Research on Multi-Access Communication Technology Based on Deep Learning

# 1. Introduction

## 1.1 Research Background

In the current information society, wireless communication technology has been widely applied and developed. With the ever-increasing number of mobile communication users, higher requirements have been placed on the efficiency and reliability of communication channels. Multi-access communication technology, as an important communication technology, is gradually becoming the key technology to solve problems such as the scarcity of communication channel resources and communication privacy security.

Traditional multi-access communication technologies mainly include Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA). However, with the rapid development of emerging technologies such as the Internet of Things and 5G communication, higher requirements have been put forward for multi-access communication technology, such as higher spectrum utilization, lower interference, and better performance.

## 1.2 Research Objective

This paper aims to study how to use deep learning technology to improve traditional multi-access communication technology and enhance the efficiency and reliability of communication channels. By applying deep learning algorithms, new multi-access communication methods are explored to solve the problems faced by traditional multi-access communication technology, and their advantages and challenges are analyzed and discussed.

## 1.3 Article Structure

This article is divided into six chapters, structured as follows:

1. Introduction: Introduces the research background, research objectives, and article structure.
2. Overview of Multi-access Communication Technology: Provides an overview of the concept of multi-access, classification of multi-access technologies, and research on multi-access communication technology in application fields.
3. Application of Deep Learning in Multi-access Communication: Introduces the basics of deep learning, explores the role of deep learning in multi-access communication, and presents deep learning-based multi-access communication algorithms.
4. Experimental Design and Results Analysis: Details the design and setup of experiments, analyzes experimental results, and discusses conclusions.
5. Advantages and Challenges of Deep Learning Multi-access Communication Technology: Explores the advantages and challenges faced by deep learning multi-access communication technology.
6. Conclusion and Outlook: Summarizes the research conclusions, looks forward to the future development direction of deep learning multi-access communication technology, and discusses the limitations of the research and the next steps.

Through the organization of the above chapters, readers can systematically understand the background, concepts, applications, experimental design and results analysis, advantages and challenges of deep learning-based multi-access communication technology research, thereby drawing conclusions and looking forward to future directions.

# 2. Overview of Multi-access Communication Technology

### 2.1 Multi-access Concept

In a communication system, multi-access is a technology that allows multiple users to share the same channel or spectrum resource at the same time. The basic principle of multi-access communication technology is to allocate different coding schemes to different users, allowing them to communicate on the same channel simultaneously without interfering with each other. The emergence of multi-access technology has greatly improved the energy efficiency and spectrum utilization of communication systems.

### 2.2 Classification of Multi-access Technologies

Multi-access communication technology can be divided into the following common classifications:

1. Time Division Multiple Access (TDMA): The communication time is divided into several time slots, with different users communicating in different time slots. Each user only transmits and receives in the time slot assigned to it. TDMA technology is suitable for communication scenarios that require low latency and high capacity, such as voice calls in mobile communication systems.

2. Frequency Division Multiple Access (FDMA): The communication spectrum is divided into multiple non-overlapping subcarrier frequency bands, with different users communicating in different frequency bands. Each user exclusively occupies a frequency band for transmission and reception operations. FDMA technology is suitable for communication scenarios that require high bandwidth and high data transmission rates, such as satellite communication systems.

3. Code Division Multiple Access (CDMA): User data are encoded, and the encoded user data are superimposed for transmission in the same frequency band. The receiving end separates the data of a specific user from the mixed signal through the corresponding decoding algorithm. CDMA technology is suitable for communication scenarios that require frequency band resource multiplexing and strong anti-interference capabilities, such as 3G and 4G mobile communication systems.

### 2.3 Application Fields of Multi-access Communication Technology

Multi-access communication technology is widely applied in various communication systems, including:

- Mobile Communication Systems: Technologies such as TDMA, FDMA, and CDMA are applied in 2G, 3G, 4G, and 5G mobile communication systems to support simultaneous communication and data transmission for