Superdirective receive arrays, whose gain can exceed that of traditional antenna arrays, have been historically challenging to realize due to high sensitivity to white noise, strong mutual coupling, and the complexity of designing receive matching networks in conventional systems. This paper proposes and analyzes a novel superdirective receiver architecture based on an array of Rydberg atomic sensors to overcome these fundamental limitations. By leveraging the quantum properties of Rydberg atoms, the proposed receiver is inherently immune to the internal thermal noise that plagues traditional receivers. As a result, the system performance is primarily limited by external background noise and fundamental quantum noise. We develop a comprehensive signal and noise model and the-oretically derive the upper bound on the directivity gain for the proposed Rydberg-based receiver. Furthermore, we prove that for multi-user scenarios, the effective channel vectors for different users become asymptotically orthogonal as the number of sensors approaches infinity, enabling high-capacity spatial multiplexing even with deep sub-wavelength element spacing. Extensive simulations demonstrate the superior performance of the proposed system over traditional superdirective receivers and validate our theoretical findings. The results highlight Rydberg atomic arrays as a promising technology for developing ultra-sensitive, compact, and broadband superdirective receivers for next-generation communication and sensing applications.

Traditional antenna arrays with a half-wavelength spacing between elements are capable of achieving a power gain proportional to the number of antennasM. Superdirective antenna arrays, however, leverage smaller antenna spacing to approach an achievable power gain ofM2, which could provide a significant performance improvement to the spectral efficiency in wireless communication systems. In this paper, we study the power scaling law of superdirective beamforming in multi-user communication systems using a uniform linear array (ULA). First, we extend superdirective precoding from single-user to multi-user multipath scenarios. Employing the basis of Legendre polynomials, we prove that the scaling laws of both the power gain and signal-to-interference-plus-noise ratio (SINR) are betweenMandM2, whereM2is achieved in the end-fire direction. To further enhance user power gains and effectively manage interference, we formulate and solve an optimization problem that maximizes the directivity gain while nullifying interference to other users. We demonstrate that this scheme can significantly improve spectral efficiency in multi-user settings, even when antenna spacing approaches zero. Moreover, we address the narrow directivity bandwidth issue, showing that the directivity of superdirective arrays decreases sharply as the frequency moves away from the center frequency, necessitating the use of multi-carrier technology to overcome this limitation. Simulation results verify the proposed power scaling law and show significant improvements in spectral efficiency with our proposed methods compared to a traditional antenna array with half-wavelength spacing.

The Holographic Interference Surface (HIS) opens up a new prospect for building a more cost-effective wireless communication architecture by performing Radio Frequency (RF) domain signal processing. In this paper, we establish a wideband channel sensing architecture for electromagnetic wave reception and channel estimation based on the principle of holographic interference theory. Dute to the nonlinear structure of holograms, interferential fringes composed of wideband RF signals exhibit severe self-interference effects in the time-frequency domain, which are inherently resistant to the classical signal processing tools. To overcome the self-interference, we propose a holographic channel recovery method, which analyzes the time-domain variation of holograms from a geometrical perspective and constructs an inverse mapping from wideband holograms to object waves. Based on the Wirtinger partial derivative and Armijo condition, we then develop a wideband hologram-based maximum likelihood (WH-ML) estimation method for estimating the channel state information (CSI) from holograms. We also propose a geometric rotation-based object wave sensing (GROWS) algorithm to address the complicated computation of ML estimation. Furthermore, we derive the Cramér-Rao lower bound (CRLB) for investigating the achievable performance of wideband holographic channel estimation. Simulation results show that under the wideband channel sensing architecture, our proposed algorithm can accurately estimate the CSI in wideband scenarios.

This study seeks to utilize large language models (LLMs) to forecast the moving ports of fluid antenna (FA). By repositioning the antenna to the locations identified by our proposed model, we intend to address the mobility challenges faced by user equipment (UE). To the best of our knowledge, this paper introduces, for the first time, the application of LLMs in the prediction of FA ports, presenting a novel model termed Port-LLM. The architecture of our model is based on the pre-trained GPT-2 framework. We designed specialized data preprocessing, input embedding, and output projection modules to effectively bridge the disparities between the wireless communication data and the data format utilized by the pre-trained LLM. Simulation results demonstrate that our model exhibits superior predictive performance under different numbers of base station (BS) antennas and varying UE speeds, indicating strong generalization and robustness ability. Furthermore, the spectral efficiency (SE) attained by our model surpasses that achieved by traditional methods in both medium and high-speed mobile environments.

This paper proposes a movable antenna (MA) assisted beamforming scheme without digital phase shifters. We perform phase shifts to signals by adjusting MA positions, achieving higher received power compared to fixed position antennas (FPAs). To compute the optimal MA positions, we propose the geometric-detection-based derivative matching optimization (DMO) method. The MAs using the DMO method demonstrate superior cost-effectiveness and energy efficiency compared to FPAs with digital phase shifters. Unlike existing MA optimization methods, the DMO method does not require complex matrix computation. It is non-iterative and can achieve the optimal MA positions under several designed geometric conditions. When the conditions are not satisfied, we prove that the normalized mean square error (NMSE) between the theoretic optimal result and the DMO result can converge to zero. Simulation results demonstrate that the DMO-assisted MA array outperforms the FPA in received power in multi-path propagation environments.

