A Scientific Analysis of a Hierarchical Active Inference Model for Coordinated Multi-Point Beamforming
DOI:
https://doi.org/10.59992/IJCI.2026.v5n5p1الكلمات المفتاحية:
Active Inference، Hierarchical Active Inference (HAI)، Coordinated Multi-Point (CoMP)، Beamforming، Free Energy Principle (FEP)، 5G، 6G، Variational Inference، Wireless Communications، Resource Allocationالملخص
Coordinated multi-point (CoMP) transmission is viewed as an innovative solution for future generation wireless systems with the ability to meet rising data rate requests. CoMP requires coordination among BSs to facilitate either resource allocation or joint transmission. However, this coordination is challenging as BSs have dynamic interference patterns and the decisions of one BS impact the decisions made by other BSs [1]. Active inference is a broad theoretical framework based around the ideas of Bayesian statistics, free-energy minimization, and constructivism. This new worldview suggests that biological agents, not just humans but also animals, do not only make decisions and interpretations, but also adapt to and perfect the internal generative models, so as to explain their environments. While conditioning on multiple stimuli and responding to the world in light of them, agents develop an adaptation mechanism to cope with unpredictable changing environments. With respect to this system, generative models will be systematically represented hierarchically according to the model. The upper levels of this hierarchy are the abstract latent variables and although harder to estimate, yet provide important contextual meaning to the lower level variables, and the variables at these lower levels are able to make sense of perception and impact on the ground, possibly directly changing it (because they affect it). With this comprehensive framework, the agents have to learn constantly, growing and adapting according to the time they see and evolve to respond to the highly evolving world around them. Beamforming optimization per-BS for CoMP transmission can be interpreted as beamforming control. There exists a local generative model that accounts for channel observations over time for each BS. The local model states are dynamic with changing channel dynamics. Assuming the beamforming parameters, BS minimizes the free-energy cost of the generative model. The free-energy control law possesses a dualistic character with respect to an approach to minimizing channel-state estimation error while allowing for maximum throughput/energy efficiency by users.
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