Mimo Ofdm Systems(Mimo Ofdm 系统)研究综述
Mimo Ofdm Systems Mimo Ofdm 系统 - In this work, we propose a two-stage tensor-based receiver for a joint channel, phase-noise (PN), and data estimation in MIMO-OFDM systems. [1] In this paper, we propose a CSI-based positioning pipeline for wireless LAN MIMO-OFDM systems operating indoors, which relies on NNs that extract a probability map indicating the likelihood of a UE being at a given grid point. [2] Compared with other non-Hermitian symmetry MIMO-OFDM systems, HU-OFDM has significant advantages in terms of power efficiency, system design flexibility, computational complexity, or hardware cost without losing reliability. [3] In this paper, we propose a new stream power allocation method for single-user beamforming for BICM MIMO-OFDM systems. [4] We consider the joint activity detection and channel estimation for massive connectivity in MIMO-OFDM systems. [5] This paper presents a simple technique for employing multiple-beamforming on the downlink of massive MIMO-OFDM systems. [6] In this paper, we consider the problem of hybrid precoding and combining for wideband millimeter wave (mmWave) and sub-terahertz (THz) MIMO-OFDM systems with beam squint effects. [7] In this article, two methods are proposed to further increase the advantages of MIMO-OFDM systems such as high access quality, high data rates and spectral efficiency. [8] This chapter demonstrates various ways for transmitting an image file using MIMO-OFDM systems which have anti-error ability to reduce BER. [9] This work proposes a PAPR reduction algorithm for solving the problem of high PAPR in MIMO-OFDM systems. [10] Simulation results illustrate that the novel semiblind estimators perform much better than existing blind/training-based sparse methods (even including the popular compressed sensing and Bayesian methods), when few training subcarriers are available (which may occur in futuristic, pilot-starved massive MIMO-OFDM systems), at much reduced complexity. [11] In this work, we propose a two-stage tensor-based receiver for joint channel, phase-noise (PN), and data estimation in MIMO-OFDM systems. [12] In this paper, we use SecretKey Capacity (SKC) on MIMO and MIMO-OFDM systems with more than one receiver antennas for the eavesdropper. [13] The BER and the capacity of MIMO-OFDM systems are analyzed by varying the parameters of the system. [14] Wireless Communications and Mobile Computing has retracted the article titled “Adaptive Blind Channel Estimation for MIMO-OFDM Systems Based on PARAFAC” [1], due to a high level of similarity identified with a previously published article, as confirmed by the editorial board [2]: Ruo-Nan Yang, Wei-Tao Zhang, Shun-Tian Lou, "Joint Adaptive Blind Channel Estimation and Data Detection for MIMO-OFDM Systems", Wireless Communications and Mobile Computing, vol. [15] This paper proposes a novel two-stage joint hybrid precoder and combiner design for