construction of C-RAN forward link,compared with the common prepass architecture,under the condition of limited prepass link.A New Stepwise Orthogonal Matching Pursuit(NSTOMP)algorithm is proposed in this paper to ensure the efficient and reliable transmission of signals in advance links under high compression rate.The algorithm is not affected by the number of atoms selected in each iteration,which is the external parameter S.By means of the dual atomic selection strategy,the misselected part in the first screening is removed,which ensures the accurate transmission of the finite bandwidth signal under a large amount of data compression.The simulation comparison and verification of the proposed NSTOMP algorithm show that the approximation accuracy of the proposed NSTOMP algorithm is superior to the common compressed sensing method and the traditional compression method under the change of sparseness K and measurement number M.Moreover,the recovery effect of the proposed algorithm is not affected by the size of S,and the signal is restored with a small S.The traditional compressed sensing algorithm cannot recover the signal when the sparsity K(internal parameter)is unknown.In order to improve the practical application effect of compressed sensing algorithm,a K-Suitable Subspace Pursuit(KSSP)algorithm based on interval location segmentation is proposed in this paper.This algorithm can predict and locate the sparsity interval without relying on the known sparsity K,and has the advantages of low iteration times and high signal recovery accuracy.The mathematical derivation and corresponding simulation verification show that the algorithm can effectively restore the transmission signal while reducing the calculation amount of the RF remote end under the condition that the sparsity K is unknown,so it has better application value in practical implementation.