Stochastic Geometry-Based Interference Prediction and Adaptive Power Control in Dense 6G Urban Networks

With the rapid emergence of ultra-dense 6G network deployments, there is an increasing demand for a robust probabilistic framework to enable accurate interference forecasting and thus effective power regulation. This work proposes a stochastic-geometry-driven adaptive power-control model that accurately estimates aggregate interference and optimizes transmit power to maintain a desired signal-to-interference-and-noise ratio level within ultra-dense metropolitan wireless systems. Employing a Poisson Point Process to model the distributions of base-station and user nodes, the work develops analytical expressions for the Laplace transform of interference, outage probability, and average achievable rate. An adaptive fractional power-control mechanism is further designed based on derived expressions in order to minimize energy expenditure while maintaining the desired reliability. Simulation results demonstrate that the proposed Adaptive Fractional Power Control(AFPC) mechanism achieves 22.2-53.3% transmit power reduction for all the schemes and provides up to 4 dB SINR gain compared to the benchmark static and full-channel-inversion schemes. In summary, the proposed framework thus provides a tractable yet operationally effective solution for interference management in sustainable large-scale 6G urban deployments.