As the volume of the electronic platform growing with the increase of their functions. It has great significance to utilize single form of signal to realize multiple functions. Since the multicarrier waveform has been widely used in communication and also show good performances in radar, this paper put forward an integrated radar and communication signal based on the OFDM. How to achieve the transmitter and how to transmit the communication data are discussed afterwards. The ambiguity function of the integrated signal is worked out to analysis the characteristics in detail, especially how the random digital sequence affect the radar performance. Theoretical analysis and simulation results show that the integrated signal can satisfy conventional radar detection and have good communication performance.
In this paper, we propose a Gated Recurrent Unit(GRU) neural network traffic prediction algorithm based on transfer learning. By introducing two gate structures, such as reset gate and update gate, the GRU neural network avoids the problems of gradient disappearance and gradient explosion. It can effectively represent the characteristics of long correlation traffic, and can realize the expression of nonlinear, self-similar, long correlation and other characteristics of satellite network traffic. The paper combines the transfer learning method to solve the problem of insufficient online traffic data and uses the particle filter online training algorithm to reduce the training time complexity and achieve accurate prediction of satellite network traffic. The simulation results show that the average relative error of the proposed traffic prediction algorithm is 35.80% and 8.13% lower than FARIMA and SVR, and the particle filter algorithm is 40% faster than the gradient descent algorithm.
In GEO satellite communications, effectiveness of the channel estimated information is insufficient due to the large time-delay in transmissions and mode selection mechanism in existing adaptive coding and modulation(ACM) is also not flexible and efficient. In view of above-mentioned problems, we propose a channel information forecast method based on corrected time-series to improve the effectiveness of estimated channel information. We propose a more efficient ACM modulo based on the channel information forecast and reinforcement learning with Q-learning. In this paper, we fit bit error rate and spectral efficiency into one criteria to ensure the fairness between performance and quality. After simulation, we get the conclusion that the error of proposed channel information forecast method is 0.792db and proposed ACM modulo not only lower the bit error rate by 43.5% but also optimize the utilization of spectrum by 42.3%.
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