Mimo Receiver(Mimo 接收器)研究综述
Mimo Receiver Mimo 接收器 - Unfortunately, this application is limited because all the channel information over the transmission link must be transmitted to MIMO receivers for complete crosstalk compensation, which indicates difficulty in applying MIMO to the transmission path via optical switches. [1] The two main components of an M-MIMO receiver are a detector and a decoder. [2] This work serves as a design guide to determine the selection of A-MIMO, TA-MIMO, or C-MIMO receivers depending on the misalignment conditions for a particular underwater application. [3] Therefore, it achieves remarkable complexity reduction and exhibits significant performance advantages compared to the existing quantized SC-MIMO receivers from the literature. [4] This algorithm has recently been applied to devise low-complexity M-MIMO receivers; however, it is limited by the fact that certain configurations of the linear equations may significantly deteriorate the performance of the RK algorithm. [5] In this work, a new, novel MIMO technique for simultaneously achieving multiplexing and diversity gains as well as completely eliminating any processing at the MIMO receiver, leading to advantages such as low complexity and low power consumption, is proposed. [6] The MIMO detection algorithm is the key for design of MIMO receiver. [7] It is known that lattice-reduction-aided (LRA) MIMO receivers can achieve full spatial diversity. [8] Our proposal was verified in the context of a MIMO receiver. [9] Compared with the existing minimum mean squared error (MMSE) based L-MUD and the generic MU-MIMO receivers, numerical simulations demonstrate valuable tradeoffs between the permissible user overloading, required number of radio frequency chains (RFCs), computational efforts, and bit error rate. [10] It turns out that, without any further measures, the MBS-MIMO receiver has the ability to compensate for frequency dependent IQ modulator gain and phase imbalance. [11] A difficulty encountered in design of MIMO Receiver in detecting of signal in noisy & large MIMO environment of the transmitted signal. [12] Since the same subcarriers are affected when the signal is subsampled at the receiver, the MBS-MIMO receiver can be used without any modification. [13] Use of low resolution ADCs has been proposed as a means to decrease power consumption in MIMO receivers. [14] 5GHz full-duplex (FD) MIMO receiver (RX) with self-adaptive ≥24dB RF/analog interference cancellation across 20MHz BW is presented. [15] As a proof of concept, a four-element beamforming-MIMO receiver (RX) covering 64-67-GHz frequency band (the FCC’s newly allocated 64–71-GHz frequency band for high-speed wireless links between small cells) enabling two-stream concurrent reception is designed and measured. [16]不幸的是，这种应用受到限制，因为传输链路上的所有信道信息都必须传输到 MIMO 接收器以进行完整的串扰补偿，这表明通过光开关将 MIMO 应用于传输路径存在困难。 [1] M-MIMO 接收器的两个主要组件是检测器和解码器。 [2] 这项工作可作为设计指南，根据特定水下应用的失准条件确定 A-MIMO、TA-MIMO 或 C-MIMO 接收器的选择。 [3] 因此，与文献中现有的量化 SC-MIMO 接收器相比，它实现了显着的复杂性降低并表现出显着的性能优势。 [4] 该算法最近被应用于设计低复杂度的 M-MIMO 接收机；然而，它受到以下事实的限制：线性方程的某些配置可能会显着降低 RK 算法的性能。 [5] 在这项工作中，提出了一种新的、新颖的 MIMO 技术，可同时实现复用和分集增益，并完全消除 MIMO 接收器的任何处理，从而具有低复杂度和低功耗等优点。 [6] MIMO检测算法是MIMO接收机设计的关键。 [7] 众所周知，格归约辅助 (LRA) MIMO 接收器可以实现全空间分集。 [8] 我们的提议在 MIMO 接收器的背景下得到了验证。 [9] 与现有的基于最小均方误差 (MMSE) 的 L-MUD 和通用 MU-MIMO 接收器相比，数值模拟证明了在允许的用户过载、所需的射频链 (RFC) 数量、计算工作量和误码之间进行有价值的权衡速度。 [10] 事实证明，在没有任何进一步措施的情况下，MBS-MIMO 接收器能够补偿频率相关的 IQ 调制器增益和相位不平衡。 [11] 在发射信号的嘈杂和大型 MIMO 环境中检测信号时遇到的 MIMO 接收器设计困难。 [12] 由于在接收机对信号进行二次采样时会影响相同的子载波，因此无需任何修改即可使用 MBS-MIMO 接收机。 [13] 已提出使用低分辨率 ADC 作为降低 MIMO 接收器功耗的手段。 [14] 展示了 5GHz 全双工 (FD) MIMO 接收器 (RX)，在 20MHz 带宽上具有自适应 ≥24dB 射频/模拟干扰消除。 [15] 作为概念验证，覆盖 64-67-GHz 频段（FCC 新分配的 64-71-GHz 频段用于小型蜂窝之间的高速无线链路）的四元素波束成形 MIMO 接收器 (RX) 可实现两个流并发接收设计和测量。 [16]
minimum mean squared 最小均方
In contrast to existing work, we propose an machine learning (ML)-enhanced MU-MIMO receiver that builds on top of a conventional linear minimum mean squared error (LMMSE) architecture. [1] Numerical results show that by appropriately setting the number of hidden neurons, the ELM achieves higher spectral efficiency and smaller BER, with fewer floating-point operations than the conventional linear MIMO receivers, namely the minimum mean squared error and maximum ratio receivers. [2]与现有工作相比，我们提出了一种机器学习 (ML) 增强型 MU-MIMO 接收器，它建立在传统的线性最小均方误差 (LMMSE) 架构之上。 [1] 数值结果表明，通过适当设置隐藏神经元的数量，ELM实现了更高的频谱效率和更小的BER，与传统的线性MIMO接收器（即最小均方误差和最大比接收器）相比，浮点运算更少。 [2]
mean squared error 均方误差
It has been shown that adopting variable-resolution (VR) ADCs in Ma-MIMO receivers can improve performance with Mean Squared Error (MSE) and throughput while providing better EE. [1]已经表明，在 Ma-MIMO 接收器中采用可变分辨率 (VR) ADC 可以通过均方误差 (MSE) 和吞吐量提高性能，同时提供更好的 EE。 [1]
Massive Mimo Receiver 大型 Mimo 接收器
For practicality, a generic power consumption model for massive MIMO receivers, including ADC resolution, symbol detection, and receiver circuits, is considered. [1] An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. [2] The goal of this paper is to establish performance bounds on the channel estimation of one-bit mmWave massive MIMO receivers for different types of channel models. [3] A polar-coded massive MIMO receiver that supports a length-1024 rate-1/2 polar code, 128 receive antennas, and eight users is designed and implemented, and delivers a throughput of 7. [4] Drawing from a recent work on negative noise correlation in quantization and statistics, we propose a novel antithetic dithered 1-bit massive MIMO receiver architecture and develop efficient channel estimation algorithms that exploit the natural and induced negative correlated noise in the system. [5] Building upon a recently proposed system model taking into account the reverberation (clutter) produced by the radar system at the massive MIMO receiver, we provide a theoretical analysis, in terms of a lower bound on the achievable uplink (UL) spectral efficiency (SE) and in terms of the mutual information of the cellular massive MIMO system, showing that for large number of antennas at the base station the radar clutter effects can be suppressed. [6] An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. [7] Methods: The existing research work namely Noise and Relevancy aware Low Complexity Detection (NRLCD) algorithm for massive MIMO receiver utilizes normalized cross correlation based pruning strategy to viably evacuate uncorrelated signals. [8]出于实用性考虑，考虑了大规模 MIMO 接收器的通用功耗模型，包括 ADC 分辨率、符号检测和接收器电路。 [1] 一种用于实现降低成本和功耗的大规模 MIMO 接收器的新兴技术是基于动态超表面天线 (DMA)，它在采集过程中固有地实现了可控压缩。 [2] 本文的目标是为不同类型的信道模型建立一位毫米波大规模 MIMO 接收器的信道估计性能界限。 [3] 设计并实现了支持长度为1024速率1/2极性码、128根接收天线和8个用户的极性编码大规模MIMO接收器，吞吐量为7。 [4] 借鉴最近关于量化和统计中负噪声相关性的工作，我们提出了一种新颖的对立抖动 1 比特大规模 MIMO 接收器架构，并开发了有效的信道估计算法，该算法利用了系统中的自然和诱导的负相关噪声。 [5] 基于最近提出的系统模型，该模型考虑了雷达系统在大规模 MIMO 接收器处产生的混响（杂波），我们根据可实现的上行链路 (UL) 频谱效率 (SE) 的下限提供了理论分析并且在蜂窝Massive MIMO系统的互信息方面，表明对于基站处的大量天线，可以抑制雷达杂波效应。 [6] 一种用于实现降低成本和功耗的大规模 MIMO 接收器的新兴技术是基于动态超表面天线 (DMA)，它在采集过程中固有地实现了可控压缩。 [7] 方法：现有的研究工作，即大规模 MIMO 接收器的噪声和相关性感知低复杂度检测 (NRLCD) 算法，利用基于归一化互相关的剪枝策略有效地疏散不相关的信号。 [8]
Multiuser Mimo Receiver
In this work we propose a multiuser MIMO receiver which learns to jointly detect in a data-driven fashion, without assuming a specific channel model or requiring CSI. [1] The use of SQLC allows grouping the users in pairs and halves the number of streams to be managed by the multiuser MIMO receiver. [2]在这项工作中，我们提出了一种多用户 MIMO 接收器，它可以学习以数据驱动的方式联合检测，而无需假设特定的信道模型或需要 CSI。 [1] SQLC 的使用允许将用户成对分组，并将由多用户 MIMO 接收器管理的流的数量减半。 [2]
Hybrid Mimo Receiver
Furthermore, for a symbol detection scenario, it is demonstrated that the proposed approach can realize reliable bit-efficient hybrid MIMO receivers capable of setting their quantization rule in light of the task. [1] , an adaptive ADC bit allocation method and an MMSE-based VAMP, are proposed for mm Wave communications of the hybrid MIMO receiver architecture. [2]此外，对于符号检测场景，证明了所提出的方法可以实现可靠的比特高效混合 MIMO 接收器，该接收器能够根据任务设置其量化规则。 [1] ，提出了一种自适应 ADC 比特分配方法和基于 MMSE 的 VAMP，用于混合 MIMO 接收器架构的毫米波通信。 [2]
Conventional Mimo Receiver
Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners. [1] Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners. [2]传统的 MIMO 接收器采用基于模型的方法以线性或迭代方式进行 MIMO 检测和信道解码。 [1] 传统的 MIMO 接收器采用基于模型的方法以线性或迭代方式进行 MIMO 检测和信道解码。 [2]
Resolution Mimo Receiver 分辨率 Mimo 接收器
The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. [1] The considered low-resolution MIMO receiver implies that the received signals simultaneously are processed by the 1-bit ADCs and the comparator network, where the latter is composed of several simple comparators with binary outputs. [2]所提出的 OBMNet 和 FBMNet 检测器具有为低分辨率 MIMO 接收器设计的独特而简单的结构，因此可以有效地训练和实现。 [1] 考虑的低分辨率 MIMO 接收器意味着接收到的信号同时由 1 位 ADC 和比较器网络处理，后者由几个具有二进制输出的简单比较器组成。 [2]
mimo receiver architecture
In this paper, we present a deep learning-based MIMO receiver architecture that consists of a ResNet-based convolutional neural network, also known as DeepRx, combined with a so-called transformation layer, all trained together. [1] , an adaptive ADC bit allocation method and an MMSE-based VAMP, are proposed for mm Wave communications of the hybrid MIMO receiver architecture. [2] Drawing from a recent work on negative noise correlation in quantization and statistics, we propose a novel antithetic dithered 1-bit massive MIMO receiver architecture and develop efficient channel estimation algorithms that exploit the natural and induced negative correlated noise in the system. [3]在本文中，我们提出了一种基于深度学习的 MIMO 接收器架构，它由一个基于 ResNet 的卷积神经网络（也称为 DeepRx）和一个所谓的转换层组成，所有这些都一起训练。 [1] ，提出了一种自适应 ADC 比特分配方法和基于 MMSE 的 VAMP，用于混合 MIMO 接收器架构的毫米波通信。 [2] 借鉴最近关于量化和统计中负噪声相关性的工作，我们提出了一种新颖的对立抖动 1 比特大规模 MIMO 接收器架构，并开发了有效的信道估计算法，该算法利用了系统中的自然和诱导的负相关噪声。 [3]