Harnessing multi-level electron transitions, Rydberg Atomic REceivers (RAREs) can detect wireless signals across a wide range of frequency bands, from Megahertz to Terahertz. This capability enables multi-band wireless communications and sensing (CommunSense). Existing research on multi-band RAREs primarily focuses on experimental demonstrations, lacking a tractable model to mathematically characterize their mechanisms. This issue leaves the multi-band RARE as a black box and poses challenges in its practical applications. To fill in this gap, this paper investigates the underlying mechanism of multi-band RAREs and explores their optimal performance. For the first time, an analytical transfer function with a closed-form expression for multi-band RAREs is derived by solving the quantum response of Rydberg atoms. It shows that a multi-band RARE simultaneously serves as amulti-band atomic mixerfor down-converting multi-band signals and amulti-band atomic amplifierthat reflects its sensitivity to each band. Further analysis of the atomic amplifier unveils that the intrinsic gain at each frequency band can be decoupled into aglobal gainterm and aRabi attentionterm. The former determines the overall sensitivity of a RARE to all frequency bands of wireless signals. The latter influences the allocation of the overall sensitivity to each frequency band, representing a unique attention mechanism of multi-band RAREs. The optimal design of the global gain is provided to maximize the overall sensitivity of multi-band RAREs. Subsequently, the optimal Rabi attentions are also derived to maximize the practical multi-band CommunSense performance. An experiment platform is built to validate the effectiveness of the derived transfer function, and numerical results confirm the superiority of multi-band RAREs.

A distinctive function of sixth-generation (6G) networks is the integration of distributed sensing and edge artificial intelligence (AI) to enable intelligent perception of the physical world. This resultant platform, termed integrated sensing and edge AI (ISEA), is envisioned to enable a broad spectrum of Internet-of-Things (IoT) applications, including remote surgery, autonomous driving, and holographic telepresence. Recently, the communication bottleneck confronting the implementation of an ISEA system is overcome by the development of over-the-air computing (AirComp) techniques, which facilitate simultaneous access through over-the-air data feature fusion. Despite its advantages, AirComp with uncoded transmission remains vulnerable to interference. To tackle this challenge, we propose AirBreath sensing, a spectrum-efficient framework that cascades feature compression and spread spectrum to mitigate interference without bandwidth expansion. This work reveals a fundamental tradeoff between these two operations under a fixed bandwidth constraint: increasing the compression ratio may reduce sensing accuracy but allows for more aggressive interference suppression via spread spectrum, and vice versa. This tradeoff is regulated by a key variable called breathing depth, defined as the feature subspace dimension that matches the processing gain in spread spectrum. To optimally control the breathing depth, we mathematically characterize and optimize this aforementioned tradeoff by designing a tractable surrogate for sensing accuracy, measured by classification discriminant gain (DG). Experimental results on real datasets demonstrate that AirBreath sensing effectively mitigates interference in ISEA systems, and the proposed control algorithm achieves near-optimal performance as benchmarked with a brute-force search.

Rydberg Atomic REceivers (RAREs) have shown compelling advantages in precise measurement of radio-frequency signals, empowering quantum wireless sensing. Existing RARE-based sensing systems primarily rely on the heterodyne-sensing technique, which introduces an extra reference source to serve as the atomic mixer. However, this approach entails a bulky transceiver architecture, requiring additional reference sources and transmitter-receiver signal decoupling. To address this problem, we propose a novel concept called selfheterodyne sensing. It utilizes the self-interference generated by the transmitted sensing signal as the reference signal, thus greatly simplifying the transceiver architecture. We derive the transmission model of self-heterodyne sensing and reveal that a self-heterodyne RARE functions as an atomic autocorrelator, where the received signal represents the autocorrelation of the transmitted signal at different delays. This characteristic translates the range of a sensing target into the frequency of the received signal. Inspired by this finding, a two-stage algorithm is devised to estimate the target range via frequency estimation and Newton refinement. Numerical results validate the superiority of the proposed quantum self-heterodyne sensing method.

One mission of sixth-generation (6G) networks is to deploy large-scale artificial intelligence (AI) models at the network edge to enable intelligent services for edge devices. The resultant platform, known as edge inference, will support a wide range of Internet-of-Things applications, such as autonomous driving, industrial automation, and augmented reality. Given the mission-critical and time-sensitive nature of these tasks, existing studies, which primarily focus on channel outage probability, may fail to ensure E2E inference accuracy and latency. To address this limitation, we propose a theoretical framework that introduces the inference outage (InfOut) probability, quantifying the likelihood that E2E inference accuracy falls below a target threshold. Under latency constraints, this framework reveals a fundamental tradeoff between communication overhead and inference reliability. To optimize this tradeoff, we derive tractable surrogate functions for InfOut probability based on a Gaussian approximation of the receive discriminant gain. Experiments demonstrate the superiority of the proposed design over conventional communication-centric approaches.

