信道状态信息(channel state information,CSI)的精确获取是大规模天线发挥效能的关键。在现有的通信系统中,上下行链路互易性不理想时,基于码本进行下行链路的CSI反馈。随着天线规模的增大,码本CSI反馈所需要的开销也越来越大。给出了...信道状态信息(channel state information,CSI)的精确获取是大规模天线发挥效能的关键。在现有的通信系统中,上下行链路互易性不理想时,基于码本进行下行链路的CSI反馈。随着天线规模的增大,码本CSI反馈所需要的开销也越来越大。给出了基于人工智能(artificial intelligence,AI)的CSI反馈压缩方法,分析了基于AI的CSI反馈的标准化影响、通信流程与面临的挑战,提供了评估结果。评估结果表明,相对于基于频域基向量压缩的码本CSI反馈,基于AI的CSI反馈在相同的反馈精度下可以显著地降低反馈开销。展开更多
在资源受限的水声网络中,使用软频率复用技术和自适应资源分配技术可以提高网络容量和能量效率。然而,水声信道的长传播时延和时变特性导致用于自适应技术的反馈信道状态信息(Channel State Information, CSI)是时变且过时的。非理想的...在资源受限的水声网络中,使用软频率复用技术和自适应资源分配技术可以提高网络容量和能量效率。然而,水声信道的长传播时延和时变特性导致用于自适应技术的反馈信道状态信息(Channel State Information, CSI)是时变且过时的。非理想的反馈CSI会降低自适应系统的性能。针对该问题,提出了一种基于多智能体深度Q网络的资源分配(Multi-agent Deep Q Network Based Resource Allocation, MADQN-RA)方法。该方法将水声软频率复用网络视为多智能体系统,并使用过时的反馈CSI序列作为系统状态。通过建立有效的奖励表达式,智能体可以跟踪时变时延水声信道的变化特性并做出相应的资源分配决策。为了进一步提高智能体的决策准确度,同时避免状态空间维度增大时的部分学习成本,结合动态状态长度方法改进了MADQN-RA。仿真结果表明,所提方法实现的系统性能优于基于其他学习的方法和基于信道预测的方法,且更接近理论最优值。展开更多
在大规模多输入多输出(multiple-input multiple-output,MIMO)系统中,基站需根据用户反馈的信道状态信息(channel state information,CSI)调制自适应编码提升谱效率。针对现有基于深度学习(deep learning,DL)的CSI反馈方法在用户端实际...在大规模多输入多输出(multiple-input multiple-output,MIMO)系统中,基站需根据用户反馈的信道状态信息(channel state information,CSI)调制自适应编码提升谱效率。针对现有基于深度学习(deep learning,DL)的CSI反馈方法在用户端实际部署时可行性较差的问题,在卷积神经网络的基础上提出了一种轻量级的CSI反馈网络,并利用深度可分离卷积技术来减少反馈网络的参数量与计算量。考虑用户端实际部署,设计了在不同压缩比条件下以及在不同环境条件下的多任务融合反馈网络。仿真将所提方法与基于DL的ConvCsiNet和ShuffleNet反馈网络在归一化均方误差和参数量与计算量等方面进行对比与分析。结果表明,所提的反馈网络在保持较高CSI重构精度的前提下,可以极大减少用户端在实际部署时所需的参数量和计算量。展开更多
In a three-dimensional （3D） multiple- input multiple-output （MIMO） system, the base station can use both horizontal and vertical spaces, transmitting spatial beam to users more accurately. This system has caught t...In a three-dimensional （3D） multiple- input multiple-output （MIMO） system, the base station can use both horizontal and vertical spaces, transmitting spatial beam to users more accurately. This system has caught the attention of researchers in recent years. The existing research on the 3D MIMO technology is based on the assumption that the base station can acquire the ideal channel state information （CSI）, which is not actually the case in real systems. Therefore, this paper introduces a limited feedback transmission scheme based on mobile station （MS） compensation in the 3D MIMO system. In this scheme, the vertical antenna gain of the 3D MIMO system compensation is assigned to the MS. Two CSI-RS ports are configured at the base station, omnidirectional CSI-RS port and partial CSI-RS port. The MS can calculate the horizontal CSI and the vertical beam gain according to omnidirectional CSI-RS port and partial CSI- RS port, respectively. Partial CSI-RS resources are used to calculate the channel after being weighted by the vertical beam vector, MS selects the optimal vertical precoding vector. Simulations show that compared with the reference strategy, the transmission scheme with limited feedback based on the MS compensation proposed in this article has more advantages. The average spectral efficiency of the system and the cell edge spectral efficiency can be greatly improved.展开更多
文摘信道状态信息(channel state information,CSI)的精确获取是大规模天线发挥效能的关键。在现有的通信系统中,上下行链路互易性不理想时,基于码本进行下行链路的CSI反馈。随着天线规模的增大,码本CSI反馈所需要的开销也越来越大。给出了基于人工智能(artificial intelligence,AI)的CSI反馈压缩方法,分析了基于AI的CSI反馈的标准化影响、通信流程与面临的挑战,提供了评估结果。评估结果表明,相对于基于频域基向量压缩的码本CSI反馈,基于AI的CSI反馈在相同的反馈精度下可以显著地降低反馈开销。
文摘在资源受限的水声网络中,使用软频率复用技术和自适应资源分配技术可以提高网络容量和能量效率。然而,水声信道的长传播时延和时变特性导致用于自适应技术的反馈信道状态信息(Channel State Information, CSI)是时变且过时的。非理想的反馈CSI会降低自适应系统的性能。针对该问题,提出了一种基于多智能体深度Q网络的资源分配(Multi-agent Deep Q Network Based Resource Allocation, MADQN-RA)方法。该方法将水声软频率复用网络视为多智能体系统,并使用过时的反馈CSI序列作为系统状态。通过建立有效的奖励表达式,智能体可以跟踪时变时延水声信道的变化特性并做出相应的资源分配决策。为了进一步提高智能体的决策准确度,同时避免状态空间维度增大时的部分学习成本,结合动态状态长度方法改进了MADQN-RA。仿真结果表明,所提方法实现的系统性能优于基于其他学习的方法和基于信道预测的方法,且更接近理论最优值。
文摘在大规模多输入多输出(multiple-input multiple-output,MIMO)系统中,基站需根据用户反馈的信道状态信息(channel state information,CSI)调制自适应编码提升谱效率。针对现有基于深度学习(deep learning,DL)的CSI反馈方法在用户端实际部署时可行性较差的问题,在卷积神经网络的基础上提出了一种轻量级的CSI反馈网络,并利用深度可分离卷积技术来减少反馈网络的参数量与计算量。考虑用户端实际部署,设计了在不同压缩比条件下以及在不同环境条件下的多任务融合反馈网络。仿真将所提方法与基于DL的ConvCsiNet和ShuffleNet反馈网络在归一化均方误差和参数量与计算量等方面进行对比与分析。结果表明,所提的反馈网络在保持较高CSI重构精度的前提下,可以极大减少用户端在实际部署时所需的参数量和计算量。
基金the National Natural Science Foundation of China Grants No.61302106,51274018,the National Science & Technology Pillar Program Grants No.2013BAK06B03 Natural Science Foundation of Hebei Province No.F2014502029 and the Fundamental Research Funds for the Central Universities Grants No.2014MS100
文摘In a three-dimensional （3D） multiple- input multiple-output （MIMO） system, the base station can use both horizontal and vertical spaces, transmitting spatial beam to users more accurately. This system has caught the attention of researchers in recent years. The existing research on the 3D MIMO technology is based on the assumption that the base station can acquire the ideal channel state information （CSI）, which is not actually the case in real systems. Therefore, this paper introduces a limited feedback transmission scheme based on mobile station （MS） compensation in the 3D MIMO system. In this scheme, the vertical antenna gain of the 3D MIMO system compensation is assigned to the MS. Two CSI-RS ports are configured at the base station, omnidirectional CSI-RS port and partial CSI-RS port. The MS can calculate the horizontal CSI and the vertical beam gain according to omnidirectional CSI-RS port and partial CSI- RS port, respectively. Partial CSI-RS resources are used to calculate the channel after being weighted by the vertical beam vector, MS selects the optimal vertical precoding vector. Simulations show that compared with the reference strategy, the transmission scheme with limited feedback based on the MS compensation proposed in this article has more advantages. The average spectral efficiency of the system and the cell edge spectral efficiency can be greatly improved.
