PaperlayerView exact revision on arXiv

Source-linked AI summary

Sum Rate Maximization for Practical RIS-Enhanced Rate-Splitting Multiple Access Systems

Qian Zhang, Xuejun Cheng, Jiaming Shi, Yufei Zhao, Rugui Yao, Ye Fan

arXiv:2610.14765v1cs.ITmath.IT

TL;DR

Existing RIS-enhanced RSMA studies rarely account for practical coupling between RIS phase shifts and reflecting amplitude. This paper proposes a variable-separation algorithm for practical RIS-enhanced RSMA sum-rate maximization, reporting over 190% gain over an ideal-RIS benchmark and significant gains over corresponding NOMA and OMA schemes.

  • Problem

    Existing studies on RIS-enhanced RSMA rarely consider amplitude–phase coupling, a practical RIS characteristic that the paper says can cause significant performance degradation when neglected.

    Source: §I · p. 1

  • Method

    The paper proposes a flexible variable-separation optimization algorithm for practical RIS-enhanced RSMA sum-rate maximization, applicable to different amplitude–phase relationships.

    Source: §I · p. 1

  • Results

    Over 190% sum-rate gain over the ideal-RIS benchmark; the practical RIS-enhanced RSMA scheme also significantly outperforms corresponding NOMA and OMA schemes.

    Source: §I · p. 1

  • Takeaways & Limitations

    The proposed algorithm is also directly applicable to practical RIS-enhanced NOMA and OMA sum-rate maximization.

    Source: §V · p. 4

Abstract

from arXiv · show

Reconfigurable intelligent surfaces (RIS) combined with rate-splitting multiple access (RSMA) provide a promising solution for improving the spectral efficiency (SE) of sixth-generation (6G) communication systems. However, most existing studies neglect the practical coupling between the RIS phase shift and amplitude, which may cause phase-shift mismatch and significant performance loss. In this paper, we investigate the multiuser sum-rate maximization problem for practical RIS-enhanced RSMA systems considering amplitude-phase coupling. To address the resulting non-convex problem, an effective variable separation algorithm is proposed. The proposed algorithm is compatible with RIS reflecting optimization under the continuous phase shift or any amplitude-phase coupling relationship and can also be directly extended to practical RIS-enhanced NOMA and OMA systems. Simulation results demonstrate performance gain of over 190\% compared with the ideal-RIS-based benchmark, while the proposed RSMA scheme significantly outperforms the corresponding NOMA and OMA schemes.

I. Introduction

RIS can reconfigure wireless propagation, while RSMA manages multiuser interference by decoding part of it and treating the rest as noise. This paper addresses the limited consideration of practical RIS amplitude–phase coupling in RIS-enhanced RSMA.

I. Introduction

  • RIS can adjust reflected-signal phase shifts to reconfigure propagation, while RSMA supports flexible multiuser interference management by partially decoding interference.The introduction frames both techniques as relevant to 6G networks.Source: §I · p. 1
  • Most prior RIS studies use an ideal phase-shift model that assumes constant reflecting amplitude, despite models of practical amplitude–phase coupling.The passage cites prior work that characterized this coupling and proposed an amplitude-feedback method.Source: §I · p. 1
  • The paper formulates multiuser sum-rate maximization for practical RIS-enhanced RSMA and proposes a variable-separation algorithm for the resulting non-convex problem.The algorithm is described as applicable to different amplitude–phase coupling relationships and extendable to practical RIS-enhanced NOMA and OMA.Source: §I · p. 1
  • The proposed scheme achieves over 190% sum-rate gain over the ideal-RIS benchmark and significantly outperforms corresponding NOMA and OMA schemes.These are the reported simulation comparisons.Source: §I · p. 1

II. System Model and Problem Formulation

The system model depicts a practical RIS-assisted RSMA downlink in which a base station serves single-antenna users through an RIS. Direct base-station-to-user links are obstructed.

II. System Model and Problem Formulation

  • A base station with M antennas and linear precoding serves K single-antenna users with assistance from an RIS equipped with N reflecting elements.Source: §II · p. 1
  • The figure presents a practical RIS-enhanced RSMA system.Source: Figure 1 · p. 2
  • The direct links between the base station and users are obstructed.Source: §II · p. 2

A. Practical RIS Model

The practical RIS model couples each element’s reflection amplitude to its phase shift. The accompanying RSMA signal model uses a common stream decoded by all users and private streams decoded by their corresponding users.

A. Practical RIS Model

  • The practical RIS model makes each element’s reflection amplitude βn vary with its phase shift θn, using a parallel-resonant-circuit-based relationship.The model’s parameters depend on the equivalent impedance circuit parameters of the reflecting elements.Source: A. Practical RIS Model · p. 2
  • RSMA combines common signals into a stream decoded by every user and assigns each user a private stream for that user alone.The base station transmits the common and private streams using linear precoding.Source: B. Signal Model · p. 2
  • Each user first treats private streams as interference and decodes the common stream, then removes it by successive interference cancellation before decoding its own private stream.Source: B. Signal Model · p. 2
  • The common rate is limited by the minimum rate across users, and the system sum rate combines that common rate with users’ private-stream rates.Source: B. Signal Model · p. 2

C. Problem Formulation

The design maximizes multiuser sum rate under practical RIS phase and amplitude–phase constraints. The practical coupling between RIS phase and amplitude, together with coupling between precoding and RIS phase shifts, makes optimization more difficult.

