Power-Control-asilomar

KO | EN

gitlite — search

#react #ai #golang #android #rust #deep-learning #awesome #cli #kubernetes #awesome-list #ios #mcp #openai #open-source

Power-Control-asilomar

(★ 42)

Asilomar 2020 code for Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks

Power-Control-asilomar 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks
## Asilomar 2020 Code
### Authors: Yasar Sinan Nasir and Dongning Guo
#### Please email me for any questions or comments: snasir [at] u.northwestern [dot] edu
> **Note:** Instructions are for Linux. 
> Please create a virtual environment and install the specified tensorflow and numpy versions:
> > `python3.6 -m pip install --user virtualenv` 
> > `virtualenv -p python3.6 venv` 
> > `source $HOME/venv/bin/activate` 
> > `pip install -r requirements.txt` 
#### Reproducing Fig. 4:
The repository comes with the data to reproduce Fig.4, run:
> * `python3 -i ./fig4.py --json-file "test_K10_N20_shadow10_episode5-2500_travel0_vmax2_5" --json-file-wmobility "train_K10_N20_shadow10_episode10-5000_travel50000_vmax2_5" --json-file-womobility "train_K10_N20_shadow10_episode10-5000_travel0_fd10" --json-file-policy-train "ddpg200_100_50"`
* **Expected Output:** 
 Test: test_K10_N20_shadow10_episode5-2500_travel0_vmax2_5; Train: train_K10_N20_shadow10_episode10-5000_travel50000_vmax2_5; policy: ddpg200_100_50 
Sum rate policy after 0 episodes: 0.91, percentage: 0.35 
Sum rate policy after 1 episodes: 2.48, percentage: 0.95 
Sum rate policy after 2 episodes: 2.49, percentage: 0.95 
Sum rate policy after 3 episodes: 2.49, percentage: 0.96 
Sum rate policy after 4 episodes: 2.51, percentage: 0.96 
Sum rate policy after 5 episodes: 2.54, percentage: 0.98 
Sum rate policy after 6 episodes: 2.55, percentage: 0.98 
Sum rate policy after 7 episodes: 2.57, percentage: 0.98 
Sum rate policy after 8 episodes: 2.56, percentage: 0.98 
Sum rate policy after 9 episodes: 2.59, percentage: 0.99 
Sum rate policy after 10 episodes: 2.58, percentage: 0.99 
Test: test_K10_N20_shadow10_episode5-2500_travel0_vmax2_5; train train_K10_N20_shadow10_episode10-5000_travel0_fd10; policy: ddpg200_100_50 
Sum rate policy after 0 episodes: 0.91, percentage: 0.35 
Sum rate policy after 1 episodes: 2.46, percentage: 0.94 
Sum rate policy after 2 episodes: 2.46, percentage: 0.94 
Sum rate policy after 3 episodes: 2.49, percentage: 0.96 
Sum rate policy after 4 episodes: 2.47, percentage: 0.95 
Sum rate policy after 5 episodes: 2.47, percentage: 0.95 
Sum rate policy after 6 episodes: 2.45, percentage: 0.94 
Sum rate policy after 7 episodes: 2.47, percentage: 0.95 
Sum rate policy after 8 episodes: 2.48, percentage: 0.95 
Sum rate policy after 9 episodes: 2.47, percentage: 0.95 
Sum rate policy after 10 episodes: 2.48, percentage: 0.95 
Sum rate per link - WMMSE: 2.61, percentage: 1.00 
Sum rate per link - FP: 2.45, percentage: 0.94 
Sum rate per link - FP w delay: 2.37, percentage: 0.91 
Sum rate per link - random: 0.93, percentage: 0.36 
Sum rate per link - max: 0.91, percentage: 0.35 
Average time for a WMMSE run: 11.74 ms 
Average time for an FP run: 6.09 ms 
Average time for a DDPGnomobility agent to determine its action 0.47 ms 
Average WMMSE iterations per run: 42.12 
Average FP iterations per run: 24.36 
![](https://raw.githubusercontent.com/sinannasir/Power-Control-asilomar/master/fig/episodeeval_test_K10_N20_shadow10_episode5-2500_travel0_vmax2_5_train_K10_N20_shadow10_episode10-5000_travel50000_vmax2_5_ddpg200_100_50.png)
![](https://raw.githubusercontent.com/sinannasir/Power-Control-asilomar/master/fig/cdf_test_K10_N20_shadow10_episode5-2500_travel0_vmax2_5_train_K10_N20_shadow10_episode10-5000_travel50000_vmax2_5_ddpg200_100_50.png)
#### Reproducing Table I:
The repository contains the pre-trained policy parameters in ./simulations/sumrate/policy, so there is no need to run training to reproduce the results. For each row of Table I, you can execute:
* > `./scripts/test20links.sh`
* > `./scripts/test40links.sh` 
 **Expected Output:**
Test: test_K20_N40_shadow10_episode5-2500_travel0_vmax2_5; train train_K10_N20_shadow10_episode10-5000_travel50000_vmax2_5; policy: ddpg200_100_50 
Sum rate per link for policy after 10 episodes: 1.97, percentage: 0.94 
Sum rate per link - WMMSE: 2.09, percentage: 1.00 
Sum rate per link - FP: 1.98, percentage: 0.94 
Sum rate per link - FP w delay: 1.87, percentage: 0.90 
