Research On Signal Intelligent Detection Technology Based On Stochastic Computing

The rapid development of wireless communication technology is making the inter-connection of everything possible.While more and more terminal devices are connected to wireless communication networks,radio spectrum resources are getting tighter and tighter.Due to the open nature of radio waves,wireless communication networks are relatively more sensitive to interference.In order to use the limited wireless spectrum resources more effectively,the management has introduced Cognitive Radio(CR)technology to dynamically share the tight spectrum resources,but this also provides opportunities for illegal users of spectrum resources.In order to avoid illegal use of radio spectrum re-sources,it is necessary to monitor its usage and confirm the legitimacy of radio signals in a given transmission band,so radio signal identification technology has become one of the research focuses in recent years.Since radio signal recognition is not only simply to determine whether a communication signal exists in the transmission channel,but also to analyze and confirm the basic characteristics of the communication signal,traditional radio signal recognition methods based on statistical signal feature analysis are usually difficult to achieve the performance requirements.With the development of deep learn-ing,neural networks,especially convolutional neural networks for image recognition,it has become a mainstream research direction to provide end-to-end radio signal intelligent recognition algorithms by drawing on the excellent results of convolutional neural net-works in the field of target recognition.Paradoxically,convolutional neural networks are complex in structure and extremely large in computation,and the computing power of edge terminal devices is often insufficient to deploy complete convolutional neural networks,so lightweight design of the network is required.Traditional approaches to lightweight design of convolutional neural networks mainly include exploring new network comput-ing architectures or finding a compromise between the reduction and performance degra-dation of existing computing architectures.Facing such a problem,this paper designs and optimizes a stochastic computation-based neural network for radio signal recognition from the perspective of system power consumption,computational latency and system re-source consumption,starting from a numerical representation that is different from the bi-nary coding system,the stochastic computation system,and finally deploys it to an FPGA testbed The verification is completed.The main work of this paper is as follows.·In the aspect of random calculation principle,based on the analysis of the principle of deterministic random calculation,a deterministic random calculation sequence structure and a high-precision adder are realized.Reduced from 2²ⁱ²to 2ⁱ.·In the aspect of network algorithm optimization for radio signal recognition,a bottom-up network structure search algorithm is applied in this paper.Based on that,the convolutional neural network obtained by the algorithm is pruned and quantized to compress its computation and storage.Without affecting the recognition rate,the overall compression ratio of the network is about 5%.·In terms of hardware implementation of radio signal recognition network,this paper designs a multiplication-add parallel structure and a sparse network storage structure based on the principle of deterministic random computing.·Completed the design and construction of the software and hardware system,the hardware code was written in Vivado development environment,the hardware code was simulated and tested on the Modelsim platform,and the system was finally implemented on the VC707 FPGA test platform.Under the condition that the recog-nition rate of convolutional neural network is 87.5%,the overall computation of the network is compressed to about 5%,and the overall power consumption of the system is about 2.45W.