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Distributed Time Synchronization in NOMA-Assisted Ultra-Dense Networks

Debjani Goswami, Indrakshi Dey, Nicola Marchetti, Suvra Sekhar Das

TL;DR

This paper tackles the problem of precise time synchronization in ultra-dense networks by proposing a GPS-free, distributed synchronization framework that relies on neighbor timing exchanges and uplink NOMA to reduce information-exchange delays. The approach decouples SBS-SB association via stable marriage and power allocation, employing an update rule for local clocks that leverages both incoming and outbound neighbor information. Key contributions include integrating information-exchange delay into the synchronization algorithm, demonstrating substantial speed gains for NOMA over OMA, and providing stability and complexity analyses for scalable deployment. The results indicate that NOMA-based information gathering yields faster synchronization, especially with limited SBs and favorable channel conditions, making the method well-suited for beyond-5G and 6G ultra-dense deployments where GPS or backhaul constraints are prohibitive.

Abstract

Ultra-dense networks (UDNs) represent a transformative access architecture for upcoming sixth generation (6G) systems, poised to meet the surging demand for high data rates. Achieving precise synchronization across diverse base stations (BSs) is critical in these networks to mitigate inter-cell interference (ICI). However, traditional centralized synchronization approaches face substantial challenges in dense urban, including limited access to Global Positioning System (GPS), dependence on reliable backhaul, and high signaling overhead demands. This study advances a low-complexity distributed synchronization solution. A primary focus is on assessing the algorithm's accuracy incorporating the effects of information exchange delays, which are pronounced in large-networks. Recognizing the pivotal role of neighbor-gathered information in the proposed approach, this research employs uplink Non-Orthogonal Multiple Access (NOMA) to reduce message-gathering delays between transmitters (TXs) and receivers (RXs). The proposed algorithm is evaluated to assess effectiveness under exchange delays, analyzing impact of system parameters like network connectivity, size, sub-bands, etc., on synchronization speed. The findings demonstrate that the NOMA-based information-gathering technique significantly accelerates network synchronization compared to orthogonal access schemes. This advancement is crucial for meeting the low-latency requirements of beyond fifth generation (5G) systems, underscoring the potential of distributed synchronization as a cornerstone for next-generation UDN deployments.

Distributed Time Synchronization in NOMA-Assisted Ultra-Dense Networks

TL;DR

This paper tackles the problem of precise time synchronization in ultra-dense networks by proposing a GPS-free, distributed synchronization framework that relies on neighbor timing exchanges and uplink NOMA to reduce information-exchange delays. The approach decouples SBS-SB association via stable marriage and power allocation, employing an update rule for local clocks that leverages both incoming and outbound neighbor information. Key contributions include integrating information-exchange delay into the synchronization algorithm, demonstrating substantial speed gains for NOMA over OMA, and providing stability and complexity analyses for scalable deployment. The results indicate that NOMA-based information gathering yields faster synchronization, especially with limited SBs and favorable channel conditions, making the method well-suited for beyond-5G and 6G ultra-dense deployments where GPS or backhaul constraints are prohibitive.

Abstract

Ultra-dense networks (UDNs) represent a transformative access architecture for upcoming sixth generation (6G) systems, poised to meet the surging demand for high data rates. Achieving precise synchronization across diverse base stations (BSs) is critical in these networks to mitigate inter-cell interference (ICI). However, traditional centralized synchronization approaches face substantial challenges in dense urban, including limited access to Global Positioning System (GPS), dependence on reliable backhaul, and high signaling overhead demands. This study advances a low-complexity distributed synchronization solution. A primary focus is on assessing the algorithm's accuracy incorporating the effects of information exchange delays, which are pronounced in large-networks. Recognizing the pivotal role of neighbor-gathered information in the proposed approach, this research employs uplink Non-Orthogonal Multiple Access (NOMA) to reduce message-gathering delays between transmitters (TXs) and receivers (RXs). The proposed algorithm is evaluated to assess effectiveness under exchange delays, analyzing impact of system parameters like network connectivity, size, sub-bands, etc., on synchronization speed. The findings demonstrate that the NOMA-based information-gathering technique significantly accelerates network synchronization compared to orthogonal access schemes. This advancement is crucial for meeting the low-latency requirements of beyond fifth generation (5G) systems, underscoring the potential of distributed synchronization as a cornerstone for next-generation UDN deployments.

Paper Structure

This paper contains 11 sections, 19 equations, 9 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Model
  4. Proposed solution to minimize information exchange Delay
  5. Small cell base station - Subband (SBS-SB) association Strategy
  6. Power Allocation Strategy
  7. Numerical Investigations
  8. Stability Analysis
  9. Computational Complexity
  10. CONCLUSION
  11. ACKNOWLEDGEMENT

Figures (9)

  • Figure 1: Radio Access Network Synchronization Requirements for beyond 5G networks ITU_T_2012.
  • Figure 2: System Model for the proposed concept.
  • Figure 3: TX-RX Configuration
  • Figure 4: Flow chart of the proposed algorithm
  • Figure 5: Network synchronization time for various network connectivities controlled by power threshold value for different network sizes.
  • ...and 4 more figures