Fluid antenna is a promising wireless communication technology that enhances communication rate by changing the antenna positions. This article proposes a new communication system that combines multiple-input single-output (MISO) fluid antennas with traditional fixed-position antennas, utilizing antenna position optimization to improve energy harvesting efficiency. In this model, we consider simultaneous wireless information and power transfer (SWIPT) which transmits identical signals from the base station to both information receiver (IR) and energy receiver (ER). We strive to enhance the power delivered to the ER by fine-tuning the positions of transmit and receive fluid antennas, along with optimizing the transmit covariance matrix, subject to a given minimum signal-to-interference-plus-noise ratio (SINR) constraint at the IR. Simulation results indicate that fluid antenna systems significantly enhance the energy harvesting efficiency of the ER compared to traditional fixed-position antennas.
Air-sea interface fluxes significantly impact the reliability and efficiency of maritime communication. Compared to sparse in-situ ocean observations, satellite remote sensing data offers broader coverage and extended temporal span. This study utilizes COARE V3.5 algorithm to calculate momentum flux, sensible heat flux, and latent heat flux at the air-sea interface, based on satellite synthetic aperture radar (SAR) wind speed data, reanalysis data, and buoy measurements, combined with neural network methods. Findings indicate that SAR wind speed data corrected via neural networks show improved consistency with buoy-measured wind speeds in flux calculations. Specifically, the bias in friction velocity decreased from -0.03 m/s to 0.01 m/s, wind stress bias from -0.03 N/m^2 to 0.00 N/m^2, drag coefficient bias from -0.29 to -0.21, latent heat flux bias from -8.32 W/m^2 to 5.41 W/m^2, and sensible heat flux bias from 0.67 W/m^2 to 0.06 W/m^2. Results suggest that the neural network-corrected SAR wind speed data can provide more reliable environmental data for maritime communication.
This paper studies energy-efficient hybrid beamforming architectures and its algorithm design in millimeter-wave communication systems, aiming to address the challenges faced by existing hybrid beamforming due to low hardware flexibility and high power consumption. To solve the problems of existing hybrid beamforming, a novel energy-efficient hybrid beamforming architecture is proposed, where radio-frequency (RF) switch networks are introduced at the front and rear ends of the phase shifter network, enabling dynamic connections between the RF chains and the phase shifter array as well as the antenna array. The system model of the proposed architecture is established, including digital precoding and analog precoding processes, and the practical hardware limitations such as quantization errors of the digital-to-analog converter (DAC) and phase shifter resolution. In order to maximize the energy efficiency, this paper derives an energy efficiency model including spectral efficiency and system power consumption, and a hybrid precoding algorithm is proposed based on block coordinate descent to iteratively optimize the digital precoding matrix, analog precoding matrix, and DAC resolution. Simulation results under the NYUSIM-generated millimeter-wave channels show that the proposed hybrid beamforming architecture and precoding algorithm have higher energy efficiency than existing representative architectures and precoding algorithms under complete and partial channel state information, while the loss of spectral efficiency compared to fully connected architecture is less than 20
In large-scale multiple-input multiple-output (U-MIMO) orthogonal frequency division multiplexing (OFDM) systems, the frequency domain channel exhibits non-stationarity, exacerbated by limited pilot positions which complicate channel estimation. This letter extends single-band channel estimation to multi-band scenarios, focusing on frequency domain non-stationary sub-6 GHz - millimeter-wave(mmWave) systems. A novel framework leveraging a multi-modal attention mechanism is proposed: it employs a multi-modal input network to extract sub-6 GHz channel details and fine-tune with limited mmWave data. An adaptive attention mechanism dynamically adjusts information weights, facilitating effective feature fusion for mmWave channel estimation. The algorithm's reliability and effectiveness in non-stationary channel estimation are validated through theoretical analysis and numerical simulations in adaptive frequency domains.
Near-field propagation, particularly that enabled by reconfigurable intelligent surfaces (RIS), has emerged as a promising research topic in recent years. However, a comprehensive literature review on RIS-based near-field technologies is still lacking. This article aims to fill this gap by providing a brief overview of near-field concepts and a systematic survey of the state-of-the-art RIS-based near-field technologies. The focus is on three key aspects: the construction of ubiquitous near-field wireless propagation environments using RIS, the enabling of new near-field paradigms for 6G networks through RIS, and the challenges faced by RIS-based near-field technologies. This technical review intends to facilitate the development and innovation of RIS-based near-field technologies.
Indoor positioning services have been widely used in various fields such as navigation,rescue and disaster relief.Indoor positioning methods based on Bluetooth sensors can be realized by some built-in functions of mobile phones,so they are easy to popularize and practical,but they often perform poorly in crowded areas and complex electromagnetic environments.To address this issue,a fingerprint indoor positioning method fusing Bluetooth and geomagnetism is proposed.Firstly,a number of Bluetooth beacons are set up in the shopping mall to be positioned,and the iBeacon function and Hall sensor of mobile phones are utilized to receive Bluetooth and geomagnetic signals respectively.Then,the Bluetooth estimated coordinates and the geomagnetism estimated coordinates are fused at the decision-making level to calculate the user position,which not only reduces the positioning error but also avoids the additional hardware resource consumption.The experimental results show that the proposed method significantly improves positioning accuracy.
Recently, the rapid development of deep learning has provided strong support for the implementation of semantic communication. A noteworthy issue is the application of the knowledge base. Deploying and generating the knowledge base necessitate substantial caching resources, and requesting it frequently may increase communication overhead. We consider the issue of improving the performance of text semantic communication systems in the context of a small-scale knowledge base, and propose a system based on entity information enhancement. In particular, we design an entity recognizer to identify the entity information in the source text and enhance the information via secondary coding. Simulation results demonstrate that the proposed system can effectively extract the entity information and achieve performance improvement at the receiver.
语义通信的目的在于传输信源数据中的语义信息,通过语义提取和压缩可大幅减少网络中需要传输的数据量,降低带宽消耗和传输时延,同时在提高传输可靠性上展现出了巨大潜力.传统通信中资源分配方案都是以优化比特传输速率而设计的,并不适用于聚焦语义信息传输的语义通信网络.为利用语义通信系统优势,需要从语义层面考虑资源分配方案的设计,以进一步提高信息传输效率.首先梳理并总结了目前语义感知通信网络中资源分配技术的研究进展,然后通过分析语义感知资源分配面临的挑战,提出了一种基于任务卸载的多维资源联合优化架构,最后以面向文本的语义任务为例,给出了两种语义感知资源分配方案,通过仿真证明了方案的有效性.
扩展现实是一种新兴的信息交互方式,相较于音视频等信息模态,为用户提供了更有沉浸感的体验.通过研究扩展现实的发展趋势,总结了相关应用及产业的通信需求和业务特点,为6G网络下的内生AI功能在扩展现实网络中的应用提供参考.首先从高宽带和低时延的需求出发,提出扩展现实网络和语义通信融合的趋势,并分析了语义通信的传输有效性,介绍其面向内容的系统模块与框架.然后基于扩展现实的业务需求,针对信息系统架构、安全接入要求和算力承载,分析了支撑具体场景的关键技术,明确语义驱动的扩展现实网络的优越性.最后总结了扩展现实网络提供的新机遇,为扩展现实网络与基于语义的内生AI融合架构及关键技术提供发展思路.
呈现了一种基于CMOS开关的单比特双极化可重构智能超表面系统.该超表面单元只需两个CMOS开关器件即可实现双极化的控制;超表面单元采用总分总的控制结构,在保证极化隔离度的情况下实现双极化同控;通过引入双层拉远交直流隔离技术,有效地解决了交直流之间的串扰问题,配合一体分离的控制方案,可以将偏压控制点放置在辐射单元的任意位置;通过在单元周围加载基片集成腔体以及在腔体中加载短路抑制柱的方式有效抑制了表面波,极大地提升了扫描角度.仿真结果表明,超表面单元的相位差在26～28 GHz的范围内都能保持180±35度的相差,开和关状态的平均插损在1dB以内;暗室测试结果表明超表面系统在±60°的扫描范围内均工作良好,波束形态清晰,旁瓣低于15 dB;外场测试结果显示该系统能够提升盲区增益25 dB以上,使系统流量达到满流.
RIS以其主动改变通信环境的优势,日益成为6G关键技术的研究重点之一.首先以RIS的运行模式、部署场景为切入点,通过与现有中继、网络控制中继器的对比,探究RIS的优势;继而详细介绍了RIS的五种工作模式的原理与区别;最后,分别从物理层和高层两个方面浅谈RIS部署在网络中对标准化工作的影响,为未来的无线网络规范提供一定的指导意义.
为了实现更高效、更灵活的MEC,提出了空中智能MEC系统,通过机载智能超表面和边缘服务器的无人机实现MEC.在该系统中,无人机既可以充当边缘服务器为地面用户提供计算服务,也可以充当通信中继,将用户的计算任务卸载到远端服务器处理.构建了以最大化系统能效为目标的模型,提出了一种基于DDPG的联合优化算法来解决非凸的能效优化问题.仿真结果表明,所提出的方案与基准方案相比,可以有效提高系统能效,并且具有良好的稳定性和收敛性.
