Channel Selection in Uncoordinated IEEE 802.11 Networks Using Graph Coloring
Abstract
:1. Introduction
- We show a graph model that is well-suited to test and evaluate the distributed channel selection techniques (Section 4.1).
- We propose and implement several channel selection techniques, modeling them as a graph coloring problem. The main requirement of these techniques is that they are based on simple and easily measurable parameters from the nodes’ point of view, such as the interference level or the number of beacon frames received (Section 4.2).
- We demonstrate that the techniques based on the measurement of the interferences outperform those based on measuring the number of beacon frames (Section 5.2 and Section 5.3).
- We show that, although the use of non-orthogonal channels is one of the main features of IEEE 802.11 networks in the 2.4 GHz frequency band, the best channel selection techniques mainly use orthogonal channels (Section 5.4).
2. Related Work
3. Graphs and Channel Selection in Wi-Fi
4. An Application of Graph Coloring to Channel Assignment in IEEE 802.11 Networks
4.1. System Model Using Graphs
4.1.1. Topological Model
4.1.2. Propagation, Interferences, SINR, and Throughput Computation
4.2. Channel Selection Techniques
4.2.1. Least Interference Channel Selection (LI) Technique
4.2.2. Beacon-Based Channel Selection Techniques
4.2.3. Baseline Techniques
4.2.4. Optimal Assignment
- We start with a random base solution , which is basically a random channel assignment for all the APs (1).
- At time t, to generate the next candidate solution , the optimizer takes the base solution and moves to a neighbor solution (2) by choosing a random access point and selecting a new random channel for it.
- When a candidate solution yields a utility loss against the base solution, there will be a probability for the optimizer to “move to it” nonetheless. As shown in Algorithm 1, this probability depends on the utility loss associated with the new contract , and also depends on a parameter known as annealing temperature (3). Annealing temperature begins at an initial value and linearly decreases to zero throughout successive iterations of the protocol (4).
- If at time t the optimizer “moves to” the neighbor solution , this solution will be used as the new base solution to generate the next neighbor (5). Otherwise, the previous will be used.
- After a fixed number of iterations, the optimizer returns the final solution, which will be the last base solution (6).
Algorithm 1: Centralized optimization based on Simulated Annealing |
Input: |
G: graph model of the Wi-Fi scenario |
T: maximum number of iterations |
: initial annealing temperature |
Output: |
S: final solution, corresponding to a channel assignment for each AP |
5. Experimental Evaluation
5.1. Experimental Settings
5.2. Comparison of Techniques
5.3. Who Should Decide? Evaluation of Different Perspectives on Channel Selection
5.4. Evaluation of the Use of Channels
6. Discussion
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AP | Access Point |
LBP | Least Beacon Power channel selection |
LBPm | Least Beacon Power masked channel selection |
LCCS | Least Congested Channel Search |
LI | Least Interference channel selection |
LNB | Least Number of Beacons channel selection |
MCS | Modulation and Coding Scheme |
SA | Simulated Annealing |
SINR | Signal-to-Interference-plus-Noise Ratio |
STA | Station |
TSC | Threshold Spectrum Coloring |
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Reference | Centralized | Distributed | Heuristic | Optimization | Cochannel |
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[1] | ✓ | ✓ | ✓ | ||
[2] | ✓ | ✓ | |||
[3] | ✓ | ✓ | ✓ | ||
[7] | ✓ | ✓ | |||
[8] | ✓ | ✓ | ✓ | ||
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[10] | ✓ | ✓ | ✓ | ✓ | |
[11] | ✓ | ✓ | ✓ | ||
[12] | ✓ | ✓ | |||
[13] | ✓ | ✓ | ✓ | ||
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[22] | ✓ | ✓ | |||
This paper | ✓ | ✓ | ✓ |
0 | 1 | 2 | 3 | 4 | 5 | ≥6 | |
1 | 0.8 | 0.5 | 0.2 | 0.1 | 0.001 | 0 |
MCS Index | Modulation Scheme | Coding Rate | Throughput (Mbit/s) | SINR Range (dB) [34] |
---|---|---|---|---|
0 | BPSK | 1/2 | 6.5 | (6.8, 7.9) |
1 | QPSK | 1/2 | 13.0 | (7.9, 10.6) |
2 | QPSK | 3/4 | 19.5 | (10.6, 13.0) |
3 | 16-QAM | 1/2 | 26.0 | (13.0, 17.0) |
4 | 16-QAM | 3/4 | 39.0 | (17.0, 21.8) |
5 | 64-QAM | 2/3 | 52.0 | (21.8, 24.7) |
6 | 64-QAM | 3/4 | 58.5 | (24.7, 28.1) |
7 | 64-QAM | 5/6 | 65.0 | ≥28.1 |
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Gimenez-Guzman, J.M.; Marsa-Maestre, I.; de la Hoz, E.; Orden, D.; Herranz-Oliveros, D. Channel Selection in Uncoordinated IEEE 802.11 Networks Using Graph Coloring. Sensors 2023, 23, 5932. https://doi.org/10.3390/s23135932
Gimenez-Guzman JM, Marsa-Maestre I, de la Hoz E, Orden D, Herranz-Oliveros D. Channel Selection in Uncoordinated IEEE 802.11 Networks Using Graph Coloring. Sensors. 2023; 23(13):5932. https://doi.org/10.3390/s23135932
Chicago/Turabian StyleGimenez-Guzman, Jose Manuel, Ivan Marsa-Maestre, Enrique de la Hoz, David Orden, and David Herranz-Oliveros. 2023. "Channel Selection in Uncoordinated IEEE 802.11 Networks Using Graph Coloring" Sensors 23, no. 13: 5932. https://doi.org/10.3390/s23135932
APA StyleGimenez-Guzman, J. M., Marsa-Maestre, I., de la Hoz, E., Orden, D., & Herranz-Oliveros, D. (2023). Channel Selection in Uncoordinated IEEE 802.11 Networks Using Graph Coloring. Sensors, 23(13), 5932. https://doi.org/10.3390/s23135932