TY - GEN
T1 - A cooperative online learning scheme for resource allocation in 5G systems
AU - Alqerm, Ismail
AU - Shihada, Basem
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2016/7/26
Y1 - 2016/7/26
N2 - The demand on mobile Internet related services has increased the need for higher bandwidth in cellular networks. The 5G technology is envisioned as a solution to satisfy this demand as it provides high data rates and scalable bandwidth. The multi-tier heterogeneous structure of 5G with dense base station deployment, relays, and device-to-device (D2D) communications intends to serve users with different QoS requirements. However, the multi-tier structure causes severe interference among the multi-tier users which further complicates the resource allocation problem. In this paper, we propose a cooperative scheme to tackle the interference problem, including both cross-tier interference that affects macro users from other tiers and co-tier interference, which is among users belong to the same tier. The scheme employs an online learning algorithm for efficient spectrum allocation with power and modulation adaptation capability. Our evaluation results show that our online scheme outperforms others and achieves significant improvements in throughput, spectral efficiency, fairness, and outage ratio. © 2016 IEEE.
AB - The demand on mobile Internet related services has increased the need for higher bandwidth in cellular networks. The 5G technology is envisioned as a solution to satisfy this demand as it provides high data rates and scalable bandwidth. The multi-tier heterogeneous structure of 5G with dense base station deployment, relays, and device-to-device (D2D) communications intends to serve users with different QoS requirements. However, the multi-tier structure causes severe interference among the multi-tier users which further complicates the resource allocation problem. In this paper, we propose a cooperative scheme to tackle the interference problem, including both cross-tier interference that affects macro users from other tiers and co-tier interference, which is among users belong to the same tier. The scheme employs an online learning algorithm for efficient spectrum allocation with power and modulation adaptation capability. Our evaluation results show that our online scheme outperforms others and achieves significant improvements in throughput, spectral efficiency, fairness, and outage ratio. © 2016 IEEE.
UR - http://hdl.handle.net/10754/621298
UR - http://www.shihada.com/node/publications/5GlearningICC.pdf
UR - http://www.scopus.com/inward/record.url?scp=84981323634&partnerID=8YFLogxK
U2 - 10.1109/ICC.2016.7511617
DO - 10.1109/ICC.2016.7511617
M3 - Conference contribution
SN - 9781479966646
BT - 2016 IEEE International Conference on Communications (ICC)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -