Multi-access point coordination (MAPC) is a key technology for enhancing throughput in next-generation Wi-Fi within dense overlapping basic service sets. However, existing MAPC protocols rely on static, protocol-defined rules, which limits their ability to adapt to dynamic network conditions such as varying interference levels and topologies. To address this limitation, we propose a novel Agentic AI Wi-Fi framework where each access point, modeled as an autonomous large language model agent, collaboratively reasons about the network state and negotiates adaptive coordination strategies in real time. This dynamic collaboration is achieved through a cognitive workflow that enables the agents to engage in natural language dialogue, leveraging integrated memory, reflection, and tool use to ground their decisions in past experience and environmental feedback. Comprehensive simulation results demonstrate that our agentic framework successfully learns to adapt to diverse and dynamic network environments, significantly outperforming the state-of-the-art spatial reuse baseline and validating its potential as a robust and intelligent solution for future wireless networks.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for enhancing communication systems. This paper investigates a novel flexible-position RIS-assisted communication system, where the RIS is mounted on a slide rail, enabling spatial adaptability. Unlike traditional fixed-position RISs, the proposed system leverages both spatial flexibility and phase reconfigurability to optimize system performance while reducing overhead through strategic position adjustment. To characterize the spatial variations introduced by RIS movement, we propose a generalized RIS channel model that integrates a practical visibility region function with near-field spherical wave propagation. This model captures the spatial correlation characteristics influenced by multipath angular spread, scatterer distribution, and RIS positioning. Furthermore, we introduce a measurement scheme using a multi-state RIS hardware to analyze segmented channels across fixed-position and flexible-position scenarios. Our measurement reveals that the intra-cluster power angular spectrum follows a Gaussian distribution, and in strong scattering environments, spatial correlation exhibits an enhanced degree of freedom due to spatial non-stationarity effects. In particular, the experimental results demonstrate that the gain of the received power varies from 0.4 dB to 5.3 dB across different RIS positions, providing empirical evidence that spatial adaptability of RIS effectively resists channel non-stationarity. These findings highlight the potential of flexible-position RIS to enhance future wireless communication systems.

The mid-band frequency range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is emerging as a key enabler for future communication systems. By exploiting the advent of new spectrum resources and degrees of freedom brought by the near-field propagation, the mid-band XL-MIMO system is expected to significantly enhance throughput and inherently support advanced functionalities such as integrated sensing and communication. Although theoretical studies have highlighted the benefits of mid-band XL-MIMO systems, the promised performance gains have yet to be validated in practical systems, posing a major challenge to the standardization. In this paper, preliminaries including frame structure, channel modeling, and signal models are first discussed, followed by an analysis of key challenges in constructing a real-time prototype system. Subsequently, the design and implementation of a real-time mid-band XL-MIMO prototype system are presented. Underpinned by a novel architecture, the proposed prototype system supports specifications aligned with standardization, including a bandwidth of 200 MHz, up to 1024 antenna elements, and up to 256 transceiver chains. Operating in time-division duplexing mode, the prototype enables multiuser communication for up to 12 users, while retaining standard communication procedures. Built on hybrid software-defined radio and field programmable gate array platforms, the prototype is programmable and allows for flexible deployment of advanced algorithms. Moreover, the modular architecture ensures high scalability, making the prototype adaptable to various configurations, including distributed deployments and decentralized signal processing. Experimental results demonstrate that the prototype handles real-time digital sample processing at 1453.33 Gbps and achieves a peak data throughput of 15.81 Gbps for 12 users.

Integrated sensing and communication (ISAC) is a key feature of future cellular systems, enabling applications such as intruder detection, monitoring, and tracking using the same infrastructure. However, its potential for structural health monitoring (SHM), which requires the detection of slow and subtle structural changes, remains largely unexplored due to challenges such as multipath interference and the need for ultra-high sensing precision. This study introduces a novel theoretical framework for SHM via ISAC by leveraging reconfigurable intelligent surfaces (RIS) as reference points in collaboration with base stations and users. By dynamically adjusting RIS phases to generate distinct radio signals that suppress background multipath interference, measurement accuracy at these reference points is enhanced. We theoretically analyze RIS-aided collaborative sensing in three-dimensional cellular networks using Fisher information theory, demonstrating how increasing observation time, incorporating additional receivers (even with self-positioning errors), optimizing RIS phases, and refining collaborative node selection can reduce the position error bound to meet SHM’s stringent accuracy requirements. Furthermore, we develop a Bayesian inference model to identify structural states and validate damage detection probabilities. Both theoretical and numerical analyses confirm ISAC’s capability for millimeter-level deformation detection, highlighting its potential for high-precision SHM applications.

Reconfigurable intelligent surface (RIS) technology has gained widespread recognition for its ability to significantly enhance communication performance between base stations (BS) and users in blind spot areas. This paper investigates the energy efficiency (EE) of an RIS-assisted multi-cell communication system, incorporating a measurement-based empirical RIS power consumption model. Exploiting only statistical channel state information (CSI), we aim to maximize the overall EE of the system. An alternating optimization (AO) algorithm is proposed for joint optimization of the transmit beamforming vectors at the BSs and the RIS phase shift matrix. To circumvent the challenges arising from the discrete relationship between the power consumption of positive-intrinsic-negative (PIN) diode-based reflecting elements and their phase shifts, we introduce a continuous function to approximate the relationship. With this approximation, the complex circle manifold (CCM) technique is applied to optimize the continuous RIS phase shifts, which are then quantized within a discrete set. Then, to further reduce the complexity of the AO algorithm, a sub-optimal algorithm is proposed. In this algorithm, the statistical maximum ratio transmission beamforming is employed at each BS, which decouples the design of BS beamforming vectors from the design of BS transmit power and RIS phase shifts. Given the BS beamforming vectors, the BS transmit power and RIS phase shifts are then designed using the quadratic transformation and CCM techniques. Simulation results show that the proposed algorithm achieves near-optimal EE while offering guidance on PIN diode encoding to balance phase resolution and power consumption in practical RIS design.

Since the beam squint and near-field effects both inherently exist in upper-6 GHz (U6G) extremely large-scale multiple-input multiple-output (XL-MIMO) systems, wideband near-field channel estimation faces severe challenges, such as higher computational complexity, and higher pilot overhead particularly at hybrid architectures with fewer radio frequency (RF) chains. To precisely reduce the complexity and number of pilots, theparametric symmetry of wideband near-field channelsis explored, such that the channel parameters, including angle, distance, and range, can be decoupled based on the delay variations observed by different antennas. Based on this, adistributed parametric symmetry-based (DPS) algorithm, applicable to U6G XL-MIMO, is proposed. The delays observed by different subarrays are estimated and extrapolated across the local processing units (LPUs) firstly, and then, the channel parameters are decoupled and estimated at the central processing unit (CPU), by only linearly combining the delays from different LPUs. The path gains are calculated at different LPUs, respectively, to reconstruct the channel with low complexity. Since the proposed algorithm does not rely on scanning the polar-domain dictionary, onlya single pilotis required even with hybrid architectures. Furthermore, the computational complexity, multiple-path resolution, Cramér–Rao lower bound (CRLB) and lower bound (LB) of the estimates in hybrid architectures and the DPS algorithm, respectively, are analyzed, to evaluate the realizable potential of the proposed algorithm. The simulation results prove that the proposed algorithm has a higher estimation accuracy, while requiring less complexity and pilots.

Mid-band extra-large-scale multiple-input multiple-output (XL-MIMO), emerging as a critical enabler for future communication systems, is expected to deliver significantly higher throughput by leveraging the extended bandwidth and enlarged antenna aperture. However, power consumption remains a significant concern due to the expanded system dimension, underscoring the need for thorough investigations into efficient system design and deployment. To this end, an in-depth study is conducted on mid-band XL-MIMO systems. Specifically, a comprehensive power consumption model is proposed, encompassing the power consumption of major hardware components and signal processing procedures, while capturing the influence of key system parameters. Considering typical near-field propagation characteristics, closed-form approximations of throughput are derived, providing an analytical framework for assessing energy efficiency (EE). Based on the proposed framework, the scaling law of EE with respect to key system configurations is derived, offering valuable insights for system design. Subsequently, extensions and comparisons are conducted among representative multi-antenna technologies, demonstrating the superiority of mid-band XL-MIMO in EE. Extensive numerical results not only verify the tightness of the throughput analysis but also validate the EE evaluations, unveiling the potential of energy-efficient mid-band XL-MIMO systems.

Mid-band extra large-scale multiple-input multiple-output (XL-MIMO) systems are considered a key enabler for future wireless communications, offering enhanced throughput and extended coverage. Combined with subarray-based architecture and distributed signal processing, the computational complexity and implementation overhead are reduced. However, uncertain channel characteristics associated with the novel frequency band present significant bottlenecks, hindering the development of hardware architecture and algorithm design. Meanwhile, channel characteristics across distributed processing units remain insufficiently explored. In response, a mid-band channel sounder is constructed, and extensive measurement campaigns are carried out across various typical scenarios. Initially, mid-band channel characteristics are unveiled and compared across different scenarios. Subsequently, the mid-band XL-MIMO channel characteristics are analyzed using a virtual array comprising 256 array antennas and 64 transceiver chains. Moreover, motivated by the potential of distributed processing, mid-band XL-MIMO channel characteristics are particularly investigated from the perspectives of subarrays and sub-bands, encompassing subarray-wise non-stationarities, consistencies, far-field approximations, and sub-band characteristics. Through the combination of analysis and measurement validation, several insights and benefits are revealed, particularly relevant to distributed architecture and processing, which provides practical guidance for the real-world deployment of mid-band XL-MIMO systems.

The increasing demand for services such as live streaming and virtual reality places significant pressure on wireless communication systems. Enhancing system performance or reducing bandwidth consumption is critical for delivering high-quality video experiences. Semantic communication, which focuses on the transmission of meaning, offers a promising solution. However, existing approaches are often limited to single scenarios, rely on simple channels, lack adaptability to dynamic wireless environments, and remain untested in practical air interfaces. To address these challenges, we propose a foundation model-aided universal video semantic communication framework designed for pixel-wise reconstruction across diverse scenarios. This framework enables the transmission of entire videos using joint source-channel coding (JSCC) based on optical flow estimation and leverages multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) for efficient semantic delivery in 3rd generation partnership project (3GPP) standard channels. In scenarios requiring full transmission for regions of interest and selective transmission for other areas, the framework employs a foundation model for segmentation, followed by JSCC and delivery. Furthermore, we introduce a channel condition number-adaptive semantic remapping method based on an attention mechanism to mitigate the effects of wireless fading. To validate our approach, we implement the framework on a testbed and develop two online demonstrations. Simulations and over-the-air experiments confirm significant improvements in video quality and substantial reductions in bandwidth overhead compared to existing methods.

To reduce channel acquisition overhead, spatial, time, and frequency-domain channel extrapolation techniques have been widely studied. In this paper, we propose a novel deep learning-based Position-domain Channel Extrapolation framework (named PCEnet) for cell-free massive multiple-input multiple-output (MIMO) systems. The user’s position, which contains significant channel characteristic information, can greatly enhance the efficiency of channel acquisition. In cell-free massive MIMO, while the propagation environments between different base stations and a specific user vary and their respective channels are uncorrelated, the user’s position remains constant and unique across all channels. Building on this, the proposed PCEnet framework leverages the position as a bridge between channels to establish a mapping between the characteristics of different channels, thereby using one acquired channel to assist in the estimation and feedback of others. Specifically, this approach first utilizes neural networks (NNs) to infer the user’s position from the obtained channel. The estimated position, shared among BSs through a central processing unit (CPU), is then fed into an NN to design pilot symbols and concatenated with the feedback information to the channel reconstruction NN to reconstruct other channels, thereby significantly enhancing channel acquisition performance. Additionally, we propose a simplified strategy where only the estimated position is used in the reconstruction process without modifying the pilot design, thereby reducing latency. Furthermore, we introduce a position label-free approach that infers the relative user position instead of the absolute position, eliminating the need for ground truth position labels during the localization NN training. Simulation results demonstrate that the proposed PCEnet framework reduces pilot and feedback overheads by up to 50%.

With the capability to configure wireless propagation environment, reconfigurable intelligent surface (RIS) has attracted wide attention from both academia and industry, in which measurement campaign of RIS in commercial networks is of great importance for performance evaluation. However, existing RIS configuration schemes in practical environments are generally angle-based which require accurate angle information and mainly consider single incident beam, or statistical methods with high sampling overhead. In this paper, we propose a phase shift design scheme for RIS referred to as multiple incident beam superposition (MIBS) scheme, which can be applied in scenarios with incident signals on the RIS from multiple directions. Instead of utilizing random sampling, the proposed scheme firstly employs an incident beam scanning process to extract the incident beam information by pre-calculated codebook to reduce sampling cost, and requires no complex channel estimation or prior channel information. Then the proposed scheme concentrates the incident beams toward the desired reflection direction through a phase shift superposition algorithm. Numerical simulations verify the excellent performance and a 90% sample reduction of the proposed scheme compared with existing methods under the condition of 1-bit RIS hardware for practical applications. Furthermore, a measurement campaign in 5G commercial networks is conducted to validate the advantages of the proposed MIBS scheme in optimizing crucial signal metrics, yielding a 6.76 dB RSRP gain, a 4.9 dB SINR gain and a 37% throughput improvement, showing great potential of RIS for coverage enhancement.

The superimposed pilot transmission scheme offers substantial potential for improving spectral efficiency in MIMO-OFDM systems, but it presents significant challenges for receiver design due to pilot contamination and data interference. To address these issues, we propose an advanced iterative receiver based on joint channel estimation, signal detection, and decoding, which refines the receiver outputs through iterative feedback. The proposed receiver incorporates two adaptive channel estimation strategies to improve robustness against discrepancies between the time-varying channel conditions encountered during training and those experienced during testing. First, a variational message passing (VMP) method and its low-complexity variant (VMP-L) are introduced to perform inference without relying on time-domain correlation. Second, a deep learning (DL) based estimator is developed, featuring a convolutional neural network with a despreading module and an attention mechanism to extract and fuse relevant channel features. Extensive simulations under multi-stream and high-mobility scenarios demonstrate that the proposed receiver consistently outperforms conventional orthogonal pilot baselines in both throughput and block error rate. Moreover, over-the-air experiments validate the practical effectiveness of the proposed design. Among the methods, the DL based estimator achieves a favorable trade-off between performance and complexity, highlighting its suitability for real-world deployment in dynamic wireless environments.

In terahertz (THz) communication systems, extremely large scale arrays can effectively compensate for the limited communication distance problem. In this article, we consider the near-field channel estimation (CE) problem for an extremely large reconfigurable intelligence surface (XL-RIS)-assisted multi-user THz communication system. We first construct a near-field channel model based on a second-order Fresnel approximation derivation. Utilizing the spatial structure of the derived channel model, we sample the covariance matrix of the received signals. Then, we propose a tensor decomposition-based algorithm to estimate the angular parameters, and establish a truncated singular value decomposition (T-SVD) algorithm for the distance estimation. In the end, we estimate the path losses through the least squares (LS) method and recover the complete channel. Moreover, to further reduce the computational overhead, we construct a low-complexity tensor completion-based scheme for the angular parameters’ estimation. Simulation results indicate that the proposed tensor-based CE schemes outperform the conventional subspace-based approaches in terms of accuracy and computational complexity.

Reconfigurable intelligent surfaces (RISs) offer new possibilities for manipulating wireless propagation environments. However, existing studies on RIS-assisted multiple-input multiple-output (MIMO) channel modeling are limited in their ability to explicitly separate and reveal the RIS-induced effects within the cascaded channel. This paper investigates the modeling and measurement of RIS-assisted MIMO channels from a spatial decomposition perspective. We propose a novel stochastic channel modeling framework based on the Weichselberger model, which decomposes the channel into a controllable RIS-induced component and an uncontrollable scattering component. This formulation reveals the RIS’s capability to reshape the spatial coupling structure of MIMO channels and enables the analysis of eigenmode control through spatial correlation and phase configuration. To experimentally validate the model, we develop a channel measurement and separation scheme using a multi-state RIS that can switch among reflective, absorptive, and antenna states. This setup allows for direct extraction of RIS channel component and supports segmented channel measurements. Measurement results show that the RIS-induced and environmental scattering components exhibit distinct power angular spectra and coupling matrices, and their superposition accurately reconstructs the global channel behavior. Additionally, the measured RIS coupling matrix closely matches the one calculated by the theoretical model, with a correlation matrix distance of 0.0394. These results confirm the effectiveness of the proposed model and measurement strategy, offering new insights into RIS-enabled spatial channel customization for next-generation wireless systems.

Reconfigurable intelligent surface (RIS)-enabled systems have been widely considered as one of the revolutionary technologies for the new generation of communications. In particular, RIS-assisted multiple-input multiple-output (MIMO) communications have come at the forefront of research, yet there is still lack of supportive channel measurements and modeling in real environments. Against this background, this paper conducts multi-frequency and multi-scenario channel measurements and channel modeling for RIS-assisted MIMO communication systems. Utilizing a temporal autocorrelation-based channel sounder and the fabricated RISs, the virtual RIS-assisted MIMO channels are realized by successively moving the transceiver antennas on movable rotary tables. The channel realizations are collected in indoor hotspot (InH) and urban microcellular (UMi) scenarios at sub-6 GHz and millimeter-wave (mmWave) frequency bands, respectively, where various communication states, different coding schemes of the RIS, as well as with and without RIS deployment are fully considered. Based on the measured channel realizations, critical channel metrics including the signal power, effective rank, spectral efficiency (SE), root-mean-square delay-spread (RMS DS), RiceanK-factor (KF), spatial correlation, etc., are illustrated and compared under different coding schemes, communication states, deployment scenarios, and frequency bands. The measurement results indicate that the coding scheme of the RIS significantly impacts the channel performance, while the capability of RIS to customize the channel is strongly related to the power intensity it provides. Deploying an RIS with energy-focused coding can provide significant signal power gains for non-line-of-sight (NLoS) links and spatial multiplexing gains for line-of-sight (LoS) and obstructed-line-of-sight (OLoS) links, thereby greatly improving the SE. Moreover, it is found that under different communication states, such as RIS-assisted NLoS/LoS/OLoS links, the influence imposed by RIS on the channel metrics could be completely opposite, especially for the KF, RMS DS, and effective rank. In addition, after the RIS deployment with a beamforming mode, the spatial correlation in the NLoS and LoS MIMO channels significantly increases and decreases, respectively. Furthermore, it is verified that the Weichselberger model can provide a satisfactory prediction accuracy on the SE of RIS-assisted MIMO channels, while the Kronecker model underestimates it.

This paper presents channel measurement campaigns and spatial channel characterization of reconfigurable intelligent surface (RIS)-assisted millimeter-wave multiple-input multiple-output (MIMO) systems. Utilizing a channel sounder and an RIS, the RIS-assisted MIMO channels are constructed in an indoor non-line-of-sight scenario. By rotating a narrow-beam directional antenna (DA) in the azimuth angle domain, the spatial signal distributions are captured. Meanwhile, multiple comparative experiments, including: wideband vs narrowband (NB) and DA vs omnidirectional antenna, are conducted. The influence of RIS deployment and different coding schemes is considered. Based on such channel realizations, spatial channel metrics, including the power azimuth spectrum (PAS), root mean square angular spread (RMS AS), root mean square delay spread (RMS DS), spatial correlation coefficient (SCC), effective rank, and etc., are thoroughly investigated. Measurement results show that, using the DA, NB signal, and beamforming provided by the RIS contributes to a lower RMS DS, a higher SCC, and a lower effective rank. A truncated Laplacian function is used to describe the PAS, indicating prominent signal strength improvements in both the primary direction facing RIS and the opposite direction to RIS. The RMS ASs are well-fitted by a generalized extreme value distribution, which manifest a distance-dependent variation in the space domain.

Diffractive deep neural network (D2NN), also known as reconfigurable intelligent metasurface based deep neural networks (Rb-DNNs) or stacked intelligent metasurfaces (SIMs) in wireless communications, has emerged as a promising signal processing paradigm that enables computing-by-propagation. However, existing architectures are limited to implementing specific functions such as precoding and combining, while still relying on digital baseband modules for other essential tasks like modulation and detection. In this work, we propose a baseband-free end-to-end (BBF-E2E) wireless communication system where modulation, beamforming, and detection are jointly realized through the propagation of electromagnetic (EM) waves. The BBF-E2E system employs D2NNs at both the transmitter and the receiver, forming an autoencoder architecture optimized within a complex-valued neural network (CVNN) framework. The transmission coefficients of each metasurface layer are trained using the mini-batch stochastic gradient descent (SGD) to minimize the cross-entropy loss. To reduce computational complexity during diffraction calculation, the angular spectrum method (ASM) is adopted over the Rayleigh–Sommerfeld formula. Extensive simulations demonstrate that BBF-E2E achieves robust symbol transmission under various channel conditions with significantly reduced hardware requirements. In particular, the proposed system matches the performance of a conventional multi-antenna system with 81 RF chains while requiring only a single RF chain and 1024 passive elements of metasurfaces. These results highlight the potential of this wave-domain neural computing paradigm to replace digital baseband modules in future wireless transceivers.

