基于模型剪枝动态调整压缩率的CSI反馈方法

doi:10.12305/j.issn.1001-506X.2023.08.37

Abstract

Abstract:

Aiming at the problem that the existing channel state information (CSI) feedback scheme based on deep learning (DL) can only perform compression feedback at a fixed compression rate (CR), a CSI feedback scheme for dynamically adjusting CR based on model pruning is proposed. Firstly, a residual channel characteristic attention (RCCA) mechanism is designed, and a CSI feedback network RCCA-Net is built, which makes full use of the amplitude-phase dependence relationship between the real parts and imaginary parts of the complex channel matrix to further improve CSI feedback-reconstruction quality. Secondly, the model RCCA-Prune is designed to dynamically adjust the CR, which uses the $\ell^2 $ norm to evaluate the contribution of each neuron to the final result, and deletes the neurons with less contribution through the model structured pruning technology to realize the dynamic adjustment of the compression rate. The simulation results show that the proposed dynamic adjustment CR scheme has better feedback performance under different CR, and has good generalization in different test environments.

Key words: channel state information, deep learning, model pruning, channel attention

CLC Number:

TN911.72

Kai SHAO, Ziqun DU, Guangyu WANG. CSI feedback method for dynamically adjusting compression rate based on model pruning[J]. Systems Engineering and Electronics, 2023, 45(8): 2615-2622.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Fig.5

