Merge pull request #94 from ShenMuyuan/cmake

updated README and removed interference-pattern example

README.md

@@ -1,51 +1,71 @@
 # ns3-ai
+
 ## Online Tutorial:
-Join us in this [online recording](https://vimeo.com/566296651) to get better knowledge about ns3-ai! The slides introduce the ns3-ai model could also be found [here](https://www.nsnam.org/wp-content/uploads/2021/tutorials/ns3-ai-tutorial-June-2021.pdf)! 
+
+Join us in this [online recording](https://vimeo.com/566296651) to get better knowledge about ns3-ai! The slides
+introduce the ns3-ai model could also be
+found [here](https://www.nsnam.org/wp-content/uploads/2021/tutorials/ns3-ai-tutorial-June-2021.pdf)!
+
 ## Description
- The [ns–3](https://www.nsnam.org/) simulator is an open-source networking simulation tool implemented by C++ and wildly used for network research and education. Currently, more and more researchers are willing to apply AI algorithms to network research. Most AI algorithms are likely to rely on open source frameworks such as [TensorFlow](https://www.tensorflow.org/) and [PyTorch](https://pytorch.org/). These two parts are developed independently and extremely hard to merge, so it is more reasonable and convenient to connect these two tasks with data interaction. Our model provides a high-efficiency solution to enable the data interaction between ns-3 and other python based AI frameworks.
 
- This module does not provide any AI algorithms or rely on any frameworks but instead is providing a Python module that enables AI interconnect, so the AI framework needs to be separately installed. You only need to clone or download this work, then import the Python modules, you could use this work to exchange data between ns-3 and your AI algorithms.
+The [ns–3](https://www.nsnam.org/) simulator is an open-source networking simulation tool implemented by C++ and wildly
+used for network research and education. Currently, more and more researchers are willing to apply AI algorithms to
+network research. Most AI algorithms are likely to rely on open source frameworks such
+as [TensorFlow](https://www.tensorflow.org/) and [PyTorch](https://pytorch.org/). These two parts are developed
+independently and extremely hard to merge, so it is more reasonable and convenient to connect these two tasks with data
+interaction. Our model provides a high-efficiency solution to enable the data interaction between ns-3 and other python
+based AI frameworks.
 
+This module does not provide any AI algorithms or rely on any frameworks but instead is providing a Python module that
+enables AI interconnect, so the AI framework needs to be separately installed. You only need to clone or download this
+work, then import the Python modules, you could use this work to exchange data between ns-3 and your AI algorithms.
 
- Inspired by [ns3-gym](https://github.com/tkn-tub/ns3-gym), but using a different approach which is faster and more flexible.
+Inspired by [ns3-gym](https://github.com/tkn-tub/ns3-gym), but using a different approach which is faster and more
+flexible.
 
 ### Features
-- High-performance data interaction module (using shared memory). 
+
+- High-performance data interaction module (using shared memory).
 - Provide a high-level interface for different AI algorithms.
 - Easy to integrate with other AI frameworks.
 
-
 ## Installation
+
 ### 1. Install this module in ns-3
-#### Get ns-3:  
+
+#### Get ns-3:
+
 This module needs to be built within ns-3, so you need to get a ns-3-dev or other ns-3 codes first.
 
 Check [ns-3 installation wiki](https://www.nsnam.org/wiki/Installation) for detailed instructions.
 
 #### Add this module
+
 ```Shell
 cd $YOUR_NS3_CODE/contrib
 git clone https://github.com/hust-diangroup/ns3-ai.git
 ```
 
-#### Rebuild ns-3
+#### Reconfigure ns-3
+
 ```Shell
-./waf configure
-./waf
+./ns3 clean
+./ns3 configure
 ```
 
 ### 2. Add Python interface
 
 #### Install
-Python3 is used and tested.
+
+Python 3 is used and tested. It's recommended to use ns3-ai under a Conda environment.
 
 ```Shell
 cd $YOUR_NS3_CODE/contrib/ns3-ai/py_interface
-
 pip3 install . --user
 ```
 
-#### Baisc usage
+#### Basic usage
+
 ``` Python
 import py_interface
 mempool_key = 1234                                          # memory pool key, arbitrary integer large than 1000
@@ -59,9 +79,21 @@ with v as o:
     print(*o)
 py_interface.FreeMemory()
 ```
+
 ## Shared Memory Pool
-The ns3-ai module interconnects the ns-3 and AI frameworks by transferring data through the shared memory pool. The memory can be accessed by both sides and controlled mainly in ns-3. The shared memory pool is defined in `ns3-ai/model/memory-pool.h`.  
-The `CtrlInfoBlock` is the control block of the all shared memory pool, the `SharedMemoryCtrl` is the control block of each shared memory, and the `SharedMemoryLockable` is the actual shared memory used for data exchange. In each memory block, we use version and nextVersion as the lock indicator. The synchronization for reading/writing locks and the events update are accomplished by the lock indicator. For every process that wants to access or modify the data, it will compare the `version` variable and the `nextVersion` variable. If they are the same, it means that the memory is reachable. Then it will add one to the next version atomically to lock the memory and also add one to the version after its operation to the memory to unlock the memory. Besides the version of the memory acts as the signal to tell different processes the current state of the memory block, which provides different methods to synchronize.
+
+The ns3-ai module interconnects the ns-3 and AI frameworks by transferring data through the shared memory pool. The
+memory can be accessed by both sides and controlled mainly in ns-3. The shared memory pool is defined
+in `ns3-ai/model/memory-pool.h`.  
+The `CtrlInfoBlock` is the control block of the all shared memory pool, the `SharedMemoryCtrl` is the control block of
+each shared memory, and the `SharedMemoryLockable` is the actual shared memory used for data exchange. In each memory
+block, we use version and nextVersion as the lock indicator. The synchronization for reading/writing locks and the
+events update are accomplished by the lock indicator. For every process that wants to access or modify the data, it will
+compare the `version` variable and the `nextVersion` variable. If they are the same, it means that the memory is
+reachable. Then it will add one to the next version atomically to lock the memory and also add one to the version after
+its operation to the memory to unlock the memory. Besides the version of the memory acts as the signal to tell different
+processes the current state of the memory block, which provides different methods to synchronize.
+
 ```
 |SharedMemoryBlock1|
 |SharedMemoryBlock2|
@@ -75,44 +107,59 @@ The `CtrlInfoBlock` is the control block of the all shared memory pool, the `Sha
 |MemoryPoolContrlBlk|
 ```
 
+## Examples
 
+### Quick Start on how to us ns3-ai - [a_plus_b](https://github.com/hust-diangroup/ns3-ai/tree/master/examples/a_plus_b)
 
-## Examples
-### Quick Statrt on how to us ns3-ai - [a_plus_b](https://github.com/hust-diangroup/ns3-ai/tree/master/examples/a_plus_b)
-This example show how you can use ns3-ai by a very simple case that you transfer the data from ns-3 to python side and calculate a + b in the python to put back the results. Please check the README in it for more details.
+This example show how you can use ns3-ai by a very simple case that you transfer the data from ns-3 to python side and
+calculate a + b in the python to put back the results. Please check the README in it for more details.
 
 ### [RL-TCP](https://github.com/hust-diangroup/ns3-ai/blob/master/examples/rl-tcp/)
-This example is inspired by [ns3-gym example](https://github.com/tkn-tub/ns3-gym#rl-tcp). We bulid this example for the benchmarking and to compare with their module.
+
+This example is inspired by [ns3-gym example](https://github.com/tkn-tub/ns3-gym#rl-tcp). We build this example for the
+benchmarking and to compare with their module.
 
 #### Build and Run
+
 Run ns-3 example:
+
 ```
-cp -r contrib/ns3-ai/example/rl-tcp scratch/
+cp -r contrib/ns3-ai/examples/rl-tcp scratch/
+cd scratch/rl-tcp/
 python3 run_tcp_rl.py --use_rl --result
 ```
 
 ### [LTE_CQI](https://github.com/hust-diangroup/ns3-ai/blob/master/examples/lte_cqi/)
-This original work is done based on [5G NR](https://5g-lena.cttc.es/) branch in ns-3. We made some changes to make it also run in LTE codebase in ns-3 mainline. We didn't reproduce all the experiments on LTE, and the results used in this document are based on NR work.
+
+This original work is done based on [5G NR](https://5g-lena.cttc.es/) branch in ns-3. We made some changes to make it
+also run in LTE codebase in ns-3 mainline. We didn't reproduce all the experiments on LTE, and the results used in this
+document are based on NR work.
 
 #### Build and Run
 
 Run ns-3 example:
-If you want to test the LSTM, you can run another python script but you may need to install [TensorFlow](https://www.tensorflow.org/) environment first. 
+If you want to test the LSTM, you can run another python script but you may need to
+install [TensorFlow](https://www.tensorflow.org/) environment first.
+
 ```Shell
+cp -r contrib/ns3-ai/examples/lte_cqi scratch/
 cd scratch/lte_cqi/
-
 python3 run_online_lstm.py 1
 ```    
-**NOTE: If the program does not exit normally, you need to run freeshm.sh to release the shared memory manually.**
+
+**NOTE: If the program does not exit normally, you need to run `freeshm.sh` to release the shared memory manually.**
 
 ### [Rate-Control](https://github.com/hust-diangroup/ns3-ai/tree/master/examples/rate-control)
-This is an example that shows how to develop a new rate control algorithm for the Wi-Fi model in ns-3 using the ns3-ai model.
+
+This is an example that shows how to develop a new rate control algorithm for the Wi-Fi model in ns-3 using the ns3-ai
+model.
+
 #### Usage
 
 Copy this example to scratch:
 
 ```shell
-cp -r contrib/ns3-ai/example/rate-control scratch/
+cp -r contrib/ns3-ai/examples/rate-control scratch/
 cd scratch/rate-control
 ```
 
@@ -129,7 +176,9 @@ python3 ai_thompson_sampling.py
 ```
 
 ## Cite our work
+
 Please use the following bibtex:
+
 ```
 @inproceedings{10.1145/3389400.3389404,
 author = {Yin, Hao and Liu, Pengyu and Liu, Keshu and Cao, Liu and Zhang, Lytianyang and Gao, Yayu and Hei, Xiaojun},