Mimo Networks(米莫网络)研究综述
Mimo Networks 米莫网络 - The proposed method greatly improves the accuracy of modulation classification in MIMO networks. [1] The cellular mMIMO networks can provide high data rate for users, however their performance is not satisfied for the cell-edge users and shadowed users. [2] When network slicing is applied to CF-mMIMO networks in order to serve multiple tenants, the deployment flexibility of CF-mMIMO networks could be further improved. [3] To fill this gap, in this paper we conduct a comparative analysis of several most viable advanced jamming schemes in the widely-used MIMO networks. [4] This method was applied to MIMO networks that operate over Rayleigh fading channels with different antenna nodes. [5] In this study, a CSI feedback scheme using random scalar quantization (RSQ) for MU-MIMO networks is proposed. [6] However, in practice MU-MIMO networks are far from their full potential due to the poor scalability problem, including high computational complexity at PHY layer and large-overhead channel contention at MAC layer. [7] This paper aims at highlighting the different trade-offs affecting various performance metrics in CF-M-MIMO networks. [8] We propose a linear precoding scheme that relaxes such infeasibility in overloaded MU-MIMO networks. [9]该方法极大地提高了MIMO网络中调制分类的准确性。 [1] 蜂窝mMIMO网络可以为用户提供高数据速率，但其性能对于小区边缘用户和影子用户来说并不满意。 [2] 当网络切片应用于 CF-mMIMO 网络以服务多个租户时，CF-mMIMO 网络的部署灵活性可以进一步提高。 [3] 为了填补这一空白，在本文中，我们对广泛使用的 MIMO 网络中几种最可行的高级干扰方案进行了比较分析。 [4] 该方法适用于在具有不同天线节点的瑞利衰落信道上运行的 MIMO 网络。 [5] 在这项研究中，提出了一种针对 MU-MIMO 网络使用随机标量量化 (RSQ) 的 CSI 反馈方案。 [6] 然而，在实践中，由于可扩展性差的问题，包括 PHY 层的高计算复杂性和 MAC 层的大开销信道竞争，MU-MIMO 网络远未发挥其全部潜力。 [7] 本文旨在强调影响 CF-M-MIMO 网络中各种性能指标的不同权衡。 [8] 我们提出了一种线性预编码方案，可以缓解过载 MU-MIMO 网络中的这种不可行性。 [9]
Massive Mimo Networks 大型 Mimo 网络
Cell-Free massive MIMO networks are recognized as possible solution in the future wireless communication. [1] This power control algorithm leads to scalable CF Massive MIMO networks in which the amount of computations conducted by each access point (AP) does not depend on the number of network APs. [2] To promote energy conservation and improve utilization of system resources, we propose a joint antenna selection and power allocation (JASPA) strategy for CCFD massive MIMO networks. [3] In this paper, we design a deep learning framework for the power allocation problems in massive MIMO networks. [4] The major challenges in massive MIMO networks are to increase the system throughput and capacity with low complexity and reliability of the wireless communication system. [5] We then tackle massive MIMO networks, and exploit our asymptotic analysis in the number of antennas to derive a low-complexity, yet highly efficient, operational mode and transmit power allocation scheme for a finite-size scenario. [6] The RIS is deployed to help conventional massive MIMO networks serve the users in the dead zone. [7] Although many precoding techniques are essentially proposed for a small-scale MIMO, they have been exploited in massive MIMO networks. [8] In this paper, a resource allocation problem is studied for downlink cell-free massive MIMO networks, where each access point (AP) serves a cluster of user equipment (UE). [9] The results demonstrate that ARDI is capable of accurately reconstructing full downlink channels when the signal-to-noise ratio is higher than 15dB, thereby expanding the channel capacity of Massive MIMO networks. [10] We then apply our proposed technique to channel estimation in massive MIMO networks. [11] The results show that 5G Massive MIMO networks require about 50% less power consumption than the 4G ones, and the NB-IoT in-band deployment requires about 10% less power than guard-band deployment. [12] Massive MIMO networks). [13] We present a modification of conjugate beamforming for the forward link of cell-free massive MIMO networks. [14] In particular, we focus on the downlink performance of massive MIMO networks with the Maximum Ratio Transmission (MRT) precoder at the base stations. [15] This study investigates the channel correlation and the power delay profile (PDP) of the propagation channels in wideband massive MIMO networks through measurements on prototypes under certain circumstance. [16] Specifically, a novel cooperative uplink transmission and detection scheme is first proposed for massive MIMO networks, where each uplink frame is divided into a number of data blocks with independent coding schemes and the following blocks are decoded based on previously detected data blocks in both service and neighboring cells. [17] In particular, for a WPT system with coverage radiusCell-Free 大规模 MIMO 网络被认为是未来无线通信中可能的解决方案。 [1] 这种功率控制算法导致了可扩展的 CF Massive MIMO 网络，其中每个接入点 (AP) 执行的计算量不依赖于网络 AP 的数量。 [2] 为了促进节能和提高系统资源的利用率，我们提出了一种用于 CCFD 大规模 MIMO 网络的联合天线选择和功率分配 (JASPA) 策略。 [3] 在本文中，我们为大规模 MIMO 网络中的功率分配问题设计了一个深度学习框架。 [4] 大规模 MIMO 网络的主要挑战是提高系统吞吐量和容量，同时降低无线通信系统的复杂性和可靠性。 [5] 然后，我们处理大规模 MIMO 网络，并利用我们对天线数量的渐近分析，为有限大小的场景推导出低复杂性但高效的操作模式和发射功率分配方案。 [6] RIS 的部署是为了帮助传统的大规模 MIMO 网络为死区的用户提供服务。 [7] 尽管许多预编码技术本质上是针对小规模 MIMO 提出的，但它们已在大规模 MIMO 网络中得到应用。 [8] 在本文中，研究了下行链路无蜂窝大规模 MIMO 网络的资源分配问题，其中每个接入点 (AP) 服务于用户设备 (UE) 集群。 [9] 结果表明，ARDI能够在信噪比高于15dB时准确重构全下行信道，从而扩大Massive MIMO网络的信道容量。 [10] 然后，我们将我们提出的技术应用于大规模 MIMO 网络中的信道估计。 [11] 结果表明，5G Massive MIMO 网络比 4G 网络功耗低约 50%，NB-IoT 带内部署比保护带部署功耗低约 10%。 [12] 大规模 MIMO 网络）。 [13] 我们提出了一种用于无蜂窝大规模 MIMO 网络前向链路的共轭波束成形的修改。 [14] 特别是，我们专注于在基站使用最大比传输 (MRT) 预编码器的大规模 MIMO 网络的下行链路性能。 [15] 本研究通过在特定情况下对原型进行测量，研究了宽带大规模 MIMO 网络中传播信道的信道相关性和功率延迟分布 (PDP)。 [16] 具体而言，首先针对大规模 MIMO 网络提出了一种新颖的协同上行传输和检测方案，其中每个上行帧被划分为具有独立编码方案的多个数据块，并根据之前在业务和服务中检测到的数据块对后续块进行解码。相邻的细胞。 [17] 特别是，对于覆盖半径 <inline-formula> <tex-math notation="LaTeX">$R_{\mathrm {n}}$ </tex-math></inline-formula> 和排除半径的 WPT 系统<inline-formula> <tex-math notation="LaTeX">$R_{\mathrm {e}}$ </tex-math></inline-formula>，随着<inline-公式> <tex-math notation="LaTeX">$R_{\mathrm {e}}$ </tex-math></inline-formula> 和 <inline-formula> <tex-math notation="LaTeX"> $R_{\mathrm {n}}$ </tex-math></inline-formula>，这证实了小蜂窝配置将是提高毫米波大规模 MIMO 网络中 WPT 性能的可行解决方案。 [18] 这种自适应子阵列架构在毫米波频率的大规模 MIMO 网络中提供了硬件复杂性和频谱效率之间的折衷。 [19] 基于提供的结果，我们得出结论，我们提出的方案增加了多小区大规模 MIMO 网络的总 SE。 [20] 为了进一步提高 5G 大规模 MIMO 网络的频谱效率，针对 TDD 系统开发了双向训练 (BiT)，以最大限度地提高下行链路加权和速率。 [21] 本文讨论了具有随机拓扑结构的Massive MIMO网络的覆盖概率分析。 [22] 结果表明，ARDI能够在信噪比高于15dB时准确重构全下行信道，从而扩大Massive MIMO网络的信道容量。 [23] 与之前的相关工作不同，大规模 MIMO 网络涉及到电路和天线的硬件损伤、传输效率和能耗。 [24]
User Mimo Networks
This article investigates the using of this new SM technique in Multi-user MIMO networks, denoted here as MU-QSM. [1] The IoT $K$ -User MIMO networks described improve upon prior multi-user capacity results through the application of a new beamformer modality. [2]本文研究了这种新的 SM 技术在多用户 MIMO 网络中的使用，这里表示为 MU-QSM。 [1] 所描述的 IoT $K$ -User MIMO 网络通过应用新的波束形成器模式改进了先前的多用户容量结果。 [2]