eess.SP - 2023-08-31

  • paper_url: http://arxiv.org/abs/2308.16882
  • repo_url: None
  • paper_authors: Chaojin Qing, Zilong Wang, Qing Ye, Wenhui Liu, Linsi He
  • for: 提高FDDFeMassive MIMO系统中的下行频道信息准确预测精度,解决由接收器扭曲引起的频率匹配问题。
  • methods: 使用减少接收器扭曲的传统方法提取上行CSI的幅特征,然后使用专门设计的轻量级幅学习网络(Dist-LeaNet)来抑制接收器扭曲和调整下行CSI的幅相似性。
  • results: 对FDD系统进行了严格的实验,结果表明,考虑接收器扭曲,提出的方案可以提高下行频道信息预测精度,同时降低传输和处理延迟。
    Abstract In frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems, the reciprocity mismatch caused by receiver distortion seriously degrades the amplitude prediction performance of channel state information (CSI). To tackle this issue, from the perspective of distortion suppression and reciprocity calibration, a lightweight neural network-based amplitude prediction method is proposed in this paper. Specifically, with the receiver distortion at the base station (BS), conventional methods are employed to extract the amplitude feature of uplink CSI. Then, learning along the direction of the uplink wireless propagation channel, a dedicated and lightweight distortion-learning network (Dist-LeaNet) is designed to restrain the receiver distortion and calibrate the amplitude reciprocity between the uplink and downlink CSI. Subsequently, by cascading, a single hidden layer-based amplitude-prediction network (Amp-PreNet) is developed to accomplish amplitude prediction of downlink CSI based on the strong amplitude reciprocity. Simulation results show that, considering the receiver distortion in FDD systems, the proposed scheme effectively improves the amplitude prediction accuracy of downlink CSI while reducing the transmission and processing delay.
    摘要 在分频分配多输入多输出(FDD)大规模多输入多输出(mMIMO)系统中,接收器损害导致的回归不匹配严重下降了频率预测性能。为解决这个问题,本文从损害抑制和回归准确的角度提出了一种轻量级神经网络基于频率预测方法。具体来说,通过在基站(BS)上提取接收器损害的干扰特征,然后通过学习在下降频率通信频道方向上,设计了专门的、轻量级的干扰学习网络(Dist-LeaNet),以抑制接收器损害并准确地做回归准确性between uplink和downlink CSI。接着,通过堆叠,一个单hidden layer基于频率预测网络(Amp-PreNet)被开发出来实现频率预测的下降频率CSI。 simulation结果表明,在考虑到FDD系统中接收器损害的情况下,提出的方案可以有效提高下降频率CSI的频率预测精度,同时降低传输和处理延迟。

Analysis and Optimization of Reconfigurable Intelligent Surfaces Based on $S$-Parameters Multiport Network Theory

  • paper_url: http://arxiv.org/abs/2308.16856
  • repo_url: None
  • paper_authors: Andrea Abrardo, Alberto Toccafondi, Marco Di Renzo
  • for: 本研究考虑了可重新配置智能表面(RIS),并使用多口网络理论来模型它。
  • methods: 我们首先比较了使用Z参数和S参数来表示RIS,并证明它们之间的等价性,并讨论它们的不同特点。然后,我们开发了一种优化RIS配置的算法,以优化电磁共振 Coupling的影响。
  • results: 我们显示,基于S参数优化算法比基于Z参数优化算法更高效,这是因为小修改步长的提案算法会导致S参数中更大的变化,从而增加算法的速度。
    Abstract In this paper, we consider a reconfigurable intelligent surface (RIS) and model it by using multiport network theory. We first compare the representation of RIS by using $Z$-parameters and $S$-parameters, by proving their equivalence and discussing their distinct features. Then, we develop an algorithm for optimizing the RIS configuration in the presence of electromagnetic mutual coupling. We show that the proposed algorithm based on optimizing the $S$-parameters results in better performance than existing algorithms based on optimizing the $Z$-parameters. This is attributed to the fact that small perturbations of the step size of the proposed algorithm result in larger variations of the $S$-parameters, hence increasing the convergence speed of the algorithm.
    摘要 在本文中,我们考虑了可重新配置智能表面(RIS),并使用多ports网络理论来建模。我们首先比较了使用$Z$-参数和$S$-参数来表示RIS,并证明它们之间的等价性,并讨论它们的不同特点。然后,我们开发了一种优化RIS配置的算法,基于优化$S$-参数,并证明这种算法在电磁共振 coupling的存在下比既有算法更好。这是因为小幅修改算法中的步长,会导致$S$-参数的大小更大变化,从而提高算法的速度增长。

On the Performance of RIS-Aided Spatial Scattering Modulation for mmWave Transmission

  • paper_url: http://arxiv.org/abs/2308.16804
  • repo_url: None
  • paper_authors: Xusheng Zhu, Wen Chen, Zhendong Li, Qingqing Wu, Ziheng Zhang, Kunlun Wang, Jun Li
  • for: investigate a state-of-the-art reconfigurable intelligent surface (RIS)-assisted spatial scattering modulation (SSM) scheme for millimeter-wave (mmWave) systems
  • methods: utilize line-of-sight (LoS) and non-LoS links in the transmitter-RIS and RIS-receiver channels, respectively, and employ the maximum likelihood detector at the receiver
  • results: derive the conditional pairwise error probability (CPEP) expression for the RIS-SSM scheme under two scenarios, and obtain the union upper bound of average bit error probability (ABEP) based on the CPEP expression, all of which are validated by Monte Carlo simulations.Here is the Chinese translation of the three key information points:
  • for: 研究一种基于智能表面(RIS)的干扰干扰(SSM)方案,用于毫米波(mmWave)系统
  • methods: 利用传输器-RIS通道中的直线视野(LoS)和非直线视野(non-LoS)链接,并在接收器上使用最大可能性探测器
  • results: derive CPEP表达式,并根据其而获得ABEP上限,所有结果经过了Monte Carlo仿真验证。
    Abstract In this paper, we investigate a state-of-the-art reconfigurable intelligent surface (RIS)-assisted spatial scattering modulation (SSM) scheme for millimeter-wave (mmWave) systems, where a more practical scenario that the RIS is near the transmitter while the receiver is far from RIS is considered. To this end, the line-of-sight (LoS) and non-LoS links are utilized in the transmitter-RIS and RIS-receiver channels, respectively. By employing the maximum likelihood detector at the receiver, the conditional pairwise error probability (CPEP) expression for the RIS-SSM scheme is derived under the two scenarios that the received beam demodulation is correct or not. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained based on the CPEP expression. Finally, the derivation results are exhaustively validated by the Monte Carlo simulations.
    摘要 在这篇论文中,我们研究了一种基于快速可配置智能表面(RIS)的扩展频率(mmWave)系统中的空间扩散调制(SSM)方案,其中假设RIS位于发送器的近距离上,而接收器则位于RIS的远距离上。为此,在发送器-RIS和RIS-接收器通道中分别使用了直线视线(LoS)和非直线视线(non-LoS)链路。通过使用最大可能性检测器(Maximum Likelihood Detector,MLD)在接收器端,我们 derivated了RIS-SSM方案的假设捷径误差概率(CPEP)表达。然后,基于CPEP表达,我们获得了union最大上限 bound of average bit error probability(ABEP)。最后,我们使用Monte Carlo仿真 validate了 derive结果。

Channel Estimation Using RIDNet Assisted OMP for Hybrid-field THz Massive MIMO Systems

