发帖
雷达卡
摘要翻译：
大规模多输入多输出(MIMO)通信的最新进展表明，在基站上配置大天线阵可以显著提高蜂窝网络的性能。Massive MIMO具有降低网络干扰、提高用户平均吞吐量的潜力。另一方面，动态时分双工(TDD)允许相邻小区以不同的上行链路(UL)和下行链路(DL)子帧配置工作，是传统静态TDD的一个有希望的增强。与静态TDD相比，动态TDD可以提供更多的灵活性来适应不同小区之间的UL和DL业务模式，但可能会在不同方向传输的小区之间产生额外的干扰。基于massive MIMO和dynamic TDD各自的特点和特点，本文提出了将massive MIMO和dynamic TDD技术结合起来，即massive MIMO技术解决MC网络中dynamic TDD技术的局限性。具体来说，我们主张在massive MIMO的MC网络中可以充分发挥动态TDD的优势，即通过增加BS天线的数量可以有效地消除BS对BS的干扰。我们用随机矩阵理论进行了详细的分析，证明了当每个用户的BS天线数目无限大时，BS-to-BS干扰对上行链路传输的影响消失了。最后，我们通过数值模拟验证了我们的分析。
---
英文标题：
《Technical Report on Efficient Integration of Dynamic TDD with Massive
MIMO》
---
作者：
Yan Huang, Brian Jalaian, Stephen Russell, and Hooman Samani
---
最新提交年份：
2018
---
分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
--
---
英文摘要：
Recent advances in massive multiple-input multiple-output (MIMO) communication show that equipping base stations (BSs) with large arrays of antenna can significantly improve the performance of cellular networks. Massive MIMO has the potential to mitigate the interference in the network and enhance the average throughput per user. On the other hand, dynamic time division duplexing (TDD), which allows neighboring cells to operate with different uplink (UL) and downlink (DL) sub-frame configurations, is a promising enhancement for the conventional static TDD. Compared with static TDD, dynamic TDD can offer more flexibility to accommodate various UL and DL traffic patterns across different cells, but may result in additional interference among cells transmitting in different directions. Based on the unique characteristics and properties of massive MIMO and dynamic TDD, we propose a marriage of these two techniques, i.e., to have massive MIMO address the limitation of dynamic TDD in macro cell (MC) networks. Specifically, we advocate that the benefits of dynamic TDD can be fully extracted in MC networks equipped with massive MIMO, i.e., the BS-to-BS interference can be effectively removed by increasing the number of BS antennas. We provide detailed analysis using random matrix theory to show that the effect of the BS-to-BS interference on uplink transmissions vanishes as the number of BS antennas per-user grows infinitely large. Last but not least, we validate our analysis by numerical simulations.
---
PDF链接：
https://arxiv.org/pdf/1804.06143
京公网安备 11010802022788号
