Deep Learning-Aided Finite-Capacity Fronthaul Cell-Free Massive MIMO with Zero Forcing
Deep Learning-Aided Finite-Capacity Fronthaul Cell-Free Massive MIMO with Zero Forcing
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DOI：
10.1109/icc40277.2020.9149210
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
M. Bashar;A. Akbari;K. Cumanan;H. Ngo;A. Burr;P. Xiao;M. Debbah
中科院分区：
文献类型：
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作者：
M. Bashar;A. Akbari;K. Cumanan;H. Ngo;A. Burr;P. Xiao;M. Debbah
我们考虑了一个无小区的大规模多输入多输出(MIMO)系统，其中信道估计和接收信号在接入点(AP)被量化并被转发到中央处理单元(CPU)。CPU采用迫零技术来检测所有用户发送的信号。为了解决非凸和速率最大化问题，提出了一种启发式次优方案，将该问题转化为几何规划问题。利用深度卷积神经网络(DCNN)，我们可以从大规模衰落(LSF)系数中确定映射，并通过使用量化信道解决优化问题来确定最优功率。根据如何解决优化问题，研究了不同的功率控制方案：i)基于小规模衰落(SSF)的功率控制；ii)基于LSF使用然后遗忘(UatF)的功率控制；以及iii)基于LSF深度学习(DL)的功率控制。基于SSF的功率控制方案需要针对SSF的每个相干间隔进行求解，这在实时系统中几乎是不可能的。数值结果表明，与已有的实用的基于LSF-UatF的功率控制方案相比，提出的基于LSF-DL的功率控制方案显著提高了系统性能。
We consider a cell-free massive multiple-input multiple-output (MIMO) system where the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU). Zero-forcing technique is used at the CPU to detect the signals transmitted from all users. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is proposed to convert the problem into a geometric programme (GP). Exploiting a deep convolutional neural network (DCNN) allows us to determine both a mapping from the large-scale fading (LSF) coefficients and the optimal power by solving the optimization problem using the quantized channel. Depending on how the optimization problem is solved, different power control schemes are investigated; i) small-scale fading (SSF)-based power control; ii) LSF use-and-then-forget (UatF)-based power control; and iii) LSF deep learning (DL)-based power control. The SSF-based power control scheme needs to be solved for each coherence interval of the SSF, which is practically impossible in real time systems. Numerical results reveal that the proposed LSF-DL-based scheme significantly increases the performance compared to the practical and well-known LSF-UatF-based power control.