面向大规模MIMO信号检测的深度学习模型混合精度量化方法OA

In the signal detection of massive multiple-input multiple-output(MIMO)systems,deploying deep learning models on resource-constrained hardware typically requires optimizing storage overhead,computational complexity,and energy consumption.Model quantization provides a feasible approach to improve deployment efficiency.This paper proposes a mixed-precision model quantization method based on quantization-unit sensitivity.First,according to the po-sition and functional role of each layer in the deep learning model,we partitioned the network into quantization units by grouping layers with similar structural characteristics;then,we transformed the hierarchical bit-allocation problem into a quantization-unit-level allocation problem to reduce the search space.Second,we designed a quantization-unit sensitiv-ity metric that jointly accounts for detection performance and energy overhead and assigned bit widths based on sensitiv-ity differences;within a limited bit-width range,we searched for an appropriate quantization configuration.Meanwhile,an energy-consumption model was constructed to include computation,weight transmission,and activation-value trans-mission,which is used to compute the sensitivity metric and evaluate quantization strategies.Simulation results showed that,compared with the full-precision model,fixed-precision quantization schemes,and conventional linear detection algorithms,the proposed method achieved a detection performance close to that of the full-precision model and outper-formed fixed-precision quantization under the same total bit-width constraint and conventional linear detection.In addi-tion,its energy consumption was significantly lower than that of the full-precision model.