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction method, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. In the performance analysis, we derive the asymptotical lower and upper bounds of the prediction error for a multipath channel, when the number of base station (BS) antennas and the port density of the FA are large enough. When the UEs move at a speed of 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction method.

This paper addresses the near-field channel reconstruction problem in frequency division duplex (FDD) mode of extremely large-scale multiple-input multiple-output (XL-MIMO) systems. To acquire accurate channel state information (CSI), we propose a joint spatial-frequency domain (JSFD) precoding-based channel reconstruction method. Specifically, we design the JSFD precoder using the frequency-independent parameters estimated from the uplink (UL) channel, which significantly reduces the pilot overhead. Moreover, we propose an array partitioning matrix pencil (APMP) method to estimate parameters at the base station (BS) side. In theoretical analysis, we derive the optimal array partitioning range to reduce the impact of the near-field effect on channel reconstruction. We also prove that the estimation error converges to zero when the number of BS antennas and the bandwidth are large enough. The numerical results demonstrate that our JSFD method performs well in the near-field region and exhibits strong robustness under both high and low signal-to-noise ratio (SNR) conditions.

As Uncrewed Aerial Vehicle (UAV) technology matures and spreads, non-cooperative UAV intrusions have significantly increased airspace safety risks. To achieve all-weather airspace awareness and multi-level threat response, integrating UAV detection and warning into existing cellular base stations is a promising solution. Currently, most UAV detection techniques involve extracting Micro-Doppler features during UAV movement. However, extracting salient Micro-Doppler motion features is challenging and requires complex mathematical algorithms. We present a novel detection network called the Shallow Brain Residual Network (SBRN) to address this critical need for timely detection and early warning systems. The SBRN is inspired by the Shallow Brain architecture, which emulates the parallel processing hierarchy of the human visual system and fundamentally differs from classical deep learning frameworks. To the best of our knowledge, the proposed SBRN model is the first architecture that systematically implements a complete shallow-brain biological architecture. Our framework achieves detection accuracy of 99.45% while eliminating complex data preprocessing requirements. The model demonstrates robust multi-class discrimination capabilities, distinguishing between six distinct UAV types and multiple avian species. Unlike conventional deep learning models, the SBRN with only 17 million parameters, enabling efficient deployment. The network exhibits fast convergence speed, achieving > 96% accuracy within the first training epoch.

In multi-user communication systems, nonlinear precoding generally exhibits higher throughput than linear pre-coding, however at the cost of higher computation complexity. With the increasing number of antennas and users, the realization of nonlinear precoder at the base station is even more challenging. To address this issue, we propose a new system architecture that employs resistive random-access memory (RRAM) circuits to reduce the computation complexity of the nonlinear Tomlinson-Harashima precoding (THP) to a linear scale. We present a computation-constraints principle for designing RRAM circuits to perform nonlinear operations and construct an LQ decomposition RRAM circuit. Since the conductance of memristor is quantized, we perform the bit precision analysis and derive the lower bound of the Signal to Interference plus Noise Ratio (SINR). Our analysis indicates that at a high Signal to Noise Ratio (SNR) or with a large number of antennas, each 1 bit increase in bit precision brings a 6 dB improvement in SINR. Simulation demonstrates the feasibility and accuracy of the RRAM-based circuit and our theoretical results. Our work proves that the RRAM array holds significant potential for implementing high-complexity nonlinear precoding algorithms and may offer a promising solution to meet the demands of future communication.

In this paper, we propose a novel phased array with movable antennas (PAMA). By replacing traditional phase shifters with movable antennas, PAMA eliminates insertion loss of phase shifters and enhances antenna performance by leveraging the increased degrees of freedom in antenna positioning. We construct a periodic approximation-based beampattern synthesis (PABS) method. It leverages the approximate periodicity of the PAMA array response to obtain a high-quality initial solution through discrete optimization with quadruple-frequency sampling, which is subsequently refined using projected gradient descent. Simulations demonstrate the effectiveness of the proposed approach for multi-beampattern synthesis and highlight the impact of antenna movement range on synthesis performance. The results show that PAMA-based beampattern synthesis enhances beamforming accuracy and improves efficiency, especially in practical scenarios where digital phase shifters are used.

Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLCF) is proposed to reduce the feedback and improve the spectral efficiency for FDD massive MIMO. Manifold learning (ML) is an effective method for dimensionality reduction. However, most ML algorithms focus only on data compression, and lack the corresponding recovery methods. Moreover, the computational complexity is high when dealing with incremental data. Considering to utilize the intrinsic manifold structure where the CSI samples reside, we propose a landmark selection algorithm to describe the topological skeleton of this manifold. Based on the learned skeleton, the local patch of the incremental CSI on the manifold can be easily determined by its nearest landmarks. This motivates us to propose an incremental CSI compression and reconstruction scheme by keeping the local geometric relationships with landmarks invariant. We theoretically prove the convergence of the proposed landmark selection algorithm. Meanwhile, the upper bound on the error of approximating CSI with landmarks is derived. Simulation results under an industrial channel model of 3GPP demonstrate that the proposed MLCF outperforms existing deep learning based algorithms.