可重构智能表面是第六代移动通信的潜在关键技术之一.为推动可重构智能表面的工程应用,借鉴现有技术的标准化经验至关重要.因此对可重构智能表面与3GPP中已标准化的网络控制中继器进行了系统性能的比较.首先对两者的技术细节进行了比对,接着通过系统建模和仿真对比了参考信号接收功率和信干噪比.通过对比两者,对可重构智能表面的标准化和工程应用提出了建议.
主编观点 智能超表面(RIS)作为一种基础性创新技术,具有低成本、低能耗、可编程、易部署等特点.通过构建智能可控无线环境,可突破传统无线环境被动适应的局限性,给未来移动通信网络带来一种全新的范式,具有广阔的技术与产业前景.当前,RIS技术的发展引起了业界的广泛关注,在理论研究、材料工艺、实现算法及工程试验等领域学术界和产业界开展了一系列推进活动,极大地促进了RIS的技术研究与工程化进程.
智能超表面基于其对空间电磁波在幅、相和极化等参数上的调控,以可编程方式主动调控和定制化无线传播信道,已在多个领域初步展现了一定的潜力.然而,现阶段研究尚缺乏更全面的基于实际网络干扰假设的大规模系统级性能研究.对RIS系统级性能研究仿真方法论进行了探讨,分析了RIS系统性能研究中需要关注的关键维度和主要因素,并基于此对RIS系统级性能空间进行了研究.
RIS是6G的潜在关键技术之一,得益于其低成本、低功耗等特性,RIS在6G网络部署中具备很大的潜力.为推动RIS技术最终落地,需开展RIS标准工作研究.以3GPP Rel-18的网络控制中继的标准化工作为参考,详细分析了RIS技术的潜在标准化工作,并阐明了RIS面临的问题与挑战,为RIS器件选型及设计提供了一定参考.
针对无线信道的随机性和不确定性导致的MEC网络任务卸载时传输速率低的问题,提出一种RIS辅助MEC方案,利用RIS增强链路的能力来支持任务卸载.通过对用户发射功率、任务卸载量、MEC计算资源调度、RIS相移矩阵等进行联合优化,最小化服务总时延.由于原问题是具有高度耦合变量的非凸问题,借助块坐标下降法将原问题分解为三个子问题,并利用拉格朗日对偶法、逐次凸近似法和交替方向乘子法进行求解.最后设计了一种交替算法,迭代求解近似最优解.仿真结果表明,与其他基准方案相比,该方案在降低服务时延方面具有更好的性能.
由于无线通信的开放性及无线信号传输的辐射性,位于信号接收范围内的非法窃听者有机会截获通信信号,获取通信信号的关键特征,甚至破译传输内容,这给无线通信安全带来了严重威胁.得益于密码学的发展,完全破译传输内容可能性较小,无线通信面临的主要威胁来自发射源位置等关键特征的暴露.考虑通信发射源位置隐私保护问题,借助分布式RIS反射真实发射源信号,构建"虚假发射源",降低窃听者对通信发射源的测向精度,实现对真实发射源位置信息的保护.基于窃听者对通信发射源的测向误差的CRLB,构建发射源位置隐私保护的性能评估指标.结果表明RIS的部署能够有效提升对真实发射源位置隐私保护性能,且分布式RIS布阵相较于集中式RIS布阵方案来说,性能提升更为显著.另外,发射源位置隐私保护性能受限于窃听者天线数量,在天线数量增加时需要部署更多的RIS才能达到相同的位置隐私保护效果.
海上搜救能力边界即海上活动边界,海上搜救能力对确保海上平台及人员安全至关重要.全时全域的通信保障是确保海上救援信息高效流转的关键,远距离、高带宽的数据传输手段在海上搜救通信中的应用探索,是现阶段需要研究的重要课题.针对目前海上搜救通信存在的险情信息报知不充分、协同救援宽带通信手段不足等问题,结合VDES的技术特点,重点基于VDE宽带信道进行搜救力量协同组网的应用模式,对VDES系统在海上搜救通信中的应用进行了分析和探索,基本满足海上救援中协同通信的宽带数据业务传输需求,提升搜救效率.
STAR-RIS作为一种新的RIS架构,有望提升未来无线网络的全域覆盖能力.针对未来网络中的通信和感知需求,研究STAR-RIS辅助ISAC系统的下行安全传输优化问题.旨在联合优化主动和被动波束成形,在满足用户服务需求的同时,提升系统的感知性能.所考虑的优化问题非凸且强耦合,难以求解.为此,提出一种基于交替迭代、半正定松弛和连续凸近似的算法求解该问题.仿真结果表明,所提出的算法在保证通信需求的同时,实现了更好的感知性能.
