Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications
Abstract
:1. Introduction
- Focusing on Device-to-Device (D2D) communication as a key component in the advancement of cellular networks, including 5G and beyond.
- Highlighting D2D communication’s potential in improving system throughput, offloading network cores, and increasing spectral efficiency.
- Emphasizing the importance of optimizing resource and power allocation to minimize co-channel interference and maximize the benefits of D2D communication.
- Conducting a comparative analysis of meta-heuristic algorithms, such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Bee Life Algorithm (BLA).
- Introducing a novel combination of matching techniques with BLA for joint channel and power allocation, enhancing the optimization process.
- Demonstrating that the combined matching algorithm and BLA outperform PSO, BLA, and GA in terms of throughput, convergence speed, and practicality.
- Investigating the use of D2D communication within cellular networks, particularly over uplink channels shared with cellular communications.
- Aiming to reduce interference between Cellular Users (CUs) and D2D users, as well as among D2D users sharing the same channel.
- Seeking to enhance overall network throughput by effectively managing channel and power allocation.
1.1. Related Work
1.2. Main Contribution
2. System Model and Problem Formulation
3. Bio-Inspired Algorithms
3.1. Individual Representation and Fitness
3.2. Genetic Algorithm (GA)
3.2.1. Crossover Operation
3.2.2. Mutation Operation
3.3. Particle Swarm Optimization (PSO)
3.4. Bee Life Algorithm (BLA)
3.4.1. Bees in Nature
3.4.2. The Bee Life Algorithm
3.4.3. Food Foraging
4. The Matching Bees Algorithm (MBA)
5. Simulation and Discussion
5.1. Convergence of the Algorithms
5.2. Network Performance Based on D2D Pairs Number
5.3. Effects of Rate Restrictions on Acceptance Ratio
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
U | The set of users |
CU and D2D pairs number, respectively | |
Resource blocks set | |
K | Resource blocks number |
A | Matrix of allocation |
Throughput | |
CUs minimum SINR | |
Gain between UEi and UEj | |
Path Loss model | |
B | Bandwidth |
CUs maximum power | |
D2D pairs maximum power | |
Crossover threshold | |
Mutation threshold | |
White Gaussian Noise | |
F | Carrier frequency |
X | Population account |
D | Number of drones |
W | Number of workers |
Y | Number of broods |
5GB | Fifth Generation and Beyond |
PPP | Poisson Point Process |
BLA | Bee Life Algorithm |
PSO | Particle Swarm Optimization |
BR | Block of Resource |
QoS | Quality of Service |
BS | Base Station |
SINR | Signal-to-Interference-plus-Noise Ratio |
CU | Cellular User |
UAV | Unmanned Aerial Vehicle |
D2D | Device to Device |
UE | User Equipment |
GA | Genetic Algorithm |
UMi | Urban Micro System |
kbps | Kilobits per second |
MBA | Matching Bees Algorithm |
Appendix A
- Generate X random bees: Initialization
- Calculate Fitness (X bees): Evaluation
- Categorization: One Queen, W workers, D drones // Reproduction: first optimization operator (endin criteria not met)
- do
- Crossover (Queen, Drone) // Queen and Drones mate with probability ThC
- while (there is a drone who did not mate with Queen)
- for (some broods)
- Mutation (brood) // The broods mutate with probability ThM
- end for // Food Foraging: second optimization operator
- for (all workers)
- Random selection (D2Dpair)
- Transmission power optimization (D2Dpair)
- end for
- Calculate Fitness (broods, new workers): Evaluation
- Keep X best bees: Selection
- end while
- Best Solution (Queen) BLA
Appendix B
- for (i 1 to X) do
- for (r RB1 to RBK) do
- if (Best fitness < fitness (i-th, r-th)) then
- Best fitness (i-th, r-th)
- end if
- end for
- end for // end of initialization of X bees with matching theory
- while (not (stopping criteria)) do
- Evaluation: calculate fitness (X bees)
- Categorization: One Queen, W workers, D drones
- Reproduction
- Crossover
- Mutation
- Food Foraging
- Optimize transmission power D2Dpair
- Calculate fitness (broods, new workers): Evaluation
- Keep X best bees: Selection while
- Best Solution (Queen)
- End MBA
Appendix C
GA | PSO | BLA | MBA | |
---|---|---|---|---|
Source | Genetics | Particle Swarms | Bees | Combination of bees and matching theory |
Optimization operators | Crossover and Mutation | Updating power and resource allocated to D2D pairs | Reproduction (Crossover and Mutation) and food foraging (Local search) | Matching theory to optimize first population and BLA (reproduction and food foraging) to enhance the optimized first population |
Number of D2D pairs supported | Multiple |
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BR# | User Equipment (UE) | Power (P) |
---|---|---|
BR1 | UE4 | P4 |
UE2 | P2 | |
UE5 | P5 | |
BR2 | UE1 | P1 |
UE6 | P6 | |
BR1 | UE7 | P7 |
Parameters | Values |
---|---|
Radius of the cell | 1000 m |
Coverage of D2D users | 50 m |
WGN | −174 |
Max power (CU and D2D) | 23 dBm |
B | 1 MHz |
F | 2.4 GHz |
nbC | 8 |
Y | 42 |
X | 20 |
D | 7 |
W | 12 |
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Benbraika, M.K.; Kraa, O.; Himeur, Y.; Telli, K.; Atalla, S.; Mansoor, W. Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications. Computers 2024, 13, 44. https://doi.org/10.3390/computers13020044
Benbraika MK, Kraa O, Himeur Y, Telli K, Atalla S, Mansoor W. Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications. Computers. 2024; 13(2):44. https://doi.org/10.3390/computers13020044
Chicago/Turabian StyleBenbraika, Mohamed Kamel, Okba Kraa, Yassine Himeur, Khaled Telli, Shadi Atalla, and Wathiq Mansoor. 2024. "Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications" Computers 13, no. 2: 44. https://doi.org/10.3390/computers13020044