TY - GEN
T1 - Detection for Uplink Massive MIMO System
T2 - 3rd EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2019
AU - Li, Lin
AU - Meng, Weixiao
N1 - Publisher Copyright:
© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2019
Y1 - 2019
N2 - In this paper, we make a compressive survey for the research on detection in uplink Massive multiple input and multiple output (MIMO) system. As one key technology in Massive MIMO system, which is also one primary subject for the fifth generation wireless communications, this research is significant to be developed. As a result of large scaled antennas, the channel gain matrix in Massive MIMO system is asymptotic diagonal orthogonal, and it is an non-deterministic polynomial hard problem to obtain the optimum bits error rate (BER) performance during finite polynomial complexity time. The traditional detection algorithms for MIMO system are not efficient any more due to poor BER performance or high computational complexity. The exiting detection algorithms for Massive MIMO system are able to solve this issue. However, there are still crucial problems for them, including employing the deep learning technology for detection in Massive MIMO system, and not work for the millimeter wave Massive MIMO system in the strong spatial correlation environment even exiting keyhole effect, which is not rich scattering, as well as application in Hetnets wireless communications, and etc. Therefore, the research on detection for uplink Massive MIMO system is still in its early stage, there are lots of significant and urgent issues to overcome in the future.
AB - In this paper, we make a compressive survey for the research on detection in uplink Massive multiple input and multiple output (MIMO) system. As one key technology in Massive MIMO system, which is also one primary subject for the fifth generation wireless communications, this research is significant to be developed. As a result of large scaled antennas, the channel gain matrix in Massive MIMO system is asymptotic diagonal orthogonal, and it is an non-deterministic polynomial hard problem to obtain the optimum bits error rate (BER) performance during finite polynomial complexity time. The traditional detection algorithms for MIMO system are not efficient any more due to poor BER performance or high computational complexity. The exiting detection algorithms for Massive MIMO system are able to solve this issue. However, there are still crucial problems for them, including employing the deep learning technology for detection in Massive MIMO system, and not work for the millimeter wave Massive MIMO system in the strong spatial correlation environment even exiting keyhole effect, which is not rich scattering, as well as application in Hetnets wireless communications, and etc. Therefore, the research on detection for uplink Massive MIMO system is still in its early stage, there are lots of significant and urgent issues to overcome in the future.
KW - Low complexity detection
KW - Massive MIMO
KW - Optimum performance
UR - https://www.scopus.com/pages/publications/85076856052
U2 - 10.1007/978-3-030-36402-1_31
DO - 10.1007/978-3-030-36402-1_31
M3 - 会议稿件
AN - SCOPUS:85076856052
SN - 9783030364014
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 287
EP - 299
BT - Advanced Hybrid Information Processing - 3rd EAI International Conference, ADHIP 2019, Proceedings, Part 1
A2 - Gui, Guan
A2 - Yun, Lin
PB - Springer
Y2 - 21 September 2019 through 22 September 2019
ER -