
Ultra dense small cell networks represent a key future\ud network solution that can help meet the exponentially rising traffic\ud requirements of modern wireless networks. Backhauling these small\ud cells is an emerging challenge to the extent that various cells\ud are likely to have different backhaul constraints. The user-centric\ud backhaul scheme has been proposed in the literature to jointly exploit\ud the diversity in users’ requirement and backhaul constraints. In\ud this work, we propose a novel scheme, termed the Memory-based\ud Hybrid Scheme, that additionally also exploits the predictability in\ud a users mobility. We compare the novel scheme to two variants\ud of memory-less user-centric backhaul implementations and show\ud significant gains in convergence time (15%), user-centric KPIs (51%\ud and 82%) at the negligible cost 2% loss in cumulative throughput.\ud The novel scheme requires additional memory in user-devices to\ud store learned values, which is nonetheless well justified in view of\ud the considerable gains achieved.
memory-based learning, user cell association, user-centric, backhaul, Heterogeneous networks (HetNets), Electrical engineering. Electronics. Nuclear engineering, user-centric;backhaul, TK1-9971
memory-based learning, user cell association, user-centric, backhaul, Heterogeneous networks (HetNets), Electrical engineering. Electronics. Nuclear engineering, user-centric;backhaul, TK1-9971
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 11 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
