Beyond Gaussian Assumptions: A General Fractional HJB Control Framework for L\'evy-Driven Heavy-Tailed Channels in 6G

TL;DR

This paper introduces a fractional HJB control framework for 6G wireless channels modeled by heavy-tailed Lévy processes, addressing non-Gaussian, non-stationary environments with abrupt fluctuations.

Contribution

It develops a novel fractional control approach using a fractional HJB equation for heavy-tailed Lévy-driven channels, with proven existence and uniqueness of solutions.

Findings

The fractional HJB framework effectively optimizes power in heavy-tailed interference scenarios.

Numerical results show improved performance over traditional methods in complex environments.

The model captures non-local effects and memory in 6G channel dynamics.

Abstract

Emerging 6G wireless systems suffer severe performance degradation in challenging environments like high-speed trains traversing dense urban corridors and Unmanned Aerial Vehicles (UAVs) links over mountainous terrain. These scenarios exhibit non-Gaussian, non-stationary channels with heavy-tailed fading and abrupt signal fluctuations. To address these challenges, this paper proposes a novel wireless channel model based on symmetric $\alpha$-stable L\'evy processes, thereby enabling continuous-time state-space characterization of both long-term and short-term fading. Building on this model, a generalized optimal control framework is developed via a fractional Hamilton-Jacobi-Bellman (HJB) equation that incorporates the Riesz fractional operator to capture non-local spatial effects and memory-dependent dynamics. The existence and uniqueness of viscosity solutions to the fractional HJB equation are rigorously established, thus ensuring the theoretical validity of the proposed control formulation. Numerical simulations conducted in a multi-cell, multi-user downlink setting demonstrate the effectiveness of the fractional HJB-based strategy in optimizing transmission power under heavy-tailed co-channel and multi-user interference.