Optimizing Clustered Cell-Free Networking for Sum Ergodic Capacity Maximization With Joint Processing Constraint

Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered cell-free networking, the wireless network is decomposed into a number of disjoint...

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Vydané v:IEEE transactions on wireless communications Ročník 24; číslo 1; s. 571 - 584
Hlavní autori: Xia, Funing, Wang, Junyuan, Dai, Lin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
bisection algorithm
Branch and bound methods
Cellular communication
Cellular networks
Clustered cell-free networking
Clustering algorithms
Combinatorial analysis
Complexity
Computer architecture
Convexity
Decomposition
Interference
joint processing constraint
Mathematical programming
Maximization
Optimization
Partitioning algorithms
Signal processing algorithms
Simulation
sum ergodic capacity maximization
Uplink
Wireless networks
ISSN:1536-1276, 1558-2248
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Shrnutí:Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered cell-free networking, the wireless network is decomposed into a number of disjoint parallel operating subnetworks with joint processing adopted inside each subnetwork independently for intra-subnetwork interference mitigation. Different from the existing works that aim to maximize the number of subnetworks without considering the limited processing capability of base-stations (BSs), this paper investigates the clustered cell-free networking problem with the objective of maximizing the sum ergodic capacity while imposing a limit on the number of user equipments (UEs) in each subnetwork to constrain the joint processing complexity. By successfully transforming the combinatorial NP-hard clustered cell-free networking problem into an integer convex programming problem, the problem is solved by the branch-and-bound method. To further reduce the computational complexity, a bisection clustered cell-free networking (<inline-formula> <tex-math notation="LaTeX">\text {B}\text {C}^{2}\text {F} </tex-math></inline-formula>-Net) algorithm is proposed to decompose the network hierarchically. Simulation results show that compared to the branch-and-bound based scheme, the proposed <inline-formula> <tex-math notation="LaTeX">\text {B}\text {C}^{2}\text {F} </tex-math></inline-formula>-Net algorithm significantly reduces the computational complexity yet achieves nearly the same network decomposition result. Moreover, our <inline-formula> <tex-math notation="LaTeX">\text {B}\text {C}^{2}\text {F} </tex-math></inline-formula>-Net algorithm achieves near-optimal performance and outperforms the state-of-the-art benchmarks with up to 25% capacity gain.
Bibliografia:ObjectType-Article-1
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2024.3496733