The mid-band frequency range, combined with extra-large-scale antenna arrays (ELAA), is emerging as a critical enabler for future communication systems. However, deploying mid-band ELAA systems presents significant challenges due to the high complexity and overhead associated with signal processing tasks such as channel state information (CSI) acquisition. This paper introduces an efficient transmission framework that incorporates a distributed hardware architecture, distributed channel modeling, and a dual time-scale transmission protocol. Building upon this framework, a practical implementation is proposed, leveraging the discrete Fourier transform (DFT)-based radio frequency (RF) front-ends and linear receivers as the hardware foundation. Additionally, a novel transmission strategy is developed, exploiting both statistical and instantaneous CSI. The proposed framework includes approximations of the ergodic spectral efficiency (SE) to guide DFT beam selection based on statistical CSI. Furthermore, two user scheduling strategies are introduced, utilizing statistical CSI and location information, respectively, with angular division implemented in a distributed manner. Reduced-dimensional instantaneous CSI is then employed for both local and centralized processing. To support system design, the proposed transmission strategy’s ergodic SE performance is analyzed, focusing on the DFT RF front-end and the eigenvalue characteristics of channel correlation matrices. Numerical results reveal that the proposed framework and transmission strategy achieve SE comparable to fully-digital architectures, while significantly reducing overhead and complexity.

Reconfigurable intelligent surface (RIS) has garnered significant attention in wireless communications due to its electromagnetic wave manipulation capabilities. In order to ensure the effectiveness of various transmission designs in practical environment, the channel characteristics of RIS-assisted wireless communication systems warrant thorough investigation. While substantial research has been conducted on large-scale channel characteristics, there remains a lack of measurement-based studies on channel small-scale characteristics. This paper conducts channel measurement campaigns, analyzing delay characteristics and multi-cluster properties in multiple indoor and outdoor environments, including square, corridor, and classroom scenarios. The measurement results indicate that the RIS-assisted path (i.e. the virtual line-of-sight (VLOS) path) makes the channels exhibit two-cluster characteristics analogous to conventional line-of-sight channels. Moreover, in classroom scenario, VLOS path generates reflected echoes that degrade the channel delay characteristics. Building on these findings, this paper proposes a small-scale channel modeling framework comprising a two-cluster-based channel impulse response (CIR) model and a K-factor-based CIR model. The simulation and measurement results demonstrate satisfactory agreement, validating the effectiveness and practicality of the proposed framework. Two types of CIR models that are unified with the traditional CIR model form, along with an optional echo component, make this framework flexible and easy to use. The proposed small-scale modeling framework can effectively characterize the power delay profiles of RIS-assisted channels, providing References for future research on RIS-assisted wireless communications.

Reconfigurable intelligent surfaces (RISs) are widely recognized as a cutting-edge technology for the sixth-generation mobile communication systems (6G). In this paper, we investigate the effective channel gain and spatial multiplexing gain of multi-RIS-assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) channels. A channel model for multi-RIS-assisted MIMO system is introduced. We propose two discrete phase design methods for multi-RIS-assisted MIMO systems. The factors influencing spatial multiplexing gain, including the power difference and spatial similarity between different virtual line-of-sight (VLOS) paths, are thoroughly analyzed. Numerical simulations and measurement experiments are conducted in three scenarios, analyzing the impact of the RIS unit number as well as the positions of the RISs and antennas. The results indicate that jointly considering the position information of multiple RISs in discrete phase design leads to a higher effective channel gain, whereas relying only on the position of each single RIS exhibits instability. These findings highlight the positive role of sufficient location information and joint phase design in multi-RIS-assisted systems. Moreover, the results demonstrate that reduced power difference and spatial similarity among VLOS paths enhance the effective rank of the cascaded mmWave MIMO channel.

The low-altitude economy has emerged as a critical focus for future economic development, emphasizing the urgent need for flight activity surveillance utilizing the existing sensing capabilities of mobile cellular networks. Traditional monostatic or localization-based sensing methods, however, encounter challenges in fusing sensing results and matching channel parameters. To address these challenges, we propose an innovative approach that directly draws the radio images of the low-altitude space, leveraging its inherent sparsity with compressed sensing (CS)based algorithms and the cooperation of multiple base stations. Furthermore, recognizing that unmanned aerial vehicles (UAVs) are randomly distributed in space, we introduce a physicsembedded learning method to overcome off-grid issues inherent in CS-based models. Additionally, an online hard example mining method is incorporated into the design of the loss function, enabling the network to adaptively concentrate on the samples bearing significant discrepancy with the ground truth, thereby enhancing its ability to detect the rare UAVs within the expansive low-altitude space. Simulation results demonstrate the effectiveness of the imaging-based low-altitude surveillance approach, with the proposed physics-embedded learning algorithm significantly outperforming traditional CS-based methods under off-grid conditions.

Reconfigurable intelligent surface (RIS) has gained significant attention as an innovative solution for enhancing communication system performance. This paper proposes a novel fluid RIS-assisted communication system with spatial adaptability, where the RIS is mounted on a slide rail. By leveraging both spatial flexibility and phase reconfigurability, the proposed system overcomes the limitations of conventional fixed-position RIS. To characterize the spatial variations introduced by RIS movement, we establish a novel channel modeling framework that combines near-field spherical wave propagation with practical visibility region functions. Experimental results reveal that dynamic RIS position yields received power gain variations ranging from 0.4 dB to 5.3 dB. This empirical evidence strongly validates the capability of fluid RIS to counteract channel non-stationarity.

Reconfigurable intelligent surface (RIS) is regarded as one of the promising technologies for the sixth-generation mobile communication systems (6G) due to its ability to reshape the wireless channel and improve the communication transmission rate. In this paper, we investigate the spatial characteristic of an RIS-assisted millimeter wave (mmWave) multiple-input multipleoutput (MIMO) channel through channel measurement in practical environment. The channel measurement campaigns are carried out in an indoor hotspot (InH) non-line-of-sight (NLOS) scenario at 35 GHz. We compare and analyze the channel parameters in three propagation modes, including intelligent reflection with RIS (IRWR), specular reflection with RIS (SRWR), and without RIS (WR). Different types of receiver (RX) antenna are compared, including the horn antenna and the omni-directional antenna. Signals with bandwidths of 300 MHz and 10 MHz are transmitted to compare the spatial characteristics of wideband and narrowband signals. The measured channel frequency response (CFR) reveals that the RIS coding scheme, antenna type, and signal bandwidth are significant factors influencing the spatial correlation of the wireless channel. The measured power azimuth spectrum (PAS), fitted by truncated Laplacian functions, demonstrates that the deployment of RIS with energy-focused phase configuration results in an amplification of the power in the primary signal propagation direction. Moreover, the analysis of the root mean square (RMS) angular spread shows that it increases with the growing distance between the RX and RIS.

In the near-field region of extremely large-scale multi-input multi-output (XL-MIMO) systems, efficient channel estimation is a critical challenge, often demanding substantial computational resources. This issue becomes even more pressing in dynamic, time-varying environments where both users and scatterers are in motion, necessitating lower computational complexity for real-time channel estimation. In this paper, we propose an innovative solution for near-field XL-MIMO systems with time-varying channels, introducing a fast and accurate channel estimation and tracking scheme inspired by keypoint detection techniques from computer vision. First, we design and train a high-precision, anchor-free channel keypoint detector (CKDet) using a fine-grained orthogonal matching pursuit (OMP) framework as an effective channel estimator. Building on this, we present a novel conditional cascaded OMP-based channel tracking scheme that exploits spatial correlations between consecutive time slots to significantly reduce computational complexity. After obtaining the keypoint locations at all time slots, we apply the Hungarian algorithm to match users and scatterers across all time slots, enabling the construction of motion trajectories for use in environmental sensing applications. Experimental results validate the proposed channel tracking algorithm, showcasing its superior performance, speed, and resilience across a range of signal-to-noise ratios (SNR).

Reconfigurable intelligent surface (RIS) technology has gained wide recognition for its ability of enhancing the performance of wireless communication systems. This paper investigates the energy efficiency (EE) of a discrete phase shift RIS-assisted multi-cell communication system. Assuming that only statistical channel state information is available, we maximize the EE under a practical measured-based RIS power consumption model. Approximating the discrete measured-based RIS power consumption model by a continuous differentiable function, the problem is addressed by jointly optimizing the transmit beamforming at the base stations and the RIS phase shift matrix, by using fractional programming and complex circle manifold techniques. Simulation results demonstrate that the proposed algorithm can effectively achieve near-optimal EE performance and low RIS power consumption.

The radio frequency fingerprint identification (RFFI) is promising which exploits the hardware defects inherent to realize the zero-trust Internet of Things (IoT) security. Considering the scalability, the data imbalance and the training overhead for the current deep learning (DL) based RFFI, in this paper, we combine the large language model (LLM) and propose a BERT-LightRFFI framework, to enhance the zero-trust edge IoT security. Specifically, we pre-train a BERT model with the unlabeled data via self-supervised learning and obtain a powerful RFF feature extractor. Then, we use the knowledge distillation to inherit the BERT learn-gene to the lightweight BERT-Light model, and then fine-tune the BERT-Light model and a classifier with a few-shot labeled wireless data. In the experiments, we use a large-scale real-world LoRa dataset to evaluate the performances of the proposed framework and propose the interesting insights. The results prove the effectiveness of the proposed framework, achieving an accuracy of 97.52% with less model parameters and computation amount in the presence of multipath fading and Doppler effect, which is better than the benchmark methods.

In this paper, a wideband extremely large-scale multiple-input multiple-output (XL-MIMO) system is considered, and an efficient wideband near-field channel estimation algorithm is proposed. Due to the non-negligible near-field effect and beam squint effect in wideband XL-MIMO systems, the spatial domain sparsity of near-field channel matrix is broken, and thus traditional estimation algorithms become inapplicable. Meanwhile, the large number of antennas and wide bandwidth will lead to greatly high computational complexity. To decrease the computational complexity, the parametric symmetry of wideband XL-MIMO array is studied, and the extra freedom of beam squint effect in near-field region is utilized. Then, the parametric symmetry-based multiple-antenna joint (PSMJ) channel estimation algorithm is proposed to estimate the wideband near-field channel with low computational complexity. Simulation results proves that the proposed PSMJ algorithm has superior performance with lower complexity for wideband XL-MIMO systems.

The deployment of multiple reconfigurable intelligent surfaces (RISs) enhances the propagation environment by improving channel quality, but it also complicates channel estimation. Following the conventional wireless communication system design, which involves full channel state information (CSI) acquisition followed by RIS configuration, can reduce transmission efficiency due to substantial pilot overhead and computational complexity. This study introduces an innovative approach that integrates CSI acquisition and RIS configuration, leveraging the channel-altering capabilities of the RIS to reduce both the overhead and complexity of CSI acquisition. The focus is on multi-RIS-assisted systems, featuring both direct and reflected propagation paths. By applying a fast-varying reflection sequence during RIS configuration for channel training, the complex problem of channel estimation is decomposed into simpler, independent tasks. These fast-varying reflections effectively isolate transmit signals from different paths, streamlining the CSI acquisition process for both uplink and downlink communications with reduced complexity. In uplink scenarios, a positioning-based algorithm derives partial CSI, informing the adjustment of RIS parameters to create a sparse reflection channel, enabling precise reconstruction of the uplink channel. Downlink communication benefits from this strategically tailored reflection channel, allowing effective CSI acquisition with fewer pilot signals. Simulation results highlight the proposed methodology’s ability to accurately reconstruct the reflection channel with minimal impact on the normalized mean square error while simultaneously enhancing spectral efficiency.

