Generative adversarial networks based adaptive modulation and coding for next-generation 5G communication systems

Abstract

Abstract In 5G based communication systems, adaptive modulation and coding (AMC) is a key approach that optimizes data transmission by constantly modifying modulation schemes and error correction coding by the current channel circumstances. AMC’s main objective is to increase data transfer efficiency and reliability while adjusting to the frequently fluctuating and unexpected nature of wireless channels. However, the channel's quality can be impacted by several variables, including distance, fading, noise, and interference in the time-varying channel. Hence it won't be easy to approximate the channel state information (CSI) accurately for time-varying channels. This paper discusses the novel rate adaptation approach that leverages generative adversarial networks (GAN) along with AMC to ensure efficient and reliable data transfer in a dynamic and often challenging environment, that maximizes data throughput even under varying conditions and offers robustness under adverse ones.

Keywords

• AMC
• GAN
• Bit error rate
• Machine learning
• Throughput