Reliable IoT Communications in 6G Non-Terrestrial Networks with Dual RIS

The increasing demand for Internet of Things (IoT) applications has accelerated the need for robust resource allocation in sixth-generation (6G) networks. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted upper mid-band communication framework. To ensure robust connectivity under severe line-of-sight (LoS) blockages, we use a two-tier RIS structure comprising terrestrial RISs (TRISs) and high-altitude platform station (HAPS)-mounted RISs (HRISs). To maximize network sum rate, we formulate a joint beamforming, power allocation, and IoT device association (JBPDA) problem as a mixed-integer nonlinear program (MINLP). The formulated MINLP problem is challenging to solve directly; therefore, we tackle it via a decomposition approach. The zero-forcing (ZF) technique is used to optimize the beamforming matrix, a closed-form expression for power allocation is derived, and a stable matching-based algorithm is proposed for device-RIS association based on achievable data rates. Comprehensive simulations demonstrate that the proposed scheme approaches the performance of exhaustive search (ES) while exhibiting substantially lower complexity, and it consistently outperforms greedy search (GS) and random search (RS) baselines. Moreover, the proposed scheme converges much faster than the ES scheme.

💡 Research Summary

The paper addresses the pressing need for reliable, high‑capacity IoT connectivity in forthcoming sixth‑generation (6G) networks, focusing on the newly allocated upper‑mid‑band (UMB) spectrum (7–15 GHz, also known as FR3 or the “Golden Band”). Recognizing that line‑of‑sight (LoS) blockages are especially severe in dense urban environments, the authors propose a two‑tier reconfigurable intelligent surface (RIS) architecture: terrestrial RISs (TRISs) mounted on building facades and a high‑altitude platform‑station (HAPS)‑mounted RIS (HRIS). This hybrid deployment aims to provide complementary coverage—TRISs supply ground‑level reflections while the HRIS offers a high‑altitude, wide‑viewpoint link that can bypass ground obstructions.

The system model consists of a single access point (AP) equipped with N antennas serving K single‑antenna IoT devices. Direct AP‑to‑device links are assumed unavailable due to severe path loss and blockage; all communication proceeds via an AP‑→‑RIS‑→‑device two‑hop cascade. Each RIS contains M_y × M_z passive elements whose phase shifts (θ) and amplitudes (κ) are controllable. The cascaded channel for device k via RIS ℓ is expressed as g_{ℓ,k}=H_ℓ Θ_ℓ h_{ℓ,k}, where H_ℓ is the AP‑to‑RIS channel, Θ_ℓ the diagonal RIS‑configuration matrix, and h_{ℓ,k} the RIS‑to‑device channel.

The core objective is to maximize the network sum‑rate Σ_k R_k, where R_k = log₂(1+SINR_{ℓ,k}) and SINR_{ℓ,k}=p_k|g_{ℓ,k}ᴴ w_k|²/(∑{i≠k}p_i|g{ℓ,k}ᴴ w_i|²+σ²). The optimization variables are (i) the beamforming matrix W (columns w_k), (ii) the power allocation vector p, and (iii) the binary device‑RIS association matrix Υ (Υ_{ℓ,k}=1 if device k is served by RIS ℓ). Constraints enforce non‑negative powers, a total AP power budget P_AP, and a one‑to‑one matching (each device assigned to exactly one RIS and each RIS to at most one device). This formulation yields a mixed‑integer nonlinear program (MINLP), which is NP‑hard.

To obtain a tractable solution, the authors decompose the problem into three sub‑problems solved iteratively:

1. Beamforming Optimization (P1) – With p and Υ fixed, zero‑forcing (ZF) beamforming is applied. The pseudo‑inverse of the effective channel matrix G =