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雷达卡
摘要翻译：
考虑了超密集云无线接入网（UD-CRAN）,其中远程无线头（RRHs）密集部署在网络中。为了减少信道估计的开销，我们重点研究了在信道状态信息有限的情况下，以用户为中心的频分双工系统中鲁棒发射波束形成的设计。具体来说，我们设想了一个完整的CSI获取过程，包括两个关键步骤：信道估计和信道量化。对于相干协作传输，相位模糊度(PA)也进行了量化。在不完全CSI的基础上，我们的目标是在满足用户速率要求和前端容量限制的前提下，优化波束形成向量，以使总发射功率最小。利用多个不确定项的统计性质，导出了可达数据率的闭式表达式。然后，我们提出了一个基于逐次凸逼近技术的求解该问题的低复杂度迭代算法。在每次迭代过程中，采用拉格朗日对偶分解法获得最优波束形成矢量。此外，还提供了一对低复杂度的用户选择算法来保证问题的可行性。仿真结果证实了我们的鲁棒算法在满足速率要求方面的准确性。最后，我们的仿真结果验证了使用单个比特对PA进行量化能够获得良好的性能。
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英文标题：
《Robust Beamforming Design for Ultra-dense User-Centric C-RAN in the Face
of Realistic Pilot Contamination and Limited Feedback》
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作者：
Cunhua Pan, Hong Ren, Maged Elkashlan, Arumugam Nallanathan, Lajos
Hanzo
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最新提交年份：
2018
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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。
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英文摘要：
The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads (RRHs) are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex (FDD) UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim for optimizing the beamforming vectors in order to minimize the total transmit power subject to users' rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the achievable data rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms are provided to guarantee the feasibility of the problem. Simulation results confirm the accuracy of our robust algorithm in terms of meeting the rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA is capable of achieving good performance.
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PDF链接：
https://arxiv.org/pdf/1804.0399
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