Intelligent technologies for adjusting the physical layer of mobile networks

Abstract

The paper investigates intelligent technologies for adjusting the physical layer of mobile networks, which are used to optimize and improve the performance of wireless communication systems. Such technologies allow to improve the efficiency of data transmission, increase bandwidth and reduce transmission errors. In particular, we consider the structure of the end-to-end physical layer based on reinforcement learning, which studies how an agent should make decisions in a certain environment to maximize some reward or expected benefit. In the context of the physical layer of the network, reinforcement learning can be used to optimize the behavior of an agent that affects data transmission and control of channel parameters.

We propose a model of a physical network layer unit with generative artificial intelligence that can be used to improve the efficiency and reliability of data transmission in wireless communication systems. The main purpose of such a model is to generate optimal signals or adjust data transmission parameters in order to maximize throughput, minimize transmission errors, and ensure high-quality communication.

The interaction between the physical network layer unit and the artificial intelligence algorithm is investigated in several ways, depending on the specific situation and context of use. The artificial intelligence algorithm can receive data from the physical layer of the network, for example, information about the status of network devices, bandwidth, noise, and delays. This data can be transmitted through a special interface or protocol from the physical layer to the artificial intelligence algorithm for further analysis and processing. The artificial intelligence algorithm can issue commands to network devices at the physical layer, for example, to configure parameters, optimize network performance, and detect abnormal behavior. These commands can be transmitted through a specific protocol or interface from the AI algorithm to the network devices. The artificial intelligence algorithm can analyze data from the physical layer of the network to detect deviations, errors, or failures in the network. When such situations are detected, the algorithm can send notifications to the network administrator or perform automatic actions to restore the network. We investigate resource optimization in the context of the interaction of a physical network layer unit with an artificial intelligence algorithm. An artificial intelligence algorithm can analyze the load on network resources at the physical level and distribute bandwidth between different devices or channels in order to achieve optimal use of available resources. The AI algorithm can analyze historical traffic data at the physical layer of the network and predict future traffic flows. This allows for effective resource planning and adaptation of network settings to meet anticipated needs.

A model of a physical layer unit with a built-in artificial intelligence algorithm in the format of a combination of hardware and software that combines the functionality of the physical layer of the network with intelligent algorithms is proposed. The methods for improving the performance of the physical layer of wireless systems using generative artificial intelligence (GAI) technology are determined: optimization of system parameters, pattern detection, data analysis and optimal training planning, and automatic adjustment of network parameters