Dynamic metasurface antennas (DMAs), with their ability to manipulate electromagnetic waves using tunable metamaterial elements, provide a promising solution for beamforming in next-generation wireless communication systems. However, practical constraints, such as discrete phase shifts and the Lorentzian constraint, pose significant challenges for their optimization. This paper investigates the beamforming optimization problem in a downlink system where a base station (BS) equipped with a DMA operates under arbitrary discrete phase shift constraints. We propose two algorithms: the closest point projection (CPP) algorithm, which efficiently projects continuous-phase solutions onto discrete domains with lower complexity, and the optimalM-phase beamforming (OMPB) algorithm, which is proven to achieve the globally optimal solution for discrete phase configurations. We derive closed-form expressions for the quantization performance loss and average signal-to-noise ratio (SNR). Our analysis reveals that the quantization-induced performance losses for 1-bit, 2-bit, and 3-bit uniform phase configurations are 3.74 dB, 0.87 dB, and 0.22 dB, respectively. Extensive Monte-Carlo simulations validate the theoretical analysis and demonstrate that the proposed methods achieve notable performance gains over traditional relaxation and random algorithms.

In traditional multiple-input multiple-output (MIMO) communication systems, the antenna spacing is often no smaller than half a wavelength. However, by exploiting the coupling between more closely-spaced antennas, a superdirective array may achieve a much higher beamforming gain than traditional MIMO. In this paper, we present a novel utilization of neural networks in the context of superdirective arrays. Specifically, a new model called MultiTransUNet-GAN is proposed, which aims to forecast the excitation coefficients to achieve “superdirectivity” or “super-gain” in the compact uniform linear or planar antenna arrays. In this model, we integrate a multi-level guided attention and a multi-scale skip connection. Furthermore, generative adversarial networks are integrated into our model. To improve the prediction accuracy and convergence speed of our model, we introduce the warm up aided cosine learning rate (LR) schedule during the model training, and the objective function is improved by incorporating the normalized mean squared error (NMSE) between the generated value and the actual value. Simulations demonstrate that the array directivity and array gain achieved by our model exhibit a strong agreement with the theoretical values. Overall, it shows the advantage of enhanced precision over the existing models, and a reduced requirement for measurement and the computation of the excitation coefficients.

The objective of this study is to address the mobility challenges faced by user equipment (UE) through the implementation of fluid antenna (FA) on the UE side. This approach aims to maintain the time-varying channel in a relatively stable state by strategically relocating the FA to an appropriate port. To the best of our knowledge, this paper introduces, for the first time, the application of large language models (LLMs) in the prediction of FA ports, presenting a novel model termed Port-LLM. Our proposed method for predicting the moving port of the FA is a two-step prediction method. To enhance the learning efficacy of our proposed Port-LLM model, we integrate low-rank adaptation (LoRA) fine-tuning technology. Additionally, to further exploit the natural language processing capabilities of pre-trained LLMs, we propose a framework named Prompt-Port-LLM, which is constructed upon the Port-LLM architecture and incorporates prompt fine-tuning techniques along with a specialized prompt encoder module. The simulation results show that our proposed models all exhibit strong generalization ability and robustness under different numbers of base station antennas and medium-to-high mobility speeds of UE. In comparison to existing methods, the performance of the port predicted by our models demonstrates superior efficacy. Moreover, both of our proposed models achieve millimeter-level inference speed.

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction methods, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. Our analysis shows that for a multi-path channel with a strong line-of-sight (LoS) path, the prediction error of our proposed MPMP method nearly converges to zero, as the number of BS antennas and the port density of the FA are large enough. For a general multi-path channel, we also derive the upper and lower bounds of the prediction error when the number of paths is large enough. When the UEs move at a speed of 60 or 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction methods.

Traditional multiple-input multiple-output (MIMO) systems exhibit power gains that are proportional to the number of antennas M. By contrast, a superdirective array has the potential to attain the power gain proportional to $M^{2}$, which may lead to great improvements in spectral efficiency. However, few early studies explore the superdirectivity in multi-user wireless communications. In this paper, we conduct a detailed study on the topic. Firstly, we extend the superdirective precoding from single-user scenarios to multi-user multipath scenarios. Utilizing the Legendre polynomials basis, we prove that the scaling laws of both the power gain and the signal-to-interference-plus-noise ratio (SINR) are between $\mathcal{O}(M)$ and $\mathcal{O}\left(M^{2}\right)$, where $\mathcal{O}\left(M^{2}\right)$ is achieved at the end-fire direction. We reveal that, for a fixed-aperture compact antenna array without considering ohmic loss and impedance mismatch loss, the power gain keeps growing with the increasing number of antennas. Furthermore, we propose a Multi-user Interference-Nulling Superdirective (MINS) precoding scheme to maximize user power gains while eliminating interference. Simulation results verify the proposed power scaling law and show significant improvements in spectral efficiency using our methods compared to the traditional MIMO.

