TY - JOUR
T1 - User-centric base station clustering and resource allocation for cell-edge users in 6G ultra-dense networks
AU - Su, Yuhan
AU - Gao, Zhibin
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2023/4
Y1 - 2023/4
N2 - Ultra-Dense Networks (UDNs) have been proposed to meet the ultra-high system capacity and ultra-high user experience rate requirements of sixth generation (6G) mobile networks. However, ultra-dense base stations (BSs) deployment poses challenges for cell-edge users such as BS selection, severe interference, resource allocation, and inter-cell handover. To tackle these obstacles, this paper formulates a joint BS clustering and resource allocation problem for cell-edge users in a 6G heterogeneous UDN system. For efficient resolution, this problem is decoupled into two sub-problems to be solved independently. We first propose a user-centric BS clustering algorithm based on many-to-many matching for the BS clustering problem in the heterogeneous UDN system; This algorithm considers the constraint of the system capacity. A many-to-many stable matching between users and small cell BSs is constructed under the optimization objective of maximizing the achievable user rate. Furthermore, we analyze the effectiveness, stability, convergence, and complexity of the BS clustering algorithm. Given established small cell BS clusters, we also propose a user-centric resource allocation algorithm based on network partitioning. This algorithm accounts for the interference of all users in the heterogeneous UDN system and maximizes intra-sub-network interference while minimizing inter-sub-network interference via a spectral clustering-based sub-network partitioning algorithm. Next, orthogonal allocation of resource blocks (RBs) is implemented within each sub-network, and RBs are spatially multiplexed between sub-networks. Numerical results confirm the benefits of the proposed methods: our algorithms outperform benchmark solutions in terms of the sum achievable system rate, average achievable user rate, and user spectral efficiency.
AB - Ultra-Dense Networks (UDNs) have been proposed to meet the ultra-high system capacity and ultra-high user experience rate requirements of sixth generation (6G) mobile networks. However, ultra-dense base stations (BSs) deployment poses challenges for cell-edge users such as BS selection, severe interference, resource allocation, and inter-cell handover. To tackle these obstacles, this paper formulates a joint BS clustering and resource allocation problem for cell-edge users in a 6G heterogeneous UDN system. For efficient resolution, this problem is decoupled into two sub-problems to be solved independently. We first propose a user-centric BS clustering algorithm based on many-to-many matching for the BS clustering problem in the heterogeneous UDN system; This algorithm considers the constraint of the system capacity. A many-to-many stable matching between users and small cell BSs is constructed under the optimization objective of maximizing the achievable user rate. Furthermore, we analyze the effectiveness, stability, convergence, and complexity of the BS clustering algorithm. Given established small cell BS clusters, we also propose a user-centric resource allocation algorithm based on network partitioning. This algorithm accounts for the interference of all users in the heterogeneous UDN system and maximizes intra-sub-network interference while minimizing inter-sub-network interference via a spectral clustering-based sub-network partitioning algorithm. Next, orthogonal allocation of resource blocks (RBs) is implemented within each sub-network, and RBs are spatially multiplexed between sub-networks. Numerical results confirm the benefits of the proposed methods: our algorithms outperform benchmark solutions in terms of the sum achievable system rate, average achievable user rate, and user spectral efficiency.
KW - 6G
KW - BS clustering
KW - Resource allocation
KW - Spectral-clustering
KW - Ultra-dense networks
UR - http://www.scopus.com/inward/record.url?scp=85145564278&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145564278&partnerID=8YFLogxK
U2 - 10.1016/j.future.2022.11.011
DO - 10.1016/j.future.2022.11.011
M3 - Article
AN - SCOPUS:85145564278
SN - 0167-739X
VL - 141
SP - 173
EP - 185
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -