Stochastic Learning Robust Beamforming for Millimeter-Wave Systems With Path Blockage

被引:36
作者
Iimori, Hiroki [1 ]
de Abreu, Giuseppe Thadeu Freitas [1 ]
Taghizadeh, Omid [2 ]
Stoica, Razvan-Andrei [3 ]
Hara, Takanori [4 ]
Ishibashi, Koji [4 ]
机构
[1] Jacobs Univ Bremen, Dept Comp Sci & Elect Engn, Focus Area Mobil, D-28759 Bremen, Germany
[2] Tech Univ Berlin, Network Informat Theory Grp, D-10623 Berlin, Germany
[3] WIOsense GmbH & Co KG, Dept Res & Dev, D-28717 Bremen, Germany
[4] Univ Electrocommun, Adv Wireless & Commun Res Ctr, Tokyo 1828585, Japan
关键词
Array signal processing; Robustness; Optimization; Downlink; Channel estimation; Quality of service; Complexity theory; Millimeter wave communication; MIMO; blockages; coordinated beamforming; machine learning; NETWORKS; DESIGN; HARDWARE;
D O I
10.1109/LWC.2020.2997366
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We introduce a new robust, outage minimum, millimeter wave (mmWave) coordinated multipoint (CoMP) beamforming scheme to combat the random path blockages typical of mmWave systems. Unlike state-of-the-art methods, which are of limited applicability in practice due to their combinatorial nature which leads to prohibitive complexity, the proposed method is based on a stochastic-learning-approach, which learns crucial blockage patterns without resorting to the well-known worst-case optimization framework. Simulation results demonstrate the superior performance of the proposed method both in terms of outage probability and effective achievable rate.
引用
收藏
页码:1557 / 1561
页数:5
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