A polarization-aware direction-of-arrival (DoA) detection scheme is conceived that leverages the intrinsic vector sensitivity of a single Rydberg atomic vapor cell to achieve quantum-enhanced angle resolution. Our core idea lies in the fact that the vector nature of an electromagnetic wave is uniquely determined by its orthogonal electric and magnetic field components, both of which can be retrieved by a single Rydberg atomic receiver via electromagnetically induced transparency (EIT)- based spectroscopy. To be specific, in the presence of a static magnetic bias field that defines a stable quantization axis, a pair of sequential EIT measurements is carried out in the same vapor cell. Firstly, the electric-field polarization angle is extracted from the Zeeman-resolved EIT spectrum associated with an electricdipole transition driven by the radio frequency (RF) field. Within the same experimental cycle, the RF field is then retuned to a magnetic-dipole resonance, producing Zeeman-resolved EIT peaks for decoding the RF magnetic-field orientation. This scheme exhibits a dual yet independent sensitivity on both angles, allowing for precise DoA reconstruction without the need for spatial diversity or phase referencing. Building on this foundation, we derive the quantum Fisher-information matrix (QFIM) and obtain a closed-form quantum Cramér-Rao bound (QCRB) for the joint estimation of polarization and orientation angles. Finally, simulation results spanning various quantum parameters validate the proposed approach and identify optimal operating regimes. With appropriately chosen polarization and magnetic-field geometries, a single vapor cell is expected to achieve sub-0.1° angle resolution at moderate RF-field driving strengths.

The intrinsic integration of Rydberg atomic receivers into wireless communication systems is proposed, by harnessing the principles of quantum physics in wireless communications. More particularly, we conceive a pair of Rydberg atomic receivers, one incorporates a local oscillator (LO), referred to as an LO-dressed receiver, while the other operates without an LO and is termed an LO-free receiver. The appropriate wireless model is developed for each configuration, elaborating on the receiver's responses to the radio frequency (RF) signal, on the potential noise sources, and on the signal-to-noise ratio (SNR) performance. The developed wireless model conforms to the classical RF framework, facilitating compatibility with established signal processing methodologies. Next, we investigate the associated distortion effects that might occur, specifically identifying the conditions under which distortion arises and demonstrating the boundaries of linear dynamic ranges. This provides critical insights into its practical implementations in wireless systems. Finally, extensive simulation results are provided for characterizing the performance of wireless systems, harnessing this pair of Rydberg atomic receivers. Our results demonstrate that LO-dressed systems achieve a significant SNR gain of approximately 40~50 dB over conventional RF receivers in the standard quantum limit regime. This SNR head-room translates into reduced symbol error rates, enabling efficient and reliable transmission with higher-order constellations.

Holographic multiple-input multiple-output (HMIMO) is envisaged as a viable manner for manipulating electromagnetic field produced or perceived by antennas, in an effort to achieve an intelligent and endogenously holography-capable wireless propagation environment. This notion garners significant interest when engaging large antenna elements at high frequencies, such as millimeter-wave or terahertz. Under these conditions, operations often occur within the Fresnel region, i.e., near-field region, where assumptions of planar wavefront no longer apply. This article investigates the codebook solution for HMIMO, unmasking a number of challenges intrinsic in the near-field context and limitations of applying codebooks specified in current standards. Specifically, we proposed a two-phase codebook design empowered by artificial intelligence (AI)-based techniques, where angular and distance ingredients are resolved respectively at each phase, functioning in tandem for facilitating an efficient beam training at reduced pilot overhead. Then from the 3rd generation partnership project (3GPP) standardization perspective, we share potential design rationales influencing standardization, along with a novel signaling procedure for HMIMO beam sweeping.

