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Non-Orthogonal Multiple-Access for Coherent-State Optical Quantum Communications Under Lossy Photon Channels

Zhichao Dong, Xiaolin Zhou, Yongkang Chen, Wei Ni, Ekram Hossain, Xin Wang

TL;DR

The paper tackles the challenge of enabling multi-user optical quantum communications (OQCs) with coherent states under lossy photon channels and atmospheric turbulence. It develops a SIC-based PNRD-Kennedy receiver and a rigorous asymptotic sum-rate framework that accounts for turbulence, background noise, and photon loss, then optimizes power allocation via successive convex approximation (SCA). A low-complexity adaptive importance-sampling variant extends scalability to larger user numbers, and extensive simulations show substantial sum-rate gains (over 20%) over plausible baselines. The work advances practical multi-user OQC design and provides a foundation for future receiver improvements, including approaches nearer the Helstrom limit like Dolinar-type schemes.

Abstract

Coherent states have been increasingly considered in optical quantum communications (OQCs). With the inherent non-orthogonality of coherent states, non-orthogonal multiple-access (NOMA) naturally lends itself to the implementation of multi-user OQC. However, this remains unexplored in the literature. This paper proposes a novel successive interference cancellation (SIC)-based Kennedy receiver for uplink NOMA-OQC systems, along with a new approach for power allocation of the coherent states emitted by users. The key idea is to rigorously derive the asymptotic sum-rate of the considered systems, taking into account the impact of atmospheric turbulence, background noise, and lossy photon channel. With the asymptotic sum-rate, we optimize the average number of photons (or powers) of the coherent states emitted by the users. Variable substitution and successive convex approximation (SCA) are employed to convexify and maximize the asymptotic sum-rate iteratively. A new coherent-state power allocation algorithm is developed for a small-to-medium number of users. We further develop its low-complexity variant using adaptive importance sampling, which is suitable for scenarios with a medium-to-large number of users. Simulations demonstrate that our algorithms significantly enhance the sum-rate of uplink NOMA-OQC systems using coherent states by over 20\%, compared to their alternatives.

Non-Orthogonal Multiple-Access for Coherent-State Optical Quantum Communications Under Lossy Photon Channels

TL;DR

The paper tackles the challenge of enabling multi-user optical quantum communications (OQCs) with coherent states under lossy photon channels and atmospheric turbulence. It develops a SIC-based PNRD-Kennedy receiver and a rigorous asymptotic sum-rate framework that accounts for turbulence, background noise, and photon loss, then optimizes power allocation via successive convex approximation (SCA). A low-complexity adaptive importance-sampling variant extends scalability to larger user numbers, and extensive simulations show substantial sum-rate gains (over 20%) over plausible baselines. The work advances practical multi-user OQC design and provides a foundation for future receiver improvements, including approaches nearer the Helstrom limit like Dolinar-type schemes.

Abstract

Coherent states have been increasingly considered in optical quantum communications (OQCs). With the inherent non-orthogonality of coherent states, non-orthogonal multiple-access (NOMA) naturally lends itself to the implementation of multi-user OQC. However, this remains unexplored in the literature. This paper proposes a novel successive interference cancellation (SIC)-based Kennedy receiver for uplink NOMA-OQC systems, along with a new approach for power allocation of the coherent states emitted by users. The key idea is to rigorously derive the asymptotic sum-rate of the considered systems, taking into account the impact of atmospheric turbulence, background noise, and lossy photon channel. With the asymptotic sum-rate, we optimize the average number of photons (or powers) of the coherent states emitted by the users. Variable substitution and successive convex approximation (SCA) are employed to convexify and maximize the asymptotic sum-rate iteratively. A new coherent-state power allocation algorithm is developed for a small-to-medium number of users. We further develop its low-complexity variant using adaptive importance sampling, which is suitable for scenarios with a medium-to-large number of users. Simulations demonstrate that our algorithms significantly enhance the sum-rate of uplink NOMA-OQC systems using coherent states by over 20\%, compared to their alternatives.

Paper Structure

This paper contains 22 sections, 4 theorems, 61 equations, 10 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contribution
  4. System Model and Assumptions
  5. Sum-Rate Analysis of Uplink NOMA-OQC
  6. Analysis of Explicit Sum-Rate
  7. Upper and Lower Bounds of Sum-Rate
  8. Asymptotically Tight Approximation of Sum-Rate
  9. Proposed Coherent-State Power Allocation
  10. SCA-Based Coherent-State Power Allocation
  11. Convergence and Complexity Analysis
  12. Convergence Analysis
  13. Complexity Analysis
  14. Low-Complexity Power Allocation With Adaptive Importance Sampling
  15. Low-Complexity Solution with Importance Sampling
  16. ...and 7 more sections

Key Result

Theorem 1

The expression for the sum-rate of the uplink NOMA-OQC system is given by

Figures (10)

  • Figure 1: An illustration of the considered uplink NOMA-OQC system, where $K$ users form an uplink NOMA cluster, each user is equipped with a laser to emit coherent states, and the BS receives those coherent states through the PNRD-Kennedy receiver. Here, LO stands for local oscillator.
  • Figure 2: Comparison of sum-rate between Algorithms 1/2, OMA, IA, and ENP.
  • Figure 3: Comparison of sum-rates for 8 users under different path loss and atmospheric turbulence scenarios.
  • Figure 4: Comparison of the sum-rate change with increasing number of users in the considered NOMA cluster.
  • Figure 5: The convergence behaviors of Algorithms 1 and 2.
  • ...and 5 more figures

Theorems & Definitions (8)

  • Theorem 1
  • proof
  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Theorem 2
  • proof