In this paper, we investigate the channel estimation problem in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, explicitly accounting for both spherical-wave propagation characteristics and spatial non-stationary effects. Building on these properties, we propose a novel model-based deep learning framework that delivers high-accuracy channel estimation with low computational complexity by tightly integrating domain knowledge and data-driven learning. Specifically, the proposed framework comprises three key unfolding networks: a sparse channel recovery network, a codebook update network, and an error cancellation network. The first network, referred to as variational Bayesian inference (VBI)-Net, is derived by unfolding the inverse-free VBI (IF-VBI) algorithm. It enables high-precision sparse channel reconstruction without requiring explicit prior assumptions, by learning the underlying precision distribution directly from data. The second network, gradient (Grad)-Net, is developed by unfolding the gradient ascent procedure, where learnable step sizes are introduced to adaptively refine the parameters of the polar-domain grids. Moreover, Grad-Net captures spatial non-stationary characteristics associated with the polar-domain representation by jointly exploiting gradient information and estimated path parameters. The third network, termed projected gradient descent (PGD)-Net, is constructed by unfolding the PGD algorithm. It iteratively refines the channel estimates and effectively suppresses residual estimation errors induced by spherical-wave propagation and spatial non-stationarity. Extensive numerical simulations demonstrate that the proposed framework significantly outperforms existing methods in both estimation accuracy and computational efficiency. Furthermore, the proposed framework achieves a superior accuracy-complexity tradeoff for practical XL-MIMO systems, delivering enhanced performance while maintaining very low computational complexity.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are pivotal to next-generation wireless communications, where dynamic RF chain architectures offer enhanced performance. However, efficient precoding in such systems requires accurate channel state information (CSI) obtained with low complexity. To address this challenge, spatial-domain channel extrapolation has attracted growing interest. Existing methods often overlook near-field spherical wavefronts or rely heavily on sparsity priors, leading to performance degradation. In this paper, we propose an adaptive near-field channel extrapolation framework for multi-subcarrier XL-MIMO systems, leveraging a strategically selected subset of antennas. Subsequently, we develop both on-grid and off-grid algorithms, where the latter refines the former’s estimates for improved accuracy. To further reduce complexity, a cross-validation (CV)-based scheme is introduced. Additionally, we analytically formulate the mutual coherence of the sensing matrix and propose a coherence-minimizing-based random pattern to ensure robust extrapolation. Numerical results validate that the proposed algorithms significantly outperform existing methods in both extrapolation accuracy and achievable rate, while maintaining low computational complexity. In particular, our proposed CV ratio offers a flexible trade-off between accuracy and efficiency, and the corresponding off-grid algorithm achieves high accuracy with complexity comparable to conventional on-grid methods.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is gaining attention as a prominent technology for enabling the sixth-generation (6G) wireless networks. However, the vast antenna array and the huge bandwidth introduce a non-negligible beam squint effect, causing beams of different frequencies to focus at different locations. One approach to cope with this is to employ true-time-delay lines (TTDs)-based beamforming to control the range and trajectory of near-field beam squint, known as the near-field controllable beam squint (CBS) effect. In this paper, we investigate the user localization in near-field wideband XL-MIMO systems under the beam squint effect and spatial non-stationary properties. Firstly, we derive the expressions for Cramér-Rao Bounds (CRBs) for characterizing the performance of estimating both angle and distance. This analysis aims to assess the potential of leveraging CBS for precise user localization. Secondly, a user localization scheme combining CBS and beam training is proposed. Specifically, we organize multiple subcarriers into groups, directing beams from different groups to distinct angles or distances through the CBS to obtain the estimates of users’ angles and distances. Furthermore, we design a user localization scheme based on a convolutional neural network model, namely ConvNeXt. This scheme utilizes the inputs and outputs of the CBS-based scheme to generate high-precision estimates of angle and distance. The numerical results derived from CRBs reveal that the inherent spatial non-stationary characteristics notably increase the CRB for angle, but have an insignificant impact on the CRB for distance estimation. In addition, the CRBs for both angle and distance decrease with increasing bandwidth and number of subcarriers. More importantly, our proposed ConvNeXt-based user localization scheme achieves centimeter-level accuracy in localization estimates.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is an emerging transceiver technology for enabling next-generation communication systems, due to its potential for substantial enhancement in both the spectral efficiency and spatial resolution. However, the achievable performance limits of various promising XL-MIMO configurations have yet to be fully evaluated, compared, and discussed. In this paper, we develop an effective degrees of freedom (EDoF) performance analysis framework specifically tailored for near-field XL-MIMO systems. We explore five representative distinct XL-MIMO hardware designs, including uniform planar array (UPA)-based with infinitely thin dipoles, two-dimensional (2D) continuous aperture (CAP) plane-based, UPA-based with patch antennas, uniform linear array (ULA)-based, and one-dimensional (1D) CAP line segment-based XL-MIMO systems. Our analysis encompasses two near-field channel models: the scalar and dyadic Green’s function-based channel models. More importantly, when applying the scalar Green’s function-based channel, we derive EDoF expressions in the closed-form, characterizing the impacts of the physical size of the transceiver, the transmitting distance, and the carrier frequency. In our numerical results, we evaluate and compare the EDoF performance across all examined XL-MIMO designs, confirming the accuracy of our proposed closed-form expressions. Furthermore, we observe that with an increasing number of antennas, the EDoF performance for both UPA-based and ULA-based systems approaches that of 2D CAP plane and 1D CAP line segment-based systems, respectively. Moreover, we unveil that the EDoF performance for near-field XL-MIMO systems is predominantly determined by the array aperture size rather than the sheer number of antennas.

The key technologies of sixth generation (6G), such as ultra-massive multiple-input multiple-output (MIMO), enable intricate interactions between antennas and wireless propagation environments. As a result, it becomes necessary to develop joint models that encompass both antennas and wireless propagation channels. To achieve this, we utilize the multi-port communication theory, which considers impedance matching among the source, transmission medium, and load to facilitate efficient power transfer. Specifically, we first investigate the impact of insertion loss, mutual coupling, and other factors on the performance of multi-port matching networks. Next, to further improve system performance, we explore two important deep unfolding designs for the multi-port matching networks: beamforming and power control, respectively. For the hybrid beamforming, we develop a deep unfolding framework, i.e., projected gradient descent (PGD)-Net based on unfolding projected gradient descent. For the power control, we design a deep unfolding network, graph neural network (GNN) aided alternating optimization (AO)-Net, which considers the interaction between different ports in optimizing power allocation. Numerical results verify the necessity of considering insertion loss in the dynamic metasurface antenna (DMA) performance analysis. Besides, the proposed PGD-Net based hybrid beamforming approaches approximate the conventional model-based algorithm with very low complexity. Moreover, our proposed power control scheme has a fast run time compared to the traditional weighted minimum mean squared error (WMMSE) method.

In this paper we study the effective degree of freedom (EDoF) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. We consider two XL-MIMO hardware designs, uniform planar array (UPA) based and continuous aperture (CAP) based XL-MIMO, as well as two representative near-field channel models: scalar Green function based and dyadic Green function with triple polarization based models. First, for UPA-based XL-MIMO with a discrete array aperture, we evaluate the EDoF performance by applying discrete channel matrices generated by the scalar or dyadic Green channel model. Then, for CAP-based XLMIMO, a tailored EDoF performance evaluation framework for a two-dimensional (2D) CAP plane based system is constructed by leveraging asymptotic analysis and extending the analysis approaches for a one-dimensional (1D) CAP line segment based system. This framework incorporates the triplepolarized auto-correlation kernel function, which can efficiently capture the impact of multiple polarization on the EDoF performance. Numerical results show that, with an increase in the number of antennas, the UPA-based XL-MIMO system can achieve an EDoF performance close to the EDoF performance for the CAP plane based XL-MIMO system. Moreover, the EDoF performance can be enhanced by the multiple polarization in channels and increased physical size of the transceiver.

The reconfigurable intelligent surface (RIS) technology holds great promise for the advancement of future sixth-generation networks. However, existing research on RIS-assisted communication systems often relies on ideal hardware and static channel conditions, which are impractical in real-world scenarios. In this study, we assess the performance of a RIS-assisted communication system, considering the combined effects of hardware impairments caused by imperfect transceivers and channel aging resulting from user mobility. To achieve this, we analyze the direct and cascade channels between the base station and the user, assuming correlated Rician distributions. We employ the linear minimum mean square estimation method to estimate the overall channel and derive a closed-form expression for the uplink spectral efficiency (SE). By formulating an optimization problem for RIS phase shift, we maximize SE using the projected gradient ascent algorithm. Monte Carlo simulations reveal the impact of channel aging and hardware impairments on system performance. While practical RIS implementations may introduce phase estimation error in the reflected signal, these errors can be mitigated through phase shift optimization. Overall, our results highlight the significant potential of RIS technology in addressing challenges posed by imperfect hardware and users’ mobility.

Deep learning (DL) has been increasingly adopted for channel state information (CSI) feedback to harness the performance gains promised by massive multiple-input multiple-output (MIMO). Existing DL-based feedback schemes prioritize the accuracy of CSI reconstruction, which results in substantial memory and computational demands, especially when they are unacceptable for user equipment (UE) with limited resources. In this paper, we propose an asymmetric pooling-based network for more efficient CSI compression, named PoolCsiNet, to reduce the associated overhead of exploiting convolutional neural networks (CNN) for CSI compression at the UE. By leveraging the local information of clustered physical channel models, PoolCsiNet incorporates a low-complexity amplitude-pooling algorithm in its encoder at the UE without requiring any trainable parameters. A corresponding decoder structure is also developed to firstly acquire a coarse CSI and then a lightweight feature refiner is constructed to enhance the coarse CSI reconstruction. The parameter-free encoder and CNN-based refiner constitute a novel asymmetric CSI network architecture. Thanks to the parameter-free design of the encoder, memory demand at the UE is minimized, thereby eliminating the need for joint training and parameter updating. Furthermore, considering the sparsity of indoor wireless channels, a PoolCsiNet+, with a dilated-amplitude-pooling (DAP) module, is further proposed to elevate the CSI reconstruction accuracy of the PoolCsiNet. Thanks to a pooling design tailored for clustered channel models, lossy compression of pooling hardly sacrifices CSI features and can be exploited to eliminate information redundancy in CSI. Experiments demonstrate that both asymmetric PoolCsiNet and PoolCsiNet+ significantly improve the quality of CSI reconstruction up to 4 dB compared with existing DL-based methods, while maintaining a memory-free profile and achieving a sevenfold reduction in computational overhead at the UE.

Cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as a promising technology for enabling future wireless communication systems. Significant attention has been generated by its considerable advantages in augmenting degrees of freedom. In this paper, we first investigate a CF XL-MIMO system with base stations equipped with XL-MIMO panels under a dynamic environment. Then, we propose an innovative multi-agent reinforcement learning (MARL)-based power control algorithm that incorporates predictive management and distributed optimization architecture, which provides a dynamic strategy for addressing high-dimension signal processing problems. Specifically, we compare various MARL-based algorithms, which shows that the proposed MARL-based algorithm effectively strikes a balance between spectral efficiency (SE) performance and convergence time. Moreover, we consider a double-layer power control architecture based on the large-scale fading coefficients between antennas to suppress interference within dynamic systems. Compared to the single-layer architecture, the results obtained unveil that the proposed double-layer architecture has a nearly 24% SE performance improvement, especially with massive antennas and smaller antenna spacing.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technology for the future sixth-generation (6G) networks to achieve higher performance. In practice, various linear precoding schemes, such as zero-forcing (ZF) and regularized zero-forcing (RZF) precoding, are capable of achieving both large spectral efficiency (SE) and low bit error rate (BER) in traditional massive MIMO (mMIMO) systems. However, these methods are not efficient in extremely large-scale regimes due to the inherent spatial non-stationarity and high computational complexity. To address this problem, we investigate a low-complexity precoding algorithm, e.g., randomized Kaczmarz (rKA), taking into account the spatial non-stationary properties in XL-MIMO systems. Furthermore, we propose a novel mode of randomization, i.e., sampling without replacement rKA (SwoR-rKA), which enjoys a faster convergence speed than the rKA algorithm. Besides, the closed-form expression of SE considering the interference between subarrays in downlink XL-MIMO systems is derived. Numerical results show that the complexity given by both rKA and SwoR-rKA algorithms has 51.3% reduction than the traditional RZF algorithm with similar SE performance. More importantly, our algorithms can effectively reduce the BER when the transmitter has imperfect channel estimation.