This paper proposes a fast and robust graph-based wavenumber-domain approach for channel estimation in holo-graphic MIMO (HMIMO) systems. Unlike conventional angulardomain methods—prone tomutual coupling, power leakage, andsampling redundancy—our framework resolves HMIMO’s high-dimensional challenges by introducing a wavenumber-domain basis via orthogonal Fourier harmonics (FHs), eliminating dependencies on antenna density. By reformulating channel estimation as its sparse recovery counterpart, we model clustered sparsity using an elliptic Markov random field (EMRF), upon which a graph-cut swap expansion (GCSE) algorithm is developed, leveraging graph-theoretic optimizations for fast convergence and low complexity. Simulations demonstrate that our method achieves robust performance against mutual coupling, varying SNRs, and antenna density with drastically less computing time.

In the near-field context, the Fresnel approximation is typically employed to mathematically represent solvable functions of spherical waves. However, these efforts may fail to take into account the significant increase in the lower limit of the Fresnel approximation, known as the Fresnel distance. The lower bound of the Fresnel approximation imposes a constraint that becomes more pronounced as the array size grows. Beyond this constraint, the validity of the Fresnel approximation is broken. As a potential solution, the wavenumber-domain paradigm characterizes the spherical wave using a spectrum composed of a series of linear orthogonal bases. However, this approach falls short of covering the effects of the array geometry, especially when using Gaussian-mixed-model (GMM)-based von Mises-Fisher distributions to approximate all spectra. To fill this gap, this paper introduces a novel wavenumber-domain ellipse fitting (WD-EF) method to tackle these challenges. Particularly, the channel is accurately estimated in the near-field region, by maximizing the closed-form likelihood function of the wavenumber-domain spectrum conditioned on the scatterers’ geometric parameters. Simulation results are provided to demonstrate the robustness of the proposed scheme against both the distance and angles of arrival.

In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems. The conventional angular-domain representation fails to capture the continuous angular power spectrum characterized by the spatially -stationary electromagnetic random field, thus leading to the ambiguous detection of the significant angular power, which is referred to as the power leakage. To tackle this challenge, the HMIMO channel is represented in the wavenumber domain for exploring its cluster-dominated sparsity. Specifically, a finite set of Fourier harmonics acts as a series of sampling probes to encapsulate the integral of the power spectrum over specific angular regions. This technique effectively eliminates power leakage resulting from power mismatches induced by the use of discrete angular-domain probes. Next, the channel estimation problem is recast as a sparse recovery of the significant angular power spectrum over the continuous integration region. We then propose an accompanying graph-cut-based swap expansion (GCSE) algorithm to extract beneficial sparsity inherent in HMIMO channels. Numerical results demonstrate that this wavenumber-domain-based GCSE approach achieves robust performance with rapid convergence.

In this paper, we investigate the employment of the extremely large-scale reconfigurable intelligent surface (XL-RIS) in dynamic near-field wireless communication systems. Given the spherical-wavefront nature in the near-field context, channel estimation becomes more challenging, in particular for time-varying channels due to the mobility of the transceivers. This implies that frequent feedback of high-dimensional cascaded channel state information (CSI) associated with XL- RIS entails substantial overhead. To tackle this challenge, we propose a channel tracking scheme for the near-field XL- RIS regime. Specifically, a Kalman filter (KF) based framework is proposed, in which two learning-based networks are employed for obtaining the channel information of the first time slot, thereby acquiring initial prior information for the KF technique. Subsequently, leveraging the known prior information and the temporal correlation of the time-varying channel, the cascaded channel matrix for the next time slot is continuously predicted. Following that, the prediction is updated and refined using the observation from the current time slot as a benchmark, enhancing the accuracy and reliability of the channel tracking process. Finally, simulation results are provided to verify the effectiveness of the proposed scheme.