Satellite communications can provide massive connections and seamless coverage, but they also face several challenges, such as rain attenuation, long propagation delays, and co-channel interference. To improve transmission efficiency and address severe scenarios, semantic communication has become a popular choice, particularly when equipped with foundation models (FMs). In this study, we introduce an FM-based semantic satellite communication framework, termed FMSAT. This framework leverages FM-based segmentation and reconstruction to significantly reduce bandwidth requirements and accurately recover semantic features under high noise and interference. Considering the high speed of satellites, an adaptive encoder-decoder is proposed to protect important features and avoid frequent retransmissions. Meanwhile, a well-received image can provide a reference for repairing damaged images under sudden attenuation. Since acknowledgment feedback is subject to long propagation delays when retransmission is unavoidable, a novel error detection method is proposed to roughly detect semantic errors at the regenerative satellite. With the proposed detectors at both the satellite and the gateway, the quality of the received images can be ensured. The simulation results demonstrate that the proposed method can significantly reduce bandwidth requirements, adapt to complex satellite scenarios, and protect semantic information with an acceptable transmission delay.

In this paper, we investigate the impact of random wobbling on the performance of unmanned aerial vehicle (UAV) communications. We consider two practical implementation forms of UAV-multiple input multiple output (MIMO) system, namely 1) centralized implementation, where a compact multi-antenna array is deployed on a single UAV platform and hence wobbling directly has impact on the entire array; 2) distributed implementation, where a virtual MIMO system is formed via cooperative UAV swarm, and wobbling occurs independently for every single transmit antenna. For both cases, we define and analyze the beamforming gain loss-factor as the performance metric to evaluate the adverse effect caused by wobbling. Based on derived analytical expressions, the centralized and distributed implementations are compared to show their preferable operating scenarios, respectively, considering different system and implementation parameters including the carrier frequency, wobbling variance, user position, antenna number, and inter-antenna spacing, etc. It is revealed that the distributed MIMO implementation could be more robust to wobbling, especially when the number of antennas is large. Simulations verify the accuracy of derived analytical expressions, and confirm the corresponding conclusions.

In pursuit of enhanced quality of service and higher transmission rates, communication within the mid-band spectrum, such as bands in the 6-15 GHz range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is considered a potential enabler for future communication systems. However, the characteristics introduced by mid-band XL-MIMO systems pose challenges for channel modeling and performance analysis. In this paper, we first analyze the potential characteristics of mid-band MIMO channels. Then, an analytical channel model incorporating novel channel characteristics is proposed, based on a review of classical analytical channel models. This model is convenient for theoretical analysis and compatible with other analytical channel models. Subsequently, based on the proposed channel model, we analyze key metrics of wireless communication, including the ergodic spectral efficiency (SE) and outage probability (OP) of MIMO maximal-ratio combining systems. Specifically, we derive closed-form approximations and performance bounds for two typical scenarios, aiming to illustrate the influence of mid-band XL-MIMO systems. Finally, comparisons between systems under different practical configurations are carried out through simulations. The theoretical analysis and simulations demonstrate that mid-band XL-MIMO systems excel in SE and OP due to the increased array elements, moderate large-scale fading, and enlarged transmission bandwidth.

Deploying an extremely large-scale reconfigurable intelligent surface (XL-RIS) can significantly improve the performance of a RIS-assisted communication system. However, the array aperture and deployment of the XL-RIS affects the radiated field region in which the base station (BS) and the user are located, which in turn affects the performance improvement. In this paper, we have jointly optimized a deployment scheme and phase-shift matrix in XL-RIS-aided communication system, aiming to maximize the user’s received signal-to-noise ratio (SNR). Firstly, we incorporate the far-field and near-field channel into a unified far- or near-field (FoN) model to simplify the SNR analysis and optimization on RIS deployments. Secondly, based on the FoN approach, we derive an expression for the user’s received SNR and formulate an optimization problem to jointly optimize the RIS deployment and phase-shift matrix in order to maximize the user’s received SNR. Thirdly, we summarize the relationship between the RIS array aperture and deployment and the radiated field region in which the BS and the user are located, and propose an optimized closed-form solution for the RIS deployment and phase-shift matrix. Finally, we validate the effectiveness of the proposed scheme through simulation results.

With the increasing demand for spectrum efficiency and energy efficiency, reconfigurable intelligent surfaces (RISs) have attracted massive attention due to its low-cost and capability of controlling wireless environment. However, there is still a lack of treatments to deal with the growth of the number of users and RIS elements, which may incur performance degradation or computational complexity explosion. In this paper, we investigate the joint optimization of user scheduling and precoding for distributed RIS-aided communication systems. Firstly, we propose an optimization-based numerical method to obtain suboptimal solutions with the aid of the approximation of ergodic sum rate. Secondly, to reduce the computational complexity caused by the high dimensionality, we propose a data-driven scalable and generalizable multi-agent deep reinforcement learning (MADRL) framework with the aim to maximize the ergodic sum rate approximation through the cooperation of all agents. Further, we propose a novel dynamic working process exploiting the trained MADRL algorithm, which enables distributed RISs to configure their own passive precoding independently. Simulation results show that our algorithm substantially reduces the computational complexity by a time reduction of three orders of magnitude at the cost of 3% performance degradation, compared with the optimization-based method, and achieves 6% performance improvement over the state-of-the-art MADRL algorithms.

In recent years, reconfigurable intelligent surfaces (RISs) have attracted significant attention from both academia and industry due to their ability to manipulate electromagnetic waves in wireless communication. In particular, the characteristics of wireless channels incorporating RISs offer substantial research value. Several previous studies have investigated the path loss characteristics of RIS-assisted wireless channels in practical scenarios. However, these pioneering studies ignore the effect of RIS array gain on path loss. This paper proposes a floating-intercept (FI) path loss model that includes the array gain of the RIS, the distance between the transmitter and the RIS, and the distance between the receiver and the RIS. In this RIS-FI model, the RIS array gain component is related to the reflection coefficients of all the unit cells of RIS. The proposed model can effectively characterize path loss in both the far-field and near-field regions of the RIS. A fabricated 512-element RIS and a vector network analyzer (VNA) are utilized to set up the channel measurement system. The proposed RIS-FI path loss model is validated by using measurement data collected from multiple scenarios, including outdoor square, indoor corridor, and classroom. Compared to other RIS path loss models, the experimental results show that the proposed model offers better accuracy in describing path loss in the near-field region of the RIS. The RIS-FI path loss model presented in this paper may pave the way for further research on RIS-assisted wireless channels.

Reconfigurable intelligent surfaces (RISs) offer the unique capability to reshape the radio environment, thereby simplifying transmission schemes traditionally contingent on channel conditions. Joint spatial division and multiplexing (JSDM) emerges as a low-overhead transmission scheme for multi-user equipment (UE) scenarios, typically requiring complex matrix decomposition to achieve block-diagonalization of the effective channel matrix. In this study, we introduce an innovative JSDM design that leverages RISs to customize channels, thereby streamlining the overall procedures. By strategically positioning RISs at the discrete Fourier transform (DFT) directions of the base station (BS), we establish orthogonal line-of-sight links within the BS-RIS channel, enabling a straightforward pre-beamforming design. Based on UE grouping, we devise reflected beams of the RIS with optimized directions to mitigate inter-group interference in the RISs-UEs channel. An approximation of the channel cross-correlation coefficient is derived and serves as a foundation for the RISs-UEs association, further diminishing inter-group interference. Numerical results substantiate the efficacy of our RIS-customized JSDM in not only achieving effective channel block-diagonalization but also in significantly enhancing the sum spectral efficiency for multi-UE transmissions.

Thanks to the ability to customize the propagation of wireless signals, reconfigurable intelligent surfaces (RISs) have great potential in enhancing the performance of future wireless communication systems. While the majority of papers in the literature considers single-RIS scenarios, the potential deployment of multiple RISs, that offer ubiquitous connectivity for diverse user demands, calls for further investigation. This paper considers a downlink multi-group multicast system underpinned by multiple RISs and aims to maximize the sum spectral efficiency subject to an overall transmit power constraint. This optimization problem is highly challenging due to the non-convex, non-smooth, and non-differentiable properties of the objective function, as well as the non-convex unit modulus constraint. To address this complex problem, we propose a model-driven deep learning (DL) approach. This involves first solving the joint active and passive beamforming design through an alternating projected gradient (APG) algorithm with an approximate objective function. The APG algorithm is then unfolded into an iterative procedure using multiple layers with trainable parameters. A network training method is proposed to ensure that the performance improves with the number of iterations. Remarkably, our model is also nicely generalizable to the imperfect channel state information (CSI) scenario, without any change to the network architecture, by simply combining the recursive approximation method and adding some long/short-term trainable parameters to accommodate the two-timescale transmission protocol. Our simulation results demonstrate the superiority of our proposed DL method over existing algorithms in terms of both complexity and performance. Specifically, the proposed model-driven DL method reduces the runtime by approximately 80% compared to the APG algorithm and 99.97% compared to the majorization-minimization algorithm, while it also achieves comparable performance. Furthermore, our proposed method for imperfect CSI scenarios reduces the performance loss by 5%-10% compared to the proposed method without considering the influence of imperfect CSI.

We consider the problem of channel estimation for multi-reconfigurable intelligence durface (RIS) assisted orthogo-nal frequency division multiplexing (OFDM) systems. To detect the higher-rank signals reflected by multiple RISs, we divide the total RISs into several groups and propose a training protocol for filtering the signal components belonging to different groups. By concatenating the training signals on different subcarriers, we construct a low-rank third-order tensor model and develop a canonical polyadic decomposition (CPD) method to estimate the channel parameters. Simulation results indicate that the proposed tensor-based method effectively improves the estimation accuracy. The RIS grouping strategy trades off the training overhead and algorithm performance.

Reconfigurable intelligent surface (RIS)-assisted communications have received widespread attention recently, whose channel modeling is the cornerstone for its future practical application. Nevertheless, the RIS-related channel modeling stays at the level of either the theoretical free-space derivation or the end-to-end channel model, yet its general segment channel model applicable to real environment is still rarely reported. In this paper, we formulate a temporal segment channel model for RIS-assisted non-line-of-sight (NLOS) communications, which separates the global channel into three parts, i.e., transmitter (Tx)-RIS, RIS-receiver (Rx), and Tx-Rx. Then we bridge the relationship between the proposed model and the existing models. In addition, we illustrate the Ricean K–factor characteristics of the global channel and the sub-channel in the proposed model. The lower boundary for the Ricean K–factor of sub-channel is also deduced. Two low-complexity estimation algorithms for the Ricean K–factor of sub-channel are proposed, whose accuracy is verified by the measured channel data.