Reconfigurable intelligent surface (RIS) is a promising technology that has the potential to change the way we interact with the wireless propagating environment. In this paper, we propose a practical two-stage spatial-oversampling codebook algorithm for the beamforming of RIS, which is based on the spatial structure of the wireless channel. This algorithm has significantly lower complexity compared to the two-dimensional full-space searching-based codebook, yet with only negligible performance loss in the experiment. Then, a series of experiments are conducted with the fabricated RIS systems, covering office, corridor, and outdoor environments, to verify the effectiveness of RIS in both laboratory and current fifth generation (5G) commercial networks. In the office and corridor scenarios, the 5.8 GHz RIS provided a power gain of $\mathbf{1 0 - 2 0 ~ d B}$ at the receiver. In commercial 5G networks, the 2.6 GHz RIS improved indoor signal strength by 4-7 dB.

The array gain of a superdirective antenna array can be proportional to the square of the number of antennas, which is much larger than the traditional array. However, the realization of the so-called superdirectivity entails accurate calculation and application of the excitations (beamforming vector). Moreover, the excitations require a large dynamic power range, especially when the number of antennas increases and the antenna spacing decreases. In this paper, we derive the closed form solution to the superdirective beamforming vector and characterize the distribution of the excitation power range for the superdirective array. We prove that as the antenna spacing tends to 0, the amplitude range of the superdirective excitations for an M-antenna array can be expressed as a list of binomial coefficients of order M-1. Moreover, to alleviate the high power range requirement, two beamforming methods are proposed to obtain the beamforming vector under a certain excitation range constraint based on Particle Swarm Optimization and convex approximation, respectively. Full-wave electromagnetic simulations validate the effectiveness of our proposed methods.

Superdirectivity has the potential to increase the array gain to the square of the number of antennas, pushing the spectral efficiency of wireless communications to a higher level. However, calculating the superdirective beamforming vector in the presence of strong coupling is a challenging task due to the lack of the coupling depiction. Another practical obstacle is the sensitivity problem—superdirective antenna arrays are susceptible to excitation errors, necessitating precise excitation controls. To address these problems, we first introduce the embedded element pattern (EEP), which describes the coupled radiation field. We propose an EEP-based beamforming (EEPB) method to achieve superdirectivity. To mitigate the sensitivity problem, we propose an EEP-aided orthogonal complement-based robust beamforming (EEP-OCRB) algorithm for computing a robust superdirective beamforming vector. Full-wave simulations and real-world experiments utilizing a prototype of a 5-dipole superdirective antenna array validate both the superdirectivity of the EEPB method and the robustness of the EEP-OCRB algorithm to excitation errors.

This paper addresses the mobility problem in extremely large antenna array (ELAA) communication systems. In order to account for the performance loss caused by the spherical wavefront of ELAA in the mobility scenarios, we propose a wavefront transformation-based matrix pencil (WTMP) channel prediction method. In particular, we design a matrix to transform the spherical wavefront into a new wavefront, which is closer to the plane wave. We also design a time-frequency projection matrix to capture the time-varying path delay due to user movement. Furthermore, we adopt the matrix pencil (MP) method to estimate channel parameters. Our proposed WTMP method can mitigate the effect of near-field radiation when predicting future channels. For an ELAA communication system in the mobility scenarios, we prove that the prediction error converges to zero with the increasing number of base station antennas. Simulation results demonstrate that our designed transform matrix efficiently mitigates the near-field effect, and that our proposed WTMP method can overcome the ELAA mobility challenge and approach the performance in stationary settings.

Most research works on reconfigurable intelligent surfaces (RIS) rely on idealized models of the reflection coefficients, i.e., uniform reflection amplitude for any phase and sufficient phase shifting capability. In practice however, such models are oversimplified. This paper introduces a realistic reflection coefficient model for RIS based on measurements. The reflection coefficients are modeled as discrete complex values that have non-uniform amplitudes and suffer from insufficient phase shift capability. We then propose a group-based query algorithm that takes the imperfect coefficients into consideration while calculating the reflection coefficients. We analyze the performance of the proposed algorithm, and derive the closed-form expressions to characterize the received power of an RIS-aided wireless communication system. The performance gains of the proposed algorithm are confirmed in simulations. Furthermore, we validate the proposed theoretical results by experiments with our fabricated RIS prototype systems. The simulation and measurement results match well with the theoretical analysis.

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communications due to its capability of optimizing the propagation environments. Nevertheless, in literature, there are few prototypes serving multiple users. In this paper, we propose a whole flow of channel estimation and beamforming design for RIS, and set up an RIS-aided multi-user system for experimental validations. Specifically, we combine a channel sparsification step with generalized approximate message passing (GAMP) algorithm, and propose to generate the measurement matrix as Rademacher distribution to obtain the channel state information (CSI). To generate the reflection coefficients with the aim of maximizing the spectral efficiency, we propose a quadratic transform-based low-rank multi-user beamforming (QTLM) algorithm. Our proposed algorithms exploit the sparsity and low-rank properties of the channel, which has the advantages of light calculation and fast convergence. Based on the universal software radio peripheral devices, we built a complete testbed working at 5.8 GHz and implemented all the proposed algorithms to verify the possibility of RIS assisting multi-user systems. Experimental results show that the system has obtained an average spectral efficiency increase of 13.48 bps/Hz, with respective received power gains of 26.6 dB and 17.5 dB for two users, compared with the case when RIS is powered-off.

The Holographic Interference Surface (HIS) provides a new paradigm for building a more cost-effective wireless communication architecture. In this paper, we derive the principles of holographic interference theory for electromagnetic wave reception and transmission, whereby the optical holography is extended to communication holography and a channel sensing architecture for holographic interference surfaces is established. Unlike the traditional pilot-based channel estimation approaches, the proposed architecture circumvents complicated processes like filtering, analog to digital conversion (ADC), down conversion. Instead, it relies on interfering the object waves with a pre-designed reference wave, and therefore reduces the hardware complexity and requires less time-frequency resources for channel estimation. To address the self-interference problem in the holographic recording process, we propose a phase shifting-based interference suppression (PSIS) method according to the structural characteristics of communication hologram and interference composition. We then propose a Prony-based multi-user channel segmentation (PMCS) algorithm to acquire the channel state information (CSI). Our theoretical analysis shows that the estimation error of the PMCS algorithm converges to zero when the number of HIS units is large enough. Simulation results show that under the holographic architecture, our proposed algorithm can accurately estimate the CSI in multi-user scenarios.

This paper addresses the mobility problem in extremely large antenna array (ELAA) communication systems. In order to account for the performance loss caused by the spherical wavefront of ELAA in the mobility scenario, we propose a wavefront transformation-based matrix pencil (WTMP) channel prediction method. In particular, we design a matrix to transform the spherical wavefront into a new wavefront, which is closer to the plane wave. We also design a time-frequency projection matrix to capture the time-varying path delay. Furthermore, we adopt the matrix pencil (MP) method to estimate channel parameters. Our proposed WTMP method can mitigate the effect of near-field radiation when predicting future channels. Theoretical analysis shows that the designed matrix is asymptotically determined by the angles and distance between the base station (BS) antenna array and the scatterers or the user when the number of BS antennas is large enough. For an ELAA communication system in the mobility scenario, we prove that the prediction error converges to zero with the increasing number of BS antennas. Simulation results demonstrate that our designed transform matrix efficiently mitigates the near-field effect, and that our proposed WTMP method can overcome the ELAA mobility challenge and approach the performance in stationary setting.

This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-time-domain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, which has not been considered so far in the literature. We prove theoretically that in the scenario with time-varying path delays, the prediction error converges to zero with the increasing number of the base station (BS) antennas, providing that only two arbitrary channel samples are known. We also derive a lower-bound of the number of the BS antennas to achieve a satisfactory performance. Simulation results under the industrial channel model of 3GPP demonstrate that our proposed MDMP method approaches the performance of the stationary scenario even when the users’ velocity reaches 120 km/h and the latency of the channel state information is as large as 16 ms.

Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLF) is proposed to reduce the amount of feedback and improve the spectral efficiency of FDD massive MIMO. In most traditional manifold learning approaches, the newly sampled data has to be combined with the existing dataset and the training process has to be done all over again, making it complex to process the incremental CSI in a wireless communication system. Also, the number of component functions required for reconstruction is proportional to the dimension of channel matrix, which limits their practicality in wideband systems. In this paper, we solve the incremental problem by introducing two groups of dictionaries. The key idea of our MLF framework is to learn these dictionaries to represent the manifold structure of CSI data. The incremental CSI is reconstructed by preserving the local manifold structure, i.e., sharing the same neighbor and coding relationships in the input space and the feature space. Experimental results under an industrial channel model of 3GPP show that the proposed algorithm outperforms existing algorithms based on compressive sensing and deep learning in terms of CSI reconstruction performance.

In most multiple-input multiple-output (MIMO) communication systems, e.g., Massive MIMO, the antenna spacing is generally no less than half a wavelength. It helps to reduce the mutual coupling and therefore facilitate the system design. The maximum array gain is the number of antennas in this settings. However, when the antenna spacing is made very small, the array gain of a compact array can be proportional to the square of the number of antennas - a value much larger than the traditional array. To achieve this so-called "superdirectivity" however, the calculation of the excitation coefficients (beamforming vector) is known to be a challenging problem. In this paper, we derive the beamforming vector of superdirective arrays based on a novel coupling matrix-enabled method. We also propose an approach to obtain the coupling matrix, which is derived by the spherical wave expansion method and active element pattern. The full-wave electromagnetic simulations are conducted to validate the effectiveness of our proposed method. Simulation results show that when the beamforming vector obtained by our method is applied, the directivity of the designed dipole antenna array has a good agreement with the theoretical values.

This paper addresses the challenge of mobility in massive multiple-input multiple-output (MIMO) communication systems. In the deployment of 5G, this problem leads to alarmingly high performance degradation. In this paper, we propose a novel multi-dimension Matrix Pencil (MDMP) channel prediction method in order to tackle this practical challenge. More specifically, our method calculates accurate estimations of path angles, delays and Doppler simultaneously. In order to do so, we exploit the angular-frequency-domain and angular-time-domain structures of the wideband channel, and propose a path pairing procedure by exploiting the super-resolution property of the estimated angles. Our method is able to deal with the realistic constraint of time-variant path delays introduced by user movements, which have not been considered so far in literature. We prove that with only two arbitrary channel samples given, that prediction error converges to zero in the scenario with time-variant delay and arbitrary delay of channel state information (CSI), if the number of base station (BS) antennas is large enough. Unlike the existing Prony-based angular-delay domain (PAD) prediction method that assumes the CSI delay is an integral multiple of the pilot interval, our MDMP method breaks such a limitation and is therefore more general. Simulation results under the clustered delay line (CDL) model of 3GPP demonstrate that in the high-mobility scenario with time-variant path delays and a large CSI delay of 16 ms, our proposed MDMP method can approach to the performance of the stationary scenario.

The estimation of Channel State Information (CSI) is one of the most difficult tasks for massive multiple-input multiple-output (MIMO) in frequency division duplex (FDD) mode. It is even more challenging in high-mobility scenarios. In this paper, we consider an FDD massive MIMO system with high-mobility and CSI delay and aim to predict the downlink (DL) channel under a realistic multipath channel model. The key novelty lies in the fact that for the first time we devise a joint angle-delay-Doppler (JADD) channel estimation framework. The main idea of our framework is to reconstruct the DL channel with the DL channel parameters estimated from the uplink (UL) channel samples and scalar feedback coefficients. To alleviate the feedback overhead, we design a wideband beamformer for the base station (BS) based on the DL angle-delay-Doppler parameters. The user equipment (UE) then estimates the DL channel parameters and feeds back some Doppler-related scalar coefficients back to the BS. We show that the feedback and DL pilot training overhead are independent of the number of BS antennas. The lower bound performance of our framework is also derived. Numerical results under the industrial channel model in rich scattering environments demonstrate that our framework works well from medium mobility scenario of 30 km/h to high mobility settings of 350 km/h.

In massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, a challenging problem is how to predict channel state information (CSI) (i.e., channel prediction) accurately in mobility scenarios. However, a practical obstacle is caused by CSI non-stationary and nonlinear dynamics in temporal domain. In this paper, we propose a spatio-temporal neural network (STNN) to achieve better performance by carefully taking into account the spatio-temporal characteristics of CSI. Specifically, STNN uses its encoder and decoder modules to capture the spatial correlation and temporal dependence of CSI. Further, the differencing-attention module is designed to deal with the non-stationary and nonlinear temporal dynamics and realize adaptive feature refinement for more accurate multi-step prediction. Additionally, an advanced training scheme is adopted to reduce the discrepancy between STNN training and testing. Evaluated on a realistic channel model with enhanced mobility and spherical waves, experimental results show that STNN can effectively improve the accuracy of prediction and perform well with respect to different signal to noise ratios (SNRs). Visualization and testing for unit root illustrate STNN is able to learn CSI time-varying patterns by alleviating series non-stationarity.

Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the “curse of mobility”. The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD) wideband precoder is proposed for channel training. Our idea consists in the exploitation of the partial channel reciprocity of FDD and the angle-delay-Doppler channel structure. More precisely, the base station (BS) estimates the angle-delay-Doppler information of the UL channel based on UL pilots using Matrix Pencil (MP) method. It then computes the wideband JADD precoders according to the extracted parameters. Afterwards, the user estimates and feeds back some scalar coefficients for the BS to reconstruct the predicted DL channel. Asymptotic analysis shows that the CSI prediction error converges to zero when the number of BS antennas and the bandwidth increases. Numerical results with industrial channel model demonstrate that our framework can well adapt to high speed (350 km/h), large CSI delay (10 ms) and channel sample noise.

Wireless communications are increasingly vulnerable to simultaneous jamming and eavesdropping attacks due to the inherent broadcast nature of wireless channels. With this focus, due to the potential of reconfigurable intelligent surface (RIS) in substantially saving power consumption and boosting information security, this paper is the first work to investigate the effect of the RIS-assisted wireless transmitter in improving both the spectrum efficiency and the security of multi-user cellular network. Specifically, with the imperfect angular channel state information (CSI), we aim to address the worst-case sum rate maximization problem by jointly designing the receive decoder at the users, both the digital precoder and the artificial noise (AN) at the base station (BS), and the analog precoder at the RIS, while meeting the minimum achievable rate constraint, the maximum wiretap rate requirement, and the maximum power constraint. To address the non-convexity of the formulated problem, we first propose an alternative optimization (AO) method to obtain an efficient solution. In particular, a heuristic scheme is proposed to convert the imperfect angular CSI into a robust one and facilitate the developing a closed-form solution to the receive decoder. Then, after reformulating the original problem into a tractable one by exploiting the majorization-minimization (MM) method, the digital precoder and AN can be addressed by the quadratically constrained quadratic programming (QCQP), and the RIS-aided analog precoder is solved by the proposed price mechanism-based Riemannian manifold optimization (RMO). To further reduce the computational complexity of the proposed AO method and gain more insights, we develop a low-complexity monotonic optimization algorithm combined with the dual method (MO-dual) to identify the closed-form solution. Numerical simulations using realistic RIS and communication models demonstrate the superiority and validity of our proposed schemes over the existing benchmark schemes.

