Subject: Cutting-Edge Advancements in Wireless Communications and Signal Processing

Dear Elman,

General Overview

This newsletter explores a collection of preprints covering critical areas in wireless communications, including integrated positioning and communication (IPAC), channel estimation, reconfigurable intelligent surfaces (RIS), and advanced signal processing techniques. A recurring theme is the use of deep reinforcement learning (DRL) and deep learning (DL) to optimize performance in complex and dynamic wireless environments.

For example, Ma et al. (2024) delve into the potential of IPAC systems on low Earth orbit (LEO) satellites, investigating the synergy between communication and positioning functionalities. Similarly, Bai et al. (2024) utilize DRL for efficient data collection in backscatter sensor networks using a UAV with a movable antenna, achieving improvements in data collection time and energy consumption.

Channel estimation is another key focus. Darya & Abdallah (2024) propose semi-blind channel estimation techniques for massive MIMO LEO satellite communications to combat channel aging. Chen et al. (2024) introduce channel customization in multi-RIS-assisted MIMO systems, integrating CSI acquisition and RIS configuration to reduce overhead and complexity.

The transformative role of RIS is a prominent theme. Shu et al. (2024) analyze the impact of RIS on the degrees of freedom (DoF) of rank-deficient channels, showcasing potential rate improvements. Qian et al. (2024) investigate STAR-RIS-assisted cell-free massive MIMO systems under electromagnetic interference and phase errors, proposing mitigation strategies. Raeisi et al. (2024) explore beyond diagonal RIS (BD-RIS) for efficient localization, highlighting its potential for high-accuracy positioning. Huang et al. (2024) examine the security implications of intelligent omni-surfaces (IOSs), proposing a fully-passive jamming attack.

Beyond communication and positioning, the collection covers signal processing in other domains. Wang et al. (2024) propose a Schrödinger Bridge-based MRI reconstruction framework using multi-contrast image guidance. Molina-Molina et al. (2024) present artifact mitigation tools for EEG signal analysis, focusing on ocular artifacts. EEG signal processing for music and voice classification (Ariza et al., 2024) and emotion recognition (Imtiaz & Khan, 2024, Wang et al., 2024) are also explored.

Several papers introduce novel methodologies. Singh et al. (2024) present exponentially consistent nonparametric clustering algorithms for data streams. Wang et al. (2024) investigate throughput maximization for movable antenna systems considering movement delay. Shi et al. (2024) introduce AMSnet-KG, a netlist dataset for LLM-based AMS circuit auto-design. Chowdhury et al. (2024) present a fast hyperspectral reconstruction algorithm for neutron computed tomography. Stutz-Tirri et al. (2024) develop a framework for efficient and physically-consistent modeling of reconfigurable electromagnetic structures.

Finally, practical implementations and experimental validations are presented. Al-ZuBi & Alouini (2024) offer a GUI tool for scanning TV white space spectrum. Vicenzo & Xu (2024) introduce a GNSS direct position estimation plug-in with multipath mitigation. Xu et al. (2024) explore mutual information-oriented ISAC beamforming design under statistical CSI. Du et al. (2024) propose a CSI feedback framework based on transmitting important values and generating others. Mishra et al. (2024) analyze coexistence of real-time source reconstruction and broadband services over wireless networks. Galeote-Cazorla et al. (2024) provide an experimental assessment of human blockage at sub-THz and mmWave frequencies.

Paper Highlights

Channel Customization for Low-Complexity CSI Acquisition in Multi-RIS-Assisted MIMO Systems

Channel Customization for Low-Complexity CSI Acquisition in Multi-RIS-Assisted MIMO Systems by Weicong Chen, Yu Han, Chao-Kai Wen, Xiao Li, Shi Jin https://arxiv.org/abs/2411.14088

Caption: This figure illustrates the proposed channel customization framework for multi-RIS assisted MIMO. It depicts the five key steps: (1) uplink training, (2) uplink channel estimation, (3) channel customization via RIS configuration, (4) downlink training, and (5) downlink channel estimation, highlighting the joint optimization of CSI acquisition and RIS configuration. This approach leverages fast-varying RIS phases to simplify channel estimation, especially in the downlink, by creating a sparse reflection channel.

