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Processing Load Allocation of On-Board Multi-User Detection for Payload-Constrained Satellite Networks

Sirui Miao, Neng Ye, Peisen Wang, Qiaolin Ouyang

TL;DR

This paper proposes to allocate the processing load among cooperative satellites for finest exploitation of distributed processing power, and develops a quadratic transformation to address the non-trivial integer matching, which leads to a system sum-rate maximization problem under stringent payload constraints.

Abstract

The rapid advance of mega-constellation facilitates the booming of direct-to-satellite massive access, where multi-user detection is critical to alleviate the induced inter-user interference. While centralized implementation of on-board detection induces unaffordable complexity for a single satellite, this paper proposes to allocate the processing load among cooperative satellites for finest exploitation of distributed processing power. Observing the inherent disparities among users, we first excavate the closed-form trade-offs between achievable sum-rate and the processing load corresponding to the satellite-user matchings, which leads to a system sum-rate maximization problem under stringent payload constraints. To address the non-trivial integer matching, we develop a quadratic transformation to the original problem, and prove it an equivalent conversion. The problem is further simplified into a series of subproblems employing successive lower bound approximation which obtains polynomial-time complexity and converges within a few iterations. Numerical results show remarkably complexity reduction compared with centralized processing, as well as around 20\% sum-rate gain compared with other allocation methods.

Processing Load Allocation of On-Board Multi-User Detection for Payload-Constrained Satellite Networks

TL;DR

This paper proposes to allocate the processing load among cooperative satellites for finest exploitation of distributed processing power, and develops a quadratic transformation to address the non-trivial integer matching, which leads to a system sum-rate maximization problem under stringent payload constraints.

Abstract

The rapid advance of mega-constellation facilitates the booming of direct-to-satellite massive access, where multi-user detection is critical to alleviate the induced inter-user interference. While centralized implementation of on-board detection induces unaffordable complexity for a single satellite, this paper proposes to allocate the processing load among cooperative satellites for finest exploitation of distributed processing power. Observing the inherent disparities among users, we first excavate the closed-form trade-offs between achievable sum-rate and the processing load corresponding to the satellite-user matchings, which leads to a system sum-rate maximization problem under stringent payload constraints. To address the non-trivial integer matching, we develop a quadratic transformation to the original problem, and prove it an equivalent conversion. The problem is further simplified into a series of subproblems employing successive lower bound approximation which obtains polynomial-time complexity and converges within a few iterations. Numerical results show remarkably complexity reduction compared with centralized processing, as well as around 20\% sum-rate gain compared with other allocation methods.
Paper Structure (11 sections, 3 theorems, 17 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 11 sections, 3 theorems, 17 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formalization
  3. Signal Model
  4. Problem Statement
  5. Analysis of MUD Processing Load
  6. Impact of Satellite-User Matchings on System Sum-Rate
  7. Proposed Workload Allocation Method for Distributed Multi-User Detection
  8. Equivalent Transformation of $\mathcal{P}_{\text{MAX-SR}}$
  9. Iterative Solution Using Successive Lower-bound Approximation
  10. Numerical Results
  11. Conclusion

Key Result

Proposition 1

The MUD processing load at the $j$-th satellite increases exponentially with the number of its target users.

Figures (5)

  • Figure 1: Illustration of processing load allocation in cooperative network.
  • Figure 2: Convergence performance: overall sum-rate of the proposed method vs. iteration numbers with varying (a) $\epsilon$ (b) $q_s$.
  • Figure 3: Comparisons on the sum-rate with varying: (a) $\epsilon$, and (b) $q_s$.
  • Figure 4: Comparisons on the sum-rate with varying $J$ under (a) $\epsilon=0.4$, and (b) $\epsilon=0.6$.
  • Figure 5: Comparisons on the sum-rate gap and the MUD processing load with varying $q_s$.

Theorems & Definitions (7)

  • Proposition 1
  • proof
  • Definition 1
  • Lemma 1
  • proof
  • Lemma 2
  • proof