mhni-es / dl_ofdm

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks

Acknowledgement

This repository is a fork from Dr. Zhongyuan Zhao paper "Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks, in IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2407-2420, Aug. 2021, doi: 10.1109/JSAC.2021.3087241." This fork of the original repository incorporates modifications to address the effects of Power Amplifier (PA) non-linearity in the OFDM receiver design. The changes aim to enhance the receiver's performance by mitigating the distortion introduced by PA non-linearities, which are prevalent in practical communication systems.

About this code

Modified source code for Deep Learning-Based OFDM Receiver.

• Modulation: BPSK, QPSK, 8-QAM, 16-QAM, of Gray mapping.
• SNR: -10:1:29 dB

Key Changes

• Implementation of a new module within the receiver's architecture to create PA non-linear effects.
• Adaptation of the learning algorithm to include training data affected by PA non-linearities, ensuring robustness and improved accuracy.
• Evaluation of the modified receiver's performance in various scenarios, demonstrating its enhanced capability to handle PA-induced distortions.

Software platform

• Matlab R2017b, R2018a (replace rayleighchan in the code for newer release)
• Python3 compatiable
• TensorFlow 1.x: tensorflow-gpu==1.15, docker tensorflow image 1.15.5-gpu-jupyter is highly recommended if you just want a quick tryout. If your GPU is not supported by that docker image, checkout latest docker images with tag [year.month]-tf1-py3 on Nvidia NGC.
• Note: newer versions of Matlab and Tensorflow are possible, but require quite some work on the code (read here).

Contents of directories

.
├── dev # latest working source code
├── test_v1 # archived for old version https://arxiv.org/abs/1810.07181v3
├── README.md 
└── LICENSE

Usage

1. Run script_rayleigh in Matlab for benchmarks
2. Run python3 run_local_ofdm.py --awgn=True in terminal for training and testing results.

For newer versions of Matlab and Tensorflow

Matlab: rayleighchan was removed and replaced by 'comm.RayleighChannel' from later versions of matlab. You may uncomment the lines 202-210, lines 293-300 in /dev/m/OFDM_Benchmark_dev.m to use the newer function. However, you need either replace parfor to for in that code to directly use the newer function, or change the code to initialize multiple identical objects of comm.RayleighChannel in lines 202-210 and use different objects (lines 293-300) in each parallel loop to enable the parfor.

Tensorflow: I use tf.contrib.graph_editor to enable the transfer learning scheme described in the paper. However, in Tensorflow 2, the tf.contrib is removed and graph_editor no longer exists. If you want to use TF2 rather than TF1, you will need to re-write the transfer learning (lines 264-365 in ofdmreceiver_np_mp.py) with whatever equivalent new methods in TF 2.

Update

1. A few missing CSV files are uploaded in commit 26ea69. These CSV files are the FIR filter coefficients for the Rayleigh channels used in the evaluation. They are necessary for experiments in fading channel. These CSV files are computed according to equation (2) in the paper, based on the channel tapes configured here. They can also be found as h.AlphaMatrix in OFDM_Benchmark_dev.m after stepping over line 283 (change parfor to for in line 282 in order to step into that block of code in Matlab).