arXiv:2610.33663v1 Announce Type: new Abstract: Neural-network receivers are proposed to undo the distortion of a power amplifier driven close to saturation, where it is most efficient. They estimate the channel from pilots, known symbols sent in every frame. The pilots pass through the same amplifier as the data, so the channel estimate carries a small error that depends on the pilot sequence. If one sequence is always sent, as by default in the Sionna simulator, the error never changes, and a network can learn it instead of undoing the amplifier. We tested this in simulated two- and four-antenna orthogonal frequency-division multiplexing links; results are for binary pilots at the highest signal-to-noise ratio tested. A network with separate weights for each subcarrier lowered the bit error rate by 14 to 22% with its training pilot but raised it by 2 to 18% with every other pilot tested, and longer training widened this gap. A convolutional neural receiver following an open-source tutorial gained 37 percentage points more on its training pilot than on unseen pilots after short training, and 9 points more after nine times longer training, much of which also appeared with a linear amplifier. The gap shrank with amplifier back-off but persisted where the in-band distortion meets the 5G limit. On a channel with short delay spread, smoothing the channel estimate across subcarriers without training lowered the error rate on all 107 pilot sequences tested, by 30% on average. Learned receivers should therefore be tested on unseen pilots and against such simple baselines.