A Sub‐Optimum Algorithm for Turning on/Off Co‐Channel Access Points in Ultra‐Dense Networks
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| Udgivet i: | Engineering Reports vol. 7, no. 11 (Nov 1, 2025) |
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John Wiley & Sons, Inc.
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| 001 | 3269845374 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2577-8196 | ||
| 024 | 7 | |a 10.1002/eng2.70483 |2 doi | |
| 035 | |a 3269845374 | ||
| 045 | 0 | |b d20251101 | |
| 100 | 1 | |a Shirvani Moghaddam, Shahriar |u Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University (SRTTU), Tehran, Iran | |
| 245 | 1 | |a A Sub‐Optimum Algorithm for Turning on/Off Co‐Channel Access Points in Ultra‐Dense Networks | |
| 260 | |b John Wiley & Sons, Inc. |c Nov 1, 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a ABSTRACT This paper proposes a sub‐optimal Kuhn–Munkres‐based resource assignment algorithm to maximize both the number of connected links and the mean throughput per link in ultra‐dense networks (UDNs) consisting of densely distributed co‐channel access points (APs) and user equipment (UEs). The proposed seven‐step algorithm first assigns UEs to APs that provide higher data rates while accounting for the interference of all APs. Next, only the interference from the selected APs is considered to identify UEs that meet the minimum throughput threshold level. In subsequent steps, considering both the interference of previously assigned APs and the remaining candidate APs, additional UEs are connected. Simulation results in MATLAB for a 250m×250m$$ 250\ \mathrm{m}\times 250\ \mathrm{m} $$ service area with 250$$ 250 $$ randomly distributed APs and varying numbers of UEs (25–250$$ 250 $$) demonstrate that the proposed algorithm achieves higher connectivity and total throughput with significantly reduced processing time compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Gray Wolf Optimization (GWO). Specifically, as the number of UEs increases from 10%$$ 10\% $$ to 100%$$ 100\% $$ of the number of APs, the proposed algorithm improves the number of connected UEs by 10%−48%$$ 10\%-48\% $$, 47%−96%$$ 47\%-96\% $$, 57%−109%$$ 57\%-109\% $$, and 22%−58%$$ 22\%-58\% $$, and the total throughput by 20%−52%$$ 20\%-52\% $$, 44%−86%$$ 44\%-86\% $$, 50%−105%$$ 50\%-105\% $$, and 22%−69%$$ 22\%-69\% $$, respectively, over the four benchmark algorithms. Moreover, owing to its lower computational complexity, the proposed method achieves at least 99%$$ 99\% $$ reduction in processing time. | |
| 653 | |a Search algorithms | ||
| 653 | |a Particle swarm optimization | ||
| 653 | |a Service areas | ||
| 653 | |a Algorithms | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Assignment problem | ||
| 653 | |a Optimization | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Shirvani Moghaddam, Kiaksar |u School of Computer Engineering, Iran University of Science and Technology (IUST), Tehran, Iran | |
| 700 | 1 | |a Ashoor, Ebrahim |u Digital Communications Signal Processing (DCSP) Research Laboratory, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University (SRTTU), Tehran, Iran | |
| 773 | 0 | |t Engineering Reports |g vol. 7, no. 11 (Nov 1, 2025) | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3269845374/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3269845374/fulltext/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3269845374/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |