Common-Loss Parameter-Efficiency Analysis of MLP and KAN Neural Receivers for Digital Communications

Abstract

Classical coherent binary phase-shift keying (BPSK) reception over additive white Gaussian noise (AWGN) channels is analytically well understood, and the optimum hard-decision detector is known. Therefore, the aim of using neural receivers in this study is not to replace the classical AWGN-BPSK detector. Instead, the AWGN-BPSK setting is deliberately selected as a theoretically verifiable benchmark for analyzing how compactly neural receiver architectures can represent a known decision behavior. This paper compares multi-layer perceptron (MLP) and Kolmogorov--Arnold Network (KAN) receivers using a multi-SNR Nyquist-rate BPSK dataset. The models are evaluated using bit error rate (BER), mean squared error (MSE), and trainable parameter count. Beyond reporting accuracy alone, this work emphasizes a common-loss parameter-efficiency perspective: when two receivers reach a comparable practical BER or loss region, the receiver with fewer parameters is more attractive for real-time deployment. The results show that both MLP and KAN receivers reproduce the expected AWGN-BPSK detection trend, while a compact KAN configuration reaches a comparable operating region with substantially fewer trainable parameters. In particular, the KAN receiver with 3281 parameters achieves a test BER of 2.45 x 10^-4, while the MLP baseline with 8513 parameters achieves a test BER of 2.50 x 10^-4. This corresponds to approximately 61.5% fewer trainable parameters at a comparable BER operating point. This reduction is important for real-time neural receivers because it affects memory footprint, parameter access, inference latency, energy consumption, and hardware feasibility on embedded, software-defined radio, FPGA, ASIC, and edge communication platforms.