Cross-layer Joint Optimization Mechanism And Construction Of Simulation Platform For Image Transmission In Deep Space Exploration

As the core of deep space exploration,deep space communication shoulders the responsibility of data exchange between ground station and flight detectors.However,due to the shortcomings such as large delay,frequent interruption,limited node resource,low SNR and dynamic and asymmetrical uplink and downlink,deep-space links cause damage and even loss of the backhaul data.Therefore,it is very important to study the high-quality,high-efficiency and high-reliability transmission scheme of the detected image data which occupies the core of the data transmission of flight detectors.In this dissertation,the framework of delay interrupt network protocol is used as the core architecture,and CS image compression technology and Spinal channel coding and decoding technology are used in the application layer and physical layer respectively to build the deep space detection image compression and transmission platform.And through the application layer,the transport layer and the physical layer between the cross-layer joint optimization,in reducing the use of feedback under the premise of deep space exploration images to achieve high quality,high efficiency and high reliability of the transmission.In deep space image compression and transmission platform,the application layer adopts the CS image compression sensing technology as the image compression,aiming at the limited node resource.Different from the traditional compression algorithm,CS image compression sensing technology merges the sampling and compression into one step,directly encodes the detected image data linearly,and transfers the heavy processing task to the decoding side.At the same time,Due to the holographic compression characteristic of CS image compression,the redundant compression value can be added to the transport layer,and the receiver can receive sufficient compression value to decode successfully.Due to the complex time-variant of the deep space channel,the physical layer adopts the Spinal rate-free coding technology as the channel coding,which can dynamically adapt to the deep-space link and reduce the feedback situation.Then,based on the architecture of the platform,this dissertation constructs a theoretical model and optimizes the solution for the cross-layer joint transmission mechanism,and makes a feasible cross-layer joint transmission scheme.This scheme dynamically adjusts the number of CS image compression redundancy values and the number of symbols of each coding block of Spinal code for different channel states,maximizes the throughput under the premise of reducing the use of feedback mechanism to achieve high quality and efficient transmission of the detected image.In order to verify and evaluate the cross-layer joint transmission scheme,this dissertation implements the software of the transport layer LTP protocol in DTN network and constructs a hardware-in-the-loop simulation platform based on deep space detection image compression and transmission platform.In this dissertation,the simulation scenario is set up as the communication between the Earth and Mars,and simulates the return process of the deep space exploration image,then verifies and evaluates the throughput performance of the cross-layer joint transmission scheme established in this dissertation.The results show that the throughput performance of the cross-layer joint scheme is 6.5% higher than that of the pre-retransmission scheme in the deep space environment,14.7% higher than that of the unpredictable addition scheme,and 21% higher than unpredictable retransmission mechanism.