Wireless Backhaul Topology Optimization with AI and Heuristic Shortcuts

The patent segments the exhaustive topology evaluation process into multiple phases: (1) generating an initial feasible topology using analytical methods, (2) identifying critical links and nodes that have disproportionate impact on performance, (3) performing detailed optimization only on these critical segments, and (4) propagating changes to the full network. This segmentation reduces computational complexity from evaluating all possible topologies to evaluating only relevant subsets.

The patent applies partial action by performing exhaustive evaluation only on a carefully selected subset of candidate topologies rather than all possible topologies. The method generates a limited set of promising candidates using analytical shortcuts and heuristics, then applies rigorous evaluation only to these partial candidates, achieving near-optimal results with tractable computational effort.

The patent performs preliminary analytical evaluation to generate an initial feasible topology and identify critical components before applying more sophisticated optimization. This preliminary action filters out obviously poor configurations and focuses subsequent computational resources on promising candidates, bridging the gap between analytical tractability and optimization quality.

The patent introduces intermediary heuristics and analytical shortcuts that act as mediators between simple analytical methods and exhaustive search. These intermediaries generate refined candidate topologies that incorporate wireless environment dynamics without requiring full exhaustive evaluation, achieving better accuracy than traditional methods while maintaining computational tractability.

The patent transforms the static topology determination process into a dynamic adaptation mechanism. The system continuously monitors network conditions (traffic patterns, link quality, node failures) and periodically re-evaluates and reconfigures the topology using the same analytical and optimization methods employed during initial setup. This enables the network to adapt to changing conditions while maintaining the simplicity of the original configuration approach.

The patent implements feedback loops where network performance metrics are continuously measured and fed back to the topology optimization process. This feedback mechanism triggers reconfiguration when performance degradation is detected, enabling the static topology to adapt dynamically to changing conditions while maintaining the simplicity of the original setup methodology.

The patent segments the large-scale network into smaller clusters or zones, applying topology optimization independently to each segment. This segmentation reduces the combinatorial explosion from considering all N nodes together to considering smaller subsets, making optimization feasible for large networks while maintaining overall network capacity and coverage.

The patent applies partial evaluation by focusing computational resources on critical subgraphs and high-impact link configurations rather than evaluating all possible topologies for the entire large network. This selective approach enables optimization of capacity-critical portions of the network without being overwhelmed by the super-exponential growth of total configuration space.