  • paper_url: http://arxiv.org/abs/2308.16638
  • repo_url: None
  • paper_authors: Hasan Nayir, Erhan Karakoca, Ali Görçin, Khalid Qaraqe
    for:* 这篇论文旨在提出一种基于Recursive Information Distillation Network(RIDNet)和Orthogonal Matching Pursuit(OMP)的混合场 THz mMIMO通道估计方法,以解决hybrid-field THz mMIMO通道估计的挑战。methods:* 该方法使用RIDNet和OMP结合,对hybrid-field THz mMIMO通道进行估计,包括远场和近场组件。results:* 实验结果表明,提议的RIDNet基于方法在所有Signal-to-Noise Ratio(SNR)域内都具有较低的通道估计误差,特别是在低SNR域。此外,结果还表明,使用RIDNet基于方法可以使用较少的RF链和尝试符号来达到与OMP算法相同的性能。
    Abstract The terahertz (THz) band radio access with larger available bandwidth is anticipated to provide higher capacities for next-generation wireless communication systems. However, higher path loss at THz frequencies significantly limits the wireless communication range. Massive multiple-input multiple-output (mMIMO) is an attractive technology to increase the Rayleigh distance by generating higher gain beams using low wavelength and highly directive antenna array aperture. In addition, both far-field and near-field components of the antenna system should be considered for modelling THz electromagnetic propagation, where the channel estimation for this environment becomes a challenging task. This paper proposes a novel channel estimation method using a recursive information distillation network (RIDNet) together with orthogonal matching pursuit (OMP) for hybrid-field THz mMIMO channels, including both far-field and near-field components. The simulation experiments are performed using the ray-tracing tool. The results indicate that the proposed RIDNet-based method consistently provides lower channel estimation errors compared to the conventional OMP algorithm for all signal-to-noise ratio (SNR) regimes, and the performance gap becomes higher at low SNR regimes. Furthermore, the results imply that the same error performance of the OMP can be achieved by the RIDNet-based method using a lower number of RF chains and pilot symbols.
    摘要 频率为teraHz(THz)的无线访问带宽更大,预计会提供下一代无线通信系统更高的容量。然而,THz频率上的跟踪损耗非常大,限制无线通信范围。大规模多输入多输出(mMIMO)技术可以提高Rayleigh距离,通过生成更高的投射高度和高度指向性的天线阵列。此外,需考虑天线系统的远场和近场组分,模拟THz电磁传播。由于这种环境的通道估计成为了一项挑战。这篇文章提出了一种使用重征信息蒸馈网络(RIDNet)和对匹配追求(OMP)算法的新通道估计方法,用于hybrid-field THz mMIMO通道,包括远场和近场组分。实验使用了射线跟踪工具。结果表明,提议的RIDNet基于方法在所有信号响应率(SNR)域内都提供了更低的通道估计错误,并且在低SNR域的性能差距变得更大。此外,结果表明,使用RIDNet基于方法可以通过使用较低的RF链和射频标志符来实现相同的错误性。

Design Challenges for the Implementation of Smart Homes

  • paper_url: http://arxiv.org/abs/2308.16602
  • repo_url: https://github.com/jettbrains/-L-
  • paper_authors: Nesreen Mufid
  • for: 这个研究的目标是设计和实现一个智能家庭模型,以提高家庭安全性和可用性。
  • methods: 该模型使用可靠的移动网络,让用户可以在外出时监控家庭内部的情况,并通过不同的探测器检测火灾、气体泄漏、水泄漏和偷窃等问题。此外,家庭内还设置了一个摄像头,为用户提供全景视图。
  • results: 该模型可以帮助用户在外出时监控家庭内部的情况,并在火灾、气体泄漏、水泄漏和偷窃等问题发生时通知用户,让用户有时间采取行动。此外,用户还可以通过移动应用程序远程控制家庭的照明系统,灯光的开关和灭火。
    Abstract Home automation for many years had faced challenges that limit its spreading around the world. These challenges caused by the high cost of Own such a home, inflexibility system (cannot be monitored outside the home) and issues to achieve optimal security. Our main objective is to design and implement a smart home model that is simple, affordable to the users. The proposed system provide flexibility to monitor the home, using the reliable cellular network. The user will be able what is inside the home when he /she is away from home. In addition to that, our model overcome the issue of the security by providing different sensors that detects smoke, gas, leakage of water and incases of burglary. Moreover, a camera will be available in the home to give a full view for the user when he/she is outside the home. The user will be informed by an application on his/she phone incase if there is a fire, water leakage and if someone break into the house. This will give the user a chance to take an action if such cases happened. Furthermore, the user can monitor the lighting system of the home, by giving the user a chance to turn the lights on and off remotely.
    摘要 家庭自动化系统在多年来一直面临着限制其在全球蔓延的挑战。这些挑战是由于高昂的家庭所有成本、不灵活的系统(不能在外部监控)以及实现优质安全性的问题所致。我们的主要目标是设计并实施一个简单、可Affordable的家庭自动化模型。提议的系统提供了外部监控家庭的灵活性,使用可靠的手机网络。用户将能够在离家时了解家内情况,并且可以通过应用程序在手机上获得相关信息。此外,我们的模型还解决了安全性问题,通过设置烟报、气体探测器、水泄漏检测器和窃贼检测器等多种感知器来实现。此外,家中还将安装一个摄像头,以为用户在外部提供全景视图。用户通过应用程序接收有关家内情况的通知,如果发生火灾、水泄漏或窃贼等情况,他们可以及时采取相应的行动。此外,用户还可以通过移动设备控制家庭照明系统,启用和灭火按钮。