Sum rate per link - random: 0.62, percentage: 0.30 
Sum rate per link - full: 0.60, percentage: 0.29 
Average time for a WMMSE run: 54.67 ms 
Average time for an FP run: 14.24 ms 
Average time for a policy agent to determine its action 0.46 ms 
Average WMMSE iterations per run: 71.53 
Average FP iterations per run: 28.92 
* > `./scripts/test60links.sh` 
 **Expected Output:**
Test: test_K20_N60_shadow10_episode5-2500_travel0_vmax2_5; train train_K10_N20_shadow10_episode10-5000_travel50000_vmax2_5; policy: ddpg200_100_50 
Sum rate per link for policy after 10 episodes: 1.58, percentage: 0.94 
Sum rate per link - WMMSE: 1.68, percentage: 1.00 
Sum rate per link - FP: 1.59, percentage: 0.94 
Sum rate per link - FP w delay: 1.50, percentage: 0.89 
Sum rate per link - random: 0.37, percentage: 0.22 
Sum rate per link - full: 0.35, percentage: 0.21 
Average time for a WMMSE run: 94.88 ms 
Average time for an FP run: 23.05 ms 
Average time for a policy agent to determine its action 0.45 ms 
Average WMMSE iterations per run: 80.06 
Average FP iterations per run: 28.22 
* > `./scripts/test100links.sh` 
 **Expected Output:**
Test: test_K20_N100_shadow10_episode5-2500_travel0_vmax2_5; train train_K10_N20_shadow10_episode10-5000_travel50000_vmax2_5; policy: ddpg200_100_50 
Sum rate per link for policy after 10 episodes: 1.14, percentage: 0.93 
Sum rate per link - WMMSE: 1.23, percentage: 1.00 
Sum rate per link - FP: 1.15, percentage: 0.93 
Sum rate per link - FP w delay: 1.09, percentage: 0.88 
Sum rate per link - random: 0.18, percentage: 0.15 
Sum rate per link - full: 0.17, percentage: 0.14 
Average time for a WMMSE run: 187.76 ms 
Average time for an FP run: 36.34 ms 
Average time for a policy agent to determine its action 0.35 ms 
Average WMMSE iterations per run: 86.11 
Average FP iterations per run: 25.71 
#### To train a policy from scratch:
We prepared a bash script file that runs all the python files required to run all simulations from scratch:
> ./scripts/train.sh
#### Demo:
It takes several hours to complete the above script, so you can try a much simpler simulation setup that just runs 2 training episodes for a 10 link scenario to get a sense of how our simulator works:
> `./scripts/demo.sh` 
* **Expected Output:** 
Deployment: train_K5_N10_shadow10_episode2-5000_travel50000_vmax2_5; policy: ddpg200_100_50; K: 10; N: 5
Averages for last 200 episodes: 
Sum rate per link - policy: 2.58 
Sum rate per link - WMMSE: 2.69 
Sum rate per link - FP: 2.62 
Sum rate per link - FP w delay: 2.51 
Sum rate per link - random: 0.91 
Sum rate per link - full: 0.85 
Average time for a WMMSE run: 4.13 ms 
Average time for an FP run: 6.16 ms 
Average time for a policy agent to determine its action 0.53 ms 
Average time for a policy mini-batch train 25.20 ms 
Average WMMSE iterations per run: 29.23 
Average FP iterations per run: 43.95 
![](https://raw.githubusercontent.com/sinannasir/Power-Control-asilomar/master/fig/spectraleff_train_K5_N10_shadow10_episode2-5000_travel50000_vmax2_5_network_0.png)
#### Configuration files:
1. DDPG hyper-parameters are in ./config/policy/ddpg200_100_50.json:
	{
	 "N_neighbors": 5,
	 "scale_R_inner": 0.75,
	 "scale_R_interf": 2.5,
	 "n_hiddens": [
	 200,
	 100,
	 50
	 ],
	 "neightresh": 2,
	 "discount_factor": 0.25,
	 "learning_rate_0_critic": 0.001,
	 "learning_rate_decay_critic": 0.9995,
	 "learning_rate_min_critic": 0.0000,
	 "learning_rate_0_actor": 0.0005,
	 "learning_rate_decay_actor": 0.9995,
	 "learning_rate_min_actor": 0.0000,
	 "target_update_count": 50,
	 "time_slot_to_pass_weights": 50,
	 "max_epsilon": 0.05,
	 "epsilon_decay": 0.9995,
	 "min_epsilon": 0.00,
	 "batch_size": 128,
	 "memory_per_agent": 1000,
	 "cuda":false
	}
	> **Note:** If you set cuda to True, running python files in parallel as done in the scripts will not work. 
	
	> **Note:** Scaling parameters are used to scale the channel gains in dB before feeding them into neural networks. 
	
	> **Note:** We provide a DQN implementation for reference. Its config file has num_actions option in addition to the ones listed above. 
2. Deployment scenarios are in ./config/deployment/. There are two types of configuration files: train and test. Please examine both before using.

Power-Control-asilomar

Power-Control-asilomar 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Power-Control-asilomar

Power-Control-asilomar 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README