The acquisition of channel state information (CSI) in Frequency Division Duplex (FDD) massive MIMO has been a formidable challenge. In this paper, we address this problem with a novel CSI feedback framework enabled by the partial reciprocity of uplink and downlink channels in the wideband regime. We first derive the closed-form expression of the rank of the wideband massive MIMO channel covariance matrix for a given angle-delay distribution. A low-rankness property is identified, which generalizes the well-known result of the narrow-band uniform linear array setting. Then we propose a partial channel reciprocity (PCR) codebook, inspired by the low-rankness behavior and the fact that the uplink and downlink channels have similar angle-delay distributions. Compared to the latest codebook in 5G, the proposed PCR codebook scheme achieves higher performance, lower complexity at the user side, and requires less feedback. We derive the feedback overhead necessary to achieve asymptotically error-free CSI feedback. Two low-complexity alternatives are also proposed to further reduce the complexity at the base station side. Simulations with the practical 3GPP channel model show the significant gains over the latest 5G codebook, which prove that our proposed methods are practical solutions for 5G and beyond.

Reconfigurable intelligent surfaces (RISs) have recently received widespread attention in the field of wireless communication. An RIS can be controlled to reflect incident waves from the transmitter towards the receiver; a feature that is believed to fundamentally contribute to beyond 5G wireless technology. The typical RIS consists of entirely passive elements, which requires the high-dimensional channel estimation to be done elsewhere. Therefore, in this paper, we present a semipassive large-scale RIS architecture equipped with only a small fraction of simplified receiver units with only 1-bit quantization. Based on this architecture, we first propose an alternating direction method of multipliers (ADMM)-based approach to recover the training signals at the passive RIS elements, We then obtain the global channel by combining a channel sparsification step with the generalized approximate message passing (GAMP) algorithm. Our proposed scheme exploits both the sparsity and lowrankness properties of the channel in the joint spatial-frequency domain of a wideband mmWave multiple-input-multiple-output (MIMO) communication system. Simulation results show that the proposed algorithm can significantly reduce the pilot signaling needed for accurate channel estimation and outperform previous methods, even with fewer receiver units.

The prospects of using a Reconfigurable Intelligent Surface (RIS) to aid wireless communication systems have recently received much attention from academia and industry. Most papers make theoretical studies based on elementary models, while the prototyping of RIS-aided wireless communication and real-world field trials are scarce. In this paper, we describe a new RIS prototype consisting of 1100 controllable elements working at 5.8 GHz band. We propose an efficient algorithm for configuring the RIS over the air by exploiting the geometrical array properties and a practical receiver-RIS feedback link. In our indoor test, where the transmitter and receiver are separated by a 30 cm thick concrete wall, our RIS prototype provides a 26 dB power gain compared to the baseline case where the RIS is replaced by a copper plate. A 27 dB power gain was observed in the short-distance outdoor measurement. We also carried out long-distance measurements and successfully transmitted a 32 Mbps data stream over 500 m. A 1080p video was live-streamed and it only played smoothly when the RIS was utilized. The power consumption of the RIS is around 1 W. Our paper is vivid proof that the RIS is a very promising technology for future wireless communications.

Massive MIMO is a key technology for 5th generation (5G) mobile communications. The large excess of base station (BS) antennas brings unprecedented spectral efficiency. However, during the initial phase of industrial testing, a practical challenge arises which undermines the actual deployment of massive MIMO and is related to mobility. In fact, testing teams reported that in moderate-mobility scenarios, e.g., 30 km/h of UE speed, the performance may drop 50% compared to the low-mobility scenario, a problem not foreseen by theoretical papers on the subject. In order to deal with this challenge, we propose a Prony-based angular-delay domain (PAD) prediction method, which is built on exploiting the angle-delay-Doppler structure of the multipath. Our theoretical analysis shows that when the number of base station antennas and the bandwidth are large, the prediction error of our PAD algorithm converges to zero for any UE velocity level, provided that only two accurate enough previous channel samples are available. Simulation results show that under the realistic channel model of 3GPP in rich scattering environment, our proposed method even approaches the performance of stationary scenarios where the channels do not vary at all.