Leveraging the strong atom-light interaction, Rydberg atomic receivers (RAREs) can measure radio waves with extreme sensitivity. Existing research primarily focuses on improving the signal detection capability of RAREs, while traditional signal processing methods at the transmitter side have remained unchanged, which leaves a large gap from the maximum channel capacity. To address this issue, we exploit the transmitter precoding in atomic multiple-input-multiple-output systems to achieve the channel capacity. To begin with, a strong-reference approximation is proposed to linearize the nonlinear magnitude-detection model of atomic receivers, allowing us to express the channel capacity analytically. Then, a new digital precoding technique, termed In-phase-and-Quadrature (IQ) aware precoding is presented, which features independent processing of I/Q data streams using four real-valued matrices. The design is shown to be capacity-achieving for the atomic MIMO system. For the case of large-scale MIMO, we extend the proposed design into the popular hybrid precoding architecture, which cascades a classical high-dimensional analog precoder with a low-dimensional version of the proposed IQ-aware digital precoder. By alternatively optimizing the digital and analog parts, the hybrid design is able to approach the performance of the optimal IQ-aware fully digital precoding. Simulation results validate the superiority of proposed IQ-aware precoding methods over existing techniques in atomic MIMO communication systems.

Densifying multiple-input multiple-output (MIMO) has attracted much attention in recent years. The strong correlations among densifying antennas provide sufficient prior knowledge about channel state information (CSI), which inspires the careful design of observation matrices (e.g., transmit precoders and receive combiners) to boost channel estimation performance. To achieve this, this work proposes to jointly design the combiners and precoders by maximizing the mutual information between the received pilots and densifying MIMO channels. Particularly, a two-dimensional ice-filling (2DIF) algorithm is proposed, which is motivated by the fact that the eigenspace of MIMO channel covariance can be decoupled into two sub-eigenspaces. By properly setting the precoder and the combiner as the eigenvectors from these two sub-eigenspaces, the 2DIF promises to generate nearoptimal observation matrices for channel estimation. Simulation results demonstrate that, the proposed 2DIF method outperforms the state-of-the-art schemes in channel estimation accuracy.

The advancement of Rydberg atoms in quantum information technology is driving a paradigm shift from classicalradio-frequency(RF) receivers to Rydberg atomic receivers. Capitalizing on the extreme sensitivity of Rydberg atoms to external electromagnetic fields, Rydberg atomic receivers are capable of realizing more precise radio-wave measurements than RF receivers to support high-performance wireless communication and sensing. Although the atomic receiver is developing rapidly in quantum-physics domain, its integration with wireless communications is at a nascent stage. In particular, systematic methods to enhance communication performance through this integration are yet to be discovered. Motivated by this observation, we propose in this paper to incorporate Rydberg atomic receivers intomultiple-input-multiple-output(MIMO) communication, a prominent 5G technology, as the first attempt on implementing atomic MIMO receivers. To begin with, we provide a comprehensive introduction on the principles of Rydberg atomic receivers and build on them to design the atomic MIMO receivers. Our findings reveal that signal detection of atomic MIMO receivers corresponds to a non-linear biasedphase retrieval(PR) problem, as opposed to the linear Gaussian model adopted in classical MIMO systems. Then, to recover signals from this non-linear model, we modify the Gerchberg-Saxton (GS) algorithm, a typical PR solver, into a biased GS algorithm to solve the biased PR problem. Moreover, we propose a novel Expectation-Maximization GS (EM-GS) algorithm to cope with the unique Rician distribution of the biased PR model. Our EM-GS algorithm introduces a high-pass filter constructed by the ratio of Bessel functions into the iteration procedure of GS, thereby improving the detection accuracy without sacrificing the computational efficiency. Finally, the effectiveness of the devised algorithms and the feasibility of atomic MIMO receivers are demonstrated by theoretical analysis and numerical simulation.

