TY - JOUR
T1 - Advancements in 5G New Radio TDD Cross Link Interference Mitigation
AU - Pedersen, Klaus
AU - Esswie, Ali
AU - Lei, Du
AU - Harrebek, Johannes
AU - Yuk, Youngsoo
AU - Selvaganapathy, Srinivasan
AU - Helmers, Hakon
N1 - Publisher Copyright:
© 2002-2012 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - This article presents enhancements that further improve the performance of 5G New Radio time division duplex (TDD) operation and performance. The focus is on mechanisms to mitigate co-channel cross link interference (CLI) between neighboring cells, providing pointers also to recent advances in remote interference mitigation between cells separated by tens to hundreds of kilometers. A sophisticated framework where User Equipment's (UEs) are used as sensors detecting CLI problems is outlined, and the underlying design rationales are presented. Solutions for simple coordination of TDD radio frame configurations between network elements are described. Solutions based on simple reinforcement learning algorithms for the network to adjust the TDD radio frame configuration are proposed, and validated for both macro and indoor factory deployments, considering both ultra-reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB) traffic. Results from extensive dynamic system-level simulations confirm that such solutions offer attractive benefits.
AB - This article presents enhancements that further improve the performance of 5G New Radio time division duplex (TDD) operation and performance. The focus is on mechanisms to mitigate co-channel cross link interference (CLI) between neighboring cells, providing pointers also to recent advances in remote interference mitigation between cells separated by tens to hundreds of kilometers. A sophisticated framework where User Equipment's (UEs) are used as sensors detecting CLI problems is outlined, and the underlying design rationales are presented. Solutions for simple coordination of TDD radio frame configurations between network elements are described. Solutions based on simple reinforcement learning algorithms for the network to adjust the TDD radio frame configuration are proposed, and validated for both macro and indoor factory deployments, considering both ultra-reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB) traffic. Results from extensive dynamic system-level simulations confirm that such solutions offer attractive benefits.
UR - http://www.scopus.com/inward/record.url?scp=85103882263&partnerID=8YFLogxK
U2 - 10.1109/MWC.001.2000376
DO - 10.1109/MWC.001.2000376
M3 - Journal article
AN - SCOPUS:85103882263
SN - 1536-1284
VL - 28
SP - 106
EP - 112
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
IS - 4
M1 - 9397423
ER -