# MIMO_JCESD
Code for the paper
"A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems", [Signal Processing 223 (2024), 109554.](https://www.sciencedirect.com/science/article/pii/S0165168424001737), [[arxiv version]](https://arxiv.org/abs/2303.03678)
We evaluate three DL methods: [DeepRx](https://ieeexplore.ieee.org/document/9345504), a lightweight DenseNet adapted to JCESD, and
a new unrolled dynamics(UD) model called Hyper-WienerNet, which uses hypernetworks to estimate unknown
parameters.
OFDM 系统中用于联合信道估计和信号检测的深度学习和迭代算法的比较研究 （138个子文件） 
epoch0 14.33MB epoch1 14.33MB epoch131 2.62MB epoch199 2.62MB epoch2 14.33MB epoch26 14.33MB epoch28 14.33MB epoch454 50.59MB epoch61 2.62MB epoch80 2.62MB epoch90 2.62MB epoch99 14.33MB LICENSE 1KB NewRadioPDSCHThroughputExample.m 45KB hSSBurst.m 12KB hSSBurstInfo.m 10KB hPRGPrecode.m 7KB hArrayGeometry.m 3KB hUpdateHARQProcess.m 1KB hNewHARQProcesses.m 921B hSkipWeakTimingOffset.m 757B README.md 578B train_DeepRX_May26-v1-1750.out 147KB train_DeepRX_May25_v1_2100.out 121KB
PDSCHLinkExampleProcessingChain.png 30KB valid_BER_vs_epoch_curve_StartEpoch0_EndEpoch100.png 27KB valid_BER_vs_epoch_curve_StartEpoch0_EndEpoch200.png 26KB valid_BER_vs_epoch_curve_start0.png 26KB deeprx-training-valid-loss.png 22KB valid_BER_vs_epoch_curve_StartEpoch0_EndEpoch100.png 22KB valid_BER_vs_epoch_curve_start0.png 22KB longRunThroughput.png 20KB Training_CE_loss_vs_epoch_start0.png 19KB Training_CE_loss_vs_epoch_start0.png 18KB Training_log_CE_loss_vs_epoch_start0.png 18KB Training_log_CE_loss_vs_epoch_start0.png 13KB mmsenet.py 84KB mmsenet.py 84KB load_data.py 36KB load_data.py 26KB train.py 25KB train.py 24KB mmsenet.py 14KB test.py 11KB test.py 10KB train.py 8KB config.py 6KB config.py 6KB densenet.py 5KB densenet.py 5KB test.py 3KB config.py 3KB head_mimo.py 2KB head_mimo.py 2KB head_mimo.py 2KB main.py 409B main.py 409B main.py 387B __init__.py 65B __init__.py 65B __init__.py 46B __init__.py 46B __init__.py 40B __init__.py 22B mmsenet.cpython-37.pyc 30KB mmsenet.cpython-37.pyc 30KB mmsenet.cpython-38.pyc 29KB mmsenet.cpython-38.pyc 26KB mmsenet.pyc 22KB mmsenet.pyc 22KB load_data.cpython-37.pyc 17KB load_data.cpython-38.pyc 16KB train.cpython-38.pyc 15KB train.cpython-37.pyc 14KB train.cpython-37.pyc 14KB train.cpython-38.pyc 14KB mmsenet.cpython-38.pyc 12KB load_data.cpython-37.pyc 12KB mmsenet.pyc 11KB load_data.cpython-38.pyc 9KB mmsenet.cpython-36.pyc 8KB test.cpython-37.pyc 7KB test.cpython-37.pyc 6KB test.cpython-38.pyc 6KB test.cpython-38.pyc 6KB train.cpython-38.pyc 6KB train.cpython-36.pyc 4KB train.cpython-36.pyc 4KB train.cpython-36.pyc 4KB densenet.cpython-37.pyc 4KB densenet.cpython-37.pyc 4KB densenet.cpython-38.pyc 4KB config.cpython-37.pyc 4KB config.cpython-38.pyc 4KB test.cpython-36.pyc 2KB test.cpython-36.pyc 2KB test.cpython-36.pyc 2KB test.cpython-38.pyc 2KB config.cpython-38.pyc 2KB head_mimo.pyc 2KB共 138 条
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