A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
Abstract
:1. Introduction
2. System Model and Problem Formulation
2.1. Matrices for Spectrum Allocation
- Available matrix L. The matrix represents the availability of licensed bands for cognitive users. If , user n can access spectrum m without interference to primary users, otherwise . As shown in Figure 1, spectrum channel B is available for , then .
- Benefit matrix B. The matrix indicates the benefit that a cognitive user gets by successful access to a licensed spectrum band, where only if .
- Interference matrix C. The three-axis matrix describes the interference relationship of any two vertices n and k when they access spectrum m. As shown in Figure 1, and overlap in some area, then , , .
- Allocation matrix A. The matrix is a spectrum allocation result which is interference free. If , cognitive user n can access spectrum m and transmission data in this band. A conflict free allocation needs to satisfy the interference constraints: .
- Degree matrix for cognitive users Z. The matrix represents the available spectrum number for each cognitive users. In Figure 1, .
- Degree ascending matrix K. The matrix is another representation of the available matrix, which incrementally orders the rows according to the degree matrix Z.
2.2. Problem Formulation and Measure Functions
- (1)
- Max-Sum-Reward-Mean (MSRM): This function is used to measure the average of total spectrum utilization in the system, which is the average of the sum user rewards.
- (2)
- Max-Proportional-Fair (MPF): The function is to measure the fairness among cognitive users accessing the spectrum in the system, which is driven by .
- (3)
- Max-Min-Reward (MMR): The function is to maximize the spectrum utilization at the bottleneck cognitive users who receive the lowest reward, which is a simple notion of fairness.
3. The IACO-Based Spectrum Allocation Method
3.1. The Basic Idea
3.2. Transform for the Spectrum Allocation Problem
3.3. Differential Evolution Process in IACO
3.4. Variable Neighborhood Search Process in IACO
3.5. The Process and Description of IACO
- (1)
- Path selection. The transfer probability between node i and s in the choice process is presented as follows:
- (2)
- Update pheromone. Using an elitist strategy to update pheromone. The pheromone concentration on path is updated as the following rules:
3.6. Pseudocode of IACO
Algorithm 1: An Improved Ant Colony Optimization Algorithm |
4. Simulation Results and Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Name of Matrix | Definition of Matrix |
---|---|
Available matrix | |
Benefit matrix | |
Interference matrix | |
Allocation matrix | |
Degree matrix for cognitive users | |
Degree ascending matrix |
Iteration | Algorithm | Relative Difference (%) | ||
---|---|---|---|---|
MSRM | MMR | MPF | ||
30 | IACO | 0.366 | 0.447 | 1.711 |
ACO | 1.144 | 1.676 | 3.017 | |
PSO | 0.324 | 1.275 | 2.083 | |
GA | 1.033 | 2.876 | 3.496 | |
100 | IACO | 0 | 0 | 0.013 |
ACO | 0 | 1.514 | 2.504 | |
PSO | 0 | 1.309 | 0.952 | |
GA | 0.472 | 2.666 | 3.224 | |
200 | IACO | 0 | 0 | 0.012 |
ACO | 0 | 1.177 | 2.299 | |
PSO | 0 | 0.616 | 0.564 | |
GA | 0.063 | 2.282 | 2.71 |
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Liu, L.; Wang, N.; Chen, Z.; Guo, L. A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm. Algorithms 2018, 11, 16. https://doi.org/10.3390/a11020016
Liu L, Wang N, Chen Z, Guo L. A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm. Algorithms. 2018; 11(2):16. https://doi.org/10.3390/a11020016
Chicago/Turabian StyleLiu, Liping, Ning Wang, Zhigang Chen, and Lin Guo. 2018. "A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm" Algorithms 11, no. 2: 16. https://doi.org/10.3390/a11020016