By deploying a large number of antennas with subhalf- wavelength spacing in a compact space, dense array systems (DASs) can fully unleash the multiplexing and diversity gains of limited apertures. To acquire these gains, accurate channel state information acquisition is necessary but challenging due to the large antenna numbers. To overcome this obstacle, this paper reveals that designing the observation matrix to exploit the high spatial correlation of DAS channels is crucial for realizing near-optimal Bayesian channel estimation. Specifically, we prove that the observation matrix design for channel estimation is equivalent to a time-domain duality of point-to-point multipleinput multiple-output precoding, except for the change in the total power constraint on the precoding matrix to the pilot-wise discrete power constraint on the observation matrix. Inspired by Bayesian regression, a novel ice-filling algorithm is proposed to design amplitude-and-phase controllable observation matrices, and a majorization-minimization algorithm is proposed to address the phase-only controllable case. Particularly, we prove that the ice-filling algorithm can be interpreted as a “quantized” water-filling algorithm, wherein the latter’s continuous power-allocation process is converted into the former’s discrete pilot-assignment process. To support the near-optimality of the proposed designs, we provide comprehensive analyses on the achievable mean square errors and their asymptotic expressions. Finally, numerical results confirm that our proposed designs achieve the near-optimal channel estimation performance and outperform existing approaches significantly.

Near-field beam training is essential for acquiring channel state information in 6G extremely large-scale multiple input multiple output (XL-MIMO) systems. To achieve low-overhead beam training, existing method has been proposed to leverage the near-field beam split effect, which deploys true-time-delay arrays to simultaneously search multiple angles of the entire angular range in a distance ring with a single pilot. However, the method still requires exhaustive search in the distance domain, which limits its efficiency. To address the problem, we propose a distance-dependent beam-split-based beam training method to further reduce the training overheads. Specifically, we first reveal the new phenomenon of distance-dependent beam split, where by manipulating the configurations of time-delay and phase-shift, beams at different frequencies can simultaneously scan the angular domain in multiple distance rings. Leveraging the phenomenon, we propose a near-field beam training method where both different angles and distances can simultaneously be searched in one time slot. Thus, a few pilots are capable of covering the whole angle-distance space for wideband XL-MIMO. Theoretical analysis and numerical simulations are also displayed to verify the superiority of the proposed method on beamforming gain and training overhead.

The advancement of Rydberg atoms is driving a paradigm shift from classical receivers to atomic receivers. Capitalizing on the extreme sensitivity of Rydberg atoms to external disturbance, atomic receivers can measure radio waves more precisely than classical receivers to support high-performance wireless communication. Although the atomic receiver is developing rapidly in the field of quantum physics, its integration with wireless communications is at a nascent stage. Particularly, systematic methods to enhance communication performance through this integration are largely uncharted. Motivated by this observation, we propose to incorporate atomic receivers into multiple-input multiple-output (MIMO) communications to implement atomic-MIMO receivers. We establish the framework of atomic-MIMO receivers by exploiting the principle of quantum sensing. Our model reveals that the signal detection of atomic-MIMO systems is intrinsically a nonlinear phase-retrieval problem, as opposed to the linear model in classical MIMO systems. To perform atomic-MIMO signal detection, we propose an Expectation-Maximization-Gerchberg-Saxton (EM-GS) algorithm based on the maximum likelihood (ML) criteria. Its novelty lies in treating the unobserved phase information as a latent variable and thereby decoupling the intricate ML problem into a sequence of tractable linear regression problems with analytical solutions. Experimental results validate the effectiveness of detecting atomic-MIMO signals using the EM-GS algorithm.

The natural integration of extremely large antenna arrays (ELAAs) and terahertz (THz) communications can potentially establish Tbps data links for 6G networks. However, due to the extremely large array aperture and wide bandwidth, a new phenomenon termed as “near-field beam split” emerges. This phenomenon causes beams at different frequencies to focus on distinct physical locations, leading to a significant loss of the beamforming gain. To address this challenging problem, we first harness a piecewise-far-field channel model to approximate the complicated near-field wideband channel. In this model, the entire large array is partitioned into several small sub-arrays. While the wireless channel’s phase discrepancy across the entire array is modeled as near-field spherical, the phase discrepancy within each sub-array is approximated as far-field planar. Built on this approximation, a phase-delay focusing (PDF) method employing delay phase precoding (DPP) architecture is proposed. Our PDF method could compensate for the intra-array far-field phase discrepancy and the inter-array near-field phase discrepancy via the joint control of phase shifters and time delayers, respectively. Theoretical and numerical results are provided to demonstrate the efficiency of the proposed PDF method in mitigating the near-field beam split effect. Finally, we define and derive a novel metric termed as the “effective Rayleigh distance” by the evaluation of beamforming gain loss. Compared to classical Rayleigh distance, the effective Rayleigh distance is more accurate in determining the near-field range for practical communications.

