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1. RIS with Nanomaterials for FutureG Applications

   Ghaleb Al Duhni, Tatiana Valera (October 2023, IEEE Nanotechnology Materials and Devices Conference (NMDC), Oct 22-25, Paestum, Italy.)

   Reconfigurable Intelligent Surfaces (RIS) also known as Intelligent Reflecting Surfaces (IRS) often depend upon metasurfaces. These typically comprise of a large array of passive elements that can be fabricated to modulate reflection amplitude or phase or both to create tunable functions that are independently controlled. Various RIS are developed to improve spectral efficiency through ultrawideband antennas, enhanced beamforming with higher gain and bandwidth, spatial reconfigurability, selective and adjustable isolation, and other desired features. Several approaches to tune the RIS performance are being explored. This paper reviews the primary approaches and the benefit of emerging tunable nanomaterials in achieving such RIS functions. Designs with 1-bit and 6-bit phase shifters are discussed in the first part. Various opportunities with nanomaterials and nanodevices to induce such phase shifts are discussed in the last part of the paper. 

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2. 1-bit Wideband Reconfigurable Intelligent Surface Design at Sub-6 Band

   Kitch_Mensah-Bonsu; Binbin_Yang; Abdullah_Eroglu; Hao_Xu; Lijun_Qian (August 2024, IEEE)

   In this paper, a 1-bit wideband reconfigurable intelligent surface (RIS) design operating at sub-6 band is proposed. The unit cells of the RIS surface are designed based on a three-layer stacked patch structure. Employing a 1-bit PIN diode to reconfigure the phase states, a phase difference of 𝟏𝟖𝟎𝟎 ± 𝟐𝟎𝟎 is observed over the frequency range of 4.81 – 5.77 GHz, corresponding to a 19.2% fractional bandwidth. The reflection magnitude of the unit cell is above –3 dB in the band of interest, and the cross-polarization of the scattered waves is found significantly lower than the single-patch structure. The RIS array with a dimension of 𝟖. 𝟐𝝀 × 𝟓. 𝟏𝝀 , corresponding to a 𝟏𝟔 × 𝟏𝟎 array is structured according to the proposed unit cell with good beam steering capabilities. 

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3. Hybrid Ris-Assisted Interference Mitigation for Spectrum Sharing

   https://doi.org/10.1109/ICASSP49357.2023.10096639  

   Wang, Fangzhou; Swindlehurst, A. Lee (June 2023, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   This paper explores reconfigurable intelligent surfaces (RIS) for mitigating cross-system interference in spectrum sharing applications. Unlike conventional reflect-only RIS that can only adjust the phase of the incoming signal, a hybrid RIS is considered that can configure the phase and modulus of the impinging signal by absorbing part of the signal energy. We investigate two spectrum sharing scenarios: (1) Spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver, and (2) Spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that optimizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is formulated, and then solved through fractional programming. Numerical results show that with a sufficient number of elements, the hybrid RIS can in many cases completely eliminate the interference, unlike a conventional non-absorptive RIS. 

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4. Clutter Suppression for Target Detection Using Hybrid Reconfigurable Intelligent Surfaces

   https://doi.org/10.1109/RadarConf2351548.2023.10149583  

   Wang, Fangzhou; Li, Hongbin; Swindlehurst, A. Lee (May 2023, 2023 IEEE Radar Conference (RadarConf23))

   Reconfigurable intelligent surface (RIS) technology is a promising approach being considered for future wireless communications due to its ability to control signal propagation with low-cost elements. This paper explores the use of an RIS for clutter mitigation and target detection in radar systems. Unlike conventional reflect-only RIS, which can only adjust the phase of the reflected signal, or active RIS, which can also amplify the reflected signal at the cost of significantly higher complexity, noise, and power consumption, we exploit hybrid RIS that can configure both the phase and modulus of the impinging signal by absorbing part of the signal energy. Such RIS can be considered as a compromise solution between conventional reflect-only and active RIS in terms of complexity, power consumption, and degrees of freedoms (DoFs). We consider two clutter suppression scenarios: with and without knowledge of the target range cell. The RIS design is formulated by minimizing the received clutter echo energy when there is no information regarding the potential target range cell. This turns out to be a convex problem and can be efficiently solved. On the other hand, when target range cell information is available, we maximize the received signal-to-noise-plus-interference ratio (SINR). The resulting non-convex optimization problem is solved through fractional programming algorithms. Numerical results are presented to demonstrate the performance of the proposed hybrid RIS in comparison with conventional RIS in clutter suppression for target detection. 

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5. Optimizing AoI in UAV-RIS Assisted IoT Networks: Off Policy vs. On Policy

   https://doi.org/10.1109/jiot.2023.3246925  

   Sherman, Michelle; Shao, Sihua; Sun, Xiang; Zheng, Jun (February 2023, IEEE Internet of Things Journal)

   In urban environments, tall buildings or structures can pose limits on the direct channel link between a base station (BS) and an Internet-of-Thing device (IoTD) for wireless communication. Unmanned aerial vehicles (UAVs) with a mounted reconfigurable intelligent surface (RIS), denoted as UAV-RIS, have been introduced in recent works to enhance the system throughput capacity by acting as a relay node between the BS and the IoTDs in wireless access networks. Uncoordinated UAVs or RIS phase shift elements will make unnecessary adjustments that can significantly impact the signal transmission to IoTDs in the area. The concept of age of information (AoI) is proposed in wireless network research to categorize the freshness of the received update message. To minimize the average sum of AoI (ASoA) in the network, two model-free deep reinforcement learning (DRL) approaches – Off-Policy Deep Q-Network (DQN) and On-Policy Proximal Policy Optimization (PPO) – are developed to solve the problem by jointly optimizing the RIS phase shift, the location of the UAV-RIS, and the IoTD transmission scheduling for large-scale IoT wireless networks. Analysis of loss functions and extensive simulations is performed to compare the stability and convergence performance of the two algorithms. The results reveal the superiority of the On-Policy approach, PPO, over the Off-Policy approach, DQN, in terms of stability, convergence speed, and under diverse environment settings 

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