Data-Aided Channel Estimation Utilizing Gaussian Mixture Models

  • paper_url: http://arxiv.org/abs/2308.16601
  • repo_url: None
  • paper_authors: Franz Weißer, Nurettin Turan, Dominik Semmler, Wolfgang Utschick
  • for: 提高多用户系统中通道估计质量
  • methods: 使用数据符号和导航符号,提出两种方法,包括基于所有接收符号的子空间估计和基于 Gaussian mixture model 的通道估计器
  • results: 对实际通道测量数据进行数值仪表示,提议方法比 studied state-of-the-art 通道估计器 superior 性能
    Abstract In this work, we propose two methods that utilize data symbols in addition to pilot symbols for improved channel estimation quality in a multi-user system, so-called semi-blind channel estimation. To this end, a subspace is estimated based on all received symbols and utilized to improve the estimation quality of a Gaussian mixture model-based channel estimator, which solely uses pilot symbols for channel estimation. Both of the proposed approaches allow for parallelization. Even the precomputation of estimation filters, which is beneficial in terms of computational complexity, is enabled by one of the proposed methods. Numerical simulations for real channel measurement data available to us show that the proposed methods outperform the studied state-of-the-art channel estimators.
    摘要 在这项工作中,我们提出了两种方法,利用数据符号以外的导航符号进行改进的通道估计质量,称为半不可见通道估计。为此,我们根据所有接收的符号 estimate一个子空间,并使用这个子空间来改进基于 Gaussian mixture model 的通道估计器,该仅使用导航符号进行通道估计。两种提出的方法均允许并行计算。而且,一种方法甚至可以在预计算估计filter的过程中启用并行计算。我们对我们手中的实际通道测量数据进行数值仿真,结果表明,我们提出的方法可以比 studied state-of-the-art 通道估计器表现更好。

Channel Estimation for XL-MIMO Systems with Polar-Domain Multi-Scale Residual Dense Network

  • paper_url: http://arxiv.org/abs/2308.16400
  • repo_url: https://github.com/HaoLei-tnunder/Channel_Estimation_for_XL-MIMO_Systems_with_Polar-Domain_Multi-Scale_Residual_Dense_Network
  • paper_authors: Hao Lei, Jiayi Zhang, Huahua Xiao, Xiaodan Zhang, Bo Ai, Derrick Wing Kwan Ng
  • for: 实现未来无线通信的广泛应用需要精准的通道状态信息,xl-mimo技术可以提供巨大的性能提升potential。
  • methods: 作者提出了基于 polar-domain sparse 的多重遗传神经网络(P-MRDN)和多scale residual dense network(P-MSRDN),以提高xl-mimo系统中的通道估计精度。
  • results: 实验结果显示,提议的方案比现有参考方案有更高的性能,并且 channel sparsity 对方案的影响相对较小。
    Abstract Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technique to enable versatile applications for future wireless communications.To realize the huge potential performance gain, accurate channel state information is a fundamental technical prerequisite. In conventional massive MIMO, the channel is often modeled by the far-field planar-wavefront with rich sparsity in the angular domain that facilitates the design of low-complexity channel estimation. However, this sparsity is not conspicuous in XL-MIMO systems due to the non-negligible near-field spherical-wavefront. To address the inherent performance loss of the angular-domain channel estimation schemes, we first propose the polar-domain multiple residual dense network (P-MRDN) for XL-MIMO systems based on the polar-domain sparsity of the near-field channel by improving the existing MRDN scheme. Furthermore, a polar-domain multi-scale residual dense network (P-MSRDN) is designed to improve the channel estimation accuracy. Finally, simulation results reveal the superior performance of the proposed schemes compared with existing benchmark schemes and the minimal influence of the channel sparsity on the proposed schemes.
    摘要 “EXTREMELY LARGE-SCALE MULTIPLE-INPUT MULTIPLE-OUTPUT(XL-MIMO)是未来无线通讯技术的应用中的一个有前途的技术。为了实现这个巨大的性能提升,精确的通道状态信息是一个基本的技术前提。在传统的大规模MIMO系统中,通道通常被Modeled为距离较远的平面波front,这给了设计低复杂度的通道估测设计提供了帮助。但是,这种稀疏性不是XL-MIMO系统中的主要特点,因为近场球面波front的影响不可忽略。为了解决传统angular-domain channel estimation scheme的自然的性能损失,我们首先提出了 polar-domain multiple residual dense network(P-MRDN),这是基于近场通道的polar-domain稀疏性改进了现有的MRDN scheme。其次,我们设计了 polar-domain multi-scale residual dense network(P-MSRDN),以提高通道估测精度。最后,我们通过实验结果表明了我们提出的方案比现有的参考方案有更高的性能,并且通道稀疏性对我们提出的方案的影响较小。”