This paper tackles the significant challenge of channel estimation in multi-RIS assisted MIMO systems, especially in millimeter-wave (mmWave) environments. RISs, while promising for enhancing wireless communication, introduce complexity in CSI acquisition. The authors propose a novel framework that deviates from the conventional sequential approach of channel estimation followed by RIS configuration. Instead, they advocate for a joint optimization strategy, leveraging the channel customization capabilities of RISs to simplify CSI acquisition for both uplink and downlink transmissions.

The key innovation lies in using fast-varying reflection phases across the RIS elements during training. This effectively isolates transmit signals from direct and reflected paths, decomposing the complex channel estimation problem into simpler, independent tasks. For uplink transmission, a positioning-based algorithm extracts partial CSI, focusing on the dominant Line-of-Sight (LoS) paths in the UE-RIS channel. This partial CSI guides the adjustment of RIS parameters to create a sparse reflection channel, enhancing the cascaded LoS path gain by the RIS array gain, approximated as: H ≈ A_b,eΞA^H_u,e, where Ξ represents the enhanced gain matrix. This simplification enables precise uplink channel reconstruction with reduced complexity. The downlink benefits from this tailored sparse channel, allowing efficient CSI acquisition with fewer pilot signals.

Simulations validate the effectiveness of this approach. The fast-varying RIS phase technique successfully separates signals from different links, with normalized mean squared error (NMSE) aligning with theoretical predictions. The positioning-based algorithm accurately extracts LoS path parameters, even in rich scattering environments. While the proposed method may introduce a slight increase in NMSE for the reconstructed channel compared to full channel estimation methods like NOMP, it achieves comparable spectral efficiency (SE). The significant reduction in pilot overhead and computational complexity, particularly in the downlink (e.g., up to 9600 fewer coarse searches for directional parameters with a 25-element RIS), makes this approach highly attractive for practical implementation. This channel customization framework presents a paradigm shift in CSI acquisition for multi-RIS assisted MIMO systems, offering a low-complexity, efficient solution that balances accuracy and overhead, especially in demanding mmWave scenarios.

Efficient Localization with Base Station-Integrated Beyond Diagonal RIS

Efficient Localization with Base Station-Integrated Beyond Diagonal RIS by Mahmoud Raeisi, Hui Chen, Henk Wymeersch, Ertugrul Basar https://arxiv.org/abs/2411.13295

Caption: Comparison of Directivity between BD-RIS, D-RIS, and AAA

This paper addresses the growing need for high-precision localization in next-generation communication systems. While Reconfigurable Intelligent Surfaces (RIS) offer a cost-effective alternative to large antenna arrays, traditional diagonal RIS (D-RIS) suffer from limited beamforming capabilities. This research introduces a novel approach using Base Station (BS)-integrated Beyond Diagonal RIS (BD-RIS) for enhanced downlink localization. BD-RIS, unlike D-RIS which only adjusts the phase of impinging waves, controls both phase and amplitude, enabling greater flexibility in passive beamforming and significantly improving localization accuracy.

The proposed system employs a linear fully-connected BD-RIS integrated with the BS, emulating a Multiple-Input Single-Output (MISO) system. Operating in transmissive mode, the BD-RIS enables passive beamforming. The analysis considers both near-field (NF) and far-field (FF) scenarios, using distinct channel models for the BS-RIS and RIS-User Equipment (UE) links. The BS-RIS channel is modeled using Rayleigh-Sommerfeld diffraction theory for NF and incorporates subcarrier information for FF. The RIS-UE channel is modeled based on Fresnel NF and LoS wideband channel for NF and FF scenarios, respectively. A predefined codebook based on Takagi's decomposition is used for systematic environmental sweeping during localization. The received signal at the UE is modeled as: y_i,t[n] = √P h_i[n]Ω_i,tg[n] + w_i,t[n], where i denotes the scenario, t the time slot, n the subcarrier index, P the transmitted power, h_i the RIS-UE channel, Ω_i,t the RIS phase shift matrix, g the BS-RIS channel, and w_i,t the additive noise.

Cramér-Rao Lower Bound (CRLB) analysis assesses the theoretical limits of localization accuracy across various system parameters, including transmitted power, BS-RIS distance, and the number of subcarriers. The results reveal that BD-RIS achieves near-active antenna array performance, significantly outperforming D-RIS. In the NF scenario, BD-RIS maintains high beamforming gain even close to the BS, unlike D-RIS whose performance degrades due to magnitude variations in the BS-RIS channel. In the FF scenario, while D-RIS benefits from frequency diversity for Time of Arrival (TOA) estimation, its limited beamforming hinders Angle of Arrival (AoA) estimation, resulting in inferior overall localization compared to BD-RIS. This study highlights BD-RIS's potential for high-accuracy positioning, bridging the gap between active and passive localization methods. The ability to control both phase and amplitude is crucial for BD-RIS's superior performance, particularly in NF scenarios, opening new research avenues in passive beamforming-based localization.

Integrated Positioning and Communication via LEO Satellites: Opportunities and Challenges

Integrated Positioning and Communication via LEO Satellites: Opportunities and Challenges by Jie Ma, Pinjun Zheng, Xing Liu, Yuchen Zhang, Tareq Y. Al-Naffouri https://arxiv.org/abs/2411.14360

Caption: This infographic illustrates the architecture of a Low Earth Orbit (LEO) satellite Integrated Positioning and Communication (IPAC) system. It depicts the interplay between the space, user, and ground segments, highlighting key elements like feeder and service links, handover mechanisms, and the impact of orbit errors on both positioning and communication performance. The visualization also showcases the flow of information through gateways, core networks, and data centers, emphasizing the integrated nature of the IPAC system.

This paper explores the promising field of Integrated Positioning and Communication (IPAC) using Low Earth Orbit (LEO) satellites for 6G non-terrestrial networks. While positioning and communication have been studied extensively individually, the synergistic potential of their integration remains largely untapped. This article provides a comprehensive analysis of LEO-based IPAC systems, examining how communication can enhance positioning accuracy and vice-versa.

LEO satellites, due to their lower altitude, offer advantages like stronger signals, lower latency, and wider coverage compared to MEO and GEO satellites. However, they also face challenges like higher path loss, greater propagation delay, and stronger Doppler effects. The paper argues that leveraging position information can significantly improve communication performance. For example, position information-enabled fast beamforming can address the challenges posed by short coherence time and rapid channel variations in LEO systems. Similarly, position information-based fast timing advance update can mitigate timing misalignment interference, a critical issue in 5G/6G due to large propagation delays.

Communication technologies can reciprocally enhance positioning capabilities. Advanced infrastructures like phased array antennas and hybrid beamforming enable the acquisition of Angle of Departure (AoD) and Angle of Arrival (AoA) observations, unavailable in traditional GNSS. These angle observations, coupled with network-level collaboration via inter-satellite communication, can significantly improve positioning accuracy. The paper presents two case studies demonstrating these synergistic benefits. One shows that UE location-based beamforming achieves higher spectral efficiency than traditional outdated channel-based beamforming. Another demonstrates that integrating antenna arrays and cooperative positioning with orthogonal signal transmission significantly reduces the positioning error bound.

The paper also acknowledges open research challenges in LEO-based IPAC. These include managing the double-edged sword of Doppler shift (beneficial for positioning but detrimental to communication), optimizing TDD vs. FDD operation, addressing satellite orbit errors, managing frequent satellite handovers, optimizing resource allocation for diverse user needs, and ensuring positioning privacy and communication security. Despite these challenges, the article highlights the significant potential of LEO-based IPAC systems for enhanced positioning and communication capabilities in the 6G era and beyond.

Conclusion

This newsletter has highlighted several key advancements in wireless communications and signal processing. The common thread weaving through these diverse research areas is the pursuit of enhanced efficiency and performance. The exploration of channel customization in multi-RIS assisted MIMO systems showcases the potential for simplifying complex channel estimation procedures, paving the way for more efficient data transmission. The innovative use of BD-RIS for localization demonstrates a promising approach for achieving high-accuracy positioning with passive beamforming, opening new avenues for research in localization technologies. Finally, the analysis of integrated positioning and communication via LEO satellites underscores the synergistic benefits of combining these functionalities, promising a new era of enhanced connectivity and location-based services in the 6G era and beyond. These advancements collectively point towards a future where wireless systems are not only faster and more reliable but also more intelligent and adaptable to the dynamic demands of a connected world.