Near-Field Beam Focusing Pattern and Grating Lobe Characterization for Modular XL-Array

In this paper, we investigate the near-field modelling and analyze the beam focusing pattern for modular extremely large-scale array (XL-array) communications. As modular XL-array is physically and electrically large in general, the accurate characterization of amplitude and phase variations across its array elements requires the non-uniform spherical wave (NUSW) model, which, however, is difficult for performance analysis and optimization. To address this issue, we first present two ways to simplify the NUSW model by exploiting the unique regular structure of modular XL-array, termed sub-array based uniform spherical wave (USW) models with different or common angles, respectively. Based on the developed models, the near-field beam focusing patterns of XL-array communications are derived. It is revealed that compared to the existing collocated XL-array with the same number of array elements, modular XL-array can significantly enhance the spatial resolution, but at the cost of generating undesired grating lobes. Fortunately, different from the conventional far-field uniform plane wave (UPW) model, the near-field USW model for modular XL-array exhibits a higher grating lobe suppression capability, thanks to the non-linear phase variations across the array elements. Finally, simulation results are provided to verify the near-field beam focusing pattern and grating lobe characteristics of modular XL-array.

Multi-timescale Channel Customization for Transmission Design in RIS-assisted MIMO Systems

The performance of transmission schemes is heavily influenced by the wireless channel, which is typically considered an uncontrollable factor. However, the introduction of reconfigurable intelligent surfaces (RISs) to wireless communications enables the customization of a preferred channel for adopted transmissions by reshaping electromagnetic waves. In this study, we propose multi-timescale channel customization for RIS-assisted multiple-input multiple-output systems to facilitate transmission design. Specifically, we customize a high-rank channel for spatial multiplexing (SM) transmission and a highly correlated rank-1 channel for beamforming (BF) transmission by designing the phase shifters of the RIS with statistical channel state information in the angle-coherent time to improve spectral efficiency (SE). We derive closed-form expressions for the approximation and upper bound of the ergodic SE and compare them to investigate the relative SE performance of SM and BF transmissions. In terms of reliability enhancement, we customize a fast-changing channel in the symbol timescale to achieve more diversity gain for SM and BF transmissions. Extensive numerical results demonstrate that flexible customization of channel characteristics for a specific transmission scheme can achieve a tradeoff between SE and bit error ratio performance.

Channel Customization for Limited Feedback in RIS-assisted FDD Systems

Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. \textcolor[rgb]{0,0,0}{Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems.} In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. \textcolor[rgb]{0,0,0}{By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed.} Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

Near Field Computational Imaging with RIS Generated Virtual Masks

Near field computational imaging has been recognized as a promising technique for non-destructive and highly accurate detection of the target. Meanwhile, reconfigurable intelligent surface (RIS) can flexibly control the scattered electromagnetic (EM) fields for sensing the target and can thus help computational imaging in the near field. In this paper, we propose a near-field imaging scheme based on holograghic aperture RIS. Specifically, we first establish an end-to-end EM propagation model from the perspective of Maxwell equations. To mitigate the inherent ill conditioning of the inverse problem in the imaging system, we design the EM field patterns as masks that help translate the inverse problem into a forward problem. Next, we utilize RIS to generate different virtual EM masks on the target surface and calculate the cross-correlation between the mask patterns and the electric field strength at the receiver. We then provide a RIS design scheme for virtual EM masks by employing a regularization technique. The cross-range resolution of the proposed method is analyzed based on the spatial spectrum of the generated masks. Simulation results demonstrate that the proposed method can achieve high-quality imaging. Moreover, the imaging quality can be improved by generating more virtual EM masks, by increasing the signal-to-noise ratio (SNR) at the receiver, or by placing the target closer to the RIS.

Communication-efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback

Deep learning (DL)-based channel state information (CSI) feedback has received significant research attention in recent years. However, previous research has overlooked the potential privacy disclosure problem caused by the transmission of CSI datasets during the training process. In this work, we introduce a federated edge learning (FEEL)-based training framework for DL-based CSI feedback. This approach differs from the conventional centralized learning (CL)-based framework in which the CSI datasets are collected at the base station (BS) before training. Instead, each user equipment (UE) trains a local autoencoder network and exchanges model parameters with the BS. This approach provides better protection for data privacy compared to CL. To further reduce communication overhead in FEEL, we quantize uplink and downlink model transmission into different bits based on their influence on feedback performance. Additionally, since the heterogeneity of CSI datasets in different UEs can degrade the performance of the FEEL-based framework, we introduce a personalization strategy to improve feedback performance. This strategy allows for local fine-tuning to adapt the global model to the channel characteristics of each UE. Simulation results indicate that the proposed personalized FEEL-based training framework can significantly improve the performance of DL-based CSI feedback while reducing communication overhead.

Quantized Phase Alignment by Discrete Phase Shifts for Reconfigurable Intelligent Surface-Assisted Communication Systems

Reconfigurable intelligent surface (RIS) has aroused a surge of interest in recent years. In this paper, we investigate the joint phase alignment and phase quantization on discrete phase shift designs for RIS-assisted single-input single-output (SISO) system. Firstly, the phenomena of phase distribution in far field and near field are respectively unveiled, paving the way for discretization of phase shift for RIS. Then, aiming at aligning phases, the phase distribution law and its underlying degree-of-freedom (DoF) are characterized, serving as the guideline of phase quantization strategies. Subsequently, two phase quantization methods, dynamic threshold phase quantization (DTPQ) and equal interval phase quantization (EIPQ), are proposed to strengthen the beamforming effect of RIS. DTPQ is capable of calculating the optimal discrete phase shifts with linear complexity in the number of unit cells on RIS, whilst EIPQ is a simplified method with a constant complexity yielding sub-optimal solution. Simulation results demonstrate that both methods achieve substantial improvements on power gain, stability, and robustness over traditional quantization methods. The path loss (PL) scaling law under discrete phase shift of RIS is unveiled for the first time, with the phase shifts designed by DTPQ due to its optimality. Additionally, the field trials conducted at 2.6 GHz and 35 GHz validate the favourable performance of the proposed methods in practical communication environment.

Static Power Consumption Modeling and Measurement of Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RISs) are anticipated to transform wireless communication in a way that is both economical and energy efficient. Revealing the practical power consumption characteristics of RISs can provide an essential toolkit for the optimal design of RIS-assisted wireless communication systems and energy efficiency performance evaluation. Based on our previous work that modeled the dynamic power consumption of RISs, we henceforth concentrate more on static power consumption. We first divide the RIS hardware into three basic parts: the FPGA control board, the drive circuits, and the RIS unit cells. The first two parts are mainly to be investigated and the last part has been modeled as the dynamic power consumption in the previous work. In this work, the power consumption of the FPGA control board is regarded as a constant value, however, that of the drive circuit is a variant that is affected by the number of control signals and its self-power consumption characteristics. Therefore, we model the power consumption of the drive circuits of various kinds of RISs, i.e., PIN diode-/Varactor diode-/RF switch-based RIS. Finally, the measurement results and typical value of static power consumption are illustrated and discussed.

Low-overhead Beam Training Scheme for Extremely Large-Scale RIS in Near-field

Extremely large-scale reconfigurable intelligent surface (XL-RIS) has recently been proposed and is recognized as a promising technology that can further enhance the capacity of communication systems and compensate for severe path loss . However, the pilot overhead of beam training in XL-RIS-assisted wireless communication systems is enormous because the near-field channel model needs to be taken into account, and the number of candidate codewords in the codebook increases dramatically accordingly. To tackle this problem, we propose two deep learning-based near-field beam training schemes in XL-RIS-assisted communication systems, where deep residual networks are employed to determine the optimal near-field RIS codeword. Specifically, we first propose a far-field beam-based beam training (FBT) scheme in which the received signals of all far-field RIS codewords are fed into the neural network to estimate the optimal near-field RIS codeword. In order to further reduce the pilot overhead, a partial near-field beam-based beam training (PNBT) scheme is proposed, where only the received signals corresponding to the partial near-field XL-RIS codewords are served as input to the neural network. Moreover, we further propose an improved PNBT scheme to enhance the performance of beam training by fully exploring the neural network's output. Finally, simulation results show that the proposed schemes outperform the existing beam training schemes and can reduce the beam sweeping overhead by approximately 95%.

Wireless Communication Using Metal Reflectors: Reflection Modelling and Experimental Verification

Wireless communication using fully passive metal reflectors is a promising technique for coverage expansion, signal enhancement, rank improvement and blind-zone compensation, thanks to its appealing features including zero energy consumption, ultra low cost, signaling- and maintenance-free, easy deployment and full compatibility with existing and future wireless systems. However, a prevalent understanding for reflection by metal plates is based on Snell's Law, i.e., signal can only be received when the observation angle equals to the incident angle, which is valid only when the electrical dimension of the metal plate is extremely large. In this paper, we rigorously derive a general reflection model that is applicable to metal reflectors of any size, any orientation, and any linear polarization. The derived model is given compactly in terms of the radar cross section (RCS) of the metal plate, as a function of its physical dimensions and orientation vectors, as well as the wave polarization and the wave deflection vector, i.e., the change of direction from the incident wave direction to the observation direction. Furthermore, experimental results based on actual field measurements are provided to validate the accuracy of our developed model and demonstrate the great potential of communications using metal reflectors.

Multi-domain Cooperative SLAM: The Enabler for Integrated Sensing and Communications

Simultaneous localization and mapping (SLAM) provides user tracking and environmental mapping capabilities, enabling communication systems to gain situational awareness. Advanced communication networks with ultra-wideband, multiple antennas, and a large number of connections present opportunities for deep integration of sensing and communications. First, the development of integrated sensing and communications (ISAC) is reviewed in this study, and the differences between ISAC and traditional communication are revealed. Then, efficient mechanisms for multi-domain collaborative SLAM are presented. In particular, research opportunities and challenges for cross-sensing, cross-user, cross-frequency, and cross-device SLAM mechanisms are proposed. In addition, SLAM-aided communication strategies are explicitly discussed. We prove that the multi-domain cooperative SLAM mechanisms based on hybrid sensing and crowdsourcing can considerably improve the accuracy of localization and mapping in complex multipath propagation environments through numerical analysis. Furthermore, we conduct testbed experiments to show that the proposed SLAM mechanisms can achieve decimeter-level localization and mapping accuracy in practical scenarios, thereby proving the application prospect of multi-domain collaborative SLAM in ISAC.