Leveraging the concept of the electromagnetic signal and information theory, holographic multiple-input multiple-output (MIMO) technology opens the door to an intelligent and endogenously holography-capable wireless propagation environment, with their unparalleled capabilities for achieving high spectral and energy efficiency. Less examined are the important issues such as the acquisition of accurate channel information by accounting for holographic MIMO’s peculiarities. To fill this knowledge gap, this paper investigates the channel estimation for holographic MIMO systems by unmasking their distinctions from the conventional one. Specifically, we elucidate that the channel estimation, subject to holographic MIMO’s electromagnetically large antenna arrays, has to discriminate not only the angles of a user/scatterer but also its distance information, namely the three-dimensional (3D) azimuth and elevation angles plus the distance (AED) parameters. As the angular-domain representation fails to characterize the sparsity inherent in holographic MIMO channels, the tightly coupled 3D AED parameters are firstly decomposed for independently constructing their own covariance matrices. Then, the recovery of each individual parameter can be structured as a compressive sensing (CS) problem by harnessing the covariance matrix constructed. This pair of techniques contribute to a parametric decomposition and compressed deconstruction (DeRe) framework, along with a formulation of the maximum likelihood estimation for each parameter. Then, an efficient algorithm, namely DeRe-based variational Bayesian inference and message passing (DeRe-VM), is proposed for the sharp detection of the 3D AED parameters and the robust recovery of sparse channels. Finally, the proposed channel estimation regime is confirmed to be of great robustness in accommodating different channel conditions, regardless of the near-field and far-field contexts of a holographic MIMO system, as well as an improved performance in comparison to the state-of-the-art benchmarks.

In this paper, we investigate the employment of reconfigurable intelligent surfaces (RISs) into vehicle platoons, functioning in tandem with a base station (BS) in support of the high-precision location tracking. In particular, the use of a RIS imposes additional structured sparsity that, when paired with the initial sparse line-of-sight (LoS) channels of the BS, facilitates beneficial group sparsity. The resultant group sparsity significantly enriches the energies of the original direct-only channel, enabling a greater concentration of the LoS channel energies emanated from the same vehicle location index. Furthermore, the burst sparsity is exposed by representing the non-line-of-sight (NLoS) channels as their sparse copies. This thus constitutes the philosophy of the diverse sparsities of interest. Then, a diverse dynamic layered structured sparsity (DiLuS) framework is customized for capturing different priors for this pair of sparsities, based upon which the location tracking problem is formulated as a maximum a posterior (MAP) estimate of the location. Nevertheless, the tracking issue is highly intractable due to the ill-conditioned sensing matrix, intricately coupled latent variables associated with the BS and RIS, and the spatial-temporal correlations among the vehicle platoon. To circumvent these hurdles, we propose an efficient algorithm, namely DiLuS enabled spatial-temporal platoon localization (DiLuS-STPL), which incorporates both variational Bayesian inference (VBI) and message passing techniques for recursively achieving parameter updates in a turbo-like way. Finally, we demonstrate through extensive simulation results that the localization relying exclusively upon a BS and a RIS may achieve the comparable precision performance obtained by the two individual BSs, along with the robustness and superiority of our proposed algorithm as compared to various benchmark schemes.

The inability of reconfigurable intelligent surfaces (RISs) to signal processing has become a critical bottleneck in terms of the precise capture of cascaded channels. There exists a pair of critical issues: 1) the high-dimensional cascaded channel to be estimated, which always entails substantial pilot overhead typically proportional to RIS elements and 2) the high possibilities of false alarms and unfaithful angle detection during cascaded channel estimates, referred to as the angular distortion, particularly in the case of conventional fully-passive RIS systems. To circumvent these issues, we investigate in this paper a multi-user multiple-input multiple-output (MIMO) system assisted by a wireless beacon (WB) enabled RIS over millimeter wave (mmWave) frequency. Specifically, WB is a functionally independent hardware that attaches parasitically to the RIS using only one radio frequency chain for broadcasting pilots, allowing for the accurate acquisition of partial cascaded channel information, e.g., some of angles of arrival/departure (AoA/AoDs) and complex gains. This efficiently eliminates the angular distortion effect and thus facilitates high precision recovery of sparse cascaded mmWave channels. Then, a hybrid structured sparsity (HSS) model is presented to capture hybrid sparse priors and approximate exact posterior distributions associated with RIS-aided twin-hop channels. We conceive of channel estimation as a compressive sensing problem in contrast to its conventional copy due to the presence of an unknown sensing matrix. To tackle this problem, an expectation-maximization (EM)-based algorithm, i.e., HSS-EM, is developed in order to fully employ the sparse priors encapsulated by the proposed HSS model for a robust and accurate recovery of the cascaded channels. Numerical results validate our analytical claims and demonstrate the significant improvement in terms of the normalized mean square error (NMSE) performance over conventional designs.