Massive multiple-input multiple-output (MIMO) for 5G is evolving into the extremely large-scale antenna array (ELAA) to increase the spectrum efficiency by orders of magnitude for 6G communications. ELAA introduces spherical-wave-based near-field communications, where channel capacity can be significantly improved for single-user and multi-user scenarios. Unfortunately, the near-field region at large incidence/emergence angles is greatly reduced with the widely studied uniform linear array (ULA). Thus, many randomly distributed users may fail to benefit from near-field communications. In this paper, we leverage the rotational symmetry of uniform circular array (UCA) to provide uniform and enlarged near-field regions at all angles, enabling more users to benefit from near-field communications. Specifically, by exploiting the geometrical relationship between UCA and users, the near-field beamforming technique for UCA is developed. Based on the analysis of near-field beamforming, we reveal that UCA is able to provide a larger near-field region than ULA in terms of the effective Rayleigh distance. Moreover, a concentric-ring codebook is designed to realize efficient codebook-based beamforming in the near-field region. In addition, we find out that UCA could generate orthogonal near-field beams along the same direction when the focal point of the near-field beam is exactly the zeros of other beams, which has the potential to further improve spectrum efficiency in multi-user communications compared with ULA. Simulation results are provided to verify the effectiveness of theoretical analysis and feasibility of UCA to enable more users to benefit from near-field communications by broadening the near-field region.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technology for future 6G communications. To realize effective precoding, channel estimation schemes are essential to acquire precise channel state information (CSI), while most existing schemes work relying on the spatial stationary assumption. In XL-MIMO systems, however, the spatial non-stationary effect naturally exists. Such effect can hardly be recognized by existing channel estimation schemes, leading to a severe accuracy loss of channel estimation. To address this problem, in this paper, we study the spatial non-stationary channel estimation for XL-MIMO systems. Specifically, we propose a group time block code (GTBC) based signal extraction scheme. The key idea is to artificially create and exploit the time-domain relevance of non-stationary effect, which allows XL-MIMO to recognize such effect in the space domain. In this way, the spatial non-stationary channel is converted to a series of spatial stationary channels. To effectively estimate these channels, a GTBC-based polar-domain simultaneous orthogonal matching pursuit (GP-SOMP) algorithm is proposed as a solution. Simulation results reveal that the proposed GP-SOMP algorithm can recognize the spatial non-stationary effect in XL-MIMO systems and realize a much more accurate channel estimation than existing schemes.

Reconfigurable intelligent surface (RIS) has been widely considered as a key technique to improve spectral efficiency for the 5th generation (5G) and beyond 5G (B5G) communications. Compared with most existing research that only focuses on the reflective RIS, the design and prototyping of a novel transmissive RIS are presented in this paper, and its enhancement to the RIS-aided communication system is experimentally demonstrated. The 2-bit transmissive RIS element utilizes the penetration structure, which combines a 1-bit current reversible dipole and a 90° digital phase shifter based on a quadrature hybrid coupler. A transmissive RIS prototype with$16\times16$elements is designed, fabricated, and measured to verify the proposed design. The measured phase shift and insertion loss of the RIS element validate the 2-bit phase modulation capability. Being illuminated by a horn feed, the prototype achieves a maximum broadside gain of 22.0 dBi at 27 GHz, and the two-dimensional beamforming capability with scan angles up to ±60° is validated. The experimental results of the RIS-aided communication system verify that by introducing the extra gain and beam steering capability, the transmissive RIS is able to achieve a higher data rate, reduce the transmit power, improve the transmission capability through obstacles, and dynamically adapt to the signal propagation direction.

Wideband extremely large-scale multiple-input-multiple-output (XL-MIMO) plays an important role in boosting the data rate for 6G networks. Because of the huge bandwidth and the large number of antennas, wideband XL-MIMO introduces a significant near-field beam split effect, where beams at different frequencies are focused on different locations. This effect results in a severe array gain loss, and existing works mainly consider to compensate for this loss by utilizing time-delay (TD) beamforming. This paper demonstrates that despite degrading the array gain, the near-field beam split effect can also contribute to the fast near-field beam training. Specifically, we first reveal the controllable near-field beam split effect. This effect indicates that TD beamforming can control the degree of the near-field beam split effect, i.e., beams at different frequencies can flexibly occupy the desired location range. Due to the similarity with the dispersion of natural light caused by a prism, we also call this effect as “near-field rainbow”. Then, by taking advantage of the near-field rainbow, a fast wideband beam training scheme is proposed to generate beams focusing on multiple locations at multiple frequencies with the help of TD beamforming. Finally, simulation results demonstrate that the proposed scheme is able to realize efficient near-field beam training with low training overheads.

