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.

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.

Compact ultra-massive antenna arrays (CUMA) share key characteristics with holographic communication systems, featuring densely spaced and individually controlled antenna elements that enable precise manipulation of electromagnetic waves. In this paper, we investigate the spectral efficiency (SE) of CUMA deployed within some constrained physical space. Departing from prior works that assume ideal isotropic antennas, we derive a closed-form expression for the SE assuming a line-of-sight (LoS) channel at the electromagnetic level, explicitly accounting for mutual coupling and antenna orientation. The analysis reveals that the channel gain is highly sensitive to both the array orientation and individual antenna directions. In the single-user case, our results show that the optimal orientation of the user array is either aligned parallel or perpendicular to the signal direction, depending on the inter-element spacing. Notably, near-optimal channel gain is achieved when individual antennas are oriented perpendicular to the signal direction. In the multi-user case, we further optimize transceiver configurations under mutual coupling constraints. Simulation results confirm that SE is strongly influenced by the directional alignment of user antennas and array placement in the near-field regime. CUMA significantly outperforms traditional half-wavelength spaced arrays in terms of SE when constrained to the same physical aperture.

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 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.

Large AI models (LAMs) offer new opportunities for wireless intelligence, but their deployment in latency- and resource-constrained systems remains challenging. To explore this in the context of channel state information (CSI) feedback for frequency-division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems, we propose a novel framework, OLAMCF, that integrates LAMs via offline codebook optimization, thereby avoiding the need for real-time inference. Specifically, the large vision model (LVM) at the core of this framework is built upon a vision-based backbone, pre-trained on large-scale image datasets and fine-tuned with site-specific CSI. This strategy allows this framework to capture the structural similarity between CSI and image to refine codewords from the conventional codebook and generate customized codebooks tailored to the specific environments. Simulation results show that our approach significantly outperforms existing schemes in both reconstruction accuracy and system throughput, without introducing additional inference latency or computational overhead. This design philosophy—extracting the best offline and discarding the rest online—offers a practical perspective on integrating LAMs into communication systems.

Mid-band extra large-scale massive multiple-input multiple-output (XL-MIMO) systems are regarded as potential enablers in future communication systems, which are also trapped in high complexity and channel state information (CSI) acquisition overhead. In this paper, an efficient distributed XL-MIMO structure is first considered, and a novel transmission strategy is proposed by utilizing the joint instantaneous and statistical CSI. Specifically, a user scheduling scheme based on user locations is first presented. Subsequently, approximations of ergodic spectral efficiency (SE) are derived, serving as the basis of analog beamforming. Additionally, the signal processing at the local units and the central unit is carried out utilizing instantaneous CSI. Numerical results demonstrate that the proposed distributed XLMIMO structure and transmission strategy, leveraging angular division in a distributed manner, are capable of achieving SE comparable to that of a fully digital structure.

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).

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.

To reduce the computational complexity of near-field channel estimation for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, the concept ofvirtual cell subarraysin subarray hybrid precoding architectures, is firstly given. Multiple subarrays with strong correlation can be flexibly and dynamically combined, to jointly extract relevant features of the channels. Based on this, an undersampling matching and oversampling refinement pursuit (UMORP) algorithm is proposed, which can detect the channel parameters of cell subarrays through an undersampling dictionary constructed by analog phase shifters. This approach facilitates the estimation of the channel of a single cell subarray with much fewer pilots and lower hardware capability requirement. Then, a multiple-path decoupled spatial extrapolation (MPDSE) scheme is proposed for fully dimensional channel reconstruction, which orthogonally decouples multiple paths from the received signal of a single cell subarray firstly, and then utilize the spatial correlation between adjacent cell subarrays to extrapolate the channels with low cost. Moreover, to further reduce the computational complexity in XL-MIMO systems, a spatial multiple cell subarrays joint extrapolation (SMCJE) scheme is also proposed. Based on the spatial geometry among cell subarrays, only several cell suabrrays are utilized to jointly estimate the distances and reconstruct the fully dimensional near-field channel, achieving a low level of computational complexity. Our simulation results verify that the proposed schemes perform better in XL-MIMO systems, while requiring fewer pilots and exhibiting much lower computational complexity.

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.

In the near-field region of an extremely large-scale multiple-input multiple-output (XL MIMO) system, channel reconstruction is typically addressed through sparse parameter estimation based on compressed sensing (CS) algorithms after converting the received pilot signals into the transformed domain. However, the exhaustive search on the codebook in CS algorithms consumes significant computational resources and running time, particularly when a large number of antennas are equipped at the base station (BS). To overcome this challenge, we propose a novel scheme to replace the high-cost exhaustive search procedure. We visualize the sparse channel matrix in the transformed domain as a channel image and design the channel keypoint detection network (CKNet) to locate the user and scatterers in high speed. Subsequently, we use a small-scale newtonized orthogonal matching pursuit (NOMP) based refiner to further enhance the precision. Our method is applicable to both the Cartesian domain and the Polar domain. Additionally, to deal with scenarios with a flexible number of propagation paths, we further design FlexibleCKNet to predict both locations and confidence scores. Our experimental results validate that the CKNet and FlexibleCKNet-empowered channel reconstruction scheme can significantly reduce the computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction tasks.

In the near-field region of an extremely large-scale MIMO (XL MIMO) system, channel reconstruction can be solved by utilizing sparse parameter estimation after transforming the received pilots at the base station (BS) into Cartesian domain. However, the process of exhaustive search over the codebook consumes significant computational resources and running time, especially when dealing with a vast number of antennas. In this study, we visualize the sparse channel matrix in the Cartesian domain as an channel image and propose a deep neural network, i.e., channel keypoint detection network (CKNet), to locate the user and scatterers. Subsequently, we employ a straightforward Newton optimization module to fine-tune the estimations. Experimental results demonstrate that the CKNet-empowered channel reconstruction scheme substantially reduces computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction.

Visibility region (VR) is known as a key channel characteristic appeared in extra-large massive MIMO (XL-MIMO) systems, which can be exploited to facilitate low-complexity transmission design. Existing VR recognition method requires an a priori location-Vrdataset, with which a user's VR can be estimated given its location. This dataset is constructed by selecting some beacon users (BUs) to estimate the VR at their locations via uplink training. Constrained by the available training resource and possible environmental variation, practical size of the dataset is usually limited; how to efficiently select BUs for better VR recognition accuracy is therefore important. To this end, we propose and compare three BU selection methods, including random selection, minimum spacing constrained (MSC) selection, and a more sophisticated method (denoted as dynamic boundary refining, DBR) which utilizes partial of BUs for exploring unknown environment, while selecting the other BUs for further refining already-estimated VR region boundaries. Simulation results show that with a small number of BUs, both MSC and DBR achieve similar VR recognition performance and outperform random selection; as the number of BUs becomes larger, DBR achieves the best recognition accuracy.

In “Channel Estimation for Massive MIMO-OTFS System in Asymmetrical Architecture,” by Chen et al., a two-stage channel estimation scheme is proposed based on the input-output relationship of orthogonal time frequency space (OTFS) modulation in asymmetrical architecture. This correspondence provides some comments and corrections to the derivation of the OTFS input-output relationship published in [1].

In this paper, an efficient near-field channel estimation algorithm, and a novel cascade channel reconstruction scheme, with significantly reduced pilot overhead and computational complexity, are proposed for hybrid extra large-scale reconfigurable intelligent surface (XL-RIS)-aided multi-input multi-output (MIMO) systems. A unique hybrid XL-RIS architecture is devised, in which the elements at the designed central subarray, and many specially selected discrete elements, are active, while others are passive. Meanwhile, a damped Newtonized orthogonal matching pursuit algorithm combining the planar and spherical wave models (DNOMP-CPSW) is proposed, in which the angle and distance parameters of multipaths are estimated through the received signals of the central subarray and the discrete active elements respectively, and the near-field channel can be reconstructed accurately with low pilot overhead and computational complexity. Moreover, to decrease the cost of cascade channel reconstruction in the considered system, a separate channel estimation scheme based on the decoupling operation (SCEDO) is proposed, which estimates the two separate channels with only 3 pilots and reduced computational complexity, and then reconstruct the cascade channel. Furthermore, the phase shift strategy of the XL-RIS with 2-bits quantization is devised, which can increase the energy of the received signal and maintain the set order to estimate multipaths in different stages of the SCEDO scheme, to improve the accuracy of the estimates, and enhance the reliability of the separate channel estimation. Simulation results verify that the proposed DNOMP-CPSW algorithm and the hybrid XL-RIS phase shift strategy can enhance the performance of the considered system. Compared with other schemes, the SCEDO scheme can reconstruct the cascade channel efficiently, with much reduced pilot overhead and computational complexity.