maximizing the average achievable sum-rate of frequency-selective millimeter-wave massive MIMO-OFDM systems. [16] This paper proposes affine-precoded superimposed pilot (SIP) design, followed by channel state information (CSI) estimation techniques for millimeter wave (mmWave) MIMO-OFDM systems. [17] The main contribution of the proposed algorithm is enabling comprehensive simulation analyses of ED performance based on the SLC method for versatile combinations of operating parameter characteristics for different working environments of MIMO-OFDM systems. [18] The forward error correction plays an important role in enhancing the performance of the MIMO-OFDM systems. [19] To be specific, a spiking reservoir computing (RC) based approach is introduced for spectrum sensing of MIMO-OFDM systems to take advantage of the spatial and temporal correlations of the environment. [20] In addition to this, the proposed method is utilized to restrict the interference in the MIMO-OFDM systems. [21] Therefore, this paper proposes a computationally-efficient hybrid precoding algorithm for mmWave MIMO-OFDM systems. [22] This technique has been investigated for MIMO OFDM systems so far. [23] It is found that around 50% reduction in per-user downlink rate could occur to the majority of users due to asynchronous reception in DM-MIMO OFDM systems. [24]在这项工作中，我们提出了一种基于张量的两级接收器，用于 MIMO-OFDM 系统中的联合信道、相位噪声 (PN) 和数据估计。 [1] 在本文中，我们提出了一种基于 CSI 的定位管道，用于在室内运行的无线 LAN MIMO-OFDM 系统，它依赖于提取概率图的 NN，该概率图指示 UE 在给定网格点的可能性。 [2] 与其他非厄米对称MIMO-OFDM系统相比，HU-OFDM在功率效率、系统设计灵活性、计算复杂度或硬件成本等方面具有显着优势，且不失可靠性。 [3] 在本文中，我们提出了一种新的流功率分配方法，用于 BICM MIMO-OFDM 系统的单用户波束成形。 [4] 我们考虑了 MIMO-OFDM 系统中大规模连接的联合活动检测和信道估计。 [5] 本文提出了一种在大规模 MIMO-OFDM 系统的下行链路上采用多波束成形的简单技术。 [6] 在本文中，我们考虑了具有波束斜视效应的宽带毫米波 (mmWave) 和亚太赫兹 (THz) MIMO-OFDM 系统的混合预编码和组合问题。 [7] 在本文中，提出了两种方法来进一步提高 MIMO-OFDM 系统的优势，例如高接入质量、高数据速率和频谱效率。 [8] 本章演示了使用具有抗误码能力以降低 BER 的 MIMO-OFDM 系统传输图像文件的各种方法。 [9] 本文提出了一种 PAPR 降低算法，用于解决 MIMO-OFDM 系统中的高 PAPR 问题。 [10] 仿真结果表明，当可用的训练子载波很少（这可能发生在未来的、缺乏导频的大规模 MIMO 中）时，新型半盲估计器的性能远优于现有的基于盲/训练的稀疏方法（甚至包括流行的压缩感知和贝叶斯方法） -OFDM 系统），复杂性大大降低。 [11] 在这项工作中，我们提出了一种基于张量的两级接收器，用于 MIMO-OFDM 系统中的联合信道、相位噪声 (PN) 和数据估计。 [12] 在本文中，我们在具有多个接收器天线的 MIMO 和 MIMO-OFDM 系统上使用 SecretKey Capacity (SKC) 作为窃听者。 [13] 通过改变系统的参数来分析MIMO-OFDM系统的BER和容量。 [14] 无线通信和移动计算已撤回题为“基于 PARAFAC 的 MIMO-OFDM 系统的自适应盲信道估计”[1] 的文章，原因是与之前发表的文章高度相似，编辑委员会确认 [2 ]：杨若楠，张伟涛，楼顺天，“MIMO-OFDM 系统的联合自适应盲信道估计和数据检测”，无线通信和移动计算，卷。 [15] 本文提出了一种新颖的两级联合混合预编码器和组合器设计，用于最大化频率选择毫米波大规模 MIMO-OFDM 系统的平均可实现和速率。 [16] 本文提出了仿射预编码叠加导频 (SIP) 设计，然后是用于毫米波 (mmWave) MIMO-OFDM 系统的信道状态信息 (CSI) 估计技术。 [17] 所提出算法的主要贡献是能够基于 SLC 方法对 ED 性能进行全面的仿真分析，以针对 MIMO-OFDM 系统的不同工作环境对操作参数特性进行多种组合。 [18] 前向纠错对提高MIMO-OFDM系统的性能起着重要作用。 [19] 具体而言，为 MIMO-OFDM 系统的频谱感知引入了一种基于尖峰水库计算 (RC) 的方法，以利用环境的空间和时间相关性。 [20] 除此之外，所提出的方法还用于限制 MIMO-OFDM 系统中的干扰。 [21] 因此，本文提出了一种用于毫米波 MIMO-OFDM 系统的计算效率高的混合预编码算法。 [22] 迄今为止，该技术已针对 MIMO OFDM 系统进行了研究。 [23] 研究发现，由于 DM-MIMO OFDM 系统中的异步接收，大多数用户的每用户下行链路速率可能会降低 50% 左右。 [24]