Mimo Detection(咪莫检测)研究综述
Mimo Detection 咪莫检测 - For a few popular tasks such as power control, beamforming, and MIMO detection, these methods achieve state-of-the-art performance while requiring less computational efforts, less channel state information (CSI), etc. [1] The at-sea experimental results have shown the effectiveness of the joint processing framework in MIMO detection of moving targets. [2] Several algorithms for MIMO detection for spatially-multiplexed signals have been developed till today. [3] The survey mainly focuses on different types of channels used in MIMO detections, the number of antennas used in transmitting signals from the source to destination, and vice-versa. [4] MIMO detection is one of the key technologies for MIMO system designs. [5] In this paper we consider maximum-likelihood (ML) MIMO detection under one-bit quantized observations and binary symbol constellations. [6] In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems. [7] Simulation results based on MIMO detections are presented to confirm the convergence gain brought by the proposed Gibbs sampling schemes. [8] In this paper, residual-based detection (RBD) algorithms are proposed for M-MIMO detection, including minimal residual (MINRES) algorithm, generalized minimal residual (GMRES) algorithm, and conjugate residual (CR) algorithm. [9] For MIMO detections, the K-Best algorithm has been widely applied for multiple-antenna wireless communications. [10] Finally, simulation results based on MIMO detection are presented to confirm the performance gain by convergence enhancement. [11] For MIMO detection, a supervised DLNN, which is designed, trained and evaluated using a Keras library and TensorFlow, is implemented in this MDM optical transmission system. [12] We have used a DLNN for MIMO detection in MDM optical transmission system and have compared its performance with Zero Forcing (ZF) detector and Semi-Definite Relaxation Row-by-Row (SDR-RBR). [13] In this paper, to tackle this problem, we design two types of low-complexity MPA over the MIMO-SCMA extended factor graph to perform joint multiuser and MIMO detection. [14] In this paper, a novel efficient algorithm for MIMO detection in MIMO-OFDM systems has been proposed. [15]对于一些流行的任务，例如功率控制、波束成形和 MIMO 检测，这些方法实现了最先进的性能，同时需要更少的计算工作、更少的信道状态信息 (CSI) 等。 [1] 海上实验结果表明了联合处理框架在 MIMO 运动目标检测中的有效性。 [2] 迄今为止，已经开发了几种用于空间复用信号的 MIMO 检测的算法。 [3] 该调查主要关注用于 MIMO 检测的不同类型的信道，用于将信号从源传输到目的地的天线数量，反之亦然。 [4] MIMO检测是MIMO系统设计的关键技术之一。 [5] 在本文中，我们考虑了一位量化观测和二进制符号星座下的最大似然 (ML) MIMO 检测。 [6] 在本文中，提出了一种新颖的迭代离散估计（IDE）算法，称为修改IDE（MIDE），以降低上行链路大规模MIMO系统中MIMO检测的计算复杂度。 [7] 给出了基于 MIMO 检测的仿真结果，以确认所提出的 Gibbs 采样方案带来的收敛增益。 [8] 本文针对M-MIMO检测提出了基于残差的检测(RBD)算法，包括最小残差(MINRES)算法、广义最小残差(GMRES)算法和共轭残差(CR)算法。 [9] 对于 MIMO 检测，K-Best 算法已广泛应用于多天线无线通信。 [10] 最后，给出了基于 MIMO 检测的仿真结果，以确认通过收敛增强获得的性能增益。 [11] 对于 MIMO 检测，在这个 MDM 光传输系统中实现了使用 Keras 库和 TensorFlow 设计、训练和评估的监督 DLNN。 [12] 我们在 MDM 光传输系统中使用 DLNN 进行 MIMO 检测，并将其性能与迫零 (ZF) 检测器和半定松弛逐行 (SDR-RBR) 检测器进行了比较。 [13] 在本文中，为了解决这个问题，我们在 MIMO-SCMA 扩展因子图上设计了两种低复杂度 MPA 来执行联合多用户和 MIMO 检测。 [14] 本文提出了一种在 MIMO-OFDM 系统中进行 MIMO 检测的高效新算法。 [15]
Massive Mimo Detection 大规模 Mimo 检测
Several numerical experiments show that the proposed algorithm outperforms known massive MIMO detection algorithms, such as an MMSE detector with belief propagation decoding. [1] Several theoretical iterative techniques that can be used to balance complexity and performance for massive MIMO detection have been proposed in the literature. [2] However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem. [3] This paper presents a deep neural network (DNN)-based massive MIMO detection method which can overcome the above limitation. [4] In practical massive MIMO detection, besides the influence of the algorithm’s own characteristics on the detection results, the hardware circuits also affect the efficiency of signal detection. [5] In this paper, a low-complexity massive MIMO detection is first proposed based on approximate EP, which relieves the computational complexity of the exact EP while maintaining the good performance. [6] Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. [7] Thus, an efficient iterative matrix inversion based on the hyper-power (HP) method is proposed for massive MIMO detection. [8] Simulation results justify the viability of the proposed detection algorithm than several existing massive MIMO detection algorithms, in terms of bit error rate performance with comparable computational complexity. [9]几个数值实验表明，所提出的算法优于已知的大规模 MIMO 检测算法，例如具有置信传播解码的 MMSE 检测器。 [1] 文献中已经提出了几种可用于平衡大规模 MIMO 检测的复杂性和性能的理论迭代技术。 [2] 然而，使用深度学习进行大规模 MIMO 检测可以实现高度的计算并行性，深度学习构成了解决信号检测问题的重要技术途径。 [3] 本文提出了一种基于深度神经网络（DNN）的大规模 MIMO 检测方法，可以克服上述限制。 [4] 在实际的大规模 MIMO 检测中，除了算法自身特性对检测结果的影响外，硬件电路也会影响信号检测的效率。 [5] 本文首先提出了一种基于近似 EP 的低复杂度大规模 MIMO 检测方法，在保持良好性能的同时减轻了精确 EP 的计算复杂度。 [6] 因此，寻找具有最佳性能和低复杂度的完美大规模 MIMO 检测算法的研究在过去十年中引起了广泛关注。 [7] 因此，针对大规模 MIMO 检测提出了一种基于超功率 (HP) 方法的高效迭代矩阵求逆方法。 [8] 仿真结果证明了所提出的检测算法比现有的几种大规模 MIMO 检测算法的可行性，在具有可比计算复杂度的误码率性能方面。 [9]
Scale Mimo Detection
In this paper, the enhanced channel hardening-exploiting message passing (ECHEMP) algorithm is proposed for large-scale MIMO detection to improve the decoding performance with less complexity cost, so as to a better decoding tradeoff. [1] Linear detectors such as Minimum Mean Square Error (MMSE), Zero Forcing are applied for large scale MIMO detection because of their low computational cost, but has the least performance. [2]本文针对大规模 MIMO 检测提出了增强型信道强化-利用消息传递 (ECHEMP) 算法，以降低复杂度成本提高解码性能，从而实现更好的解码权衡。 [1] 线性检测器，例如最小均方误差 (MMSE)、迫零，因其计算成本低而被应用于大规模 MIMO 检测，但性能最低。 [2]
Iterative Mimo Detection
In this article, we present a new iterative MIMO detection algorithm based on inexact alternating direction method of multipliers. [1] This research presents the performance of Gibbs sampling iterative MIMO detection and decoding scheme with maximum ratio combining (MRC). [2]在本文中，我们提出了一种基于乘法器的不精确交替方向方法的新的迭代 MIMO 检测算法。 [1] 本研究展示了吉布斯采样迭代 MIMO 检测和解码方案与最大比率合并 (MRC) 的性能。 [2]
Joint Mimo Detection
We propose a deep-learning approach for the joint MIMO detection and channel decoding problem. [1] We propose a deep-learning approach for the joint MIMO detection and channel decoding problem. [2]我们为联合 MIMO 检测和信道解码问题提出了一种深度学习方法。 [1] 我们为联合 MIMO 检测和信道解码问题提出了一种深度学习方法。 [2]
mimo detection algorithm Mimo检测算法
Finally, simulation of adopting different mMIMO detection algorithms are presented, which shows the Alternating Direction Method Of Multipliers (ADMM)-based infinity-norm (ADMIN) detector has the best performance. [1] Several numerical experiments show that the proposed algorithm outperforms known massive MIMO detection algorithms, such as an MMSE detector with belief propagation decoding. [2] In order to overcome the impacts of imperfect CSI for Internet of things, we propose a deep convolutional neural network (DCNN) based MIMO detection algorithm, where the DCNN is trained offline and works online to refine the imperfect CSI and improve the bit error rate of the wireless systems. [3] In this article, we present a new iterative MIMO detection algorithm based on inexact alternating direction method of multipliers. [4] The MIMO detection algorithm is the key for design of MIMO receiver. [5] In this paper, a novel and robust GSM-MIMO detection algorithm are proposed based on artificial bee colony optimization with mutation operator. [6] In this paper, we propose a novel information updating scheme for EP MIMO detection algorithm to achieve high performance with low complexity. [7] Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. [8] Simulation results justify the viability of the proposed detection algorithm than several existing massive MIMO detection algorithms, in terms of bit error rate performance with comparable computational complexity. [9] Moreover, simulation results of adopting various mMIMO detection algorithms are presented. [10]最后，给出了采用不同mMIMO检测算法的仿真， 它显示了基于无穷范数的乘法器交替方向法 (ADMM) (ADMIN) 检测器具有最佳性能。 [1] 几个数值实验表明，所提出的算法优于已知的大规模 MIMO 检测算法，例如具有置信传播解码的 MMSE 检测器。 [2] 为了克服不完善的CSI对物联网的影响，我们提出了一种基于深度卷积神经网络（DCNN）的MIMO检测算法，其中DCNN离线训练并在线工作以细化不完善的CSI并提高误码率。无线系统。 [3] 在本文中，我们提出了一种基于乘法器的不精确交替方向方法的新的迭代 MIMO 检测算法。 [4] MIMO检测算法是MIMO接收机设计的关键。 [5] 本文提出了一种基于变异算子人工蜂群优化的新型鲁棒GSM-MIMO检测算法。 [6] 在本文中，我们提出了一种新的 EP MIMO 检测算法信息更新方案，以实现低复杂度的高性能。 [7] 因此，寻找具有最佳性能和低复杂度的完美大规模 MIMO 检测算法的研究在过去十年中引起了广泛关注。 [8] 仿真结果证明了所提出的检测算法比现有的几种大规模 MIMO 检测算法的可行性，在具有可比计算复杂度的误码率性能方面。 [9] 此外，还给出了采用各种mMIMO检测算法的仿真结果。 [10]
mimo detection problem
In spite of the existence of various different SDRs for the MIMO detection problem in the literature, very little is known about the relationship between these SDRs. [1] The structure of the network is obtained from an iterative algorithm arising from the application of ADMM to the maximum-likelihood MIMO detection problem. [2] Neural network has been applied into MIMO detection problem and has achieved the state-of-the-art performance. [3]尽管文献中针对 MIMO 检测问题存在各种不同的 SDR，但对这些 SDR 之间的关系知之甚少。 [1] 网络的结构是通过将 ADMM 应用于最大似然 MIMO 检测问题而产生的迭代算法获得的。 [2] 神经网络已应用于 MIMO 检测问题，并取得了最先进的性能。 [3]
mimo detection technique Mimo检测技术
The proposed MBM-mMIMO detection technique exploits the upper bound on support recovery error and iteratively minimizes the residual error associated with the estimated transmit vector. [1] Though machine learning-based MIMO detection techniques outperform conventional symbol detection techniques, in large user massive MIMO, they suffer from maintaining an optimal bias-variance trade-off to yield optimal performance from an individual model. [2] The implementation of MIMO detection techniques become a difficult missionas the computational complexity increases with the number of transmitting antenna and constellation size. [3]所提出的 MBM-mMIMO 检测技术利用了支持恢复误差的上限，并迭代地最小化与估计的发射向量相关的残余误差。 [1] 尽管基于机器学习的 MIMO 检测技术优于传统的符号检测技术，但在大型用户大规模 MIMO 中，它们会受到保持最佳偏差-方差折衷以从单个模型中获得最佳性能的困扰。 [2] MIMO检测技术的实施成为一项艰巨的任务，因为计算复杂度随着发射天线数量和星座大小的增加而增加。 [3]
mimo detection scheme Mimo检测方案
Compared with the traditional MIMO detection schemes, the MOMS scheme is more robust in the sparse multipath channel scenarios, and can obtain most spatial degrees of freedom (DoF) in terms of antenna aperture and channel geometry. [1] Using computer simulations, it is shown that the error performance can be significantly improved and computational complexity reduced compared to those of state-of-the-art MIMO detection schemes. [2] The main contributions of this paper include first, a proposal of newly developed iterative interference cancellation technique with parallel processing for MDL-impact mitigation, second, a comparative study on performance of MIMO detection schemes, and third, a demonstration of the longest dense SDM transmission over multicore (MC)-FMF. [3]与传统的 MIMO 检测方案相比，MOMS 方案在稀疏多径信道场景下更加鲁棒，并且在天线孔径和信道几何方面可以获得最大的空间自由度（DoF）。 [1] 使用计算机模拟表明，与最先进的 MIMO 检测方案相比，可以显着提高错误性能并降低计算复杂度。 [2] 本文的主要贡献包括：第一，新开发的具有并行处理的 MDL 影响缓解迭代干扰消除技术的提议，第二，MIMO 检测方案性能的比较研究，第三，最长密集 SDM 传输的演示在多核 (MC)-FMF 上。 [3]
mimo detection method
This paper presents a deep neural network (DNN)-based massive MIMO detection method which can overcome the above limitation. [1] Using the total degradations (TD) criterion, an appropriate quiescent point is determined to decrease the impact of non-linearity level, and hence the performances of different MIMO detection methods are compared. [2]本文提出了一种基于深度神经网络（DNN）的大规模 MIMO 检测方法，可以克服上述限制。 [1] 使用总退化 (TD) 标准，确定适当的静止点以降低非线性水平的影响，从而比较不同 MIMO 检测方法的性能。 [2]