Machine Learning Phase Ambiguity Limiting Wireless Networks

The patent applies parameter changes by transforming the phase ambiguity problem into a constrained optimization problem. Specifically, it limits the phase search space to a finite set of discrete phase values (e.g., 4, 8, or 16 predefined phase options) rather than searching across all possible continuous phase values. This discretization of the phase parameter significantly reduces the complexity of machine learning training while maintaining network performance optimization.

The patent implements partial action by selectively exploring only a subset of possible phase solutions rather than exhaustively searching all phase combinations. By predefined limiting the phase options to a manageable set (e.g., 4 or 8 discrete phases), the system achieves sufficient solution comprehensiveness for practical wireless communication scenarios while dramatically reducing training time and computational resources required.

The patent applies segmentation by dividing the continuous phase space into discrete segments or bins. Instead of treating phase as a continuous variable requiring fine-grained search, the phase range is segmented into a finite number of discrete levels (e.g., 4, 8, or 16 phase quantization levels). This segmentation maintains solution optimality within each segment while enabling much faster processing and convergence during machine learning training.