Extremely large-scale MIMO (XL-MIMO) communication is a promising technology to improve the capacity for future 6G networks. With a very large number of antennas, the near-field property of XL-MIMO systems becomes significant. Unlike the classical far-field line-of-sight (LoS) channel with only one available data stream, significantly increased degrees of freedom (DoFs) are available in the near-field LoS channel. However, limited by the small number of radio frequency (RF) chains, the existing hybrid precoding architecture widely used for 5G is not able to fully utilize the extra DoFs in the near-field region. In this paper, to exploit the near-field effect as a new possibility for capacity improvement, the distance-aware precoding (DAP) architecture is developed, where each RF chain can be flexibly configured to active or inactive according to the distance-related DoFs. Moreover, based on the developed DAP architecture, a DAP algorithm is proposed to optimize the number of activated RF chains and precoding matrices to match the increased DoFs. Finally, simulation results verify that, the proposed DAP scheme can efficiently utilize the extra DoFs in the near-field region to improve the spectrum efficiency.

Accurate channel prediction is vital to address the channel aging issue in mobile communications with fast time-varying channels. Existing channel prediction schemes are generally based on the sequential signal processing, i.e., the channel in the next frame can only be sequentially predicted. Thus, the accuracy of channel prediction rapidly degrades with the evolution of frame due to the error propagation problem in the sequential operation. To overcome this challenging problem, we propose a transformer-based parallel channel prediction scheme to predict future channels in parallel. Specifically, we first formulate the channel prediction problem as a parallel channel mapping problem, which predicts the channels in next several frames in parallel. Then, inspired by the recently proposed parallel vector mapping model named transformer, a transformer-based parallel channel prediction scheme is proposed to solve this formulated problem. Relying on the attention mechanism in machine learning, the transformer-based scheme naturally enables parallel signal processing to avoid the error propagation problem. The transformer can also adaptively assign more weights and resources to the more relevant historical channels to facilitate accurate prediction for future channels. Moreover, we propose a pilot-to-precoder (P2P) prediction scheme that incorporates the transformer-based parallel channel prediction as well as pilot-based channel estimation and precoding. In this way, the dedicated channel estimation and precoding can be avoided to reduce the signal processing complexity. Finally, simulation results verify that the proposed schemes are able to achieve a negligible sum-rate performance loss for practical 5G systems in mobile scenarios.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is promising to meet the high rate requirements for future 6G. To realize efficient precoding, accurate channel state information is essential. Existing channel estimation algorithms with low pilot overhead heavily rely on the channel sparsity in the angular domain, which is achieved by the classical far-field planar-wavefront assumption. However, due to the non-negligible near-field spherical-wavefront property in XL-MIMO, this channel sparsity in the angular domain is not achievable. Therefore, existing far-field channel estimation schemes will suffer from severe performance loss. To address this problem, in this paper, we study the near-field channel estimation by exploiting the polar-domain sparsity. Specifically, unlike the classical angular-domain representation that only considers the angular information, we propose a polar-domain representation, which simultaneously accounts for both the angular and distance information. In this way, the near-field channel also exhibits sparsity in the polar domain, based on which, we propose on-grid and off-grid near-field XL-MIMO channel estimation schemes. Firstly, an on-grid polar-domain simultaneous orthogonal matching pursuit (P-SOMP) algorithm is proposed to efficiently estimate the near-field channel. Furthermore, an off-grid polar-domain simultaneous iterative gridless weighted (P-SIGW) algorithm is proposed to improve the estimation accuracy. Finally, simulations are provided to verify the effectiveness of our schemes.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) with hybrid precoding is a promising technique to meet the high rate requirements for future 6G. To realize efficient precoding, accurate channel estimation is essential. Existing channel estimation algorithms with low pilot overhead heavily rely on the channel sparsity in angle domain, which is achieved by the classical far-field planar-wavefront assumption. However, this sparsity is not available, due to the non-negligible near-field spherical-wavefront property in XL-MIMO. Therefore, existing far-field estimation schemes will suffer from severe performance loss. To address this problem, in this paper, we study the near-field channel estimation by exploiting the polar-domain sparsity. Specifically, unlike the classical angle-domain representation that only considers the angle information of channel, we propose a polar-domain representation, which simultaneously accounts both the angle and distance information. In this way, the near-field channel also exhibits sparsity in polar domain. Exploiting this polar-domain sparsity, we propose an polar-domain simultaneous orthogonal matching pursuit (P-SOMP) algorithm to efficiently estimate the near-field channel. Finally, simulations are provided to verify the effectiveness of our schemes.