Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Enhancing NOMA Handover Performance Using Hybrid AI-Driven Modulated Deterministic Sequences

Sumita Majhi, G Vasantha Reddy, Pinaki Mitra

TL;DR

This work proposes a hybrid method that combines Gold-Walsh modulated sequences with Deep Q-Networks (DQN) to intelligently manage interference during NOMA handovers and achieves a 95.2\% handover success rate, which is an improvement of up to 23.1 percentage points.

Abstract

Non-Orthogonal Multiple Access (NOMA) is an information-theoretical approach used in 5G networks to improve spectral efficiency, but it is prone to interference during handovers. In this work, we propose a hybrid method that combines Gold-Walsh modulated sequences with Deep Q-Networks (DQN) to intelligently manage interference during NOMA handovers. This method optimizes sequence selection and power allocation dynamically. As a result, it achieves a 95.2\% handover success rate, which is an improvement of up to 23.1 percentage points. It also delivers up to 28\% throughput gain and reduces interference by up to 41\% in various mobility scenarios. All improvements are statistically significant (\(p < 0.001\)). The DQN trains in \(4{,}200 \pm 400\) episodes with a complexity of \(O(N \log N + d \cdot h + \log B)\) and can be deployed in real-time.

Enhancing NOMA Handover Performance Using Hybrid AI-Driven Modulated Deterministic Sequences

TL;DR

This work proposes a hybrid method that combines Gold-Walsh modulated sequences with Deep Q-Networks (DQN) to intelligently manage interference during NOMA handovers and achieves a 95.2\% handover success rate, which is an improvement of up to 23.1 percentage points.

Abstract

Non-Orthogonal Multiple Access (NOMA) is an information-theoretical approach used in 5G networks to improve spectral efficiency, but it is prone to interference during handovers. In this work, we propose a hybrid method that combines Gold-Walsh modulated sequences with Deep Q-Networks (DQN) to intelligently manage interference during NOMA handovers. This method optimizes sequence selection and power allocation dynamically. As a result, it achieves a 95.2\% handover success rate, which is an improvement of up to 23.1 percentage points. It also delivers up to 28\% throughput gain and reduces interference by up to 41\% in various mobility scenarios. All improvements are statistically significant (). The DQN trains in episodes with a complexity of \(O(N \log N + d \cdot h + \log B)\) and can be deployed in real-time.
Paper Structure (22 sections, 1 theorem, 9 equations, 9 figures)

This paper contains 22 sections, 1 theorem, 9 equations, 9 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model and Problem Formulation
  4. NOMA System Architecture
  5. Channel Model
  6. Handover Challenge Formulation
  7. Problem Formulation
  8. Proposed Hybrid Framework
  9. Modulated Sequence Design
  10. Deep Q-Network Based Intelligent Controller
  11. Performance Evaluation and Results
  12. Simulation Setup and Baseline Methods
  13. Handover Success Rate Analysis
  14. Throughput Performance
  15. Interference Mitigation
  16. ...and 7 more sections

Key Result

Theorem 1

The proposed DQN algorithm converges to the optimal Q-function $Q^{*}$ with probability 1, provided the following conditions hold:

Figures (9)

  • Figure 1: System Model.
  • Figure 2: Handover Success Rate Comparison Across Different Methods.
  • Figure 3: Statistical Distribution Analysis of Handover Success Rates.
  • Figure 4: Throughput Performance vs User Velocity.
  • Figure 5: Interference Level Comparison (Lower is Better).
  • ...and 4 more figures

Theorems & Definitions (2)

  • Theorem 1: DQN Convergence
  • proof : Proof Sketch