Abstract:The move to next-generation wireless communications with extremely large-scale antenna arrays (ELAAs) brings the communications into the radiative near-field (RNF) region, where distance-aware focusing is feasible. However, high-frequency RNF links are highly vulnerable to blockage in indoor environments dominated by half-space obstacles (walls, corners) that create knife-edge shadows. Conventional near-field focused beams offer high gain in line-of-sight (LoS) scenarios but suffer from severe energy truncation and effective-rank collapse in shadowed regions, often necessitating the deployment of auxiliary hardware such as Reconfigurable Intelligent Surfaces (RIS) to restore connectivity. We propose a beamforming strategy that exploits the auto-bending property of Airy beams to mitigate half-space blockage without additional hardware. The Airy beam is designed to ``ride'' the diffraction edge, accelerating its main lobe into the shadow to restore connectivity. Our contributions are threefold: (i) a Green's function-based RNF multi-user channel model that analytically reveals singular-value collapse behind knife-edge obstacles; (ii) an Airy analog beamforming scheme that optimizes the bending trajectory to recover the effective channel rank; and (iii) an Airy null-steering method that aligns oscillatory nulls with bright-region users to suppress interference in mixed shadow/bright scenarios. Simulations show that the proposed edge-riding Airy strategy achieves a Signal-to-Noise Ratio (SNR) improvement of over 20 dB and restores full-rank connectivity in shadowed links compared to conventional RNF focusing, virtually eliminating outage in geometric shadows and increasing multi-user spectral efficiency by approximately 35\% under typical indoor ELAA configurations. These results demonstrate robust RNF multi-user access in half-space blockage scenarios without relying on RIS.
From: Yifeng Qin Dr. [view email]
[v1]
Wed, 14 Jan 2026 02:58:15 UTC (4,313 KB)
[v2]
Fri, 16 Jan 2026 11:09:06 UTC (4,301 KB)
[v3]
Sat, 24 Jan 2026 10:30:31 UTC (4,301 KB)
[v4]
Wed, 26 Aug 2026 06:38:27 UTC (1,529 KB)
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