To harness the benefits of both reconfigurable intelligent surface (RIS) and dynamic metasurface antenna (DMA), we consider the hybrid RIS and DMA assisted multiple-input multiple-output (MIMO) multiple-access channel (MAC) over spatially correlated Rician fading, in which multiple multi-antenna users send the transmitted signals to the DMA-based base station (BS) with the assistance of a RIS. The objective is to maximize the achievable ergodic sum-rate by jointly designing the transmit covariance matrix of users, the phase shift matrix of RIS, and the DMA weight matrix at BS only with statistical channel state information. By capitalizing on large random matrix theory, a closed-form asymptotic ergodic sum-rate is first obtained. Then, we propose a modified water-filling algorithm to design the optimal transmit covariance matrix under the power consumption and specific absorption rate constraints. Next, we design the phase shift matrix of RIS via the projected gradient ascent algorithm, subject to the non-convex unit-modular constraint. To find the constrained DMA weight matrix, we further resort to the optimal solution of the unconstrained DMA problem and adopt the alternating optimization method. The proposed algorithm is numerically shown to improve the sum-rate compared to the baseline schemes, verifying the effectiveness of the proposed schemes.

Extra large-scale multiple-input multiple-output (XL-MIMO) is a key technology for future wireless communication systems. This paper considers the effects of visibility region (VR) at the base station (BS) in a non-stationary multi-user XL-MIMO scenario, where only partial antennas can receive users’ signal. In time division duplexing (TDD) mode, we first estimate the VR at the BS by detecting the energy of the received signal during uplink training phase. The probabilities of two detection errors are derived and the uplink channel on the detected VR is estimated. In downlink data transmission, to avoid cumbersome Monte-Carlo trials, we derive a deterministic approximate expression for ergodic average energy efficiency (EE) with the regularized zero-forcing (RZF) precoding. In frequency division duplexing (FDD) mode, the VR is estimated in uplink training and then the channel information of detected VR is acquired from the feedback channel. In downlink data transmission, the approximation of ergodic average EE is also derived with the RZF precoding. Invoking approximate results, we propose an alternate optimization algorithm to design the detection threshold and the pilot length in both TDD and FDD modes. The numerical results reveal the impacts of VR estimation error on ergodic average EE and demonstrate the effectiveness of our proposed algorithm.

In this paper, an efficient near-field channel reconstruction and user equipment (UE) localization scheme is proposed for extremely large antenna array (ELAA) systems using a subarray hybrid precoding architecture. Considering the non-negligible signal amplitude and phase variations across the different receive antennas, a more realistic channel model is adopted. The channel environment, with an approximate smooth ground surface, is modeled. In fact, the channel can be divided into a line-of-sight (LoS) path, a reflection path and some non-LoS (NLoS) paths. Based on the sparsity of the channel in the spatial domain, the damped Newtonized orthogonal matching pursuit (DNOMP) algorithm is also proposed to accurately estimate the multipaths, and reconstruct the channel. Then, a UE localization algorithm is proposed, which can detect the existence of the LoS path and locate the UE. A joint localization algorithm is also devised to further increase the positioning reliability. Simulation results verify that the DNOMP algorithm can reconstruct the channel with better NMSE performance than other schemes. The localization algorithm can locate the UE with low error whenever the LoS path exists or not, with an accuracy close enough to the position error bound (PEB), while the joint localization algorithm can further enhance the positioning reliability.

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.

We investigate a new deployment form of reflective intelligent surface (RIS), which aims at enhancing the quality of service of a main communication system in a target region, while without the need of changing its transmission protocol and scheme (i.e., the RIS is “transparent” to the main system). To this end, we mathematically formulate a coverage enhancement problem, where a RIS is used transparently in the sense that the BS can be unaware of its existence, while the minimum channel link strength, measured from every BS antenna to any point in the target region, can be maximized. The formulated problem is non-convex with mixed discrete-continuous variables. To tackle this challenge, we recast it into a convex feasibility problem via spatial sampling and semi-definite relaxation. Based on a derived analytical upper bound on the link strength difference between any two location points, we further characterize the coverage-similarity region of a given location, and accordingly propose an improved spatial sampling scheme for efficient implementation. Simulation results show that the proposed transparent RIS design achieves better coverage performance than benchmark schemes. More importantly, it can effectively improve the communication performance without affecting the transmission scheme originally adopted by the main communication system.

We propose a novel two-phase separate channel estimation scheme in reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Based on the sparsity of the channel, the parameters in the user equipment (UE)-RIS channel and RIS-base station (BS) channel can be estimated in two phases and then utilized for channel reconstruction. Different from the cascaded estimation, the proposed method can achieve separate channel estimation and, thus, has higher practicability and creates room for more ingenious transceiver design. Moreover, a new pilot protocol for the RIS phase shift matrix configuration is proposed, such that new users will need only limited pilot resources. Through simulations, we prove that the proposed scheme can achieve precise channel reconstruction with low pilot overhead.

Extra large-scale multiple-input multiple-output (MIMO) has been recognized as one of the potential development directions of massive MIMO. By employing even more antennas than massive MIMO in the fifth-generation era, extra large-scale MIMO can further exploit the spatial domain resources and enable ultra high data rates, low latency communications as well as emerging applications, such as sensing and localization, in sixth-generation mobile communication systems. However, with the increase of the size of the antenna array, and the decrease of the distance between a user and the array, new channel properties, that did not manifest in conventional massive MIMO, start to kick in. Most importantly, existing research strategies pertaining to massive MIMO cannot be directly applied or simply extended to fit the extra large-scale MIMO case. Moreover, increasing the number of antennas will inevitably boost the total cost, which refers to not only the high hardware cost, but also the burden of vast processing and computations as well as the substantial training overhead. In this paper, we make a survey on the state-of-the-art on the new channel properties of and low-cost designs for extra large-scale MIMO systems. Particularly, we pursue a mathematical analysis to explain why the new features appear and illustrate how they affect the system model. Furthermore, we summarize and compare the low-cost designs from various perspectives and give our suggestions from a practical deployment point of view.

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 orthogonal time frequency space (OTFS) is poised to become a pivotal technology for the next generation of mobile communications, due to its inherent robustness against Doppler shift. By combining OTFS technology with massive multiple-input multiple-output (MIMO) technology, users can experience high-quality communication services even in highly mobile scenarios. In this letter, we extend the massive MIMO-OTFS system to an asymmetrical architecture with unequal number of transceiver radio frequency chains. To overcome the channel inconsistency and recover the downlink channel by partial uplink channel, we utilize coprime patterns and propose a channel estimation algorithm that firstly extracts the angle parameters from the virtual array and then estimates the remaining channel parameters, which effectively reduces the three-dimensional search space to two dimensions. Our numerical simulations demonstrate that the proposed algorithm enhances the accuracy of channel estimation with much lower complexity.

Reconfigurable intelligent surfaces (RISs) provide an interface between the electromagnetic world of wireless propagation environments and the digital world of information science. Simple yet sufficiently accurate path loss models for RISs are an important basis for theoretical analysis and optimization of RIS-assisted wireless communication systems. In this paper, we refine our previously proposed free-space path loss model for RISs to make it simpler, more applicable, and easier to use. The impact of the antenna’s directivity of the transmitter, receiver, and the unit cells of the RIS on the path loss is explicitly formulated as an angle-dependent loss factor. The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size. Based on the proposed model, the properties of a single unit cell are evaluated in terms of scattering performance, power consumption, and area, which allows us to unveil fundamental considerations for deploying RISs in high frequency bands. Two fabricated RISs operating in the millimeter-wave (mmWave) band are utilized to carry out a measurement campaign. The measurement results are shown to be in good agreement with the proposed path loss model. In addition, the experimental results suggest an effective form to characterize the power radiation pattern of the unit cell for path loss modeling.

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.

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.

Dual-polarized antenna arrays are widely used to reduce the array aperture and expand the channel capacity in multiple-input multiple-output (MIMO) systems. However, a challenge for doubling the number of antennas is how to reconstruct the large-dimensional channel with low complexity. In this paper, we prove the similarity of the delays, AOAs, and the number of the paths between channels in different polarizations, which is the property of polarization-independency, and verify it through both theoretical analysis and experiments. On basis of the similarity among polarizations, we propose a dual-polarized channel extrapolation scheme with low pilot cost and low computational complexity. Simulations and experiments are conducted to examine the performance of the proposed extrapolation scheme. Results show that the proposed dual-polarized channel extrapolation scheme is feasible in practice and can achieve a good channel reconstruction performance with reduced computational complexity.

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.