Although reconfigurable intelligent surface (RIS) has been touted as a technology star for future wireless networks, the critical bottleneck still lies in the accurate acquisition of channel state information (CSI). The vast majority of state-of-the-art cascaded channel estimates entail the pilot overhead typically proportional to the number of RIS elements, which results in a long training time and thus may not be tolerable in high-mobility scenarios. In this paper, we investigate the channel tracking for RIS-aided high-mobility millimeter wave (mmWave) communications. By leveraging the angular domain representation of cascaded channels, we initially demonstrate the dynamic dual-structured sparsity (DDS), i.e., i) the angular cascaded channel matrices associated with different users share the identical non-zero rows while differ in their non-zero columns and ii) the cascaded channel support exhibits temporal correlation inherent to the dynamic nature of mobile channels. Then, a layered processing with dynamic dual-structured sparsity (LP-DDS) framework is customized to provide sparse priors for the exact distributions of cascaded channels. In this case, the joint estimate of the angular cascaded channel and Doppler shift is formulated as a compressive sensing (CS) problem while taking into account the temporal correlation of dynamic channels, which, however, is highly intractable due to the ill-conditioned sensing matrix. To tackle this issue, we propose an efficient algorithm, namely DDS-VBIMP, where in particular, both variational Bayesian inference (VBI) and message passing techniques are complemented each other to achieve parameter updates by taking full advantage of the sparse priors as captured by LP-DDS. We demonstrate through our analyses that the proposed DDS-VBIMP can significantly reduce pilot overhead. Simulation results reveal the superiority and robustness of the proposed DDS-VBIMP as compared to various benchmark schemes.

Doppler-shift-induced channel aging effect significantly erodes the system performance due to the channel mismatch that evolves with time. This paper aims to investigate the joint beamforming strategy in reconfigurable intelligent surface (RIS)-aided high-mobility communications with channel aging effect. Specifically, a novel frame structure is first proposed for alleviating the heavy signaling overhead in the RIS-aided high-mobility scenario. Furthermore, a deep reinforcement learning (DRL)-based algorithm is devised for rapidly co-designing the precoder at the base station (BS) and the passive beamforming at the RIS relying exclusively upon partial channel state information (CSI) and real-time environment feedback, instead of only employing the outdated estimated CSI. The proposed joint beamforming scheme is capable of adapting to the dynamic propagation environment by exploiting the channel correlation between consecutive instants. Finally, simulation results demonstrate that the proposed algorithm can effectively mitigate the performance degradation caused by channel aging while being computationally efficient and outperforms several benchmark schemes.

Two key impediments to reconfigurable intelligent surface (RIS)-aided vehicular communications are, respectively, the double fading experienced by the signal on RIS-aided cascaded links and the high-mobility-induced intractability of acquiring channel state information (CSI). To overcome these challenges, a novel kind of RIS is presented in this paper, namely active reconfigurable intelligent omni-surface (RIOS), each element of which is supported by active loads, that concurrently transmits and reflects the incident signal amplified rather than just reflecting it as compared to the case of a passive reflecting-only RIS. We consider the use of an active RIOS to a vehicular communication system for mitigating double fading effect. Specifically, the active RIOS is mounted on the vehicle window to enhance transmission for users in the vehicle and for adjacent vehicles. We aim to jointly optimize the transmit precoding matrix at the base station (BS) and RIOS coefficient matrices to minimize the BS’s transmit power relying exclusively upon the imperfect knowledge of the large-scale CSI. To significantly relax the frequency of channel information updates, initially an efficient transmission protocol is put forward to reap the high active RIOS beamforming gain with low channel training overhead by appropriately tailoring the time-scale of CSI acquisition. Then, two algorithms, namely an alternating optimization (AO)-based algorithm and a constrained stochastic successive convex approximation (CSSCA)-based algorithm, are developed to tackle with the investigated resource allocation problem, whose pros and cons are elaborated, respectively. Simulation results substantiate the significant performance improvement of active RIOS as well as determine the validity and robustness of our proposed algorithms over various benchmark schemes.

