Noncoherent magnitude-based MIMO (NMB-MIMO) has been envisaged as a promising architecture for the low-cost implementation of massive machine-type communications (mMTC). Nevertheless, the existing channel estimation methods are not applicable to NMB-MIMO due to its unique magnitude-only detection mechanism. This paper introduces an innovative channel estimation framework specifically designed for NMB-MIMO systems. Initially, we deploy a local oscillator (LO) to transmit known reference signals, and the NMB-MIMO channel estimation is formulated as a compressive phase retrieval problem by leveraging the inherent angular-domain sparsity of wireless channels. To address the proposed problem, we subsequently develop an efficient universal compressive phase retrieval (UCPR) algorithm. Simulation results demonstrate the robustness of the proposed UCPR approach, validating its effectiveness in accurately reconstructing NMB-MIMO channels.

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.

The paradigm shift from massive MIMO to extremely large MIMO (XL-MIMO) catalyzes significant improvements in the spectral efficiency and spatial resolution of MIMO systems. However, the large-scale arrays also lead to the near-field effect, which indicates the coexistence of far-field and near-field user equipments (UEs). This paper proposes a unified beam training framework applicable to both far-field and near-field scenarios, even including thosewithin the Fresnel distance. Specifically, our proposed beam training method builds upon a rigorous wavenumber-domain spectrum analysis based on the geometric propagation characteristics. By estimating the non-zero boundaries rather than the specific non-zero entries in the wavenumber-domain spectrum, we introduce an efficient and fidelity-robust algorithm to estimate the location of the UE, which is termedellipse-fitting localization (EFL). Simulation results validate the effectiveness of our beam training framework across far-field and near-field scenarios, even within the Fresnel distance.

The mutual coupling (MC) effect refers to the phenomenon where the voltage on one antenna element induces excitation currents on nearby antenna elements. This paper initially derives precise MC matrices grounded in meticulous circuit and antenna theories, unveiling the asymmetry of MC effects between the transmitter (Tx) and receiver (Rx). This asymmetry arises from the fact that the transmitted signal is the voltage on the antenna elements, whereas the received signal represents the voltage input to the low-noise amplifiers (LNAs). Upon this approach, an MC-aware hybrid beamforming methodology is introduced to mitigate distortions in radiation patterns precipitated by MC effects. Furthermore, we undertake comprehensive performance analysis across various antenna topologies, including rectangular, hexagonal, circular, and concentric circular configurations. Simulation results substantiate the necessity for addressing MC effects and demonstrate the effectiveness of our proposed MC-aware hybrid beamforming methodology. Moreover, these results indicate that MC effects can enhance the capacity under certain antenna separations and topologies, highlighting that MC effects are not merely detrimental but potentially beneficial in densely-packed MIMO.

The large antenna aperture in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems results in a hybrid far and near-field communication. Existing hybrid-field beam training works mostly treat plane waves as spherical ones with infinite distance, thereby inheriting several limitations associated with spherical wave based training, such as protocol incompatibility, high overhead, and complex hierarchical codebook design. To address these issues, we propose a unified far-field and near-field wavenumber domain beam training framework, involving a semi-codebook-based beam sweeping scheme and a hierarchical training strategy. The core idea is reinterpreting spherical wave as a superposition of plane waves, retaining accuracy while inheriting the charming protocol compatibility, low overhead, and simple codebook design provided by plane waves. Moreover, due to the linearity of plane waves, the ideal beam pattern with negligible power leakage can be easily obtained by the proposed phased-shifted alternative minimization (PS-AltMin) codeword design method. Finally, numerical results show that the proposed wavenumber domain beam training methods have a significant achievable rate gain compared to the benchmarks, which comes from the use of the semi-codebook-based transmission technique and the unified channel model for both far-field and near-field.