Multiband operation in millimeter wave (mmWave) will obtain lots of performance gain by offering larger bandwidth. However, the prerequisite is the acquisition of accurate channel state information (CSI), which is a knotty task with hybrid analog/digital architecture. This study devotes to efficiently reconstruct and track the time drifting multiband channel to keep CSI precise in time division duplex (TDD) mmWave multiple-input-multiple-output system with hybrid analog/digital architecture. Utilizing spatial reciprocity, an efficient multiband channel reconstruction scheme is introduced, which elaborately estimates the central sub-band channel and then reconstructs side sub-band channels from the central one. To this end, a beam training-based Newtonized orthogonal matching pursuit (BT-NOMP) algorithm is proposed to estimate the central sub-band channel. With the help of frequency-independent parameters extracted from BT-NOMP, side sub-bands channel can be well reconstructed with additional low-complexity path gains estimation process. Furthermore, to avoid frequent channel reconstruction in a slightly drifting channel meanwhile keep the CSI accurate, a rotated beam-based channel tracking algorithm is developed using historical observations of channel parameters. Numerical results prove the efficiency of the proposed multiband channel reconstruction scheme and the accuracy of the channel tracking algorithm.

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.

The proof-of-work (PoW) mining process requires a large amount of intensive computing, which leads to some plights such as heavy equipment and fixed access nodes in traditional blockchain networks. A novel mobile blockchain network with the help of a mobile edge computing (MEC) server is presented, where all mobile users participate in the PoW mining process. The traditional Bitcoin network adjusts the target difficulty value to ensure a stable block time. However, for MEC-assisted mobile blockchain networks, the adjusted difficulty value needs to be broadcast to all mobile users, which results in expensive communication costs. To maintain a stable block time of mobile blockchain networks, we formulate the delay-limited computation offloading strategy of the PoW-based mining task as a non-cooperative game that maximizes an individual revenue in the MEC-assisted mobile blockchain network. Specifically, the non-cooperative game problem can be divided into multiple sub-game optimization problems to obtain final solutions for all users. We analyze the sub-game optimization problem and prove the existence of Nash equilibrium (NE) of the non-cooperative game. Moreover, we design an alternating iterative algorithm based on the continuous relaxation and greedy rounding (CRGR) to achieve the NE of this game. Given the sub-optimal delay-limited computation offloading results, we also derive the optimal transmit power for an individual user within the maximum mining delay range. From the analytical results, we can see that the proposed CRGR-based alternating iterative algorithm can efficiently attain the sub-optimal delay-limited computation offloading strategies of all mobile users in the polynomial time. The individual transmit power increases accordingly with the delay-limited computation offloading strategies of all users. Numerical results demonstrate that the proposed CRGR-based alternating iterative algorithm has fast convergence and good stability.

Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this article, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RISs, which may pave the way for further theoretical studies and practical applications in this field.

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.

This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The spatial non-stationarity, which is the key feature of the future extremely large aperture massive MIMO system, is considered. Instead of the channel matrix, the channel model parameters are learned by neural networks to save the overhead and improve the accuracy of channel reconstruction. By viewing the channel as an image, we introduce You Only Look Once (YOLO), a powerful neural network for object detection, to enable a rapid estimation process of the model parameters, including the detection of angles and delays of the paths and the identification of visibility regions of the scatterers. The deep learning-based scheme avoids the complicated iterative process introduced by the algorithm-based parameter extraction methods. A low-complexity algorithm-based refiner further refines the YOLO estimates toward high accuracy. Given the efficiency of model-driven deep learning and the combination of neural network and algorithm, the proposed scheme can rapidly and accurately reconstruct the non-stationary downlink channel. Moreover, the proposed scheme is also applicable to widely concerned stationary systems and achieves comparable reconstruction accuracy as an algorithm-based method with greatly reduced time consumption.

This paper tackles the problem of channel state information acquisition in mobile frequency- division-duplex large scale antenna systems and proposes a novel low-complexity low overhead method to track time-varying channels. Given the spatial reciprocity between uplink and downlink, the frequency independent parameters are tracked from the uplink, greatly reducing the training and feedback overhead in the downlink. The uplink tracking method consists of two major modules. The first detection module works at the initial time instance to accurately estimate parameters by a comprehensive algorithm. Then, the second tracking module works at the subsequent instances to track the changes by utilizing a low-overhead algorithm as well as the parameters obtained at the previous instance. Especially, a simplified dictionary is further designed to decrease the computational complexity of the tracking module. Numerical results demonstrate that the proposed tracking method can successfully detect the newly occurred and disappeared paths, and accurately trace the changes of the time-varying channel.

5G millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate multilateration: large bandwidth, large antenna arrays, and increased densities of base stations allow for unparalleled delay and angular resolution. However, combining localization into communications and designing joint position and velocity estimation algorithms are challenging problems. This paper considers the joint estimation in three-dimensional (3-D) lens antenna array based mmWave CRAN architecture. We embed multilateration into communications and explain its benefits for the initial access and beam training stages. We propose a closed-form solution for the joint estimation problem by forming the pseudo-linear matrix representation and designing the weighted least squares estimator with hybrid measurements. The proposed method is proven asymptotically unbiased and confirmed by simulations to achieve the Cramer- Rao lower bound and attain the desired sub-decimeter level accuracy.

Massive multiple-input multiple-output systems deploying a large number of antennas at the base station considerably increase the spectrum efficiency by serving multiple users simultaneously without causing severe interference. However, the advantage relies on the availability of the downlink channel state information (CSI) of multiple users, which is still a challenge in frequency-division-duplex transmission systems. This paper aims to solve this problem by developing a full transceiver framework that includes downlink channel training (or estimation), CSI feedback, and channel reconstruction schemes. Our framework provides accurate reconstruction results for multiple users with small amounts of training and feedback overhead. Specifically, we first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP) algorithm to extract the frequency-independent parameters (i.e., downtilts, azimuths, and delays) from the uplink. Then, by leveraging the information from these frequency-independent parameters, we develop an efficient downlink training scheme to estimate the downlink channel gains for multiple users. This training scheme offers an acceptable estimation error rate of the gains with a limited pilot amount. Numerical results verify the precision of the eNOMP algorithm and demonstrate that the sum-rate performance of the system using the reconstructed downlink channel can approach that of the system using perfect CSI.

In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. First, we estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high-estimation accuracy. The simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting the promising application of the proposed scheme in large-scale antenna array systems.

This paper faces to the frequency-division-duplex (FDD) three-dimensional (3D) multi-antenna system and proposes a novel downlink channel reconstruction scheme which costs only a small amount of overhead. Uniform planar array (UPA) is employed at the base station (BS) to exploit the 3D space. In the uplink, we first estimate the frequency-independent parameters (delay, vertical and horizontal angles of arrival) by introducing and extending the Newtonized orthogonal matching pursuit (NOMP) algorithm. Both the orthogonal matching pursuit dictionary and the Newton refinement step (NRS) are redesigned according to the spatial propagation model of the signal for the NOMP algorithm. Then, we calculate the downlink gains and feed them back to the BS. The costs for downlink pilots and uplink feedback can be very small. Finally, downlink channel is reconstructed by using the frequency-independent parameters and the downlink gains. Simulation results show that using more antennas helps improve the mean-squared error (MSE) performance of the proposed scheme and can even achieve a better MSE performance than using the linear minimum mean-square error method.

This paper studies analog beam selection schemes of discrete Fourier transform (DFT) based hybrid beamforming systems. We first derive approximations of the achievable rates when maximum-ratio combining (MRC) receiver and maximum-ratio transmitting (MRT) precoder are used in the uplink and downlink, respectively. It is shown that the achievable rate of the hybrid beamforming system is improved with the increase of the number of radio frequency chains. Also, it is found that the orthogonality condition among the line-of-sight (LoS) paths from different users directly determines the interference cancellation capability of the MRC receiver or the MRT precoder. Based on our analytical results, we propose two novel DFT beam selection schemes, referred to as exhausted searching and per-user selection. Numerical results show that the first scheme achieves higher rate while the second one is a simple suboptimal strategy with low complexity, which is practically more attractive.

In this paper, we analyze the effect of phase noise on the downlink ergodic sum-rate of a cloud radio access network. The system comprises of one baseband processing unit (BBU) on the cloud server which coordinates M multi-antenna remote radio heads (RRHs) serving K single-antenna users using regularized zero-forcing precoding. We assume the BBU has all users' data and imperfect channel state information and communicate with RRHs via optical fibers which are referred to as fronthaul links. The effect of phase noise both at RRHs and users is also taken into consideration. A deterministic approximation of downlink ergodic sum-rate is derived based on large dimensional random matrix theory when the numbers of antennas at RRHs and users are asymptotically large with a fixed ratio. From simulation results, it is confirmed that the deterministic approximation is accurate and the effect of phase noise is shown to result in a significant reduction in system performance.

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.