Research On The Key Techniques Of Compressed Sensing In Broadband Wireless Communication

As people’s requirements of wireless data transfer rate are continuo-usly improving, the techniques of broadband wireless communications are constantly moving forward. Currently, the LTE-Advanced has become the mainstream4G communication standard and3GPP introduced the HetNet and Small Cell to improve the system spectrum efficiency further, resulting in the emergence of higher-order modulation, like256QAM. Meanwhile, the compressed sensing theory became the research hotpot and one of the most promising techniques in current signal processing field as soon as it was proposed. It can fully utilize the sparsity of the ori-ginal signal, simple and compress the original signal simultaneously by linear transforming, and finally reconstruct signal accurately by the reco-nstruction algorithm in the undistortion or limit-distortion condition so that it can bring significant improvement in the process of signal’s storage and transfer.Firstly, by the research on related concepts of CS and its realization process, this article makes us approximately understand the three key steps of CS, namely the sparse representation of the signal, linear measurement and the reconstruction algorithm. As the result, this article finds out the combination point of CS and256QAM and its potential so that it provides the theoretical foundation for the implement of CS-256QAM.Then, this article proposes the structure of CS-256QAM and focuses on its feasibility and simulation’s performance. Through the mathematical derivation, this article finds out the possible problem when the CS theory is applied to the current256QAM. Meanwhile, this article proposes a hybrid solution, namely the joint Randomized-Greedy algorithm, to solve the problem of minimizing W-MSE and get the approximate optimal constellation mapping. Consequently, this solution can improve the system’s signal transfer rate as much as possible at the same time of guaranteeing the acceptable reconstructed signal’s distortion so that it can significantly enhance the system’s anti-noise performance. Also, this article further verifies the feasibility of CS-256QAM and its strong potential through the simulation of the system’s performance.Finally, this article proposes and researches on3adaptive structures of CS-256QAM on the basis of the former mathematical derivation and its performance simulation. By the comparison of system performance in different compression ratios, different reconstruction algorithms, different channel qualities and different sampling quantization errors, this article finds out an adaptive structure which is not only satisfied with the system requirements but-also can improve the system throughput as much as possible. Furthermore, by comparing three structures’ hardware requirements and simulation performance, this article gives out the best adaptive CS-256QAM structure under the current conditions and its design diagram.