Fig.6

Table 2

Fig.7

Table 3

Fig.8

Fig.9

References 27

1	LIU Z Y , ZHANG L , DING Z . Exploiting bi-directional channel reciprocity in deep learning for low rate massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2019, 8 (3): 889- 892. doi: 10.1109/LWC.2019.2898662
2	SHEN W Q , DAI L L , LI Y , et al. Channel feedback codebook design for millimeter-wave massive MIMO systems relying on lens antenna array[J]. IEEE Wireless Communications Letters, 2018, 7 (5): 736- 739. doi: 10.1109/LWC.2018.2818130
3	LIN Y P, WU P H, PHOONG S M. Statistics-aided codebook designs for limited feedback beamforming systems over millimeterwave channels[C]//Proc. of the IEEE 23rd International Conference on Digital Signal Processing, 2018.
4	SHEN W Q , DAI L L , ZHANG Y , et al. On the performance of channel-statistics-based codebook for massive MIMO channel feedback[J]. IEEE Trans.on Vehicular Technology, 2017, 66 (8): 7553- 7557. doi: 10.1109/TVT.2017.2656908
5	ADHIKARY A , NAM J , AHN J Y , et al. Joint spatial division and multiplexing—the large-scale array regime[J]. IEEE Trans.on Information Theory, 2013, 59 (10): 6441- 6463. doi: 10.1109/TIT.2013.2269476
6	JIANG Z , MOLISCH A F , CAIRE G , et al. Achievable rates of FDD massive MIMO systems with spatial channel correlation[J]. IEEE Trans.on Wireless Communications, 2015, 14 (5): 2868- 2882. doi: 10.1109/TWC.2015.2396058
7	ADHIKARY A , Al SAFADI E , SAMIMI M K , et al. Joint spatial division and multiplexing for mm-wave channels[J]. IEEE Journal on Selected Areas in Communications, 2014, 32 (6): 1239- 1255. doi: 10.1109/JSAC.2014.2328173
8	JOUNG J , KURNIAWAN E , SUN S . Channel correlation mo-deling and its application to massive MIMO channel feedback reduction[J]. IEEE Trans.on Vehicular Technology, 2016, 66 (5): 3787- 3797.
9	CUI Y M, FANG Y L. Research on PCA data dimension reduction algorithm based on entropy weight method[C]//Proc. of the 2nd International Conference on Machine Learning, Big Data and Business Intelligence, 2020: 392-396.
10	LIU J E, WANG K, LIU H B. Distributed compressed sensing of doubly selective channel in massive MIMO systems[C]//Proc. of the World Conference on Computing and Communication Technologies, 2021: 21-25.
11	GE Y, ZENG Z, ZHANG T, et al. Spatio-temporalcorrelated channel feedback for massive MIMO systems[C]//Proc. of the IEEE/CIC International Conference on Communications in China, 2018.
12	WEN C K , SHIH W T , JIN S . Deep learning for massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2018, 7 (5): 748- 751. doi: 10.1109/LWC.2018.2818160
13	LI X Y , WU H M . Spatio-temporal representation with deep neural recurrent network in MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2020, 9 (5): 653- 657. doi: 10.1109/LWC.2020.2964550
14	GUO J , WEN C K , JIN S , et al. Convolutional neural network-based multiple-rate compressive sensing for massive MIMO CSI feedback: design, simulation, and analysis[J]. IEEE Trans.on Wireless Communications, 2020, 19 (4): 2827- 2840. doi: 10.1109/TWC.2020.2968430
15	WANG Z , LI Y , WANG C , et al. A-OMP: an adaptive OMP algorithm for underwater acoustic OFDM channel estimation[J]. IEEE Wireless Communications Letters, 2021, 10 (8): 1761- 1765. doi: 10.1109/LWC.2021.3079225
16	ZHANG Z Y , CAI X , LI C G , et al. One-bit quantized massive MIMO detection based on variational approximate message passing[J]. IEEE Trans.on Signal Processing, 2017, 66 (9): 2358- 2373.
17	LIU T T , XING L , SUN Z G . Study on convergence of plug-and-play ISTA with adaptive-kernel denoisers[J]. IEEE Signal Processing Letters, 2021, 28, 1918- 1922. doi: 10.1109/LSP.2021.3111594
18	HU J, SHEN L, SUN G. Squeeze-and-Excitation networks[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
19	LOHIT S , KULKARNI K , KERVICHE R , et al. Convolutional neural networks for noniterative reconstruction of compressively sensed images[J]. IEEE Trans.on Computational Imaging, 2018, 4 (3): 326- 340. doi: 10.1109/TCI.2018.2846413
20	MOUSAVI A, DASARATHY G, BARANIUK R G. Deepcodec: adaptive sensing and recovery via deep convolutional neural networks[C]//Proc. of the 55th Annual Allerton Conference on Communication, Control, and Computing, 2017: 744-744.
21	YAO H T , DAI F , ZHANG S L , et al. DR2-net: deep residual reconstruction network for image compressive sensing[J]. Neurocomputing, 2019, 359, 483- 493. doi: 10.1016/j.neucom.2019.05.006
22	赵小强, 宋昭漾. 多级跳线连接的深度残差网络超分辨率重建[J]. 电子与信息学报, 2019, 41 (10): 2501- 2508. doi: 10.11999/JEIT190036
	ZHAO X Q , SONG Z Y . Super-resolution reconstruction of deep residual network with multi-level skip connections[J]. Journal of Electronics & Information Technology, 2019, 41 (10): 2501- 2508. doi: 10.11999/JEIT190036
23	TANG Y H , WANG Y H , XU Y X , et al. Scop: scientific control for reliable neural network pruning[J]. Advances in Neural Information Processing Systems, 2020, 33, 10936- 10947.
24	HE Y, LIU P, WANG Z W, et al. Filter pruning via geometric median for deep convolutional neural networks acceleration[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 4340-4349.
25	VADERA S , AMEEN S . Methods for pruning deep neural networks[J]. IEEE Access, 2022, 10, 63280- 63300. doi: 10.1109/ACCESS.2022.3182659
26	MEIJERINK A , MOLISCH A F . On the physical interpretation of the Saleh-Valenzuela model and the definition of its power delay profiles[J]. IEEE Trans.on Antennas and Propagation, 2014, 62 (9): 4780- 4793. doi: 10.1109/TAP.2014.2335812
27	ALKHATEEB A. DeepMIMO: a generic deep learning dataset for millimeter wave and massive MIMO applications[EB/OL]. [2022-05-05]. https://deepmimo.net/.