The integration of reconfigurable intelligent surface (RIS) into millimeter wave (mmWave) vehicular communications offers the possibility to unleash the potential of future proliferating vehicular applications. However, the high-mobility-induced rapidly varying channel state information (CSI) has been making it challenging to obtain the accurate instantaneous CSI (I-CSI) and to cope with the incurable high signaling overhead. The situation may become worse when the RIS with a large number of passive reflecting elements is deployed. To overcome this challenge, we investigate in this paper a robust transmission scheme for the time-varying RIS-aided mmWave vehicular communications, in which, specifically, a multi-antenna base station (BS) serves vehicle user equipments (VUEs) with the help of RIS at the mmWave frequency. The uplink average achievable rate is maximized relying only upon the imperfect knowledge of statistical CSI. Considering the time-varying characteristics, we first propose an effective transmission protocol by reasonably configuring the time-scale of CSI acquisition in order to significantly relax the frequency of channel information updates, which constitutes one of the most critical issues in RIS-aided vehicular communications. Then, the formulated resource allocation problem is discussed in the single- and multi-VUE case, respectively. To be specific, for the single-VUE case, a closed-form expression of the average rate is derived by extracting the statistical characteristics of mmWave channels, and an alternating optimization (AO)-based algorithm is proposed. For the multi-VUE case, we develop an efficient algorithm, called JAPMC, to circumvent the unavailability of the closed-form of the objective function and probabilistic constraint by constructing quadratic surrogates of that. Simulation results confirm the effectiveness and robustness of our proposed algorithms as compared to benchmark schemes.

Reconfigurable intelligent surfaces (RISs) have the capability of reconfiguring the wireless environment in a favorable way to improve the quality-of-service (QoS) of wireless communications. This makes RISs a promising candidate to enhance vehicle-to-everything (V2X) applications. This paper investigates the spectrum sharing problem in RIS-aided vehicular networks, in which multiple vehicle-to-vehicle (V2V) links reuse the spectrum already occupied by vehicle-to-infrastructure (V2I) links. To overcome the difficulty of acquiring instantaneous channel state information (CSI) due to the fast varying nature of some V2X channels, we rely upon large-scale (slowly varying) CSI in order to fulfill the QoS requirements of V2I and V2V communications. Particularly, we aim to maximize the sum capacity of V2I links that are used for high-rate content delivery and to guarantee the reliability of V2V links that are used for the exchange of safety information. The transmit power of vehicles, the multi-user detection (MUD) matrix, the spectrum reuse of V2V links, and the RIS reflection coefficients are jointly optimized, which results in a mixed-integer and non-convex optimization problem. To tackle this problem, the outage probability of each V2V link is first approximated by introducing an analytical expression to simplify the formulated problem. By leveraging the block coordinate descent (BCD) method, the considered optimization problem is decomposed into three sub-problems, whose optimal solutions are provided independently and updated alternately to obtain a near-optimal solution. Simulation results verify the theoretical analysis and the effectiveness of the proposed algorithm, as well as unveil the benefits of introducing RISs for enhancing the QoS performance of vehicular communications.

This paper studies a joint communication, computing and caching resource allocation problem in vehicular networks to improve user satisfaction and reduce costs. We propose a double-scale deep reinforcement learning (DSDRL) framework that combines on-policy strategy and off-policy strategy to enable dynamic resources allocation, which considers not only the diversity and difference of services but also the costs of network operators. Simulation results show that the proposed scheme can effectively improve the long-term revenue of network operators.