Massive MIMO is widely touted as an enabling technology for 5th generation (5G) mobile communications and beyond. On paper, the large excess of base station (BS) antennas promises unprecedented spectral efficiency gains. Unfortunately, during the initial phase of industrial testing, a practical challenge arose which threatens to undermine the actual deployment of massive MIMO: user mobility-induced channel Doppler. In fact, testing teams reported that in moderate-mobility scenarios, e.g., 30 km/h of user equipment (UE) speed, the performance drops up to 50% compared to the low-mobility scenario, a problem rooted in the acute sensitivity of massive MIMO to this channel Doppler, and not foreseen by many theoretical papers on the subject. In order to deal with this “curse of mobility”, we propose a novel form of channel prediction method, named Prony-based angular-delay domain (PAD) prediction, which is built on exploiting the specific angle-delay-Doppler structure of the multipath. In particular, our method relies on the high angular-delay resolution which arises in the context of 5G. Our theoretical analysis shows that when the number of base station antennas and the bandwidth are large, the prediction error of our PAD algorithm converges to zero for any UE velocity level, provided that only two accurate enough previous channel samples are available. Moreover, when the channel samples are inaccurate, we propose to combine the PAD algorithm with a denoising method for channel estimation phase based on the subspace structure and the long-term statistics of the channel observations. Simulation results show that under a realistic channel model of 3GPP in rich scattering environment, our proposed method is able to overcome this challenge and even approaches the performance of stationary scenarios where the channels do not vary at all.

Channel state information (CSI) feedback is a challenging issue in frequency division duplexing (FDD) massive MIMO systems. This paper studies a cooperative feedback scheme, where the users first exchange their CSI with each other through device-to-device (D2D) communications, then compute the precoder by themselves, and feedback the precoder to the base station (BS). Analytical results are derived to show that the cooperative precoder feedback is more efficient than the CSI feedback in terms of interference mitigation. To reduce the delays for CSI exchange, we develop an adaptive CSI exchange strategy based on signal subspace projection and optimal bit partition. Numerical results demonstrate that the proposed cooperative precoder feedback scheme with adaptive CSI exchange significantly outperforms the CSI feedback scheme, even under moderate delays for CSI exchange via D2D.

This paper considers the problem of efficient feedback design for massive multiple-input multiple-output (MIMO) downlink transmissions in frequency division duplexing (FDD) bands, where some partial channel state information (CSI) can be directly exchanged between users via device-to-device (D2D) communications. Drawing inspiration from classical point-to-point MIMO, where efficient mechanisms are obtained by feeding back directly the precoder, this paper proposes a new approach to bridge the channel feedback and the precoder feedback by the joint design of the feedback and precoding strategy following a team decision framework. Specifically, the users and the base station (BS) minimize a common mean squared error (MSE) metric based on their individual observations on the imperfect global CSI. The solutions are found to take similar forms as the regularized zero-forcing (RZF) precoder, with additional regularizations that capture any level of uncertainty in the exchanged CSI, in case the D2D links are absent or unreliable. Numerical results demonstrate superior performance of the proposed scheme for an arbitrary D2D link quality setup.

In this paper we propose a novel robust channel estimation algorithm exploiting path diversity in both angle and power domains, relying on a suitable combination of the spatial filtering and amplitude based projection. The proposed approach is able to cope with a wide range of system and topology scenarios, including those where interference channel may overlap with desired channels in terms of multipath angles of arrival (AoA) or exceed them in terms of received power. We establish the analytical conditions under which the proposed channel estimator is fully decontaminated.

This paper considers the problem of interference control in networks where base stations signals are coherently combined (aka network MIMO). Building on an analogy with so-called massive MIMO, we show how second-order statistical properties of channels can be exploited when the massive MIMO array corresponds in fact to many antennas randomly spread over a two-dimensional network. Based on the classical one-ring model, we characterize the low-rankness of channel covariance matrices and show the rank is related to the scattering radius. The application of the low-rankness property to channel estimation's denoising and low complexity interference filtering is highlighted.

Pilot contamination is known to severely limit the performance of large-scale antenna (“massive MIMO”) systems due to degraded channel estimation. This paper proposes a twofold approach to this problem. First we show analytically that pilot contamination can be made to vanish asymptotically in the number of antennas for a certain class of channel fading statistics. The key lies in setting a suitable condition on the second order statistics for desired and interference signals. Second we show how a coordinated user-to-pilot assignment method can be devised to help fulfill this condition in practical networks. Large gains are illustrated in our simulations for even small antenna array sizes.

This paper addresses the problem of channel estimation in multi-cell interference-limited cellular networks. We consider systems employing multiple antennas and are interested in both the finite and large-scale antenna number regimes (so-called "massive MIMO"). Such systems deal with the multi-cell interference by way of per-cell beamforming applied at each base station. Channel estimation in such networks, which is known to be hampered by the pilot contamination effect, constitutes a major bottleneck for overall performance. We present a novel approach which tackles this problem by enabling a low-rate coordination between cells during the channel estimation phase itself. The coordination makes use of the additional second-order statistical information about the user channels, which are shown to offer a powerful way of discriminating across interfering users with even strongly correlated pilot sequences. Importantly, we demonstrate analytically that in the large-number-of-antennas regime, the pilot contamination effect is made to vanish completely under certain conditions on the channel covariance. Gains over the conventional channel estimation framework are confirmed by our simulations for even small antenna array sizes.