系统参数	取值
基站天线数	32×32
用户天线数	1
系统带宽/GHz	0.5
OFDM子载波数	1 024
OFDM采样因子	1
多径数目	3

参数	CR
参数	1/4	1/8	1/16	1/32
模型参数量/个	2 103 086	1 054 254	529 838	267 630
模型大小/M	8.96	4.95	2.95	1.95
Saleh-Valenzuela/dB	-17.21	-16.46	-16.13	-15.97
DeepMIMO/dB	-12.92	-10.87	-9.96	-9.55

模型	参数	CR
模型	参数	1/4	1/8	1/16	1/32
RCCA-Prune	模型参数量/个	2 103 086	1 054 254	529 838	267 630
RCCA-Prune	模型大小/M	8.96	4.95	2.95	1.95
SM-CsiNet+	模型参数量/个	2 103 072	2 365 984	2 431 904	2 448 480
SM-CsiNet+	模型大小/M	8.97	9.96	10.22	10.28

[1]	Meng WANG, Bing ZHU. Application of uncertainty modeling in 2D and 3D object detection [J]. Systems Engineering and Electronics, 2023, 45(8): 2370-2376.
[2]	Tianshu CUI, Dong WANG, Zhen HUANG. Automatic modulation classification based on lightweight network for space cognitive communication [J]. Systems Engineering and Electronics, 2023, 45(7): 2220-2226.
[3]	Yu JIANG, Qi YUAN, Zhitao HU, Weiwei WU, Xin GU. Airport arrival and departure delay time prediction based on meteorological factors [J]. Systems Engineering and Electronics, 2023, 45(6): 1722-1731.
[4]	Yang CHEN, Canhui LIAO, Kun ZHANG, Jian LIU, Pengju WANG. A signal modulation indentification algorithm based on self-supervised contrast learning [J]. Systems Engineering and Electronics, 2023, 45(4): 1200-1206.
[5]	Ye ZHANG, Yi HOU, Kewei OUYANG, Shilin ZHOU. Survey of univariate sequence data classification methods [J]. Systems Engineering and Electronics, 2023, 45(2): 313-335.
[6]	Zhengtu SHAO, Dengrong XU, Wenli XU, Hanzhong WANG. Radar active jamming recognition based on LSTM and residual network [J]. Systems Engineering and Electronics, 2023, 45(2): 416-423.
[7]	Ruize LI, Shuanghui ZHANG, Yongxiang LIU. Computational efficient structural sparse ISAR imaging method based on convolutional ADMM-net [J]. Systems Engineering and Electronics, 2023, 45(1): 56-70.
[8]	Xiao HAN, Shiwen CHEN, Meng CHEN, Jincheng YANG. Open-set recognition of LPI radar signal based on reciprocal point learning [J]. Systems Engineering and Electronics, 2022, 44(9): 2752-2759.
[9]	Limin ZHANG, Kaiwen TAN, Wenjun YAN, Yuyuan ZHANG. Radar emitter recognition based on multi-level jumper residual network [J]. Systems Engineering and Electronics, 2022, 44(7): 2148-2156.
[10]	Guodong JIN, Yuanliang XUE, Lining TAN, Jiankun XU. Advances in object tracking algorithm based on siamese network [J]. Systems Engineering and Electronics, 2022, 44(6): 1805-1822.
[11]	Xiaofeng ZHAO, Yebin XU, Fei WU, Jiahui NIU, Wei CAI, Zhili ZHANG. Ground infrared target detection method based on global sensing mechanism [J]. Systems Engineering and Electronics, 2022, 44(5): 1461-1467.
[12]	Hong ZOU, Chenyang BAI, Peng HE, Yaping CUI, Ruyan WANG, Dapeng WU. Edge service placement strategy based on distributed deep learning [J]. Systems Engineering and Electronics, 2022, 44(5): 1728-1737.
[13]	Dong CHEN, Yanwei JU. Ship object detection SAR images based on semantic segmentation [J]. Systems Engineering and Electronics, 2022, 44(4): 1195-1201.
[14]	Jingming SUN, Shengkang YU, Jun SUN. Pose sensitivity analysis of HRRP recognition based on deep learning [J]. Systems Engineering and Electronics, 2022, 44(3): 802-807.
[15]	Yunxiang YAO, Ying CHEN. Target tracking network based on dual-modal interactive fusion under attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(2): 410-419.

CSI feedback method for dynamically adjusting compression rate based on model pruning

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 12

References 27

Related Articles 15

Recommended Articles

Metrics

Comments