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User Clustering for Coexistence between Near-field and Far-field Communications

Kaidi Wang, Zhiguo Ding, George K. Karagiannidis

TL;DR

The paper tackles NF–FF coexistence by enabling FF users to be served on NF beams using NOMA, under NF QoS constraints. It develops three SIC decoding strategies and models beam assignment as an overlapping coalitional game to form multiple, coexisting clusters, optimizing the FF sum-rate while guaranteeing $R_{ ext{min}}$ for NF users. In each NF cluster, the authors derive an effective SIC decoding order to realize the proposed strategies, and they prove convergence to a stable clustering with complexity $O(CKN)$. Simulation results show notable sum-rate gains over benchmarks, with strategies 2 and 3 providing higher performance at the expense of fairness, and the clustering algorithm achieving near-optimal performance with substantially reduced complexity relative to global optimization.

Abstract

This letter investigates the coexistence between near-field (NF) and far-field (FF) communications, where multiple FF users are clustered to be served on the beams of legacy NF users, via non-orthogonal multiple access (NOMA). Three different successive interference cancellation (SIC) decoding strategies are proposed and a sum rate maximization problem is formulated to optimize the beam assignment and decoding order. The beam assignment problem is further reformulated as an overlapping coalitional game, which facilitates the design of the proposed clustering algorithm. The optimal decoding order in each cluster is also derived, which can be integrated into the proposed clustering. Simulation results demonstrate that the proposed clustering algorithm is able to significantly improve the sum rate of the considered system, and the developed strategies achieve different trade-offs between sum rate and fairness.

User Clustering for Coexistence between Near-field and Far-field Communications

TL;DR

The paper tackles NF–FF coexistence by enabling FF users to be served on NF beams using NOMA, under NF QoS constraints. It develops three SIC decoding strategies and models beam assignment as an overlapping coalitional game to form multiple, coexisting clusters, optimizing the FF sum-rate while guaranteeing for NF users. In each NF cluster, the authors derive an effective SIC decoding order to realize the proposed strategies, and they prove convergence to a stable clustering with complexity . Simulation results show notable sum-rate gains over benchmarks, with strategies 2 and 3 providing higher performance at the expense of fairness, and the clustering algorithm achieving near-optimal performance with substantially reduced complexity relative to global optimization.

Abstract

This letter investigates the coexistence between near-field (NF) and far-field (FF) communications, where multiple FF users are clustered to be served on the beams of legacy NF users, via non-orthogonal multiple access (NOMA). Three different successive interference cancellation (SIC) decoding strategies are proposed and a sum rate maximization problem is formulated to optimize the beam assignment and decoding order. The beam assignment problem is further reformulated as an overlapping coalitional game, which facilitates the design of the proposed clustering algorithm. The optimal decoding order in each cluster is also derived, which can be integrated into the proposed clustering. Simulation results demonstrate that the proposed clustering algorithm is able to significantly improve the sum rate of the considered system, and the developed strategies achieve different trade-offs between sum rate and fairness.
Paper Structure (15 sections, 28 equations, 4 figures, 1 algorithm)

This paper contains 15 sections, 28 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. SIC Decoding Design
  5. SIC Decoding Strategy 1
  6. SIC Decoding Strategy 2
  7. SIC Decoding Strategy 3
  8. Problem Formulation
  9. User Clustering and Decoding Order Design
  10. Coalitional Game based User Clustering
  11. Complexity Analysis
  12. Convergence and Stability
  13. SIC Decoding Order Design
  14. Simulation Results
  15. Conclusion

Figures (4)

  • Figure 1: The impact of transmit power. $L=64$, $K=5$, and $N=20$.
  • Figure 2: The impact of the number of NF users. $L=64$, $N=20$, and $P_t=30$ dBm.
  • Figure 3: The impact of the number of FF users. $L=64$, $K=5$, and $P_t=30$ dBm.
  • Figure 4: The convergence of the user clustering algorithm. $L=64$, $K=5$, $N=20$, and $P_t=30$ dBm.

Theorems & Definitions (2)

  • Definition 1
  • Definition 2