In New Radio (NR) massive multiple input multiple output (MIMO) system, accurate acquisition of downlink channel state information (CSI) at the base station (BS) is of great importance. However, the overhead of CSI feedback becomes larger with the increased number of BS antennas. In order to further reduce feedback overhead, deep learning (DL)-based approaches are recently proposed to solve this problem. In this paper, we propose a DL model using a neural network for implicit CSI feedback mechanism to compare the existing feedback method based on the eType II codebook introduced by the 3rd Generation Partnership Project (3GPP) Rel-16. Simulation results show that DL-based approach has higher performance gains in comparison with the standard method and shows great potential in future MIMO systems.

High-accuracy indoor positioning plays an important role in our daily life, due to the growing demand for indoor navigation, emergency rescue, product positioning in industrial environments, and other use cases. With the development of wireless communication technology, there are more options to improve indoor positioning. The time-domain channel impulse response (CIR) which contains the characteristics of multipath is an excellent input choice. In this paper, we focus on combining CIR received from new radio (NR) technology with deep learning methods to solve indoor positioning problems. We analyze some potential aspects to improve previous indoor positioning algorithms and propose new architecture. It should be mentioned that there is no need to identify line-of-sight (LoS) or non-line-of-sight (NLoS) paths when using our method. The simulation is based on a typical indoor factory scenario defined for 3GPP NR positioning enhancements study with low LoS probability. The model based on the proposed network obtains an outstanding performance with the ninety percent positioning error less than 0.5m, which is much better than typical positioning models. In addition, experiments are provided to show the efficiency of different improvement strategies.

In this paper, we investigate the performance of an RIS-aided wireless communication system subject to outdated channel state information that may operate in both the near- and far-field regions. In particular, we take two RIS deployment strategies into consideration: (i) the centralized deployment, where all the reflecting elements are installed on a single RIS and (ii) the distributed deployment, where the same number of reflecting elements are placed on multiple RISs. For both deployment strategies, we derive accurate closed-form approximations for the ergodic capacity, and we introduce tight upper and lower bounds for the ergodic capacity to obtain useful design insights. From this analysis, we unveil that an increase of the transmit power, the Rician-$K$factor, the accuracy of the channel state information and the number of reflecting elements help improve the system performance. Moreover, we prove that the centralized RIS-aided deployment may achieve a higher ergodic capacity as compared with the distributed RIS-aided deployment when the RIS is located near the base station or near the user. In different setups, on the other hand, we prove that the distributed deployment outperforms the centralized deployment. Finally, the analytical results are verified by using Monte Carlo simulations.

We study the precoding for the high-rank MIMO in the LTE-A Pro systems. Unlike the low-rank precoding, layer mapping has a relatively large impact on the performance for high-rank precoding. First, we construct a model on the relationship between layer mapping and precoding. Then we derive the system throughput with the resulting model, so that the performance of the layer mapping and precoding can be evaluated. Further, we operate a sub-space optimization for the codebook-based precoding to maximize the system throughput. Simulation results show that after optimizing layer mapping, high-rank precoding achieves much better performance.

Ultra-dense network deployment is a clear trend considered for the next generation networks. To allow flexible deployment of small cells, self-backhauled small cell architecture is one of the important types of the future ultra-dense network architecture. In this paper, densification and virtualization are considered in both cell and user aspects. Virtualization of radio access network provides a key solution to mobility and interference issues. It enables user centric radio access. On the terminal side, more devices are expected to be connected in the network. Similarly, virtualization can be applied to the terminal side. This enables virtual resource allocation and coordination among terminals to boost up the user capability and overall network performance. This paper proposes a novel small cell architecture which considers multi-layer virtualization for dense heterogeneous network.

Massive MIMO is a promising technique to provide high data rate with good energy efficiency for the future wireless cellular communication. However, its performance benefit often can be realized only when accurate channel state information (CSI) is available at the transmitter to perform accurate beamforming. With large number of antennas, full CSI consumes too much overhead to feed back without compression. To reduce CSI feedback overhead, CSI feedback scheme with dual stage precoding structure is designed to quantize the long term spatial channel correlation information and short term linear precoder information. In this paper, we discuss how to optimize this dual stage precoding scheme in the typical dual-polarized massive MIMO system. The eigenvalues of spatial correlation matrix are used to improve feedback efficiency. By relaxing the constant modulus constraint in codebook design, more flexible long term precoding can be used and adapt to the channel. A specific structure of long term precoding matrix for dual-polarized MIMO system is proposed to ensure the orthogonality of the final precoder for multi-layer transmission.