The integration of reconfigurable intelligent surfaces (RIS) with cell-free massive MIMO (CF mMIMO) represents a compelling paradigm for satisfying the stringent reliability and latency demands of ultra-reliable low-latency communication (URLLC). In this framework, distributed access points (APs) provide substantial macro-diversity gains, while dynamically controllable RIS elements facilitate enhanced signal propagation. This paper investigates a practical RIS-aided CF mMIMO system designed for URLLC applications, where communications occur through RIS-reflected links under realistic spatially correlated Rayleigh fading channels, with practical impairments such as RIS phase estimation errors and electromagnetic interference explicitly considered. To evaluate reliability in the short-packet regime, we adopt the decoding error probability (DEP) as the performance metric and derive its analytical expression based on user-side SINR. We formulate a non-convex optimization problem to minimize the maximum DEP among users by jointly optimizing AP beamforming, RIS phase shifts, and blocklength allocation. A hybrid solution framework is proposed, combining deep reinforcement learning for continuous variables with a differential evolution (DE) algorithm for discrete blocklength optimization. Simulation results demonstrate the superior performance of the proposed method over alternating optimization and genetic algorithm (GA) baselines. Notably, increasing the number of AP antennas and transmission blocklength improves network availability, although gains saturate due to inter-user interference and diminishing returns. Moreover, the proposed DE-based algorithm for blocklength optimization consistently outperforms the GA method in terms of both solution quality and computational efficiency.

Reconfigurable intelligent surfaces (RIS) are emerging as a crucial technology to address the energy consumption challenges posed by the widespread deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF mMIMO) systems within future sixth-generation (6G) networks. However, most existing studies on RIS-aided CF mMIMO systems assume ideal hardware and static channel conditions, which deviate from practical deployment scenarios. This work analyzes the performance of a RIS-aided CF mMIMO system by incorporating the combined effects of hardware impairments from non-ideal transceivers and channel aging caused by user mobility. We first characterize both direct and cascaded channels between APs and user equipment, modeling them using correlated Rician fading to capture realistic propagation effects. The overall channel is then estimated via the minimum mean square error method under perfect and imperfect line-of-sight phase knowledge, and we derive an analytical expression for the instantaneous spectral efficiency (SE). We also derive the closed-form expressions of the use-and-then-forget bound with the maximum-ratio transmission precoding method. Building on these insights, we establish an efficient joint optimization framework for beamforming in the AP and phase-shift adaptations in the RIS, exploring an alternating optimization method and a deep-reinforcement learning (DRL)-based algorithm. The numerical results validate our theoretical analysis, illustrating the impact of hardware impairments and channel aging on SE. Although the DRL-based method is scalable and adapts well to dynamic environments, its high computational and memory demands pose challenges for real-time deployment, highlighting a trade-off between performance and feasibility.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) system in the presence of electromagnetic interference (EMI) and channel aging with a Rician fading channel model between the base station (BS) and user equipment (UE). Specifically, we derive the closed-form expression for downlink spectral efficiency (SE) with maximum ratio transmission (MRT) precoding. The Monte-Carlo simulation supports the theoretical results, demonstrating that amplifying the weight of the line-of-sight (LoS) component in Rician fading channels can boost SE, while EMI has a detrimental impact. Furthermore, continuously increasing the number of RIS elements is not an optimal choice when EMI exists. Nonetheless, RIS can be deployed to compensate for SE degradation caused by channel aging effects. Finally, enlarging the RIS elements size can significantly improve system performance.

Reconfigurable intelligent surfaces (RISs) and cell-free (CF) massive multiple-input multiple-output (MIMO) are two promising technologies for sixth-generation (6G) networks. This paper investigates the achievable uplink sum rate of a RIS-aided CF massive MIMO system considering electromagnetic interference (EMI) at the RISs and imperfect channel state information (CSI). Our focus is on proposing an integrated approach that optimizes the beamforming at the access points (APs) and the RIS phase shift alternately using successive convex approximation and penalty convex-concave procedures to maximize the uplink sum rate. The results demonstrate that the proposed algorithm significantly improves the performance of the RIS-aided CF massive MIMO system and effectively mitigates the interference caused by EMI and imperfect CSI. Additionally, we find that the negative impact of EMI becomes more pronounced as the channel uncertainty increases. Moreover, increasing the number of RIS reflecting elements proves beneficial, but the returns diminish as the number of RIS elements becomes sufficiently large. Furthermore, deploying RIS beyond a certain limit of EMI power leads to degradation in system performance.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

Extremely large-scale MIMO (XL-MIMO) is a promising technique for future 6G communications. The sharp increase in the number of antennas results in a transition of electromagnetic propagation from the far-field to the near-field. Due to the near-field effect, the exhaustive near-field beam training at all angles and distances requires very high overhead. The improved fast near-field beam training scheme based on time-delay structure can reduce the overhead, but it suffers from very high hardware costs and energy consumption caused by time-delay circuits. In this paper, we propose a near-field two dimension (2D) hierarchical beam training scheme to reduce the overhead without the need for extra hardware circuits. Specifically, we first formulate the multi-resolution near-field codewords design problem covering different angle and distance coverages. Next, inspired by phase retrieval problems in digital holography imaging technology, we propose a Gerchberg-Saxton (GS)-based algorithm to acquire the theoretical codeword by considering the fully digital architecture. Based on the theoretical codeword, an alternating optimization algorithm is proposed to acquire the practical codeword considering the hybrid digital-analog architecture. Finally, with the designed multi-resolution codebooks, we propose a near-field 2D hierarchical beam training scheme to significantly reduce the training overhead, which is verified by extensive simulation results.

