报告时间：4月10日09：30
报告地点：临江楼A103
主持人：李鹏教授
报告一题目：6G网络的速率分割多址：预编码和资源分配算法
Title: Rate-splitting multiple access for 6G networks: precoding and resource allocation algorithms
报告摘要：在本报告中，我们将研究基于速率分裂的多天线系统下行链路的预编码和资源分配技术。具体来说，将详细介绍线性和非线性预编码策略，以及流合并策略，这些对于在用户终端部署多天线和提高系统总速率性能至关重要。我们还将介绍适用于基于速率分裂系统的功率分配方法，这些方法基于穷尽搜索策略和高效的随机梯度下降学习算法。此外，将展示和讨论基于速率分裂的多天线网络与共位基站和无小区方案，并探讨未来的研究方向。
Abstract: In this presentation, we will examine precoding and resource allocation techniques for the downlink of rate-splitting-based multiple-antenna systems. In particular, linear and nonlinear precoding strategies will be detailed along with stream combining strategies that are fundamental for the deployment of multiple antennas at user terminals and for improving the sum-rate performance of systems. We will also present power allocation approaches suitable for rate-splitting-based systems, which are based on exhaustive search strategies and efficient stochastic gradient-based learning algorithms. Furthermore, rate-splitting-based multiple-antenna networks with co-located base stations and cell-free schemes will be presented and discussed along with future research directions.
报告时间：4月11日09：30
报告地点：临江楼A101
主持人：李鹏教授
报告二题目：无小区网络的鲁棒MMSE预编码和功率分配技术
Title: Robust MMSE Precoding and Power Allocation Techniques for Cell-Free Networks
报告摘要：本报告将中考虑一个覆盖区域内部分散用户的无小区大规模多输入多输出网络的下行信道。我们提出了一种迭代鲁棒线性最小均方误差（RMMSE）预编码技术，以及最优和统一的功率分配。我们还推导了所提出的迭代RMMSE预编码器和功率分配方法的可实现速率表达式。数值结果显示，在存在完美和不完美的信道状态信息的情况下，所提出的迭代MMSE预编码器和功率分配技术在可实现的总速率和比特错误率方面优于现有的共轭波束成形（CB）、迫零（ZF）和MMSE方案。
Abstract: In this presentation, we consider the downlink channel of a cell-free massive multiple-input multiple-output (MIMO) network with access points (APs) that serve users that are distributed over an area. We present an iterative robust linear minimum mean-square error (RMMSE) precoding technique along with optimal and uniform power allocation. We also derive achievable rate expressions for the proposed iterative RMMSE precoder and power allocation approaches. Numerical results show that the proposed iterative MMSE precoder and power allocation techniques outperform existing conjugate beamforming (CB), zero-forcing (ZF) and MMSE schemes in terms of achievable sum-rate and bit error rate (BER), in the presence of perfect and imperfect channel state information (CSI).
报告时间：4月12日09：30
报告地点：临江楼A103
主持人：李鹏教授
报告三题目：物联网网络的能效分布式和联邦学习
Title: Energy-efficient distributed and federated learning for IoT networks
报告摘要：本报告将提出一种利用粗量化信号进行能效分布式学习的框架，应用于物联网网络。具体来说，我们开发了分布式量化感知最小均方、递归最小二乘和联邦学习算法，这些算法可以使用少量比特量化的信号以能效高的方式学习参数，同时需要较低的计算成本。此外，我们还开发了一种偏差补偿策略，以进一步提高所提出的学习算法的性能。我们对所提出的算法进行了统计分析，并推导了预测均方偏差的解析表达式。同时，我们进行了计算复杂度评估，并研究了所提出和现有技术的功耗消耗情况。数值结果评估了在物联网网络中的参数估计任务中所提出的学习算法与现有技术的比较。
Abstract: In this presentation, we will present an energy-efficient distributed learning framework using coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop distributed quantization-aware least-mean, recursive least-squares and federated learning algorithms that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. Moreover, we develop a bias compensation strategy to further improve the performance of the proposed learning algorithms. We carry out a statistical analysis of the proposed algorithms and derive analytical expressions for predicting the mean-square deviation. A computational complexity evaluation and a study of the power consumption of the proposed and existing techniques are then presented. Numerical results assess the proposed learning algorithms against existing techniques for parameter estimation tasks in IoT networks.
主讲人简介：
Rodrigo C. de Lamare was born in Rio de Janeiro, Brazil, in 1975. He received his Diploma in electronic engineering from the Federal University of Rio de Janeiro in 1998 and the MSc and PhD degrees in electrical engineering from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio) in 2001 and 2004, respectively. Since January 2006, he has been with the Communications Group, School of Physics, Engineering and Technology, University of York, United Kingdom, where he is a Professor. Since April 2013, he has also been a Professor at PUC-RIO. Dr de Lamare is a senior member of the IEEE and an elected member of the IEEE Signal Processing for Communications and Networking Committee and the IEEE Sensor Array and Multichannel Signal Processing. He served as editor for IEEE Wireless Communications Letters and IEEE Transactions on Communications, and is currently an associate editor of IEEE Transactions on Signal Processing. His research interests lie in communications and signal processing, areas in which he has published over 500 papers in international journals and conferences.
电子与信息工程学院
2024年4月9日