Mutual coupling is essentially the physical phenomenon where the voltage on one antenna induces excitation currents on its surrounding antennas. Mutual coupling usually occurs in holographic MIMO (HMIMO) systems, distorting the intended radiation pattern of the array. Nevertheless, the impacts of mutual coupling on wideband HMIMO have not yet been fully investigated in the literature. To fill this knowledge gap, this paper initially unveils the frequency-dependent nature of mutual coupling, which emanates from frequency-dependent antenna impedance and mutual impedance. Subsequently, this paper proposes an innovative frequency-dependent coupling-aware beamforming methodology to mitigate frequency-dependent mutual coupling (FDMC). Upon this approach, both FDMC and frequency-dependent channels (FDC) can be simultaneously mitigated for wideband HMIMO communications. Simulation results validate the necessity of mitigating FDMC and substantiate the efficacy of our proposed beamforming-based mitigation in enhancing the wideband performance of HMIMO.

The mutual coupling effect refers to the phenomenon where the voltage on one antenna excites currents on its nearby antennas in holographic multiple-input-multiple-output (HMIMO) systems, which can distort the radiation pattern of the array. To precisely evaluate the impact of the mutual coupling effect, a widely-used method in the literature is to compute the mutual coupling matrix. However, computing the mutual coupling matrix for an electromagnetically-large array can be significantly time-consuming. Faced with this issue, this paper initially proposes a novel fast Hash-based derivation (FHD) to compute the accurate mutual coupling matrix by avoiding repetitive computation of numerical integrals. Subsequently, a rigorous combinatorial analysis on our proposed algorithm is provided, demonstrating the computational complexity can be reduced from $\mathcal{O}\left( {{N^2}} \right)$ to less than $\mathcal{O}(N)$ for a uniform planar array (UPA) equipped with N antennas. Finally, simulation results demonstrate that our proposed algorithm is capable of fast and accurate computation for the mutual coupling matrix.

This paper addresses the critical challenges of fractional delay and Doppler effects in low earth orbit (LEO) satellite orthogonal time-frequency space (OTFS) systems, where conventional channel estimation methods suffer from high complexity and inadequate prior utilization. To overcome these limitations, we propose a fast and robust framework with: 1) A subspace-constrained variational Bayesian inference (SC-VBI) mechanism that reduces complexity by decoupling high-dimensional matrix operations and 2) A statistical Delay-Doppler-prior (SD2P) integration scheme leveraging elevation angle distributions from real-world LEO orbital dynamics to enhance sparse signal recovery (SSR) accuracy. Departing from traditional sparse Bayesian learning (SBL) approaches, our algorithm mitigates the curse of dimensionality through iterative sub-space updates while embedding physical-layer Doppler characteristics into Bayesian priors. Simulations under 3GPP LEO configurations demonstrate that the proposed method achieves a ten-times compression in computational time and 60% normalized mean square error (NMSE) improvement.

Rydberg atomic receivers have been envisaged as a revolutionary technology for future wireless communications and sensing. In order to detect the angle of arrival (AoA), researchers have typically constructed arrays comprisingmultipleRydberg atomic receivers. This paper presents a novel finding: The AoA of an incident signal can be accurately recovered even with asingleRydberg atomic receiver, by harnessing the phenomenon of inner-vapor interference. Firstly, we apply the micro-element method to the atomic vapor and derive a closed-form expression for the laser transmission in the presence of interference between the incident and local oscillator (LO) radio frequency (RF) signals. Secondly, we propose a robust method to estimate the AoA based on the particle swarm optimization (PSO) algorithm. Simulation results substantiate the effectiveness of our proposed scheme with practical parameter settings, verifying the applicability of AoA detection using a single Rydberg atomic receiver.

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.

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.

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.

Weather radar images provide critical information for mesoscale weather nowcasting which plays significant roles in a range of fields including civil aviation and navigation. Differed from traditional radar exploration methods, this paper presents a novel prediction model based on a deep recurrent neural network (DeepRNN). The approach converts the task of nowcasting to a task of image series prediction. We first design a new loss function that pays more attention to the changes of images in the input sequence. In the mean while, an image discriminator is incorporated into the model to improve the visual quality of predicted images. Furthermore, optical flow is explored to preserve the the motion information. The prediction results are evaluated based on widely used statistic scores. The experimental results show that the proposed model leads to significant improvement in tasks of 2 hours forecasting of radar echo.