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.

The evolution of multi-input multi-output (MIMO) will develop toward extremely large-scale MIMO (XL-MIMO) for future 6G communications. With the extension of the antenna array, the electromagnetic propagation change from far-field to near-field. Because of the near-field effect, the exhaustive near-field beam training scanning all angles and distances involves very high overhead. The existing fast near-field beam training scheme with extra time-delay circuits can reduce the overhead, but it suffers from very high hardware costs and energy consumption. To solve this issue, we propose a low-overhead near-field two dimension (2D) hierarchical beam training after carefully designing the near-field multi-resolution codebooks. Specifically, we first formulate the problem of designing near-field multi-resolution codewords, which have various angle coverage and distance coverage. Next, we propose a Gerchberg-Saxton (GS)-based algorithm to obtain the theoretical codeword by considering the ideal fully digital architecture, and an alternating optimization algorithm is then proposed to acquire the practical codeword by considering the hybrid digital-analog architecture. Finally, we generate multi-resolution codebooks and propose a near-field 2D hierarchical beam training scheme. Simulation results demonstrate that the proposed scheme can provide a tradeoff between the achievable rate performance and overhead in near-field XL-MIMO beam training.

Accurate channel estimation is essential to empower extremely large-scale MIMO (XL-MIMO) with ultra-high spectral efficiency in 6G networks. With the sharp increase in the antenna array aperture of the XL-MIMO system, the electromagnetic propagation field will change from far-field to near-field. Unfortunately, due to the near-field effect, the existing near-field XL-MIMO channel model mismatches the practical mixed line-of-sight (LoS) and non-line-of-sight (NLoS) channel feature. In this paper, a mixed LoS/NLoS near-field XL-MIMO channel model is proposed to accurately describe the LoS and NLoS path components simultaneously, where the LoS path component is modeled by the geometric free space propagation assumption while NLoS path components are modeled by the near-field array response vectors. Then, to define the range of near-field for XL-MIMO, the MIMO Rayleigh distance (MIMO-RD) is derived. Next, a two stage channel estimation algorithm is proposed, where the LoS path component and NLoS path components are estimated separately. Numerical simulation results demonstrate that, the proposed two stage scheme is able to outperform the existing methods.

Accurate channel model and channel estimation are essential to empower extremely large-scale MIMO (XL-MIMO) in 6G networks with ultra-high spectral efficiency. With the sharp increase in the antenna array aperture of the XL-MIMO scenario, the electromagnetic propagation field will change from far-field to near-field. Unfortunately, due to the near-field effect, most of the existing XL-MIMO channel models fail to describe mixed line-of-sight (LoS) and non-line-of-sight (NLoS) path components simultaneously. In this paper, a mixed LoS/NLoS near-field XL-MIMO channel model is proposed to match the practical near-field XL-MIMO scenario, where the LoS path component is modeled by the geometric free space propagation assumption while NLoS path components are modeled by the near-field array response vectors. Then, to define the range of near-field for XL-MIMO, the MIMO Rayleigh distance (MIMO-RD) and MIMO advanced RD (MIMO-ARD) is derived. Next, a two stage channel estimation algorithm is proposed, where the LoS path component and NLoS path components are estimated separately. Moreover, the Cramér-Rao lower bound (CRLB) of the proposed algorithm is derived in this paper. Numerical simulation results demonstrate that, the proposed two stage scheme is able to outperform the existing methods in both the theoretical channel model and the QuaDRiGa channel emulation platform.

Hybrid precoding design is a high-complexity problem due to the coupling of analog and digital precoders as well as the constant modulus constraint for the analog precoder. Fortunately, the deep learning based hybrid precoding methods can significantly reduce the complexity, but the performance remains limited. In this paper, inspired by the attention mechanism recently developed for machine learning, we propose an attention-based hybrid precoding scheme for millimeter-wave (mmWave) MIMO systems with improved performance and low complexity. The key idea is to design each user’s beam pattern according to its attention weights to other users’. Specifically, the proposed attention-based hybrid precoding scheme consists of two parts, i.e., the attention layer and the convolutional neural network (CNN) layer. The attention layer is used to identify the features of inter-user interferences. Then, these features are processed by the CNN layer for the analog precoder design to maximize the achievable sum-rate. Simulation results demonstrate that the attention layer could mitigate the inter-user interferences, and the proposed attention-based hybrid precoding with low complexity can achieve higher achievable sum-rate than the existing deep learning based method.