TY - JOUR
T1 - Resource Allocation with Automated QoE Assessment in 5G/B5G Wireless Systems
AU - Dudin, Basel
AU - Ali, Najah Abu
AU - Radwan, Ayman
AU - Taha, Abd Elhamid M.
N1 - Funding Information:
AcKnoWLedgment This work is made possible by the generous support of Alfaisal University through its Internal Research Grant #19204, FCT/MEC through national funds, and when applicable co-funded by a FEDER PT2020 partnership agreement under projects UID/EEA/50008/2019 and 5G-AHEAD IF/FCT-IF/01393/2015/CP1310/CT0002.
Funding Information:
This work is made possible by the generous support of Alfaisal University through its Internal Research Grant #19204, FCT/MEC through national funds, and when applicable co-funded by a FEDER PT2020 partnership agreement under projects UID/EEA/50008/2019 and 5G-AHEAD IF/FCT-IF/01393/2015/CP1310/CT0002.
Publisher Copyright:
© 2012 IEEE.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 5G and B5G systems will be designed to better utilize the scarce spectrum. This improvement will be realized through enhanced allocation and management schemes, as well the added viability of utilizing unlicensed spectrum. Meanwhile, 5G/ B5G emphasize an ever more personalized communication experience through adapting network and spectrum choice to user requirements and experience. QoE thus becomes a critical basis for resource allocation in future networks. However, current QoE approaches are largely static and offline, making them unfit for the highly dynamic and demanding nature of future communication. To overcome this limitation, we propose a resource allocation architecture with automated QoE assessment. The architecture builds on recent advances in affective computing and sensing, and accounts for allocation considerations in a mixed (licensed/unlicensed) context.
AB - 5G and B5G systems will be designed to better utilize the scarce spectrum. This improvement will be realized through enhanced allocation and management schemes, as well the added viability of utilizing unlicensed spectrum. Meanwhile, 5G/ B5G emphasize an ever more personalized communication experience through adapting network and spectrum choice to user requirements and experience. QoE thus becomes a critical basis for resource allocation in future networks. However, current QoE approaches are largely static and offline, making them unfit for the highly dynamic and demanding nature of future communication. To overcome this limitation, we propose a resource allocation architecture with automated QoE assessment. The architecture builds on recent advances in affective computing and sensing, and accounts for allocation considerations in a mixed (licensed/unlicensed) context.
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U2 - 10.1109/MNET.2019.1800463
DO - 10.1109/MNET.2019.1800463
M3 - Article
AN - SCOPUS:85070394133
VL - 33
SP - 76
EP - 81
JO - IEEE Network
JF - IEEE Network
SN - 0890-8044
IS - 4
M1 - 8782880
ER -