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 multiple-input multiple-output (XL-MIMO) technology, offering vast spatial degrees of freedom by deploying a huge number of antennas, is a promising enabling technology to empower sixth-generation mobile networks (6G). The XL-MIMO channel model is a prerequisite of XL-MIMO technology optimization, system design, and performance evaluation. In this paper, we provide an overview of challenges and ongoing research in XL-MIMO channel measurement, characterization, and modeling. In particular, characterizing and modeling near-field effects and spatial non-stationarity (SnS) are discussed. Also, the channel modeling methods that can describe these new channel characteristics are surveyed. Furthermore, open issues in XL-MIMO channel measurement, characterization, and modeling are presented to give insights into future XL-MIMO channel research.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multiuser near-field wideband communication system is investigated, in which a robust deep reinforcement learning (DRL) based algorithm is proposed to enhance the users’ achievable rate by jointly optimizing the active beamforming at the base station (BS) and passive beamforming at the STAR-RIS. To mitigate the beam split issue, the delay-phase hybrid precoding structure is introduced to facilitate wideband beamforming. Considering the coupled nature of the STAR-RIS phase-shift model, the passive beamforming design is formulated as a problem of hybrid continuous and discrete phase-shift control, and the proposed algorithm controls the high-dimensional continuous action through hybrid action mapping. Additionally, to address the issue of biased estimation encountered by existing DRL algorithms, a softmax operator is introduced into the algorithm to mitigate this bias. Simulation results illustrate that the proposed algorithm outperforms existing algorithms and overcomes the issues of overestimation and underestimation.

With the development of millimeter-wave (mmWave) communication systems, large-scale reconfigurable intelligent surfaces (RISs) have gained considerable attention as a promising technology for signal strength enhancement and coverage extension. However, as the antenna scale and bandwidth increase, RIS-assisted wideband orthogonal frequency division multiplexing (OFDM) communication systems face challenges due to the near-field range expansion and the beam split effect over the high-frequency band, complicating the acquisition of channel state information (CSI). To tackle these challenges, we present a codebook-based three-stage beam training scheme by using the beam split effect to bypass CSI estimation. Specifically, by analyzing the beam split effect in RIS-assisted OFDM communication systems, we propose a beam-split-aware codebook capable of covering both the near and far fields with fewer codewords compared to conventional narrow-band codebooks. Using such a codebook, a three-stage beam training mechanism is adopted to obtain the optimal codeword with low time overhead, thereby facilitating subsequent beamforming. Simulation results demonstrate that the proposed scheme outperforms existing near- and far-field codebook-based schemes in terms of the beam training resolution and sum rate in the hybrid near-far field.

This study proposes a two-timescale transmission scheme for extremely large-scale reconfigurable intelligent surface aided (XL-RIS-aided) massive multi-input multi-output (MIMO) systems in the presence of visibility regions (VRs). The beamforming of base stations (BSs) is designed based on rapidly changing instantaneous channel state information (CSI), while the phase shifts of RIS are configured based on slowly varying statistical CSI. Specifically, we first formulate a system model with spatially correlated Rician fading channels and introduce the concept of VRs. Then, we derive a closed-form approximate expression for the achievable rate and analyze the impact of VRs on system performance and computational complexity. Then, we solve the problem of maximizing the minimum user rate by optimizing the phase shifts of RIS through an algorithm based on accelerated gradient ascent. Finally, we present numerical results to validate the performance of the considered system from different aspects and reveal the low system complexity of deploying XL-RIS in massive MIMO systems with the help of VRs.

Near-field communication using large-scale antenna arrays is one of the hot research topics in the sixth-generation (6G) wireless communication. Reconfigurable intelligent surface (RIS) is a cost-effective method for manipulating electromagnetic waves in the near field. We propose a 2-bit dual-polarized RIS that has the merits of low cost, low power consumption, high phase accuracy, and polarization diversity. Each element consists of an aperture-coupled microstrip patch, two single-pole-four-throw (SP4T) switches, and two groups of microstrip delay lines. Two-bit phase shift is achieved by using only one SP4T switch that controls the connection of four parallel delay branches. Dual polarization is generated by placing two orthogonal slots with two 2-bit phase shifters. A 15×15 RIS prototype operating in the 3.6 GHz band is fabricated and measured. The beam can be scanned in the ±60° range, with a peak aperture efficiency of 40.1% for horizontal polarization and 38.3% for vertical polarization. What is more, the total power consumption of the RIS is merely about 100 mW, which is very attractive for massive deployment in 6G near-field communication.

This paper investigates the joint estimation of multi-targets’ position and velocity for a terahertz multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system operating in the near field based on tensor decomposition. The waveforms transmitted from shared antennas carry communication messages and are orthogonal to each other in the frequency domain. The estimation of the position and velocity of multiple targets in the considered near-field scenario is challenging because it involves spherical wavefronts. A signal model based on spherical wavefronts enables higher resolution on spatial position, which, if properly designed, can be used to improve the estimation accuracy. In this paper, we propose a CANDE-COMP/PARAFAC (CP) decomposition-based near-field localization (CP-NFL) algorithm for the joint estimation of the position and velocity of multiple targets. In our proposed method, the received signal is expressed as a third-order tensor; based on its factor matrices we convert the original non-convex optimization problem into a convex one and solve it with CVX tools. Our analysis reveals that the uniqueness in CP decomposition can be guaranteed and the computational complexity of our proposed method is linear to the sum of the third powers of the number of sub-carriers, OFDM symbols, antennas, and targets. Numerical results show that our proposed method has a clear advantage over the existing method in terms of estimation accuracy and computational complexity.