Generative AI for Advanced UAV Networking

Geng Sun, Wenwen Xie, Dusit Niyato, Hongyang Du, Jiawen Kang, Jing Wu, Sumei Sun, Ping Zhang
Paper arXiv

Abstract

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions.

Run the Program

1) Install the following packets using pip:


      pip install langchain
      pip install openai

2) Set up LangChain.


          llm = OpenAI(api_key="")
          llm = ChatOpenAI()
          from langchain.text_splitter import RecursiveCharacterTextSplitter
          from langchain_community.embeddings import HuggingFaceBgeEmbeddings
          from langchain.vectorstores import Chroma
          from langchain.memory import ConversationBufferMemory
          from langchain.chains import ConversationalRetrievalChain
          from langchain.prompts.chat import ChatPromptTemplate
          from langchain_core.output_parsers import StrOutputParser
          from langchain_core.runnables import RunnablePassthrough

3) Load information.


          with open('information.txt',encoding='utf-8') as f:
              state_of_the_union = f.read()
          text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=30)
          texts = text_splitter.create_documents([state_of_the_union])
          chunks = text_splitter.split_documents(texts)

4) Embed and Create DB.


          model_name = "moka-ai/m3e-base"
          model_kwargs = {'device': 'cpu'}
          encode_kwargs = {"normalize_embeddings": True}
          embedding = HuggingFaceBgeEmbeddings(
                model_name=model_name,
                model_kwargs=model_kwargs,
                encode_kwargs=encode_kwargs,
                query_instruction="Generate the vector fot the text"
            )
          db = Chroma.from_documents(chunks, embedding)

5) Retrieve.


          retriever = db.as_retriever()
          retriever_chain = (
            {"context": retriever, "question": RunnablePassthrough()}
            | prompt
            | llm
            | StrOutputParser()
          )
          retriever.search_kwargs['k'] = 10

6) Search.


          searchResult = retriever_chain.invoke(inputs)

7) Generation.


          Use Generative diffusion model to solve optimization problem. (https://github.com/HongyangDu/GDMOPT)

BibTeX

@article{sun2024,
        title={Generative AI for Advanced UAV Networking},
        author={Geng Sun, Wenwen Xie, Dusit Niyato, Hongyang Du, Jiawen Kang, Jing Wu, Sumei Sun, Ping Zhang},
        journal={arXiv preprint arXiv:2404.10556},
        year={2024}
      }