Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks
Abstract
:1. Introduction
- To calculate accuracy, recall, precision, and F-score of the Hybrid Quantum Deep Learning model.
- To calculate power consumption upon implementing the Base Station Optimizer net model.
- To attain efficient and sustainable resource management and base station optimization using a Hybrid Quantum Deep Learning model.
2. Smart 6G Networks
2.1. AI-Assisted Network
2.2. Function of AI in Upcoming 6G Networks
- AI for Physical Layer: This involves tasks such as waveform recognition, categorization, frequency encryption and decryption, AI-assisted positioning, perception, localization, network assessment, and stabilization. AI also contributes to creating AI-friendly boundary devices.
- AI for MAC Layer RRM (Radio Resource Management): In this context, AI is employed for user clustering, proactive resource allocation, flexible power control, and interference management.
- AI for Control and Management: In the control and management layer, AI plays a pivotal role in dynamic network adaptation, active segment administration, self-management, and policy implementation, critical system enforcement, dimensioning and monitoring, and security enhancement.
- AI for Higher-Level RRM: Here, AI facilitates the creation of an AI-aided multilateral system for Radio Access Network (RAN) slice management, slice access mechanisms, segment provisioning, traffic management, and mobility administration.
3. Proposed Model
3.1. Hybrid Quantum Deep Learning Model
Algorithm 1. 6G communication system procedure for slice prototype |
Begin |
Step 1: Set eMBB, URLLC, mMTC, mFile as zero trajectory of size L |
Step 2: Initialize slice 0 |
Step 3: The network request is initialized centered on various key categories |
While i ≤ L |
T1 = eMBB+L/size of(eMBB) ∗ 100 |
T2 = mMTC+L/size of(mMTC) ∗ 100 |
T3 = URLLC+L/size of (URLLC) ∗ 100 |
T4 = mFileL/size of (mFileL) ∗ 100 |
if (condition function) |
if (high-level data speed and T1 ≤ 90%) eMBB + L = reqi |
Else if ((reliable&& less delay) and T2 ≤ 90%) mMTC + L = reqi |
Else if ((low-level data speed&&high-level intensity) & S3 ≤ 90%) |
URLLC+L = reqi |
Else mFilen = reqi |
Else mFilen = reqi |
End if |
End while |
3.2. Base Station Optimizer Net Research Methodology
- Input Layer—raw data such as user data, geospatial information, network configuration parameters, and traffic patterns are taken as input for preprocessing to standardize and normalize it for the model!
- Feature Extraction Layer—Convolutional Neural Networks (CNNs) or other feature extraction techniques are used to find appropriate patterns and features from the input data.
- Optimization Layer—Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks are utilized to capture temporal dependencies and dynamic changes in the network. Optimization algorithms are applied to find the best configuration for the placement of the base station, load balancing, and resource allocation.
- Decision Layer—fully connected neural networks are utilized for making decisions based on the extracted features and optimized parameters. It recommends power settings, frequency bands, base station placement, and other configurations.
- Training and Learning Mechanism—the model is trained using historical performance data and simulation results. It enhances continuously and adapts to changing network conditions by reinforcement learning or any other adaptive learning techniques.
- Number of features: 14, including transmitter location, receiver location, transmitter power, channel frequency, number of users, receiver power, channel noise, number of packets, packet length, load on devices, number of neighboring base stations, distance from neighboring base stations, bandwidth, and delay in the network.
- Second layer: 12 nodes with fully connected layer.
- Third layer: 8 nodes with fully connected layer.
- Fourth layer: 2 nodes with fully connected layer.
- Fifth layer: Softmax activation function. The base station is ON for output >0.5; the base station is OFF for output <0.5.
- Final layer: Classification layer providing the ultimate output based on the Softmax function’s value.
- Channel frequency = 100 THz
- Network bandwidth = 100 GHz
- Delay in the network = 5 × 10−5 s
- Transmitter power: 20 dB
- Receiver power: 3 dB
- Transmitter and receiver locations: randomly generated within a 1000 × 1000-dimensional area using MATLAB’s random function
- Channel noise: −0.5 dB
- Number of packets: 200
- Load on each device: 30 dB
- Each base station allowed 45 partner nodes
4. Results and Discussions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Base Station ID | Latitude | Longitude | Frequency Band | Power (dBm) | Users Count | Traffic Load (Mbps) | SNR (dB) | Throughput (Mbps) | Interference (dB) |
---|---|---|---|---|---|---|---|---|---|
BS001 | 34.0522 | −118.2437 | 3.5 GHz | 40 | 120 | 50 | 30 | 45 | 10 |
BS002 | 34.0522 | −118.2537 | 28 GHz | 30 | 90 | 35 | 25 | 30 | 15 |
BS003 | 34.0622 | −118.2437 | 3.5 GHz | 45 | 150 | 70 | 35 | 60 | 8 |
S. No | Constraint | Value |
---|---|---|
1 | Number of layers | 4 |
2 | Number of Hidden layers | 5 |
3 | Activation Function | Relu |
4 | Metrics performance | Time, accurateness, specificity, F-measurable, correct–incorrect values, variable exercise and assessment sets |
Metrics | Proposed Model | RNN Model | CNN Model |
---|---|---|---|
Accuracy | 98% | 94.5% | 92% |
Recall | 96.64% | 95.6% | 93.5% |
Precision | 96.16% | 92.5% | 92% |
F-Score | 94.76% | 93% | 91.7% |
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Share and Cite
Suresh, K.; Kannadasan, R.; Joshua, S.V.; Rajasekaran, T.; Alsharif, M.H.; Uthansakul, P.; Uthansakul, M. Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks. Sustainability 2024, 16, 7253. https://doi.org/10.3390/su16177253
Suresh K, Kannadasan R, Joshua SV, Rajasekaran T, Alsharif MH, Uthansakul P, Uthansakul M. Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks. Sustainability. 2024; 16(17):7253. https://doi.org/10.3390/su16177253
Chicago/Turabian StyleSuresh, Krishnamoorthy, Raju Kannadasan, Stanley Vinson Joshua, Thangaraj Rajasekaran, Mohammed H. Alsharif, Peerapong Uthansakul, and Monthippa Uthansakul. 2024. "Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks" Sustainability 16, no. 17: 7253. https://doi.org/10.3390/su16177253