Receiver-Distribution-Based Alphabet Design for Structured-Light Communication

Due to the deteriorating effect of atmospheric/oceanic turbulence on optical signals, the design of laser source profiles (viewed as alphabets) plays an important role in free-space optical communication. Alphabet design becomes difficult in such random media when separation between transmitted fields does not reliably translate into separable receiver distributions and no tractable receiver-side criterion is available. We introduce receiver-distribution alphabet design, which learns operational pairwise distinguishability from held-out cross-entropy induced by classifiers with calibration on receiver-intensity observations. The resulting empirical scores support finite-pool selection that jointly protects the least distinguishable selected pair and improves aggregate pairwise separability, without assuming an analytic channel metric. We evaluate this principle in physics-based simulations of structured-light propagation through random media, where the resulting alphabet improves mean classification accuracy by $10.65$ percentage points over an unadapted binary reference on propagation realizations disjoint from selection. These results support receiver-distribution-aware alphabet design when source-space geometry alone does not reliably predict receiver separability.