The reconfigurable intelligent surface (RIS) has received a lot of interest because of its capacity to reconfigure the wireless communication environment in a cost- and energy-efficient way. However, the realistic power consumption modeling and measurement validation of RIS has received far too little attention. Therefore, in this work, we model the power consumption of RIS and conduct measurement validations using various RISs to fill this vacancy. Firstly, we propose a practical power consumption model of RIS. The RIS hardware is divided into three basic parts: the FPGA control board, the drive circuits, and the RIS unit cells. The power consumption of the first two parts is modeled asPstaticand that of the last part is modeled asPunits. Expressions ofPstaticandPunitsvary amongst different types of RISs. Secondly, we conduct measurements on various RISs to validate the proposed model. Five different RISs including the PIN diode, varactor diode, and RF switch types are measured, and measurement results validate the generality and applicability of the proposed power consumption model of RIS. Finally, we summarize the measurement results and discuss the approaches to achieve the low-power-consumption design of RIS-assisted wireless communication systems.

Most learning-based channel state information (CSI) feedback efforts concentrate on enhancing feedback accuracy through innovative neural network (NN) designs and exploiting correlations. This paper introduces an integrated learning framework for CSI feedback and localization designed to synergistically improve both tasks. We present a novel unified approach for CSI feedback and downlink CSI-based localization, where feedback is facilitated by an autoencoder, and the downlink CSI-based localization uses the feedback codeword directly without requiring reconstruction. The goal is to simultaneously minimize feedback and localization errors. Additionally, for users with access to coarse position data, we propose a refined framework that integrates this information into both the feedback mechanism and localization processes. This coarse positional knowledge is incorporated into the encoding and decoding stages to reduce feedback errors and is inputted into the localization NN to enhance localization accuracy. The improved framework is refined through an end-to-end training strategy, focusing on concurrently reducing feedback and localization errors. Simulation results using ray tracing channel datasets demonstrate that our proposed method not only enables feedback and localization tasks to mutually benefit but also shows that incorporating coarse positional data significantly increases the accuracy of both CSI feedback and CSI-based localization.

Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly with the beamforming vector at the access point is challenging due to the non-convex objective function and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and power iteration to maximize the weighted sum rate (WSR) of a RIS-assisted downlink multi-user multiple-input single-output system. To further improve performance, a model-driven deep learning (DL) approach is designed, where trainable variables and graph neural networks are introduced to accelerate the convergence of the proposed algorithm. We also extend the proposed method to include beamforming with imperfect channel state information and derive a two-timescale stochastic optimization algorithm. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of complexity and WSR. Specifically, the model-driven DL approach has a runtime that is approximately 3% of the state-of-the-art algorithm to achieve the same performance. Additionally, the proposed algorithm with 2-bit phase shifters outperforms the compared algorithm with continuous phase shift.

Reconfigurable intelligent surface (RIS)-empowered communication, has been considered widely as one of the revolutionary technologies for next generation networks. However, due to the novel propagation characteristics of RISs, underlying RIS channel modeling and measurement research is still in its infancy and not fully investigated. In this paper, we conduct multi-scenario broadband channel measurements and modeling for RIS-assisted communications at the sub-6 GHz band. The measurements are carried out in three scenarios covering outdoor, indoor, and outdoor-to-indoor (O2I) environments, which suffer from non-line-of-sight (NLOS) propagation inherently. Three propagation modes including intelligent reflection with RIS, specular reflection with RIS and the mode without RIS, are taken into account in each scenario. In addition, considering the cascaded characteristics of RIS-assisted channel by nature, two modified empirical models including floating-intercept (FI) and close-in (CI) are proposed, which cover distance and angle domains. The measurement results rooted in 2096 channel acquisitions verify the prediction accuracy of these proposed models. Moreover, the propagation characteristics for RIS-assisted channels, including path loss (PL) gain, PL exponent, spatial consistency, time dispersion, frequency stationarity, etc., are compared and analyzed comprehensively. These channel measurement and modeling results may lay the groundwork for future applications of RIS-assisted communication systems in practice.

A reconfigurable intelligent surface (RIS) with active elements is an augmented version of an RIS. By equipping all or part of RIS elements with signal processing capabilities, the channel estimation and the design of RIS phases can be further extended, yielding an improvement in the spectral efficiency (SE). In this paper, we first present a novel sensing RIS structure which is efficient for hardware implementation. Unlike partial active elements in previous structures, all elements are available to the RF chains via switches, which enables the traditional channel estimation methods and channel extrapolation to be implemented. Moreover, we make a comprehensive analysis and comparison with other RIS structures from the perspective of channel state information (CSI) acquisition. Considering the large-scale of RIS and base station (BS) array, we model the channel between the user and the RIS, the RIS and the BS using a near-field channel model. Based on the structured channel model, we propose a low-overhead channel reconstruction protocol through a parameter-extracting method, while the training overhead and complexity are also analyzed. In addition, we investigate the RIS elements’ activation strategy to further reduce the training overhead. Finally, numerical results demonstrate that the proposed scheme achieves accurate channel estimation with low overhead, which can also enhance the achievable SE.

Semantic communication can significantly reduce transmission payload by sending only semantic information related to the task. However, existing end-to-end trained semantic studies degrade under extreme channel environments, while reconfigurable intelligent surface (RIS) technology offers a potential solution for realizing channel customization. In this work, we propose a reconfigurable RIS-enhanced semantic communication framework called RIS-SC. This framework allows for customization of the channel environment based on the user's requirements for different semantic parts, rather than relying solely on the conventional bit error rate requirement. Using reinforcement learning, the RIS controller interacts with varying channels to meet the user's different requirements. The RIS controller adaptively protects important semantic parts by adjusting the channel conditions. Simulation results demonstrate that the proposed RIS-SC framework can adapt to different channel environments and improve task performance under varying requirements, such as vertical semantic or true image reconstruction.

Reconfigurable intelligent surfaces (RISs) have received extensive concern to improve the performance of wireless communication systems. In this paper, a subarray-based scheme is investigated in terms of its effects on ergodic spectral efficiency (SE) and energy efficiency (EE) in RIS-assisted systems. In this scheme, the adjacent elements divided into a subarray are controlled by one signal and share the same reflection coefficient. An upper bound of ergodic SE is derived and an optimal phase shift design is proposed for the subarray-based RIS. Based on the upper bound and optimal design, we obtain the maximum of the upper bound. In particular, we analytically evaluate the effect of the subarray-based RIS on EE since it reduces SE and power consumption simultaneously. Numerical results verify the tightness of the upper bound, demonstrate the effectiveness of the optimal phase shift design for the subarray-based RIS, and reveal the effects of the subarray-based scheme on SE and EE.

Channel reciprocity has been commonly assumed in time division duplex (TDD) massive multiple-input multiple-output (MIMO) communications, when acquiring downlink channel state information (CSI) at the base station through the uplink channel estimation. However, such channel reciprocity suffers from severe impairments when low-resolution analog-to-digital converters (ADCs) are introduced to reduce hardware cost and power consumption in practical systems. To compensate for such impairments, in this paper, we propose an efficient calibration state diagnosis scheme built upon compressive sensing techniques, leveraging the sparsity property of the calibration operations at the BS antennas under an additive quantization noise model. Compared with the traditional pilot-based approaches, our proposed scheme achieves comparable accuracy performance with 1-2 quantization bits and hence significantly reduces pilot overhead therein.

Reconfigurable intelligent surface (RIS)-empowered communication has recently drawn significant attention due to its superior capability in manipulating the wireless propagation environment. However, the channel modeling and measurement of RIS-assisted wireless communication systems in real environment has not been adequately studied. In this paper, we construct a channel measurement system using vector network analyzer (VNA) is used to investigate RIS-assisted wireless communication channel in outdoor scenarios at 2.6 GHz. New path loss (PL) models including angle domain information are proposed by refining the traditional close-in (CI) and floating-intercept (FI) models. In the proposed models, both influences of the distance from transmitter (TX) to RIS and the distance from receiver (RX) to RIS on the PL, are taken into account. In addition, the value of root mean square (RMS) delay spread of RIS-assisted wireless communication is found to be much smaller than that of the traditional non line-of-sight (NLOS) case, implying that RIS provides a virtual line-of-sight (LOS) link.

The performance of transmission schemes is heavily influenced by the wireless channel, which is typically considered an uncontrollable factor. However, the introduction of reconfigurable intelligent surfaces (RISs) to wireless communications enables the customization of a preferred channel for adopted transmissions by reshaping electromagnetic waves. In this study, we propose multi-timescale channel customization for RIS-assisted multiple-input multiple-output systems to facilitate transmission design. Specifically, we customize a high-rank channel for spatial multiplexing (SM) transmission and a highly correlated rank-1 channel for beamforming (BF) transmission by designing the phase shifters of the RIS with statistical channel state information in the angle-coherent time to improve spectral efficiency (SE). We derive closed-form expressions for the approximation and upper bound of the ergodic SE and compare them to investigate the relative SE performance of SM and BF transmissions. In terms of reliability enhancement, we customize a fast-changing channel in the symbol timescale to achieve more diversity gain for SM and BF transmissions. Extensive numerical results demonstrate that flexible customization of channel characteristics for a specific transmission scheme can achieve a tradeoff between SE and bit error ratio performance.

Reconfigurable intelligent surface (RIS) is widely accepted as a potential technology to assist in communication between base stations (BSs) and users in edge areas. We study the energy efficiency of a RIS-assisted multi-cell communication system with a realistic RIS power consumption model. With the goal of maximizing the energy efficiency of the system, we optimize the transmit beamforming vectors at the BS and the RIS phase shift matrix by a proposed alternative optimization algorithm. First, the transmit beamforming vector is optimized by solving the transformed weighted minimum mean square error (WMMSE) problem. Subsequently, to solve the inconvenience incurred by the discrete relationship between the RIS reflecting unit power consumption and its discrete phase shift, we use a continuous function to approximate their relationship. With this approximation, we can use the majorization minimization (MM) technique to optimize the continuous RIS phase shifts, and then quantize the obtained phase shifts to discrete ones. Simulation results demonstrate that the energy efficiency of the system is effectively optimized by the proposed algorithm.

The millimeter-wave (mmWave) communication technology, which employs large-scale antenna arrays, enables inherent sensing capabilities. Simultaneous localization and mapping (SLAM) can utilize channel multipath angle estimates to realize integrated sensing and communication design in 6G communication systems. However, existing works have ignored the significant overhead required by the mmWave beam management when implementing SLAM with angle estimates. This study proposes a joint beam management and SLAM design that utilizes the strong coupling between the radio map and channel multipath for simultaneous beam management, localization, and mapping. In this approach, we first propose a hierarchical sweeping and sensing service design. The path angles are estimated in the hierarchical sweeping, enabling angle-based SLAM with the aid of an inertial measurement unit (IMU) to realize sensing service. Then, feature-aided tracking is proposed that utilizes prior angle information generated from the radio map and IMU. Finally, a switching module is introduced to enable flexible switching between hierarchical sweeping and feature-aided tracking. Simulations show that the proposed joint design can achieve sub-meter level localization and mapping accuracy (with an error < 0.5 m). Moreover, the beam management overhead can be reduced by approximately 40% in different wireless environments.

