ofdm-ris co-design

This study addresses the multi-objective mixed-integer nonlinear programming problem of jointly designing OFDM waveforms and configuring reconfigurable intelligent surfaces (RIS) for 6G. Synthesizing insights from 78 studies published between 2021 and 2026, it proposes the first cross-paradigm classification framework encompassing model-based convex relaxation, heuristic search, deep reinforcement and unsupervised learning, as well as emerging approaches integrating foundation models, diffusion-based generative AI, and quantum optimization. The work identifies a key property of neural network inference—maintaining constant latency under antenna array scaling (N = 16–128)—and establishes a standardized benchmark. Results demonstrate that machine learning methods achieve 95–99% of the spectral efficiency of model-driven approaches while accelerating inference by 10²–10⁴ times, and highlight six open challenges, including the lack of unified benchmarks, hardware-aware deployment constraints, and safety concerns regarding large models in real-time control.

To address the frequency-response modeling mismatch of conventional diagonal reconfigurable intelligent surfaces (RISs) in broadband OFDM systems, this paper proposes a circuit-level modeling and joint optimization framework for broadband non-diagonal RISs (BD-RISs). We first establish a frequency-dependent linear model based on admittance parameters, explicitly capturing the wideband non-diagonal reflection characteristics. Subsequently, we formulate a joint optimization problem integrating BD-RIS reflection coefficients and transmitter power allocation across OFDM subcarriers. Leveraging OFDM signal modeling, time–frequency domain channel mapping, and tailored non-convex optimization algorithms, simulations demonstrate that the proposed method significantly improves average spectral efficiency—achieving up to a 23.7% rate gain in typical scenarios—with higher circuit complexity yielding greater performance benefits from accurate wideband modeling. This work overcomes key bottlenecks in wideband RIS modeling and optimization.

Conventional diagonal reconfigurable intelligent surfaces (RISs) can only modulate signal phase, lacking independent control over channel singular values. Method: This paper proposes a novel non-diagonal RIS architecture—block-diagonal RIS (BD-RIS)—enabling joint dynamic reshaping of both amplitude and phase in MIMO channels. We formulate a non-diagonal phase response model, develop a geodesic-optimization-based framework to characterize singular-value regions, and integrate alternating optimization with singular-value bounding theory for rigorous analysis. Contributions/Results: BD-RIS significantly expands the dynamic range and trade-off space of individual singular values. It enables multi-stream transmission even at low SNR, approaching the theoretical degrees-of-freedom limit. Moreover, BD-RIS delivers pronounced rate gains in high-dimensional MIMO and large-scale RIS deployments, while supporting energy-efficient multi-stream communication under low-power constraints.

This paper addresses the sum-rate maximization problem in reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D)-underlaid cellular networks, jointly optimizing uplink transmit powers, base station (BS) receive beamforming, and RIS passive phase shifts. It is the first work to integrate RIS into D2D-celullar spectrum sharing scenarios. A block coordinate descent (BCD)-based joint optimization framework is proposed: closed-form optimal solutions are derived for power allocation and BS receive beamforming; for the non-convex RIS phase optimization, an efficient algorithm combining quadratic transformation and semidefinite relaxation (SDR) is designed. The proposed method significantly reduces computational complexity. Numerical results demonstrate substantial sum-rate gains over RIS-free baseline schemes under typical system configurations, thereby validating the effectiveness and practicality of RIS for enhancing D2D-underlaid cellular networks.

Conventional RIS beamforming methods rely on uniform phase quantization, failing to account for the inherent non-uniform phase response and heterogeneous bit-resolution of practical RIS elements—leading to suboptimal performance. Method: This work establishes, for the first time, a joint discrete optimization framework for RIS-assisted MISO systems incorporating non-uniform phase configurations. We propose the Phase-Adaptive Traversal (PAT) algorithm, which guarantees global optimality, and its low-complexity linear approximation, E-PAT, scalable to multi-user scenarios. The framework integrates non-uniform phase modeling, divide-and-conquer search, and discrete optimization. Contribution/Results: PAT achieves provably optimal solutions in single-user settings; E-PAT attains significant performance gains with linear complexity in multi-user cases. Simulations demonstrate substantial improvements in energy efficiency and achievable rate over uniform quantization baselines.

This work addresses the challenges of slow convergence, high computational complexity, and lack of user prioritization in joint signal enhancement and suppression using reconfigurable intelligent surfaces (RIS) in multi-user wireless systems. To overcome these limitations, the authors propose a unified RIS optimization framework that incorporates adaptive gradient scaling for fast, parameter-free convergence, a low-complexity beamforming recovery method that avoids matrix decomposition, and a novel user prioritization mechanism based on RIS subarray allocation, complemented by a modular architecture supporting flexible addition or removal of components. Evaluated across three representative scenarios, the proposed scheme closely approaches theoretical performance bounds, significantly outperforms conventional semidefinite relaxation methods, and demonstrates near-optimality, scalability, and effectiveness in both cooperative and competitive multi-user environments under real-world channel conditions.

本文提出一种基于级联智能超表面的全波域宽带多用户MIMO OFDM发射机，通过物理合成基带链路解决宽带传输问题。

This work addresses the waveform design challenge in dual-functional integrated sensing and communication (ISAC) systems employing OFDM by jointly optimizing subcarrier allocation and power distribution to balance communication rate and sensing accuracy. The study reveals that communication performance hinges on the number of allocated subcarriers, while sensing precision—particularly delay estimation—depends critically on their spatial distribution. To this end, the authors propose a subcarrier allocation criterion based on the trade-off between Fisher information gain for sensing and communication rate loss, along with a bounded water-filling power allocation structure. A joint path coefficient and delay estimation scheme is further developed to guide waveform optimization. By leveraging quadratic transformation and Lagrangian dual decomposition, closed-form iterative updates for the optimization variables are derived. Experimental results demonstrate that the proposed method significantly outperforms existing baselines in both delay estimation accuracy and achievable communication rate.

This study reveals that reconfigurable intelligent surfaces (RIS), while optimizing a primary communication link, can induce significant unintended interference on nearby secondary links—even when the two are spatially separated and operate on distinct carrier frequencies. Through real-world experiments conducted in the FR1 band using the CorteXlab platform and Greenerwave RIS hardware, the work systematically evaluates the impact of RIS configurations on the received power and channel phase of secondary links under representative coexistence scenarios, including both co-channel and inter-channel conditions. The experiments provide the first empirical evidence that conventional assumptions relying on frequency-domain isolation to mitigate interference no longer hold in RIS-enabled environments, thereby underscoring the critical need for cross-link compatibility considerations in RIS deployment strategies.

This study addresses the flexible trade-off between communication and sensing performance in integrated sensing and communication (ISAC) systems assisted by bi-diagonal reconfigurable intelligent surfaces (BD-RIS). The work proposes a joint optimization framework for transmit precoding vectors and BD-RIS phase-shift matrices, enhancing sum-rate through multi-user interference management while improving sensing performance via an approximation of sensing beamforming gain. Notably, this is the first work to incorporate BD-RIS into ISAC systems, leveraging its inter-element connectivity to introduce additional degrees of freedom and enable synergistic co-optimization of communication and sensing. An alternating optimization algorithm is developed to efficiently solve the resulting non-convex weighted problem, yielding closed-form updates for both precoding and phase shifts. Simulation results demonstrate that the proposed scheme significantly outperforms conventional diagonal RIS in achieving a superior communication-sensing performance trade-off.