Interference Management in UAV-Assisted Multi-Cell Networks
Abstract
1. Introduction
- This paper presents the first formulation of the abovementioned fairness-aware sum-rate optimization problem as a mixed-integer non-linear program (MINLP).
- A surrogate mixed-integer linear program (MILP) formulated to tackle the non-convexity in our MINLP is presented. Considering the fact that solving our MILP is challenging in large-scale networks, this paper outlines a GA-based two-stage approach to solve our sum-rate maximization problem efficiently. In Stage-1, we use a genetic algorithm to address the combinatorial challenges of BS sub-band assignment and UAV location selection. In Stage-2, we then solve a linear program to determine the optimal transmit power for both BSs and UAVs.
- This paper presents the first study of the aforementioned problems with the following aspects: (i) the number of BSs, (ii) the coverage area of each cell, and (iii) the number of UE per cell. The results show that our proposed two-stage approach closely approximates the optimal solution in small-scale networks. Critically, it outperforms competitive benchmarks in terms of sum-rate and fairness.
2. Related Works
Notation | Description | Unit |
1. Sets | ||
Set of BSs | — | |
Set of UAVs | — | |
UAVs in cell k | — | |
Set of UE | — | |
UE in cell k | — | |
UE in cell k associated with BS k | — | |
UE in cell k associated with UAV v | — | |
Set of sub-bands | — | |
Set of time slots | — | |
Candidate locations for UAV v | — | |
Links between UAV v and UE | — | |
2. Constants | ||
Distance from BS k to UE u | m | |
Distance from BS k to UAV location | m | |
Distance from UAV location to UE u | m | |
Maximum BS transmit power | W | |
Transmit power of UAV v | W | |
Noise power | W/Hz | |
3. Variables | ||
Fraction of total power used in slot t | – | |
Transmit power of BS k in slot t | W | |
1 if sub-band s is assigned to BS k | – | |
1 if UAV v placed at location | – | |
1 if link is active | – |
3. Preliminaries
3.1. UAV Assignment and Association
3.2. Channel Model
3.3. Data Transmissions
3.3.1. Uncoordinated Slot
3.3.2. Coordinated Slot
3.4. Problem Statement
4. A Genetic Algorithm-Based Solution
- Initialization: GA begins by randomly generating an initial population of chromosomes.
- Selection: It evaluates the fitness scores of each chromosome. It preferentially selects chromosomes with a higher fitness score to form the basis of the next generation and serve as parent chromosomes.
- Crossover: It selects parent chromosomes and combines them via a so-called crossover operation to produce new chromosomes/offspring, aiming to integrate characteristics of existing chromosomes that exhibit higher fitness.
- Mutation: A mutation operation introduces randomness within chromosomes, thereby enhancing the exploration of the solution space and helping to escape local optima.
4.1. Genotypes Encoding
4.2. Fitness Evaluation
4.3. Selection
4.4. Crossover
4.5. Mutation
4.6. Population Update
4.7. Discussion
4.8. Computational Complexity Analysis
5. Results
- (brute-force search): It generates exhaustive combinations of both the cell channel assignment and UAV location selection solutions. It then solves each of them using LP (P3) to determine the optimal solution. Note that BF can only be used for small-scale networks.
- : It follows the same GA procedure as described in Section 4. However, it solves problem (P2) and uses the resulting objective value as the fitness for a given chromosome. Note that there is a trade-off between computational efficiency and optimality because is required to solve problem (P3) for each chromosome in one population and over all generations.
- GA−Rand: It adopts a standard GA-based approach as outlined in [36] to manage interference levels at UE in dense wireless networks. Briefly, it solves a sub-band allocation problem with the objective of maximizing the minimum SINR among all UE. Since the originally proposed method is not directly applicable to UAV-assisted networks, we extend it by randomly selecting a candidate location for each UAV.
5.1. Optimality Gap
5.2. Number of Base Stations
5.3. Coverage per Cell
5.4. Number of UE per Cell
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Value(s) | Parameter | Value(s) |
---|---|---|---|
K | 5–20 | BS coverage | 20–60 m |
B | 1 MHz | 4–6 | |
5 W | dBm/Hz | ||
2–10 | 2–5 | ||
5–20 | 100 | ||
2 GHz | 200 | ||
1.5 m | 25 | ||
100–300 | 0.5 |
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Jiang, M.; Ren, H.; Qi, Y.; Wu, T. Interference Management in UAV-Assisted Multi-Cell Networks. Information 2025, 16, 481. https://doi.org/10.3390/info16060481
Jiang M, Ren H, Qi Y, Wu T. Interference Management in UAV-Assisted Multi-Cell Networks. Information. 2025; 16(6):481. https://doi.org/10.3390/info16060481
Chicago/Turabian StyleJiang, Muchen, Honglin Ren, Yongxing Qi, and Ting Wu. 2025. "Interference Management in UAV-Assisted Multi-Cell Networks" Information 16, no. 6: 481. https://doi.org/10.3390/info16060481
APA StyleJiang, M., Ren, H., Qi, Y., & Wu, T. (2025). Interference Management in UAV-Assisted Multi-Cell Networks. Information, 16(6), 481. https://doi.org/10.3390/info16060481