Depth prediction from a single monocular image is a challenging yet valuable task, as often a depth sensor is not available. The state-of-the-art approach [1] combines a deep fully convolutional network (DFCN) with a conditional random field (CRF), allowing the CRF to correct and smooth the depth values estimated by the DFCN according to efficient contextual modeling. However, using the output of the DFCN as unary input for CRF is limited by using only the last layer of the DFCN. The middle layers of the DFCN have been shown to carry useful information for other scene understanding tasks, which may help to improve the prediction quality. This paper proposes a novel super-column superpixel (SCSP) feature that is the combination of multiple layers of the DFCN after a super-pixel pooling process. The proposed approach based on the SCSP features reduces the root mean square (rms) error of the prediction by more than 16% in NYUv2 dataset.

The forced landing problem has become one of the main impediments to UAV's entering civilian airspace. Unfortunately there is no robust forced landing site detection system that will reliably detect a safe landing site. One of the main reasons for this is the difficulty in considering the various classes of surface, to determine whether they are safe or not. We propose a robust UAV landing site detection system using midlevel discriminative patches. The training and tuning process uses a dataset containing 1600 randomly selected Google map images with weak labels.We then show how the output from multiple mid-level discriminative patch detectors can be combined to indicate the level or danger for a given region. The proposed technique reliably detects safe landing areas in UAV imagery, and achieves improved performance over the state-of-the art. The proposed system outperforms the baseline system by 29.4% for completeness and 33.9% for correctness, and is invariant to the changes of illumination, sharpness and resolution of images.

Recently, internal buffer module and multi-level parallel components have already become the standard elements of SSDs. The internal buffer module is always used as a write cache, reducing the erasures and thus improving overall performance. The multi-level parallelism is exploited to service requests in a concurrent or interleaving manner, which promotes the system throughput. These two aspects have been extensively discussed in the literature. However, current buffer algorithms cannot take full advantage of parallelism inside SSDs. In this paper, we propose a novel write buffer management scheme called Parallelism-Aware Buffer (PAB). In this scheme, the buffer is divided into two parts named as Work-Zone and Para-Zone respectively. Conventional buffer algorithms are employed in the Work-Zone, while the Para-Zone is responsible for reorganizing the requests evicted from Work-Zone according to the underlying parallelism. Simulation results show that with only a small size of Para-Zone, PAB can achieve 19.2% ~ 68.1% enhanced performance compared with LRU based on a page-mapping FTL, while this improvement scope becomes 5.6% ~ 35.6% compared with BPLRU based on the state-of-the-art block-mapping FTL known as FAST.

Multi-core architectures have become mainstream. Trends suggest that the number of cores integrated on a single chip will increase continuously. However, lock contention in operating systems can limit the parallel scalability on multi-cores so significantly that the speedup decreases with the increasing number of cores (thrashing). Although the phenomenon can be easily reproduced experimentally, most existing lock models are not able to do so. To overcome this challenge, this paper develops a discrete event simulation model which has the capability of capturing both the sequential execution in critical sections and the contention for shared hardware resources. The model is evaluated using a series of typical parameter configurations which can represent different degrees of lock contention. Experimental results suggest that the thrashing phenomenon can be observed when the model parameters are selected properly. To further understand this phenomenon, statistics such as the percentage of time spent waiting for locks and the number of cores waiting for a lock are exploited to characterize the lock thrashing. In addition, the model sensitivity to changes in memory latency and hardware architectures are also examined. Finally, we use this model to compare three methods which are proposed for preventing the lock thrashing.

Multi-core architectures have become mainstream. Trends suggest that the number of cores integrated on a single chip will continue to increase. However, lock contention in applications or kernels can degrade the scalability so significantly that the speedup decreases with the increasing number of cores (thrashing). Although the phenomenon can be easily reproduced on real multi-core platforms, existing lock models are not able to do so. To overcome the disadvantage, this paper proposes an analysis model which has the capability of capturing both the sequential execution of critical sections and the overhead of lock implementation. Numerical results indicate that thrashing can be observed by using the proposed model. Furthermore, this model can also be exploited to compare different mechanisms designed for avoiding the lock thrashing.