Heterogeneous Resource Allocation for Ensuring End-to-End Quality of Service in Multi-hop Integrated Access and Backhaul Network

April 04, 2025 ยท Declared Dead ยท ๐Ÿ› arXiv.org

๐Ÿ‘ป CAUSE OF DEATH: Ghosted
No code link whatsoever

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Shuaifeng Zhang arXiv ID 2504.03576 Category cs.NE: Neural & Evolutionary Citations 1 Venue arXiv.org Last Checked 4 months ago
Abstract
Faced with increasing network traffic demands, cell dense deployment is one of significant means to utilize spectrum resources efficiently to improve network capacity. Multi-hop integrated access and backhaul (IAB) architectures have emerged as a cost-effective solution for network densification. Meanwhile, dynamic time division duplex (D-TDD) is a promising solution to adapt to highly dynamic scenarios with asymmetric uplink and downlink traffic. Thus, dynamic resource allocation between backhaul and access links and high spectral efficiency under ensuring reliable transmission are two key objectives of IAB research. However, due to huge solution space, there are some challenges in multi-hop IAB with D-TDD if only an integrated optimization problem (IOP) is considered. To handle these challenges, we decompose the IOP into sub-problems to reduce the solution space. To tackle these sub-problems, we formulate them separately as the non-cooperative games and design the corresponding utility functions to guarantee the existence of Nash equilibrium solutions. Also, to achieve the system-wide solution, we propose a single-leader heterogeneous multi-follower Stackelberg-game-based resource allocation scheme, which can combine the solving results of all the sub-problems to get the IOP approximate solution. Simulation results show that the proposed scheme can improve throughput performance while meeting spectrum energy efficiency constraints.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

๐Ÿ“œ Similar Papers

In the same crypt โ€” Neural & Evolutionary

๐Ÿ”ฎ ๐Ÿ”ฎ The Ethereal

LSTM: A Search Space Odyssey

Klaus Greff, Rupesh Kumar Srivastava, ... (+3 more)

cs.NE ๐Ÿ› IEEE TNNLS ๐Ÿ“š 6.0K cites 11 years ago

Died the same way โ€” ๐Ÿ‘ป Ghosted