A physics-based perspective for understanding and utilizing spatial resources of wireless channels

To satisfy the increasing demands for transmission rates of wireless communications, it is necessary to use spatial resources of electromagnetic (EM) waves. In this context, EM information theory (EIT) has become a hot topic by integrating the theoretical framework of deterministic mathematics and stochastic statistics to explore the transmission mechanisms of continuous EM waves. However, the previous studies were primarily focused on frame analysis, with limited exploration of practical applications and a comprehensive understanding of its essential physical characteristics. In this paper, we present a three-dimensional (3-D) line-of-sight channel capacity formula that captures the vector EM physics and accommodates both near- and far-field scenes. Based on the rigorous mathematical equation and the physical mechanism of fast multipole expansion, a channel model is established, and the finite angular spectral bandwidth feature of scattered waves is revealed. To adapt to the feature of the channel, an optimization problem is formulated for determining the mode currents on the transmitter, aiming to obtain the optimal design of the precoder and combiner. We make comprehensive analyses to investigate the relationship among the spatial degree of freedom, noise, and transmitted power, thereby establishing a rigorous upper bound of channel capacity. A series of simulations are conducted to validate the theoretical model and numerical method. This work offers a novel perspective and methodology for understanding and leveraging EIT, and provides a theoretical foundation for the design and optimization of future wireless communications.

Pilot-Based SFO Estimation for Bistatic Integrated Sensing and Communication

Enabling bistatic radar sensing within the context of integrated sensing and communication (ISAC) for future sixth generation mobile networks demands strict synchronization accuracy, which is particularly challenging to be achieved with over-the-air synchronization. Existing algorithms handle time and frequency offsets adequately, but provide insufficiently accurate sampling frequency offset (SFO) estimates that result in degradation of obtained radar images in the form of signal-to-noise ratio loss and migration of range and Doppler shift. This article introduces an SFO estimation algorithm named tilt inference of time offset (TITO) for orthogonal frequency-division multiplexing (OFDM)-based ISAC. Using available pilot subcarriers, TITO obtains channel impulse response estimates and extracts information on the SFO-induced delay migration to a dominant reference path with constant range, Doppler shift, and angle between transmit and receive ISAC nodes. TITO then adaptively selects the delay estimates that are only negligibly impaired by SFO-induced intersymbol interference, ultimately employing them to estimate the SFO. Assuming a scenario without a direct line-of-sight (LoS) between the aforementioned transmitting and receiving ISAC nodes, a system concept with a relay reflective intelligent surface (RIS) is used to create the aforementioned reference path is proposed. Besides a mathematical derivation of accuracy bounds, simulation and measurements at 26.2 GHz are presented to demonstrate TITO's superiority over existing methods in terms of SFO estimation accuracy and robustness.

Electromagnetic Property Sensing Based on Diffusion Model in ISAC System

Integrated sensing and communications (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel ISAC scheme that utilizes the diffusion model to sense the electromagnetic (EM) property of the target in a predetermined sensing area. Specifically, we first estimate the sensing channel by using both the communications and the sensing signals echoed back from the target. Then we employ the diffusion model to generate the point cloud that represents the target and thus enables 3D visualization of the target's EM property distribution. In order to minimize the mean Chamfer distance (MCD) between the ground truth and the estimated point clouds, we further design the communications and sensing beamforming matrices under the constraint of a maximum transmit power and a minimum communications achievable rate for each user equipment (UE). Simulation results demonstrate the efficacy of the proposed method in achieving high-quality reconstruction of the target's shape, relative permittivity, and conductivity. Besides, the proposed method can sense the EM property of the target effectively in any position of the sensing area.

Integrating sensing and communications: Simultaneously transmitting and reflecting digital coding metasurfaces

Wireless networks are undergoing a transformative shift, driven by the crucial factors of cost effectiveness and sustainability. Digital coding metasurfaces (DCMs) might play a key role in realizing cost-effective digital modulators by harnessing energy embedded in electromagnetic waves traversing through the air. Integrated sensing and communication (ISAC) optimize power and spectral resources by combining sensing and communication functionalities on a shared hardware platform. This article presents a tutorial-style overview of the applications and advantages of DCMs in ISAC-based networks. Emphasis is placed on the dual-functionality of ISAC, necessitating the design of DCMs with simultaneously transmitting and reflecting (STAR) capabilities for comprehensive space control. Additionally, the article explores key signal processing challenges and outlines future research directions stemming from the convergence of ISAC and emerging STAR-DCM technologies.

Holography inspired self-controlled reconfigurable intelligent surface

Among various promising candidate technologies for the sixth-generation (6G) wireless communications, recent advances in microwave metasurfaces have sparked a new research area of reconfigurable intelligent surfaces (RISs). By controllably reprogramming the wireless propagation channel, RISs are envisioned to achieve low-cost wireless capacity boosting, coverage extension, and enhanced energy efficiency. To reprogram the channel, each meta-atom on RIS needs an external control signal, which is usually generated by base station (BS). However, BS-controlled RISs require complicated control cables, which hamper their massive deployments. Here, we eliminate the need for BS control by proposing a self-controlled RIS (SC-RIS), which is inspired by the optical holography principle. Different from the existing BS-controlled RISs, each meta-atom of SC-RIS is integrated with an additional power detector for holographic recording. By applying the classical Fourier-transform processing to the measured hologram, SC-RIS is capable of retrieving the user's channel state information required for beamforming, thus enabling autonomous RIS beamforming without control cables. Owing to this WiFi-like plug-and-play capability without the BS control, SC-RISs are expected to enable easy and massive deployments in the future 6G systems.

