AI assisted PHY in future wireless systems: Recent developments and challenges

Abstract

Nowadays, the rapid development of artificial intelligence (AI) provides a fresh perspective in designing future wireless communication systems. Innumerable attempts exploiting AI methods have been carried out, which results in the state-of-the-art performance in many different areas of wireless communications. In this article, we present the most recent and insightful developments that demonstrate the potentials of AI techniques in different physical layer (PHY) components and applications including channel characterization, channel coding, intelligent signal identification, channel estimation, new PHY for random access in massive machine-type communication (mMTC), massive multiple-input multiple-output (MIMO) power control and PHY resource management. Open challenges and potential future directions are identified and discussed along this research line.

Automatic multi‐objective particle swarm optimization method for effective Doherty power amplifier design

“…As a result of unprecedented market demand, wireless communication technology is continually developing, due to the increase in data transmission and the rise in the requirement for high-speed communication in complex scenarios. [1][2][3] In a wireless communication system, radio frequency power amplifiers (RFPAs) play a crucial role in determining the quality of the overall communication system. 4 PAs, however, have suffered significant degradation in efficiency in modern wireless communication systems because of high peak-to-average power ratio (PAPR) signals.…”

AI-Based Beam Management in 3GPP: Optimizing Data Collection Time Window for Temporal Beam Prediction

“…With the development of AI technology that has also made significant advancements in other aspects of the physical layer in wireless communication [2], including channel estimation, reference signal (RS) reduction, channel coding, random access, channel state information (CSI) feedback, and positioning. For example, [3] exploits deep learning for positioning, utilizing CSI as the model input to enhance positioning performance in indoor scenarios.…”

Broadband PA design using Bayesian algorithms with different covariance functions

“…Increasing market demand for wireless communication systems has resulted in the rapid development of modern wireless communication systems, including 5G and beyond 5G techniques 1–3 . As one of the most critical components of a wireless system, RFPAs play an important role in the performance of the entire system.…”

“…Increasing market demand for wireless communication systems has resulted in the rapid development of modern wireless communication systems, including 5G and beyond 5G techniques. [1][2][3] As one of the most critical components of a wireless system, RFPAs play an important role in the performance of the entire system. Due to these factors, circuit designers are increasingly focusing on PAs with high performance features, such as wide bandwidth, high output power, and high PAE.…”