Wireless Environmental Information Theory: A New Paradigm towards 6G Online and Proactive Environment Intelligence Communication

TL;DR

The paper proposes wireless environmental information theory (WEIT) and a corresponding environment intelligence communication (EIC) paradigm to move beyond offline statistical channel models toward online, proactive channel management for 6G. WEI is defined, classified, and linked to an entropy measure $S_e$ with a mapping to channel parameters, enabling a quantitative bridge between environment and propagation. The EIC-WEI architecture combines multimodal sensing, AI-based online channel prediction, and WEK-driven decision making to continuously adapt transmission strategies with reduced pilot overhead. Validation on tasks such as cell coverage, CSI/beam prediction, and resource allocation demonstrates notable improvements over traditional statistical models and confirms the feasibility of real-time WEI-enabled channel prediction. Challenges in accuracy, complexity, and generalization are discussed, along with future directions including large WEK-based models and digital twin integrations for ubiquitous 6G environment intelligence.

Abstract

The channel is one of the five critical components of a communication system, and its ergodic capacity is based on all realizations of statistic channel model. This statistical paradigm has successfully guided the design of mobile communication systems from 1G to 5G. However, this approach relies on offline channel measurements in specific environments, and the system passively adapts to new environments, resulting in deviation from the optimal performance. With the pursuit of higher capacity and data rate of 6G, especially facing the ubiquitous environments, there is an urgent need for a new paradigm to combat the randomness of channel, i.e., more proactive and online manner. Motivated by this, we propose an environment intelligence communication (EIC) based on wireless environmental information theory (WEIT) for 6G. The proposed EIC architecture is composed of three steps: Firstly, wireless environmental information (WEI) is acquired using sensing techniques. Then, leveraging WEI and channel data, AI techniques are employed to predict channel fading, thereby mitigating channel uncertainty. Thirdly, the communication system autonomously determines the optimal air-interface transmission strategy based on real-time channel predictions, enabling intelligent interaction with the physical environment. To make this attractive paradigm shift from theory to practice, we answer three key problems to establish WEIT for the first time. How should WEI be defined? Can it be quantified? Does it hold the same properties as statistical communication information? Furthermore, EIC aided by WEI (EIC-WEI) is validated across multiple air-interface tasks, including CSI prediction, beam prediction, and radio resource management. Simulation results demonstrate that the proposed EIC-WEI significantly outperforms the statistical paradigm in decreasing overhead and performance optimization.

Figures (15)

• Figure 1: The timeline of wireless channel research by different methodologies.
• Figure 2: WEIT: From passive environment-adaptation to environment intelligence communication.
• Figure 3: Illustration for the WEI: definition, classification and properties.
• Figure 4: The relationship between WEI and environmental entropy.
• Figure 5: Environment intelligence communication for 6G system.