Research On Key Technologies Of Intelligent Communication System In The Presence Of α-Stable Noise

The rapid development of wireless communication and increasingly complicated electromagnetic environment pose unprecedented demands on intelligent communication systems.Non-Gaussian noise in practical wireless environment leads to more complicated electromagnetic environment as described in the existing research results.And the performance of the communication algorithms based on Gaussian noise decrease under the non-Gaussian noise.It is meaningful to study the intelligent communication system under non-Gaussian noise.In this paper,the intelligent communication system is studied to obtain more reliable and stable communication performance and α-stable distribution model is selected to describe the complex non-Gaussian noise.Firstly,the key technologies to be solved are given based on the influence of α-stable distribution noise on the performance of communication algorithms,and spectrum sensing,channel estimation and the design of intelligent decision engine are all considered.Then,a new communication system with abilities of learning and decision is constructed based on Deep Reinforcement Learning(DRL),named intelligent communication system.And DQN(Deep Q-Net)is applied to the modeling of intelligent decision engine.The simulations verified the feasibility of the intelligent decision engine.Throughput rate performance of different decision results given by mature Adaptive Modulation and Coding(AMC)algorithm and intelligent decision engine are compared.Obviously,intelligent decision engine made a better decision,which provides theoretical support and new ideas for the research of intelligent communication in the physical layer.The key technologies of intelligent communication systems under α-stable distributed noise can be summarized as follows:Firstly,a fractional low-order moment(FLOM)detection algorithm with dynamic threshold is proposed for spectrum sensing under α-stable distributed noise due to the poor adaptive ability of existing algorithms.Balance parameter is introduced to the FLOM detection algorithm to adjust the detection threshold dynamically to adapt to the complicated communication environment.It can reduce the repeated calculation for detection threshold every time.And the detection threshold is more accurate as it is obtained based on the statistics of the actual detection sequence,which reduces the complexity of the detection algorithm.It can be concluded that the spectrum sensing algorithm proposed in this paper has improved the detection probability according to the simulation results,and the detection threshold based on the actual sequence makes the algorithm have good adaptabilitySecondly,the channel estimation algorithms under α-stable distributed noise are studied.Calculation method for segmentation threshold of the loss function is improved in the sparse channel estimation algorithm,which reduces the strict dependence on prior knowledge and maintains the ability to suppress large-value pulses in α-stable distribution noise.Besides,a new algorithm for group sparse channel estimation based on distortion constraint is proposed.The quadratic complexity framework applied the distortion constraint function to the Recursive Least Squares(RLS)group sparse channel estimation algorithm,which aimed at suppressing the excessive estimation error caused by non-Gaussian noise and improving the ability to explore the group sparse characteristics of channel under non-Gaussian noise.According to the simulation results,it can be concluded that the improved sparse channel estimation algorithm reduces the complexity while maintaining the estimation performance.The group-sparse channel estimation algorithm based on distortion constraint can obtain smaller minimum mean square deviation(MSD)under α-stable distribution noise,which meet the demand of intelligent communication systemsFinally,a new intelligent decision engine is designed by combining the advantages of current communication systems with the characteristics of deep enforcement learning(DRL).And an intelligent communication system with capabilities of self-learning and decision-making is proposed,which addresses the urgently demand for intelligent communication systems in complex electromagnetic environments.Deep Q-Net(DQN)is the core of the intelligent decision engine,and we have innovatively proposed a learning scheme based on the cross-sample strategy of offline knowledge and online update sample.Long Short-Term Memory(LSTM)neural network guarantees better learning and decision results based on its own memory ability.The intelligent decision engine selects the communication frequency based on the spectrum sensing results,and trains and learns according to the feedback information of the communication to adapt to the current communication environment gradually.And a reasonable communication scheme will be given to obtain reliable and stable transmission performance.Throughput performance obtained by the intelligent decision engine and AMC decision-making scheme are compared under the same simulation conditions.A better decision scheme given by intelligent decision engine verifies the effectiveness of the proposed intelligent decision engine in the α-stability distribution noise.