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Extremely large-scale antenna array (ELAA) at millimeter wave (mmWave) and Terahertz (THz) band has been considered a key technology for combating high attenuation in high-frequency bands in future 6G communications. Uniform circular arrays (UCAs) have attracted much attention because of their ability to provide flat beamforming gain at all angles. To realize efficient beamforming, beam training is widely used to acquire channel state information. However, with a large antenna number, the beam training overhead in ELAA systems becomes overwhelming. Moreover, with a large bandwidth, the beam defocus effect severely degrades beam training accuracy. To address these issues, this paper proposes a frequency-dependent focusing (FDF)-based beam training scheme to realize effective beam training in near-field wideband ELAA systems with UCA. Specifically, we first analyze the FDF property of UCA, where signals at different subcarriers can simultaneously focus on different distances. Then, by exploiting the FDF property to search different distances using different subcarriers simultaneously, we design a hierarchical codebook and propose an FDF-based beam training scheme. To reveal the effectiveness of the proposed scheme, we compare its necessary beam training overhead with that of existing schemes. Finally, the simulation results demonstrate that the proposed scheme can achieve accurate beam training in near-field wideband UCA systems with a low beam training overhead.

Large-scale multiple-input multiple-output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. For large-scale MIMO systems, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. Unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas, a spectrum-efficient superimposed pilot design for downlink large-scale MIMO scenarios is proposed, where frequency-domain pilots of different transmit antennas occupy completely the same subcarriers in the frequency domain. Meanwhile, spatial–temporal common sparsity of large-scale MIMO channels motivates us to exploit the emerging theory of structured compressive sensing (CS) for reliable MIMO channel estimation, which is realised by the proposed structured subspace pursuit (SSP) algorithm to simultaneously recover multiple channels with low pilot overhead. Simulation results demonstrate that the proposed scheme performs well and can approach the performance bound.

The minimum mean square error (MMSE) signal detection algorithm is near-optimal for uplink multi-user large-scale multiple-input–multiple-output (MIMO) systems, but involves matrix inversion with high complexity. It is firstly proved that the MMSE filtering matrix for large-scale MIMO is symmetric positive definite, based on which a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion is proposed. The complexity can be reduced from 𝒪(K3) to 𝒪(K2), where K is the number of users. The convergence proof of the proposed algorithm is also provided. Simulation results show that the proposed signal detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) at millimeter-wave (mmWave) and terahertz (THz) bands plays an important role in supporting extreme high beamforming gain as well as ultra-wideband spectrum resources. Unfortunately, accurate wideband XL-MIMO channel estimation suffers from a new challenge called the near-field beam split effect. Prior studies either neglect the accurate near-field channel model or fail to exploit the beam split effect, resulting in poor channel estimation accuracy for wideband XL-MIMO. To tackle this problem, this paper proposes a bilinear pattern detection (BPD)-based approach to accurately recover the wideband XL-MIMO channel. Specifically, by analyzing the characteristics of near-field wideband channels, we first reveal the bilinear pattern of the near-field beam split effect, which implies that the sparse support set of near-field channels in both the angle and the distance domains can be regarded as a linear function against frequency. Then, inspired by the classical simultaneously orthogonal matching pursuit technique, we use the bilinear pattern to estimate the angle-of-arrival (AoA) and distance parameters of each near-field path component at all frequencies. In this way, the entire wideband XL-MIMO channel can be recovered by compressed sensing algorithms. Moreover, we provide the computational complexity of the proposed algorithm compared with existing algorithms. Finally, simulation results demonstrate that our scheme can achieve the accurate estimation of the near-field wideband XL-MIMO channel in the presence of near-field beam split effect.

In future 5G communication system, radio resources can be effectively reused by cognitive radio networks (CRNs), where a lot of secondary users (SUs) are able to access the spectrum of primary users (PUs). In this paper, we analyze the optimal spectrum access and power control of SUs on multiple bands with the target of maximizing the average sum rate (ASR) of SUs. Specifically, based on the stochastic geometry, the random distributions of PUs and SUs are modeled by Poisson point processes (PPPs), based on which we derive out the closed-form outage probabilities and obtain the ASR of SUs. Then, we formulate the maximization problem of ASR on multiple bands under the constraints of outage probabilities. With the help of convex optimization, the optimal density of SUs is obtained in closed-form when the power of SUs is fixed. The convexity of ASR is also verified, and we evaluate the optimal power of SUs when the density of SUs is fixed. Based on these two obtained results, a spectrum access and power control algorithm is further proposed to maximize the ASR of SUs on multiple bands. Simulation results demonstrate that the proposed algorithm can achieve a higher maximum ASR of SUs over the average power allocation algorithm, and the density and power boundary of SUs are constrained by PUs as well as the interference in the networks.

概要传统无线通信系统广泛利用了远场空间资源。随着6G网络的出现, 近场资源的探索和利用势在必行。这些资源为无线通信系统引入了新的物理空间维度。通过利用更高频段并结合智能超表面(RIS)、超大规模多入多出(XL-MIMO)和无蜂窝网络等技术, 近场通信将成为6G网络的关键推动因素。这种范式转变挑战了传统的远场平面波假设, 需要重新评估空间资源管理策略。尽管传统系统已有效利用远场空间资源, 但在6G网络中采用近场空间资源为重新定义无线通信系统提供了机会。这种向近场通信的转变促进了对创新技术范式的研究。近场通信有可能显著提高频谱效率、数据传输速率和空间分辨率, 从而在增强现实、高精度定位、通感一体化以及安全无线能量传输等领域实现先进应用。影响近场通信开发和应用的关键因素包括近场传播和信道建模、提高空间资源利用率、硬件挑战以及工程实践与标准化。这些领域强调了近场通信的多面性, 反映了在标准化工作的同时, 对建模、技术、硬件开发和工程实践进步的需求。近场通信具有推动无线技术发展的变革潜力, 为消费者、工业和安全等领域的应用提供了新的可能。在此背景下, 中国工程院院刊《信息与电子工程前沿(英文)》邀请张平院士担任主编, 赵亚军总工、戴凌龙教授、Marco di Renzo教授担任执行主编, 组织出版了“近场通信理论与应用”专刊。专刊收录12篇文章, 包括2篇综述、5篇研究、5篇通讯, 内容涵盖近场传播基本原理、信道模型的发展、传统机制在近场环境中面临的限制等, 此外包含XL-MIMO信道研究、同时无线信息和能量传输(SWIPT)系统以及RIS的最新研究进展, 以及它们在增强通信系统中的应用。

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.

Time domain synchronous OFDM outperforms cyclic prefix OFDM in spectral efficiency and fast synchronisation, but suffers from the difficulty of supporting 256QAM and the performance, loss over doubly selective fading channels due to severe inter-block-interference (IBI). In this reported work, the groundbreaking theory of compressive sensing is explored to solve those open problems, whereby the IBI-free region of small size within the received training sequence is used to recover the high-dimensional channel, without any IBI cancellation, and partial priori information of the channel is further exploited to reduce the complexity.