TY - GEN
T1 - Multi-Flow Carrier Aggregation in Heterogeneous Networks: Cross-Layer Performance Analysis
AU - Alorainy, Abdulaziz
AU - Hossain, Md. Jahangir
AU - Alouini, Mohamed-Slim
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2017/2/9
Y1 - 2017/2/9
N2 - Multi-flow carrier aggregation (CA) has recently been considered to meet the increasing demand for high data rates. In this paper, we investigate the cross-layer performance of multi-flow CA for macro user equipments (MUEs) in the expanded range (ER) of small cells. We develop a fork/join (F/J) queuing analytical model that takes into account the time varying channels, the channel scheduling algorithm, partial CQI feedback and the number of component carriers deployed at each tier. Our model also accounts for stochastic packet arrivals and the packet scheduling mechanism. The analytical model developed in this paper can be used to gauge various packet-level performance parameters e.g., packet loss probability (PLP) and queuing delay. For the queuing delay, our model takes out-of-sequence packet delivery into consideration. The developed model can also be used to find the amount of CQI feedback and the packet scheduling of a particular MUE in order to offload as much traffic as possible from the macrocells to the small cells while maintaining the MUE's quality of service (QoS) requirements.
AB - Multi-flow carrier aggregation (CA) has recently been considered to meet the increasing demand for high data rates. In this paper, we investigate the cross-layer performance of multi-flow CA for macro user equipments (MUEs) in the expanded range (ER) of small cells. We develop a fork/join (F/J) queuing analytical model that takes into account the time varying channels, the channel scheduling algorithm, partial CQI feedback and the number of component carriers deployed at each tier. Our model also accounts for stochastic packet arrivals and the packet scheduling mechanism. The analytical model developed in this paper can be used to gauge various packet-level performance parameters e.g., packet loss probability (PLP) and queuing delay. For the queuing delay, our model takes out-of-sequence packet delivery into consideration. The developed model can also be used to find the amount of CQI feedback and the packet scheduling of a particular MUE in order to offload as much traffic as possible from the macrocells to the small cells while maintaining the MUE's quality of service (QoS) requirements.
UR - http://hdl.handle.net/10754/623181
UR - http://ieeexplore.ieee.org/document/7848965/
UR - http://www.scopus.com/inward/record.url?scp=85015857490&partnerID=8YFLogxK
U2 - 10.1109/GLOCOMW.2016.7848965
DO - 10.1109/GLOCOMW.2016.7848965
M3 - Conference contribution
SN - 9781509024827
BT - 2016 IEEE Globecom Workshops (GC Wkshps)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -