Beamforming and RIS-Aided Ambient Backscatter Communications with Residual-Feature SVM Detection

Ambient backscatter communication (AmBC) enables battery-free connectivity by modulating data onto existing radio-frequency (RF) signals, eliminating the need for dedicated power sources. However, its reliability degrades when direct wireless channels are obstructed or severely faded. Reconfigurable intelligent surfaces (RISs) offer a solution by creating a favorable propagation environment through the control of the phases of incident signals, thereby strengthening wireless links. This paper investigates an RIS-aided AmBC system that jointly exploits physical-layer reconfiguration and statistical learning to restore detection reliability under such conditions. The RIS phase profile is aligned for the source-RIS-tag link, while a multi-antenna reader applies receive beamforming to steer toward the RF source and the tag separately. At the reader, a hypothesis-based minimum mean square error (MMSE) equalizer reconstructs the ambient symbol under each candidate tag state and produces a pair of residual features, which are classified by a support vector machine (SVM) with a Gaussian kernel. We make the physical-layer-to-learning coupling explicit, i.e., RIS phase alignment and beamforming improve the signal-to-interference-plus-noise ratio (SINR), thereby rendering the residual features more separable and reducing classification error. Simulation results show that the proposed RIS-beamforming-SVM detector achieves substantial bit-error-rate (BER) gains over RIS-energy, SVM, and SVM-beamforming baselines across a wide SINR range, that the spectral-efficiency gains are governed more strongly by the number of RIS elements than by the number of reader antennas, and that performance saturates with a moderate RIS size, allowing near-optimal operation at reduced hardware cost.