The slide presents a title on "Load-Balancing in Multi-Cell MIMO-NOMA," focusing on resource allocation strategies and algorithms. It serves as a title slide introducing the topic.

Load-Balancing in Multi-Cell MIMO-NOMA

Resource Allocation Strategies and Algorithms

Source: IEEE Transactions on Communications Paper Summary

This agenda slide outlines the structure of the presentation, starting with an Introduction & Motivation followed by Problem Formulation. It then covers Proposed Algorithms, Simulation Results, and ends with Contributions & Conclusions.

Presentation Agenda

1. Introduction & Motivation
2. Problem Formulation
3. Proposed Algorithms
4. Simulation Results
5. Contributions & Conclusions

Source: IEEE Transactions on Communications: 'Load-Balancing during Resource Allocation in Multi-Cell MIMO-NOMA'

This section header slide introduces the topic of "Load Balancing in MIMO-NOMA Networks" with the first section titled "1. Introduction and Motivation." It features section number "01" and a subtitle emphasizing the motivation for load balancing in multi-cell MIMO-NOMA networks.

Load Balancing in MIMO-NOMA Networks

01

1. Introduction and Motivation

Motivating Load Balancing in Multi-Cell MIMO-NOMA Networks

Source: IEEE Transactions on Communications: 'Load-Balancing during Resource Allocation in Multi-Cell MIMO-NOMA'

• User distribution across multiple cells
• Interference management visualization
• Resource allocation balancing
• Enhanced network performance

Load balancing in MIMO-NOMA enhances throughput in dense networks, addresses user-cell imbalance issues, and improves overall spectral efficiency. It also effectively manages interference in multi-cell environments.

Motivation for Load Balancing in MIMO-NOMA

• Enhances throughput in dense networks
• Addresses user-cell imbalance issues
• Improves overall spectral efficiency
• Manages interference in multi-cell MIMO-NOMA

Source: IEEE Trans. Commun. 'Load-Balancing during Resource Allocation in Multi-Cell MIMO-NOMA'

This section header slide introduces Section 2: Problem Formulation and System Model. It features a subtitle on the mathematical modeling of resource allocation in multi-cell MIMO-NOMA networks.

2. Problem Formulation and System Model

2

Problem Formulation and System Model

Mathematical modeling of resource allocation in multi-cell MIMO-NOMA networks

Source: IEEE Transactions on Communications: 'Load-Balancing during Resource Allocation in Multi-Cell MIMO-NOMA'

• Multiple base stations (BSs) serving users.
• MIMO beams for spatial multiplexing.
• NOMA power allocation across users.
• Load-balancing in resource allocation.

The slide provides an overview of a multi-cell MIMO-NOMA system where multiple base stations use multiple antennas for spatial multiplexing and NOMA for power-domain superposition coding to serve user clusters simultaneously. It also describes sum-rate optimization that jointly handles user-BS association, power allocation, and beamforming under per-BS load constraints to boost spectral efficiency.

System Model Overview

Source: IEEE Transactions on Communications: 'Load-Balancing during Resource Allocation in Multi-Cell MIMO-NOMA'

This slide introduces Section 3 on Proposed PTLO-based Algorithms. It highlights the specific algorithms LB-BB-MIMO-NOMA and LB-M2O-MIMO-NOMA.

3. Proposed PTLO-based Algorithms

03

Proposed PTLO-based Algorithms

LB-BB-MIMO-NOMA and LB-M2O-MIMO-NOMA

Source: IEEE Transactions on Communications: 'Load-Balancing during Resource Allocation in Multi-Cell MIMO-NOMA'

The "Algorithm Comparison" slide presents key features of advanced load balancing algorithms like LB-BB (block-based partitioning for balanced resources), LB-M2O (multi-to-one mapping for consolidation), and PTLO (particle swarm for rapid convergence). It highlights superior performance in fairness, throughput, sum rates, latency reduction in MIMO-NOMA networks, and scalable design for large-scale scenarios.

Algorithm Comparison

Source: IEEE Trans. Commun. 'Load-Balancing in Multi-Cell MIMO-NOMA'

The simulation results demonstrate superior throughput, faster convergence to optimal load balance, and reduced load variance across cells compared to baseline algorithms. The proposed approach outperforms traditional MIMO-NOMA schemes and enhances system performance in multi-cell setups.

4. Simulation Results

• Superior throughput compared to baseline algorithms
• Faster convergence to optimal load balance
• Reduced load variance across cells
• Outperforms traditional MIMO-NOMA schemes
• Enhanced system performance in multi-cell setups

Source: IEEE Transactions on Communications: 'Load-Balancing during Resource Allocation in Multi-Cell MIMO-NOMA'

• 25%: Throughput Increase

Up to 25% higher throughput

• 30%: Load Balance Improvement

30% better load balancing

• 7: Simulation Cells

7 cells modeled

• 20: Users per Cell

20 users per cell

This slide introduces Section 5, titled "Key Contributions and Conclusions." It highlights innovative load-balancing solutions for multi-cell MIMO-NOMA networks.

5. Key Contributions and Conclusions

05

Key Contributions and Conclusions

Innovative load-balancing solutions for multi-cell MIMO-NOMA networks

Source: IEEE Transactions on Communications: 'Load-Balancing during Resource Allocation in Multi-Cell MIMO-NOMA'

This conclusion slide highlights novel PTLO algorithms for MIMO-NOMA, with proven simulation gains and practicality for 5G+ networks. It proposes future real-world deployment and closes with innovative load-balancing achieved, urging exploration in 5G+ networks.

Key Contributions & Conclusions

• Novel PTLO algorithms for MIMO-NOMA

• Proven gains in simulations
• Practical for 5G+ networks

Future: Real-world deployment

Closing: Innovative load-balancing achieved. Call-to-action: Explore deployment in 5G+ networks.

Source: IEEE Transactions on Communications: 'Load-Balancing during Resource Allocation in Multi-Cell MIMO-NOMA'