Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

Bulti, Dinkisa A.; Hämäläinen, Jyri; Haile, Beneyam B.; Wondie, Yihenew

doi:10.1016/j.dcan.2021.10.006

Cited by 4 publications

(2 citation statements)

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“…In [34], authors used clustering for the purpose of finding appropriate LPN to offload a UE. Here, the purpose of using cell clustering is different.…”

Section: Clustering and Multiple Associationmentioning

confidence: 99%

“…We denote the threshold velocity as

v_{th}

and individual UE speed as

v_i

. The cell clusters are to serve the low speed users while the MC‐tier serves the high speed users. With the same procedure as in [34], cells that are highly interfering are grouped together. Let the neighbour list of the

j{\text{th}}

cell cluster contained in

CC_j

with cluster centre

MC_j

.…”

Section: Clustering and Multiple Associationmentioning

confidence: 99%

See 1 more Smart Citation

On the performance of multiple association with mobility support in 5G ultra‐dense networks: Realistic network simulation

Bulti

Wondie

2023

IET Communications

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A user equipment (UE) in ultra-dense networks (UDN) has a high chance of being in close proximity to many cells. multiple association (MA) can be used to serve users with high data rate demands in such a deployment scenario. However, because of the small footprint of the low power node (LPN), mobile users may experience highly frequent cell re-association issues. This paper focuses on solutions for UEs with high data rate demands as well as mobility management. User-centric clustering, control, and user-plane split usages are designed and investigated in coordination with the MA scheme. The clustering involves two stages: a master clustering and user-centric clustering. This approach goes with mobility management in long term evolution advanced (LTE-A)/fifth generation (5G) and can easily be integrated with the existing system. By setting some predefined threshold, the scheme attempts to treat UEs separately, based on their speed. In addition, a clustering approach was developed that generates virtual cells with which UEs are associated. The combination of MA with clustering and mobility support improves cooperation between the most appropriate cells to serve a specific UE. To validate the performance gains, synthetic system level and realistic network deployment simulations were used. The results show that the proposed scheme solves the frequent re-association issues and improves the user throughput performance. In the realistic network evaluation, up to 65.7% decrease in re-association frequency was obtained for the UEs average speed of 90 Km/h with cluster size K = 4. In addition, the 50th percentile UE throughput when k = 4 showed about 5.5% increase compared with the case when K = 2 for pedestrian and low speed users. BACKGROUNDThe advancements in wireless communication networks are helping network operators meet the exponentially rising service demand. Indeed, in the next generation networks, each user equipment (UE) is expected to make use of multiple applications simultaneously, each with its specific quality of service (QoS) requirements, for example, augmented reality or virtual reality applications with high data rate, low latency and high reliability requirements [1]. In this regard, the fifth generation (5G) mobile communication network supports high data rates, massive connectivity, lower latency (around 5 ms) in the user plane and (around 10 ms) in the control plane, and devices with very high mobility [2]. Among the technologies enabling 5G networks are: ultra-network densification, Massive multiple input multiple output (Massive MIMO), milli-meter waveThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…In [34], authors used clustering for the purpose of finding appropriate LPN to offload a UE. Here, the purpose of using cell clustering is different.…”

Section: Clustering and Multiple Associationmentioning

confidence: 99%

“…We denote the threshold velocity as