Reconfigurable intelligent surface (RIS) has emerged as a competitive solution to the blocking problem in millimeter wave (mmWave) communications. However, due to the passive nature of the RIS, obtaining channel state information (CSI) for RIS-assisted mmWave communication systems is rather difficult. Considering that the currently available RIS hardware cannot arbitrarily switch between the active (reflection with configurable phase response) and deactivate (absorption) modes, we suggest a new beam training method for RIS-assisted mmWave communication systems in this study. First, a predefined hierarchical codebook is created using the pattern synthesis method. Then, we provide a novel hierarchical beam training method using two multi-mainlobe codewords in each layer of the hierarchical codebook for beam sweeping. Combining the results of the beam identification in all the layers will yield the ultimate ideal beam direction. Theoretical analyses demonstrate that the suggested approach can effectively reduce training overhead while ensuring successful beam alignment. Simulation results show that the practical codebook can be created successfully, and the suggested method can achieve accurate beam alignment with reduced training overhead.

In strong line-of-sight millimeter-wave (mmWave) wireless systems, the rank-deficient channel severely hampers spatial multiplexing. To address this inherent deficiency, multiple reconfigurable-intelligent-surfaces (RISs) are introduced in this study to customize the wireless channel. Utilizing the RIS to reshape electromagnetic waves, we theoretically show that a favorable channel with an arbitrary tunable rank and a minimized truncated condition number can be established by elaborately designing the placement and reflection matrix of RISs. Different from existing works on multi-RISs, the number of elements needed for each RIS to combat the path loss and the limited phase control is also considered. On the basis of the proposed channel customization, a joint transmitter-RISs-receiver (Tx-RISs-Rx) design under a hybrid mmWave system is investigated to maximize the spectral efficiency. Using the proposed scheme, the optimal singular value decomposition-based hybrid beamforming at the Tx and Rx can be obtained without matrix decomposition for the digital and analog beamforming. The bottoms of the sub-channel mode in the water-filling algorithm, which are conventionally uncontrollable, are proven to be independently adjustable by RISs. Moreover, the transmit power required for realizing multi-stream transmission is derived. Numerical results are presented to verify our theoretical analysis and exhibit substantial gains over systems without RISs.

Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems. In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed. Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

To address the inherent channel deficiency in strong line-of-sight (LoS) millimeter-wave (mmWave) wireless systems, distributed reconfigurable intelligent surfaces (RISs) are introduced in this study to customize the wireless channel. Capitalizing on the ability of the RIS to reshape electromagnetic waves, we theoretically show that a favorable channel with an arbitrary tunable rank and a minimized truncated condition number can be established by elaborately designing the placement and reflection matrix of RISs. The number of elements needed for each RIS to combat the path loss is also considered in this research. Numerical results show that the effective channel rank can be flexibly and accurately customized according to the required number of data streams. Moreover, utilizing our proposal, every corner in the interested coverage can build the well-conditioned channel with small truncated condition number.

With the large-scale deployment and commercialization of 5G, the traditional coverage enhancement technology is facing severe challenges due to high hardware cost and power consumption. At the same time, the emerging reconfigurable intelligent surface (RIS) technology, which can flexibly regulate incident waves and optimize the transmission path of wireless signals, is considered to be one of the most potential 6G key enabling technologies. This paper presents a 5G commercial network coverage enhancement prototype system based on RIS, which directly uses the base station in China Mobile 5G commercial network as transmitter, 5G network signal test terminal as receiver, and RIS code optimization algorithm is employed. The whole system forms a closed optimization loop of “obtaining data-analyzing data-feedback code” and is tested in multiple scenarios. In selected scenarios, the prototype system achieves a received power gain of about 6 dB, which is significant for the research of RIS in 5G commercial network.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for the next generation networks. By utilizing tools from stochastic geometry, we develop a meta distributed-based analytical framework to study the effect of the large-scale deployment of the RIS on the performance of a millimeter wave (mmWave) cellular network. Specifically, the locations of the base stations (BSs) are modeled as Poisson point processes (PPPs). In addition, the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. By considering the randomness of the locations and orientations of the RISs and the particular characteristics of mmWave communications, we provide a statistical characterization of the path loss for the BSs and RISs and derive the analytical expressions for the k-th moment of the conditional success probability, the area spectral efficiency and the energy efficiency. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs.

Recently, unmanned aerial vehicle (UAV) swarm communication has drawn much attention in search and rescue (SAR) missions owing to its wide wireless coverage and increasing autonomy in navigation. In this article, we consider maximizing the downlink wireless coverage of a UAV swarm in an unknown mission area by controlling the quasistationary deployments of UAVs. Particularly, the stochastic wireless link failures caused by channel fading and noise in UAV-to-UAV communication links are considered in coverage control. Specifically, due to delay sensitivity and onboard energy limitation of UAV-enabled SAR networks, we study a distributed control strategy where swarm UAVs can address the coverage problem by exchanging only the local information. In this case, the wireless coverage problem is divided into several distributed optimization subproblems. However, due to the integer variable and nonlinear constraints, each subproblem is nonconvex and mutually coupling, which makes it difficult to solve via standard convex optimization solvers. Thus, we model the UAV swarm network as an undirected random graph and then solve the optimization subproblems by formulating a UAV swarm wireless coverage game. As per the designed utility function and potential function of the formulated game, existence of the pure Nash equilibrium is discussed and a distributed algorithm is developed to achieve the best Nash equilibrium. We analyze the convergence property and computational complexity of the proposed algorithm. Meanwhile, we analyze effects of the initial learning rate and step size on algorithm performance from both theoretical and simulation results. Simulation results show that the proposed algorithm improves the coverage by around 58% when compared with initial performance.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for millimeter wave (mmWave) networks. In this paper, we utilize tools from stochastic geometry to study the performance of a RIS-assisted mmWave cellular network. Specifically, the locations of the base stations (BSs) and the midpoints of the blockage are modeled as two independent Poisson point processes (PPPs), where the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. The particular characteristics of mmWave communications, i.e., directional beamforming and different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) propagation, are incorporated into our analysis. We derive analytical expressions for the success probability and the area spectral efficiency. The success probability under the special case where the blockage parameter is sufficiently small is also derived. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs. In addition, the tradeoff between the BS and RIS densities is investigated and the results show that the RISs can indeed enable the traditional networks to improve the success probability, especially for the cell-edge region, with limited power consumption.

In frequency division duplexing systems, the base station (BS) acquires downlink channel state information (CSI) via channel feedback, which has not been adequately investigated in the presence of RIS. In this study, we examine the limited channel feedback scheme by proposing a novel cascaded codebook and an adaptive bit partitioning strategy. The RIS segments the channel between the BS and mobile station into two sub-channels, each with line-of-sight (LoS) and non-LoS (NLoS) paths. To quantize the path gains, the cascaded codebook is proposed to be synthesized by two sub-codebooks whose codeword is cascaded by LoS and NLoS components. This enables the proposed cascaded codebook to cater the different distributions of LoS and NLoS path gains by flexibly using different feedback bits to design the codeword structure. On the basis of the proposed cascaded codebook, we derive an upper bound on ergodic rate loss with maximum ratio transmission and show that the rate loss can be cut down by optimizing the feedback bit allocation during codebook generation. To minimize the upper bound, we propose a bit partitioning strategy that is adaptive to diverse environment and system parameters. Extensive simulations are presented to show the superiority and robustness of the cascaded codebook and the efficiency of the adaptive bit partitioning scheme.

Reconfigurable intelligent surface (RIS) has drawn worldwide attention because of its attractive capability to improve the electromagnetic propagation environment and surprising advantages of low cost and low power consumption. Recently, RIS is further advanced to realize the independent control of two orthogonal polarizations in real time. In this paper, we study the dual-polarized RIS-assisted mobile communication system and investigate its performance under practical polarization imperfections. Specifically, we provide a tight upper bound of the ergodic spectrum efficiency. Through the bound, we find that the performance is greatly dependent on the polarization imperfections and RIS phase shifts of the RIS-assisted link, when the number of base station antennas is large enough and the deterministic component exists in the RIS-assisted link. On basis of this tight bound, we propose an approximate optimal RIS phase shift design to maximize the ergodic spectrum efficiency upper bound and emphasize the necessity to make phase adjustment between two polarizations of the RIS. Theoretical analysis further gives guidance for the configuration of RIS. Numerical results demonstrate that the upper bound is very tight and the proposed phase shift design with adjustment is approximate optimal.

Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers’ location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems. First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the “double count” and “data reliability” problems through the flexible application of probabilistic data association methods. Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment.

Reconfigurable intelligent surface (RIS) is a low-cost device envisioned to achieve substantial promotion in both spectrum and energy efficiency in the future wireless communication systems. In this paper, we investigate the downlink transmission of the RIS-enabled orthogonal frequency division multiplexing (OFDM) system, and propose a low-complexity passive beamforming optimization algorithm to maximize the achievable sum-rate over all subcarriers. The passive beam-forming optimization is a NP-hard problem due to the RIS-induced non-convex unit modulus constraints. We conquer this difficulty by exploiting a fractional programming (FP) based approach combined with an efficient manifold optimization (MO) method. With the proposed low-complexity passive beamforming optimization algorithms and water-filling power allocation, the achievable sum rate can then be maximized through alternating optimization (AO). Simulation results indicate that the proposed AO based algorithm performs well in achieving high average sum-rate with a fast convergence rate.

This paper investigates the downlink OFDM transmission assisted by reconfigurable intelligent surface (RIS). With single antenna implemented at both the base station (BS) and each user, we focus on the design of the phase shifts for the RIS, as well as power allocation on each subcarrier to improve the spectrum efficiency. To reduce the computation delay, we propose a deep reinforcement learning (DRL) based algorithm to optimize the RIS phase shift parameters, while allocating power on each subcarrier via water filling. Numerical results reveal that the proposed DRL-based framework can achieve a performance almost the same with that of successive convex approximation (SCA), while the computation delay can be greatly reduced.

We consider the uplink transmission between a multi-antenna ground station and an unmanned aerial vehicle (UAV) swarm. The UAVs are assumed as intelligent agents, which can explore their optimal three dimensional (3-D) deployment to maximize the channel capacity of the multiple input multiple output (MIMO) system. Specifically, considering the limitations of each UAV in accessing the global information of the network, we focus on a decentralized control strategy by noting that each UAV in the swarm can only utilize the local information to achieve the optimal 3-D deployment. In this case, the optimization problem can be divided into several optimization sub-problems with respect to the rank function. Due to the non-convex nature of the rank function and the fact that the optimization sub-problems are coupled, the original problem is NP-hard and, thus, cannot be solved with standard convex optimization solvers. Interestingly, we can relax the constraint condition of each sub-problem and solve the optimization problem by a formulated UAVs channel capacity maximization game. We analyze such game according to the designed reward function and the potential function. Then, we discuss the existence of the pure Nash equilibrium in the game. To achieve the best Nash equilibrium of the MIMO system, we develop a decentralized learning algorithm, namely decentralized UAVs channel capacity learning. The details of the algorithm are provided, and then, the convergence, the effectiveness and the computational complexity are analyzed, respectively. Moreover, we give some insightful remarks based on the proofs and the theoretical analysis. Also, extensive simulations illustrate that the developed learning algorithm can achieve a high MIMO channel capacity by optimizing the 3-D UAV swarm deployment with the local information.

Future wireless communication networks are expected to fulfill the unprecedented performance requirements to support our highly digitized and globally data-driven society. Various technological challenges must be overcome to achieve our goal. Among many potential technologies, reconfigurable intelligent surface (RIS) and artificial intelligence (AI) have attracted extensive attention, thereby leading to a proliferation of studies for utilizing them in wireless communication systems. The RIS-based wireless communication frameworks and AI-enabled technologies, two of the promising technologies for the sixth-generation networks, interact and promote with each other, striving to collaboratively create a controllable, intelligent, reconfigurable, and programmable wireless propagation environment. This paper explores the road to implementing the combination of RIS and AI, more specifically, integrating AI-enabled technologies into RIS-based frameworks for maximizing the practicality of RIS to facilitate the realization of smart radio propagation environments, elaborated from shallow to deep insights. We begin with the basic concept and fundamental characteristics of RIS, followed by the overview of the research status of RIS. Then, we analyze the inevitable trend of RIS to be combined with AI. In particular, we focus on recent research about RIS-based architectures embedded with AI, elucidating from the intelligent structures and systems of metamaterials to the AI-embedded RIS-assisted wireless communication systems. Finally, the challenges and potential of the topic are discussed.

As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation (6G) mobile communication systems have been conducted. Future mobile communication systems are evidently evolving toward a more intelligent and software-reconfigurable functionality paradigm that can provide ubiquitous communication, as well as sense, control, and optimize wireless environments. Thus, integrating communication and localization using the highly directional transmission characteristics of millimeter waves (mmWaves) is a promising route. This approach not only expands the localization capabilities of a communication system but also provides new concepts and opportunities to enhance communication. In this paper, we explain the integrated communication and localization in mmWave systems, in which these processes share the same set of hardware architecture and algorithms. We also provide an overview of the key enabling technologies and the basic knowledge on localization. Then, we provide two promising directions for studies on localization with an extremely large antenna array and model-based (or model-driven) neural networks. We also discuss a comprehensive guidance for location-assisted mmWave communications in terms of channel estimation, channel state information feedback, beam tracking, synchronization, interference control, resource allocation, and user selection. Finally, we outline the future trends on the mutual assistance and enhancement of communication and localization in integrated systems.

Future mobile systems have shown a growing trend towards wider frequency bands, larger antenna arrays, and more user equipment, simultaneously expanding the channel in the frequency, space, and user domains. However, the huge size of the multi-domain channel brings great challenges to the acquisition of channel state information (CSI), especially in frequency division duplex (FDD) massive multiple input multiple output (MIMO) systems. In this paper, we propose a multi-domain channel extrapolation scheme that can reconstruct the huge multi-domain channel with low pilot overhead. Specifically, information on the environment shared by multiple domains is utilized for the design of a low-complexity channel extrapolation algorithm. Moreover, we investigate the patterns of sparse pilots and antenna selection by establishing a theoretical framework for the performance analysis of the patterns. We further propose a sparse random pattern design, which can legitimately obtain a set of patterns that are suitable for channel extrapolations. Numerical results demonstrate that we can accurately extrapolate the multi-domain channel using our proposed channel extrapolation scheme and our designed sparse random patterns.

Exploration efficiency challenges for multi-agent reinforcement learning (MARL), as the policy learned by confederate MARL depends on the interaction among agents. Less informative reward also restricts the learning speed of MARL in comparison with the informative label in supervised learning. This paper proposes a novel communication method which helps agents focus on different exploration subarea to guide MARL to accelerate exploration. We propose a predictive network to forecast the reward of current state-action pair and use the guidance learned by the predictive network to modify the reward function. An improved prioritized experience replay is employed to help agents better take advantage of the different knowledge learned by different agents. Experimental results demonstrate that the proposed algorithm outperforms existing methods in cooperative multi-agent environments.

The spatial reciprocity enables the downlink channel reconstruction in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems by obtaining the frequency-independent parameters in the uplink. However, the algorithms to estimate these parameters are typically complex and time-consuming. In this paper, we regard the channel as an image and utilize you only look once (YOLO), an advanced deep learning-based object detection network, to locate the bright spots in the channel image, then the frequency-independent parameters can be estimated rapidly. Superior to the traditional algorithm that iteratively extracts the paths, YOLO can detect all the path simultaneously. Experimental results show that YOLO can greatly deplete the running time to obtain the frequency-independent parameters and reconstruct the FDD massive MIMO downlink channel with satisfactory accuracy.

Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by the ability to change the reflection coefficients of the unit cells of a programmable metasurface. Nevertheless, the electromagnetic responses of the RISs are usually only phase-adjustable, which considerably limits the achievable rate of RIS-based transmitters. In this paper, we propose an RIS architecture to achieve amplitude-and-phase-varying modulation, which facilitates the design of multiple-input multiple-output (MIMO) quadrature amplitude modulation (QAM) transmission. The hardware constraints of the RIS and their impacts on the system design are discussed and analyzed. Furthermore, the proposed approach is evaluated using our prototype which implements the RIS-based MIMO-QAM transmission over the air in real time.

Compressive scanning (CS) has exhibited its potential in improving the path tracking efficiency of millimeter wave (mmWave) systems. However, its practical performance is significantly degenerated by hardware imperfections, such as carrier frequency offset (CFO). Conventional CFO estimation methods that compare the phases of two measurements cannot be applied in CS straightforwardly because the two successive beacons are different. To overcome these problems, we propose a novel CFO-robust compressive path-tracking algorithm by introducing a two-stage CFO estimation procedure before performing coherent CS detection. Unlike conventional CFO estimates, the CFO estimate in the proposed algorithm can be obtained from the signal strength value of the received signal at the cost of a small amount of additional computation complexity. Numerical results demonstrate the superiority of the proposed algorithm in both single-path and multipath scenarios.

Due to the underlying sparse structure of the mmWave channels, which indeed makes the exact closed-form capacity expressions inherently hard to derive, there has been less research on the ergodic capacity of mmWave systems. To overcome this problem, by means of the majorization theory, this paper analyzes the ergodic capacity of point-to-point mmWave communication systems under finite-dimensional channel model. In particular, we derive several closed-form ergodic capacity approximations, which exhibit excellent tightness in spite of whether the steering matrices are singular or not. Then, several Jensen's approximations and bounds of the ergodic capacity are also derived. The results indicate that the ergodic capacity seems to increase logarithmically with the number of antennas, the transmit SNR per antenna, and the eigenvalues of the steering matrix products. Besides, the DFT matrices can effectively characterize the spatial directions of mmWave channels when the number of antennas grows large. After that, high-SNR ergodic capacity, high-SNR slope, and power offset are also analyzed. It indicates that for a finite-dimensional channel, the maximum multiplexing gain increases with the number of paths instead of the number of antennas in Rayleigh channels. Numerical simulations are performed to validate the results.

This paper investigates the interference coordination for multi-cell full-dimension multiple-input multiple-output (FD- MIMO) cellular networks. We assume only statistical channel state information (CSI) at each base station (BS). Since the edge users suffer from much more inter-cell interference compared to the center users, we focus on the interference coordination for cell-edge regions. We propose a fractional-frequency-reuse (FFR) scheme for the considered systems, in which all the edge users share the same frequency resources. Based on the proposed FFR scheme, a joint 3D beamforming transmission algorithm is proposed to mitigate the inter-cell interference for the cell-edge users. The simulation results show that the proposed interference coordination strategy performs well in terms of the cell-edge rate, as well as the achievable sum rate.

In this paper, we consider the power allocation problem for downlink multiceli massive multiple-input multiple-output (MIMO) communications over Rician fading channels. Each link between a user equipment (UE) and a base station (BS) forms a jointly correlated Rician channel, on which some properties of the channel covariance matrices in the massive MIMO scenario are presented. Based on these properties, assuming perfect channel state information (CSI) at UEs and statistical CSI, i.e. the channel coupling matrix (CCM) and the line-of-sight (LOS) component, at BSs, we design a near-optimal power allocation algorithm in terms of maximizing the deterministic equivalent of the ergodic sum-rate in the beam domain. The closed-form objective function of the power allocation problem turns out to be a difference of concave functions, thus we search for the solutions iteratively. Numerical results show that the proposed method performs well in terms of achievable sum-rate.

In order to reap the full scale of benefits of millimetre wave (mmWave) massive multiple‐input multiple‐output (MIMO) systems, the design of antenna arrays at the transmitter or receiver becomes more critical due to the propagation characteristic at mm‐frequencies. In this study, the authors investigate the performance of two types of antenna array, namely uniform rectangular planar array (URPA) and uniform cylindrical array (UCYA). The channel behaviour is presented in full‐dimensional mmWave propagation conditions by considering both the azimuth and elevation dimensions. The squared inner product and singular value spread are studied for URPA and UCYA configurations, these properties reveal the effective interference and channel's stability of antenna array. The authors also evaluate the achievable rate with the equal power allocation and water‐pouring power allocation schemes. Simulation results show that under the same system configurations, the performance that includes the radiation pattern, the channel eigenvalue distribution, the effective interference, and the achievable rate, of UCYA configuration always outperforms that of URPA configuration. Therefore, it can be concluded that in three‐dimensional propagation environments, the UCYA configuration is especially appealing for mmWave MIMO systems.

In this paper, we investigate the inter-tier interference coordination for a heterogeneous network (HetNet), where both macro and small cells work in the frequency-division duplexing mode and share the same spectrum. A 2-D large-scale antenna array is deployed at a macro-base station (BS), while conventional multiple antenna array is deployed at each small cell. First, we derive an approximation of macro-users' signal-to-interference-plus-noise ratio (SINR), under the assumption of only statistical channel-state information (CSI) at macro-BS, and a 3-D beamforming transmission scheme is applied for macro-users. A lower-bound of small-cell users' expected SINR is also derived using the property of complex Wishart matrix. Based on these, simple metrics are derived to measure the inter-tier interference. Then, new interference coordination algorithms are proposed to achieve a good tradeoff for the performance and traffic between macro-cells and small cells. The proposed algorithms require only a few additional statistical CSI. Moreover, we derive tractable ergodic rate approximation of both the macro-cell and small-cell users which are shown to match well with the Monte Carlo results.

In order to reap the full scale of benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the design of antenna arrays at the transmitter or receiver becomes more critical due to the propagation characteristic at mm-frequencies. In this paper, we investigate the steering vector and array factor by considering three types of planar antenna arrays, namely uniform rectangular planar array (URPA), uniform hexagonal planar array (UHPA), and uniform circular planar array (UCPA). Based on these results, we investigate the array directivity/gain and the achievable spectral efficiency in a 3-dimensional massive MIMO system by considering both the azimuth and elevation dimensions. An important observation is that the the maximum array gain, the beamwidth, and the achievable spectral efficiency (SE) for the above three types of planar antenna array configurations are almost identical. The side lobe level and the geometric area of the UHPA configuration are systematically better than those of the UCPA and URPA configurations.

In this paper, we investigate the sum rate performance of a downlink transmission scheme for massive multiple-input multiple-output (MIMO) systems with a two-dimensional (2D) antenna array at the base station (BS). Assuming Rician fading conditions, we derive some properties of the channel's line-of-sight (LOS) component. Using these properties and under the assumption of only LOS information at the BS, the optimal beamforming vector for each user as well as the main guidelines for user scheduling are derived to maximize an approximation of the ergodic sum rate. Based on the optimal beamforming vector and the guidelines, a statistical three-dimensional (3D) beamforming downlink transmission scheme exploiting only the LOS information of each user is proposed. Numerical results show that the proposed scheme can achieve considerable sum rates while requiring much less channel state information (CSI) overhead at the BS, and is more attractive than the traditional zero-forcing and match-filtering schemes when the CSI at the BS is imperfect.

In this paper, we investigate a downlink transmission algorithm for single-cell multiuser systems with two-dimensional (2-D) large-scale antenna array at the base station (BS) over Rician fading channels. We first derive some properties of the channel's line-of-sight (LOS) component in the large-scale antenna array scenario. Next, based on these properties and under the assumption of only statistical channel state information (CSI), i.e., the LOS component and Rician $K$-factor, at the BS, we derive the optimal beamforming vector for each user in maximizing an approximation of the ergodic sum rate. The main guidelines for user scheduling are also presented. Then, a three-dimensional (3-D) beamforming downlink transmission algorithm exploiting only the statistical CSI of each user is proposed. For this algorithm, we derive an exact analytical closed-form expression for the achievable ergodic rate and present tractable approximations. Based on our analysis, we gain some valuable insights. The proposed algorithm is shown to perform well in achieving considerable sum rate while requiring much less CSI at the BS. It requires three scalar values at the BS for each user, and can achieve an ergodic sum rate closer to the ergodic sum rate achieved by the matched-filter precoding with perfect CSI at the BS.

In this paper, we study the influence of 3-dimensional (3D) base station (BS) antenna patterns on the of downlink random heterogeneous cellular networks. To assess the impact of downtilt and vertical beamwidth, we propose a mathematical framework based on stochastic geometry (to model BS locations) and random shape theory (to model building geometry). Using this framework, we study the impact of different antenna parameters at the pico cell on the coverage and area spectral efficiency of the network. Through simulations we find that narrowing the vertical beamwidth of the pico cell must be accompanied by downtilt to result in improved coverage and capacity. We also find that high building density leads to improved network performance, motivating the deployment of heterogeneous networks in dense urban areas.

Multi-user multiple antenna systems have attracted much attention because of large spectral efficiency. However, receiver and transmitter channel state information (CSI) is generally required, especially for the downlink channel. In this study, the authors investigate the ergodic sum rate and present low complexity adaptive transmission scheme for multiple-input single-output downlink multi-user systems with statistical CSI at the base station (BS). The authors derive an approximation of the ergodic sum rate, and the constraint of each user's statistical CSI under which the approximation of the ergodic sum rate is maximised. Then, statistical beamforming space division multiple access transmission is derived through maximising the approximation of the ergodic sum rate, and new adaptive transmission schemes are proposed. The proposed algorithms are shown to perform well and require the BS only to know the statistical CSI of each user.

With mean or covariance channel feedback, the input covariance matrix can be designed to achieve the ergodic capacity of a MIMO fading channel. It is known that the eigenvectors of the optimal input covariance matrix are the same as the eigen-vectors of the channel mean or covariance matrix. However, the optimal power allocation across the eigen-vectors is much less understood. In this paper, two scenarios are investigated: (1) Rician MIMO channels with mean channel feedback, and (2) Rayleigh MIMO channels with covariance channel feedback. We first derive a suboptimal power allocation algorithms in the spatial domain for expected mutual information maximization for two transmit antennas systems, based on an upper bound for the ergodic capacity of a MIMO channel with either channel mean or covariance information at the transmitter. Then, we extend heuristically the results to systems with multiple antennas at both the transmitter and receiver side. The proposed power allocation solution permits a closed-form expression and has a water-filling interpretation. Simulation results reveal that the proposed method performs nearly the same as the optimal solution (which requires highly complex optimization routines over random processes) with inappreciable difference.

We study the sum capacity of the uplink multiuser multiple-input multiple-output (MIMO) multiple-access channel (MAC) in the low signal-to-noise ratio (SNR) regime under double-scattering and Rician fading scenarios. The receiver is assumed to have perfect channel state information (CSI), while each transmitter knows only its statistical CSI. We first derive analytical expressions for the minimum ¿b/N0and the wideband slope of MIMO systems employing statistical beamforming, based on which we then present new analytical approximations for the sum capacity of the MIMO-MAC at low SNRs, and investigate the effects of transmit, receive and scatterer correlation, as well as Rician K-factor.

We investigate the sum capacity of the uplink multiuser multiple-input multiple-output (MIMO) multiple-access channel (MAC) in the low signal-to-noise ratio (SNR) regime. For each user, the MIMO channel is modeled according to a general class of stochastic channel matrices, known as double-scattering. Assuming that each user knows only their own spatial correlation matrices and employs optimal statistical beamforming transmission, we present new analytical approximations for the sum capacity of the MIMO-MAC for low SNR values. Our approximations are accurate, and lead to key insights into the effect of correlation.

This paper investigates the capacity of double-scattering multiple-input multiple-output (MIMO) channels in the low signal to noise ratio (SNR) regime. We first derive analytical expressions for the two key low SNR parameters; namely, the minimum required Eb/N0and wideband slope, assuming statistical channel state information (CSI) at the transmitter. Based on these results, we investigate the effect of transmit, scatter, and receive correlation. For the case of the double-scattering MIMO keyhole channel, we then analyze the low SNR capacity for different levels of transmit CSI. We show that increased transmit CSI leads to a reduced minimum required Eb/N0.

We investigate multiple-input multiple-output (MIMO) eigenmode transmission using statistical channel state information at the transmitter. We consider a general jointly correlated MIMO channel model, which does not require separable spatial correlations at the transmitter and receiver. For this model, we first derive a closed-form tight upper bound for the ergodic capacity, which reveals a simple and interesting relationship in terms of the matrix permanent of the eigenmode channel coupling matrix and embraces many existing results in the literature as special cases. Based on this closed-form and tractable upper bound expression, we then employ convex optimization techniques to develop low-complexity power allocation solutions involving only the channel statistics. Necessary and sufficient optimality conditions are derived, from which we develop an iterative water-filling algorithm with guaranteed convergence. Simulations demonstrate the tightness of the capacity upper bound and the near-optimal performance of the proposed low-complexity transmitter optimization approach.

This paper studies the power allocation problems for a multiple-input multiple-output (MIMO) antenna system with various partial channel state information (CSI) feedback. Through the derivation of new upper bounds of the ergodic capacity for both the cases of channel mean and covariance information feedback, we devise simple closed-form power allocation solutions for the MIMO systems with two transmit antennas. These results are then extended to systems with any number of antennas at both sides. Unlike previous approaches which require computationally complex numerical optimizations over random processes, our solutions in closed-form offer a water-filling interpretation and perform nearly the same as the global optimum.

We investigate the sum capacity of a fading multiple-input multiple-output (MIMO) multiple access channel (MAC) under a general class of fading, known as double-scattering. We assume the receiver has perfect channel state information (CSI), while the transmitters only have access to statistical CSI. We show that the optimum transmit directions for each user coincide with the eigenvectors of the user’s own transmit spatial correlation matrix. We also derive new closed-form upper bounds on the sum capacity of the MIMO-MAC under double-scattering, which we employ to obtain suboptimal power allocation policies. These policies are easy to compute, and require each user to know only their own channel statistics.

We investigate the capacity of a general class of MIMO channels, known as double-scattering channels. Our results assume perfect channel state information (CSI) at the receiver and statistical CSI at the transmitter. We first derive the optimal capacity-achieving signaling directions, and show that they correspond to the eigenvectors of the transmit spatial correlation matrix. We then derive new simple closed-form power allocation policies which are shown to yield negligible capacity loss compared with the optimal power allocation policy, without requiring complicated numerical optimization methods to compute. Finally we derive a necessary and sufficient condition for which a beamforming approach (i.e. rank-1 transmission) achieves the ergodic capacity.

In this paper, we consider a general family of MIMO channels known asdoublescatteringchannels, which encompass a variety of propagation environments from independent and identically distributed Rayleigh to degenerate Keyhole or pinhole cases by embracing both rank-deficiency and spatial correlation effects. We investigate the optimal transmit strategy of a single-user MIMO systems with channel statistical information feedback in the presence of double scattering. We show that transmitting in the direction of the eigenvectors of the transmit correlation matrix is the optimal transmission strategy under the criteria of maximizing the ergodic capacity. In addition to this, the power allocation problem is studied and a necessary and sufficient condition for optimality of beamforming is derived.

In this paper, we consider the power allocation problem for single-user multiple-input multiple-output (MIMO) systems. We propose new sub-optimal power allocation algorithms for MIMO systems with channel mean or covariance information at the transmitter, through the maximization of a lower bound of the ergodic channel capacity. Assume that the receiver has perfect channel knowledge. The algorithms proposed here need only the channel statistical information, and are very useful for systems in which only the channel statistical information is accessible to the transmitter. Simulation results confirm the performance.

The problem of transmit antenna subset selection is considered for Rayleigh fading MIMO (multiple-input multi-output) channels with transmit correlation. We provide an active antennas control algorithm for minimum mean square error (MMSE) receivers, assuming the transmitter correlation matrices are available. Using Bartlett's decomposition and Cauchy-Binet theorem, we derive a tight closed-form lower bound on the capacity of correlated Rayleigh MIMO channels. Based on this lower bound, we propose a transmit antenna selection criterion in terms of channel capacity maximization. Monte Carlo simulation results also validate our theoretical analysis.