Method for unmanned-aerial-vehicle networking communication

Abstract

The invention relates to the technical field of wireless communication, specifically to a method for unmanned-aerial-vehicle networking communication, aiming to further improve the performance of unmanned-aerial-vehicle networking communication under the asynchronous condition. The method for unmanned-aerial-vehicle networking communication comprises steps of: coding and modulating of a convolution channel in the multi-access stage; signal superposition at a relay node; channel decoding of, on the basis of a BP algorithm, and network coding of superposed signals; and receiving of broadcast signals and reversion of the other's information. The method of the invention keeps the robustness of a communication system and achieves a channel coding gain, for example, at least 2.4 dB when compared with the Viterbi decoding method. The method improves the bit error rate performance of the system, for example, by about 2 dB if the bit error rate is 10-5 when compared with a physical-layer network coding method which does not involve channel coding. In addition, the method enhances the reliability of a unmanned-aerial-vehicle networking communication system and achieves great popularization and application prospects.

Description

Technical field [0001] The present invention relates to the technical field of wireless communication, in particular to a method of UAV networking communication. Background technique [0002] Applying the physical layer network coding in the two-way relay system transmission model under synchronous conditions can increase the network throughput by 50% and 100%, respectively, and the information transmission rate by 50% compared with the direct network coding transmission mode and the traditional information transmission mode. % And 100%, these show the superiority of physical layer network coding. However, in the actual working environment of UAV as a relay platform, due to the influence of various asynchronous factors (for example, different channel distances, different types, and the influence of oscillators, crystal oscillators and other equipment set between different nodes), it is difficult to To achieve complete synchronization of the two source nodes, there is a carrier p...

AI Technical Summary

Problems solved by technology

However, in the actual working environment of UAV as a relay platform, due to the influence of various asynchronous factors (for example, different distances and types of channels, and the influence of oscillators, crystal oscillators and other equipment set between different nodes), it is difficult to The complete synchronization of the two source nodes is achieved, so that there are carrier phase offsets, carrier frequency offsets, and symbol offsets between the signals received by the relay node from the two source nodes, resulting in loss of physical layer network coding performance

[0003] Combining BPSK (Binary Phase Shift Keying, BPSK for short) modulation and QPSK (Quadrature Phase Shift Keying, QPSK for short) modulation, which are common in the prior art, directly with physical layer network coding, will Causes a higher bit error rate in physical layer network coding

For BPSK modulation, due to the carrier phase offset and symbol offset, the bit error rate of the physical layer network code will drop by 3dB in the worst case; for QPSK modulation, when the carrier phase offset is π / 4, the physical BER drops by 6dB in the worst case for layer network coding

[0004] In order to improve the performance attenuation caused by carrier phase offset and symbol offset to the communication system, a BP algorithm is proposed for the physical layer network coding under asynchronous conditions. The influence of the system, but the influence of different modulation methods on the communication system is ignored

For the signal superimposed at the relay node in the two-way relay system transmission model, the BP algorithm can improve the performance degradation of the physical layer network coding caused by various asynchronous factors, but it is different from the physical layer network coding under ideal conditions. There is still a certain gap in performance

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