Electromagnetic Information Theory: Fundamentals and Applications for 6G Wireless Communication Systems

In wireless communications, electromagnetic theory and information theory constitute a pair of fundamental theories, bridged by antenna theory and wireless propagation channel modeling theory. Up to the fifth generation (5G) wireless communication networks, these four theories have been developing relatively independently. However, in sixth generation (6G) space-air-ground-sea wireless communication networks, seamless coverage is expected in the three-dimensional (3D) space, potentially necessitating the acquisition of channel state information (CSI) and channel capacity calculation at anywhere and any time. Additionally, the key 6G technologies such as ultra-massive multiple-input multiple-output (MIMO) and holographic MIMO achieves intricate interaction of the antennas and wireless propagation environments, which necessitates the joint modeling of antennas and wireless propagation channels. To address the challenges in 6G, the integration of the above four theories becomes inevitable, leading to the concept of the so-called electromagnetic information theory (EIT). In this article, a suite of 6G key technologies is highlighted. Then, the concepts and relationships of the four theories are unveiled. Finally, the necessity and benefits of integrating them into the EIT are revealed.

Multiperson Detection and Vital-Sign Sensing Empowered by Space-Time-Coding RISs

Passive human sensing using wireless signals has attracted increasing attention due to its superiorities of non-contact and robustness in various lighting conditions. However, when multiple human individuals are present, their reflected signals could be intertwined in the time, frequency and spatial domains, making it challenging to separate them. To address this issue, this paper proposes a novel system for multiperson detection and monitoring of vital signs (i.e., respiration and heartbeat) with the assistance of space-time-coding (STC) reconfigurable intelligent metasurfaces (RISs). Specifically, the proposed system scans the area of interest (AoI) for human detection by using the harmonic beams generated by the STC RIS. Simultaneously, frequencyorthogonal beams are assigned to each detected person for accurate estimation of their respiration rate (RR) and heartbeat rate (HR). Furthermore, to efficiently extract the respiration signal and the much weaker heartbeat signal, we propose an improved variational mode decomposition (VMD) algorithm to accurately decompose the complex reflected signals into a smaller number of intrinsic mode functions (IMFs). We build a prototype to validate the proposed multiperson detection and vital-sign monitoring system. Experimental results demonstrate that the proposed system can simultaneously monitor the vital signs of up to four persons. The errors of RR and HR estimation using the improved VMD algorithm are below 1 RPM (respiration per minute) and 5 BPM (beats per minute), respectively. Further analysis reveals that the flexible beam controlling mechanism empowered by the STC RIS can reduce the noise reflected from other irrelative objects on the physical layer, and improve the signal-to-noise ratio of echoes from the human chest.

Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface: Opportunities and Challenges

Reconfigurable intelligent surfaces (RISs) have flexible and exceptional performance in manipulating electromagnetic waves and customizing wireless channels. These capabilities enable them to provide a plethora of valuable activity-related information for promoting wireless human sensing. In this article, we present a comprehensive review of passive human sensing using radio frequency signals with the assistance of RISs. Specifically, we first introduce fundamental principles and physical platform of RISs. Subsequently, based on the specific applications, we categorize the state-of-the-art human sensing techniques into three types, including human imaging,localization, and activity recognition. Meanwhile, we would also investigate the benefits that RISs bring to these applications. Furthermore, we explore the application of RISs in human micro-motion sensing, and propose a vital signs monitoring system enhanced by RISs. Experimental results are presented to demonstrate the promising potential of RISs in sensing vital signs for manipulating individuals. Finally, we discuss the technical challenges and opportunities in this field.

Can Electromagnetic Information Theory Improve Wireless Systems? A Channel Estimation Example

Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of degrees-of-freedom (DoF), system capacity, and characteristics of the electromagnetic channel. However, these works do not clarify how EIT can improve wireless communication systems. To answer this question, in this paper, we provide a novel demonstration of the application of EIT. By integrating EM knowledge into the classical MMSE channel estimator, we observe for the first time that EIT is capable of improving the channel estimation performace. Specifically, the EM knowledge is first encoded into a spatio-temporal correlation function (STCF), which we term as the EM kernel. This EM kernel plays the role of side information to the channel estimator. Since the EM kernel takes the form of Gaussian processes (GP), we propose the EIT-based Gaussian process regression (EIT-GPR) to derive the channel estimations. In addition, since the EM kernel allows parameter tuning, we propose EM kernel learning to fit the EM kernel to channel observations. Simulation results show that the application of EIT to the channel estimator enables it to outperform traditional isotropic MMSE algorithm, thus proving the practical values of EIT.

RIS-Assisted Joint Uplink Communication and Imaging: Phase Optimization and Bayesian Echo Decoupling

Achieving integrated sensing and communication (ISAC) via uplink transmission is challenging due to the unknown waveform and the coupling of communication and sensing echoes. In this paper, a joint uplink communication and imaging system is proposed for the first time, where a reconfigurable intelligent surface (RIS) is used to manipulate the electromagnetic signals for echo decoupling at the base station (BS). Aiming to enhance the transmission gain in desired directions and generate required radiation pattern in the region of interest (RoI), a phase optimization problem for RIS is formulated, which is high dimensional and nonconvex with discrete constraints. To tackle this problem, a back propagation based phase design scheme for both continuous and discrete phase models is developed. Moreover, the echo decoupling problem is tackled using the Bayesian method with the factor graph technique, where the problem is represented by a graph model which consists of difficult local functions. Based on the graph model, a message-passing algorithm is derived, which can efficiently cooperate with the adaptive sparse Bayesian learning (SBL) to achieve joint communication and imaging. Numerical results show that the proposed method approaches the relevant lower bound asymptotically, and the communication performance can be enhanced with the utilization of imaging echoes.