The Massive Multiple Input Multiple Output (MIMO) system is a core technology of the next generation communication . Traditional compressive sensing based CSI feedback hasbecome a bottleneck problem that is limited in piratical . CQNet outperforms the state-of-the-art method with less computational overhead by achieving an average performanceimprovement of 8.07% in both outdoor and indoor scenarios . The original accuracy loss of 67.19% onaverage is reduced to 21.96% on average, and the compression rate is increased by 8 times on the original benchmark . In addition, this paper also investigates the reasons for the decrease in model accuracy at large compression rates and proposes a strategy to embed a quantization layer to achieve effective compression, by which the original accuracy Loss is reduced . It also proposes a solution to the reduction in the original benchmarks and proposes to use a Quantization layer .

Author(s) : Sijie Ji, Weiping Sun, Mo Li

Links : PDF - Abstract

Code :

https://github.com/oktantod/RoboND-DeepLearning-Project


Coursera

Keywords : original - proposes - accuracy - compression - layer -

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