Page 70 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 4 – AI and machine learning solutions in 5G and future networks
P. 70
ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4
Table 3 – Application scenarios
BS location RL‑based ICI mitigation algorithms
Longitude Latitude MC Sarsa Q‑learning Dynamic Q
116.395659 39.959522 –2.036 –1.724 1.488 6.319
117.212147 39.161901 –4.732 –2.543 0.563 5.783
111.713038 40.832723 –7.931 –3.472 –1.239 4.374
111.710787 40.832027 –6.293 –2.174 0.897 4.978
111.709219 40.837586 –6.517 –2.573 1.296 5.381
REFERENCES [10] Shendi Wang, John Thompson, Peter M.
Grant. “Closed‑Form Expressions for ICI/ISI in Fil‑
[1] Emil Björnson, Erik G Larsson, and Thomas L
Marzetta. “Massive MIMO: Ten myths and one crit‑ tered OFDM Systems for Asynchronous 5G Uplink”.
ical question”. In: IEEE Communications Magazine IEEE Transactions on Communications 65.11
(2017), 4886–4898. DOI: 10 . 1109 / TCOMM .
54.2 (2016), pp. 114–123.
2017.2698478.
[2] Jakob Hoydis, Stephan Ten Brink, and Mérouane
[11] Viswakumar.
Debbah. “Massive MIMO in the UL/DL of cellular
“Performance evaluation of 5G waveforms: UFMC
networks: How many antennas do we need?” In:
IEEE Journal on selected Areas in Communications and FBMC‑OQAM with Cyclic Pre ix‑OFDM System”.
In: 2019 9th International Conference on Advances
31.2 (2013), pp. 160–171.
in Computing and Communication (ICACC) 2019,
[3] Xudong Zhu, Zhaocheng Wang, Linglong Dai, and 6–10. DOI: 10 . 1109 / ICACC48162 . 2019 .
Chen Qian. “Smart pilot assignment for mas‑
8986195.
sive MIMO”. In: IEEE Communications Letters 19.9
[12] Mariana Dirani and Zwi Altman. “A cooperative re‑
(2015), pp. 1644–1647.
inforcement learning approach for inter‑cell inter‑
[4] Vishnu V Ratnam, Andreas F Molisch, Ozgun Y Bur‑
ference coordination in OFDMA cellular networks”.
salioglu, and Haralabos C Papadopoulos. “Hybrid 8th International Symposium on Modeling and
beamforming with selection for multiuser massive Optimization in Mobile, Ad Hoc, and Wireless Net‑
MIMO systems”. In: IEEE Transactions on Signal
works. IEEE. 2010, pp. 170–176.
Processing 66.15 (2018), pp. 4105–4120.
[13] Sicong
[5] Xiaoguang Zhao, Elena Lukashova, Florian
Cheng, Mugen “Reinforce‑
Kaltenberger, and Sebastian Wagner. “Practi‑
ment learning‑based interference control for ultra‑
cal hybrid beamforming schemes in massive mimo dense small cells”. In: 2018 IEEE Global Communi‑
5g NR systems”. In: WSA 2019; 23rd International
cations Conference (GLOBECOM). IEEE. 2018, pp. 1–6.
ITG Workshop on Smart Antennas. VDE. 2019,
pp. 1–8. [14] Meryem Simsek, Mehdi Bennis, and Ismail Gü venç.
frequency‑and time‑domain
[6] Deepak Mishra and Håkan Johansson. “Optimal
channel estimation for hybrid energy beamform‑ inter‑cell interference coordination HetNets”.
ing under phase shifter impairments”. In: IEEE IEEE Transactions on Vehicular Technology
64.10 (2014), pp. 4589–4602.
Transactions on Communications 67.6 (2019),
pp. 4309–4325. [15] Joy Iong‑Zong Chen, Lee, Wen
Wu. “Performance evaluation of BER for
[7] Kwihoon Kim, Joohyung Lee, and Junkyun Choi.
“Deep learning based pilot allocation scheme (DL‑ Massive‑MIMO M‑ary scheme over
PAS) for 5G massive MIMO system”. In: IEEE Com‑ Three‑Dimension correlated channel”. In: Computers
& Electrical Engineering 65 (2018), 196–206.
munications Letters 22.4 (2018), pp. 828–831.
[8] A Daeinabi, K Sandrasegaran, and X Zhu. “Survey [16] Vladimir Poulkov Pavlina Koleva, Oleg Asenov,
of intercell interference mitigation techniques in Georgi Iliev inter‑
LTE downlink networks”. In: Australasian Telecom‑ cell interference for L role
munication Networks and Applications Conference approach” Telecommunication Systems
55.4 (2014), pp. 481–489.
(ATNAC) 2012. IEEE. 2012, pp. 1–6.
[9] Beatriz Soret, Klaus I Pedersen, Niels TK Jørgensen,
and Vı́ctor Fernández‑López. “Interference coordi‑
nation for dense wireless networks”. In: IEEE Com‑
munications Magazine 53.1 (2015), pp. 102–109.
54 © International Telecommunication Union, 2021