Resource Allocation with Automated QoE Assessment in 5G/B5G Wireless Systems
Ali, N. Ali
; Taha, A. M. Taha
IEEE Network Vol. 33, Nº 4, pp. 76 - 81, July, 2019.
ISSN (print): 0890-8044
ISSN (online): 1558-156X
Scimago Journal Ranking: 2,77 (in 2019)
Digital Object Identifier: 10.1109/MNET.2019.1800463
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.