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Age of Information Analysis for CR-NOMA Aided Uplink Systems with Randomly Arrived Packets

Yanshi Sun, Yanglin Ye, Zhiguo Ding, Momiao Zhou, Lei Liu

TL;DR

This paper tackles the freshness of status updates (AoI) in uplink systems by integrating CR-NOMA as an add-on to a TDMA-based legacy network under randomly arriving updates and LCFS queuing. It develops a rigorous analytical framework and derives closed-form AoI expressions for four schemes: TDMA-NRT, NOMA-NRT, TDMA-RT, and NOMA-RT, using Poisson arrivals and Markov-chain reasoning. Key findings show that CR-NOMA can significantly reduce AoI compared with TDMA, and that retransmission strategies notably improve AoI at low arrival rates, with a comprehensive numerical validation of the theoretical results. The work provides practical insights into how slot duration, user count, packet size, and arrival rates shape AoI, offering guidance for implementing CR-NOMA in real-time monitoring networks while outlining avenues for future enhancements such as RSMA and multi-user slots.

Abstract

This paper studies the application of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) to reduce age of information (AoI) for uplink transmission. In particular, a time division multiple access (TDMA) based legacy network is considered, where each user is allocated with a dedicated time slot to transmit its status update information. The CR-NOMA is implemented as an add-on to the TDMA legacy network, which enables each user to have more opportunities to transmit by sharing other user's time slots. A rigorous analytical framework is developed to obtain the expressions for AoIs achieved by CR-NOMA with and without re-transmission, by taking the randomness of the status update generating process into consideration. Numerical results are presented to verify the accuracy of the developed analysis. It is shown that the AoI can be significantly reduced by applying CR-NOMA compared to TDMA. Moreover, the use of re-transmission is helpful to reduce AoI, especially when the status arrival rate is low.

Age of Information Analysis for CR-NOMA Aided Uplink Systems with Randomly Arrived Packets

TL;DR

This paper tackles the freshness of status updates (AoI) in uplink systems by integrating CR-NOMA as an add-on to a TDMA-based legacy network under randomly arriving updates and LCFS queuing. It develops a rigorous analytical framework and derives closed-form AoI expressions for four schemes: TDMA-NRT, NOMA-NRT, TDMA-RT, and NOMA-RT, using Poisson arrivals and Markov-chain reasoning. Key findings show that CR-NOMA can significantly reduce AoI compared with TDMA, and that retransmission strategies notably improve AoI at low arrival rates, with a comprehensive numerical validation of the theoretical results. The work provides practical insights into how slot duration, user count, packet size, and arrival rates shape AoI, offering guidance for implementing CR-NOMA in real-time monitoring networks while outlining avenues for future enhancements such as RSMA and multi-user slots.

Abstract

This paper studies the application of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) to reduce age of information (AoI) for uplink transmission. In particular, a time division multiple access (TDMA) based legacy network is considered, where each user is allocated with a dedicated time slot to transmit its status update information. The CR-NOMA is implemented as an add-on to the TDMA legacy network, which enables each user to have more opportunities to transmit by sharing other user's time slots. A rigorous analytical framework is developed to obtain the expressions for AoIs achieved by CR-NOMA with and without re-transmission, by taking the randomness of the status update generating process into consideration. Numerical results are presented to verify the accuracy of the developed analysis. It is shown that the AoI can be significantly reduced by applying CR-NOMA compared to TDMA. Moreover, the use of re-transmission is helpful to reduce AoI, especially when the status arrival rate is low.
Paper Structure (26 sections, 5 theorems, 102 equations, 17 figures)

This paper contains 26 sections, 5 theorems, 102 equations, 17 figures.

Table of Contents

  1. Introduction
  2. System model
  3. Update arrival and queuing process
  4. Multiple Access Strategies
  5. TDMA
  6. CR-NOMA
  7. With and without re-transmission
  8. Without re-transmission
  9. With re-transmission
  10. Performance metric
  11. Analysis on AoI for TDMA-NRT, NOMA-NRT, TDMA-RT and NOMA-RT
  12. AoI analysis for TDMA-NRT
  13. AoI analysis for NOMA-NRT
  14. AoI analysis for TDMA-RT
  15. AoI analysis for NOMA-RT
  16. ...and 11 more sections

Key Result

Theorem 1

The average AoI achieved by the considered TDMA-NRT scheme, denoted by $\bar{\Delta}_{m}^{TDMA-NRT}$, can be expressed as: where $\Gamma=\frac{MT}{1-e^{\lambda_mMT}}+\frac{1}{\lambda_m}+T$.

Figures (17)

  • Figure 1: Illustration of the AoI of a status updating process.
  • Figure 2: Illustration of the status updating process and the corresponding AoI evolution for TDMA-NRT,NOMA-NRT, TDMA-RT and NOMA-RT. It can be seen that by using NOMA and retransmission mechanism, more transmission opportunities can be provided, which can significantly reduce the instantaneous AoI.
  • Figure 3: Average AoI achieved by TDMA-NRT, TDMA-RT, NOMA-NRT and NOMA-RT. $\lambda_m=\lambda_{m'}=0.1$, $M=8$, $T=3$.
  • Figure 4: Impact of packet arrival rates on AoI for TDMA-NRT, TDMA-RT, NOMA-NRT and NOMA-RT. $N=1$ bit, $T=1$, $\lambda_m=\lambda_{m'}$.
  • Figure 5: Impact of the duration of a time slot on AoI for TDMA-NRT, TDMA-RT, NOMA-NRT and NOMA-RT. $N=2$ bits, $\lambda_m=\lambda_{m'}=0.1$.
  • ...and 12 more figures

Theorems & Definitions (5)

  • Theorem 1
  • Theorem 2
  • Theorem 3
  • Lemma 1
  • Theorem 4