C. Problem Formulation

  • The objective is to maximize multiuser sum rate while constraining RIS phases to [−π, π] and enforcing the practical amplitude–phase relationship.Source: C. Problem Formulation · p. 2
  • The formulation includes a maximum base-station transmit-power constraint and a quality-of-service threshold.Source: C. Problem Formulation · p. 2
  • Unlike the ideal model’s independent, constant amplitude and phase, practical RIS amplitude varies with phase; coupling between precoding vectors and the RIS matrix further complicates the problem.Source: C. Problem Formulation · p. 2

III. Beamforming Optimization

The algorithm separates variables to address the non-convex sum-rate problem, then alternates between precoding and RIS phase-shift updates.

III. Beamforming Optimization

  • The proposed algorithm separates variables to solve the non-convex optimization problem.Auxiliary variables ξ and {ξk} are introduced to form an equivalent reformulation.Source: §III · p. 2
  • The reformulation includes constraints on the common and user-specific rates.The constraints require ξ and ξk to meet γth, with γc,k ≥ ξ and γk ≥ ξk.Source: §III · p. 2
  • Because w and Θ are coupled, the algorithm alternately updates these variable blocks while holding the other fixed.Source: §III · p. 2

A. Optimize Precoding Vector

With the RIS phase-shift matrix fixed, the method transforms the precoding subproblem so its SINR constraints can be handled and the resulting problem solved as a convex optimization.

A. Optimize Precoding Vector

B. Optimize Phase-Shift Matrix

For fixed precoding, the method reformulates the phase-shift subproblem and exploits its element-wise structure to optimize practical RIS phases.

B. Optimize Phase-Shift Matrix

  • With w fixed, the method separates Θ from complicated constraints through an equivalent reformulation.The reformulation introduces µ > 0 and ν, a scaled dual variable.Source: B. Optimize Phase-Shift Matrix · p. 3
  • The Θ update is transformed into a convex problem by handling the phase-shift constraints through the reformulated subproblem.Source: B. Optimize Phase-Shift Matrix · p. 3
  • The practical amplitude-phase coupling constraint is incorporated into the objective before the phase-shift subproblem is decomposed.Source: B. Optimize Phase-Shift Matrix · p. 3
  • The separable objective and constraints yield N independent element-wise problems, solved by one-dimensional line search over feasible phase shifts.Source: B. Optimize Phase-Shift Matrix · p. 3
  • The algorithm also supports 1-bit discrete phase shifts by adjusting the search range to {−π, 0}.A projection method is proposed to quantify performance loss from projecting continuous phase shifts to 1-bit values.Source: B. Optimize Phase-Shift Matrix · p. 3

IV. simulation results

Simulations show that practical-RIS RSMA outperforms corresponding NOMA and OMA schemes, while phase-shift optimization also improves on ideal-to-practical transfer and post-optimization quantization baselines.

IV. simulation results

  • The simulations use Rician fading, M = 8 BS antennas, N = 32 RIS elements, and K = 2 users.The BS is at (0, 0, 0)m, the RIS at (40, 40, 0)m, and users are distributed within an 8-meter-radius circle centered at (50, 0, 0)m.Source: §IV · pp. 3–4
  • RIS-enhanced RSMA consistently outperforms corresponding NOMA and OMA schemes in multiuser sum rate as BS transmit power increases.Fig. 2 compares practical-RIS schemes; the proposed algorithm’s convergence is also shown.Source: §IV · p. 4, Figure 2 · p. 4
  • The proposed algorithm’s sum rate gradually improves and converges as iterations increase; simulation results are averaged over 200 independent experiments.Source: §IV · p. 4
  • The proposed scheme achieves approximately twofold performance gains over “Ideal to Practical” and improves sum rate by more than half over continuous-phase optimization followed by 1-bit quantization.Directly applying ideal-amplitude-optimized phase shifts to a practical RIS causes significant performance loss.Source: §IV · p. 4
  • Practical-RIS optimal phase shifts concentrate around π and −π, where reflection amplitude approaches its maximum, unlike the approximately uniform ideal-RIS phase distribution.The practical-RIS amplitude is minimized at zero phase shift.Source: §IV · p. 4

V. conclusion

The paper optimizes precoding vectors and RIS phase shifts for practical RIS-enhanced RSMA, and proposes an algorithm compatible with amplitude-phase coupling relationships. The approach also extends to practical RIS-enhanced OMA and NOMA, and simulations show it outperforms benchmark schemes.

V. conclusion

  • The proposed scheme outperformed benchmark schemes in system performance in simulations.Source: §V · p. 4
  • An effective alternating-optimization algorithm is compatible with all amplitude-response and phase-shift coupling relationships.Source: §V · p. 4
  • The algorithm can also be applied to practical RIS-enhanced OMA and NOMA multiuser sum-rate maximization.Source: §V · p. 4
Loading 2610.14765v1…