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High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems

Teppei Otsuka, Aohan Li, Hiroki Takesue, Kensuke Inaba, Kazuyuki Aihara, Mikio Hasegawa

TL;DR

The proposed coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems is compared to simulated annealing, a conventional-NOMA pairing scheme, deep Q learning based scheme, and an exhaustive search scheme to show superior results.

Abstract

Non-orthogonal multiple access (NOMA) technique is important for achieving a high data rate in next-generation wireless communications. A key challenge to fully utilizing the effectiveness of the NOMA technique is the optimization of the resource allocation (RA), e.g., channel and power. However, this RA optimization problem is NP-hard, and obtaining a good approximation of a solution with a low computational complexity algorithm is not easy. To overcome this problem, we propose the coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems. The CIM is an Ising system that can deliver fair approximate solutions to combinatorial optimization problems at high speed (millisecond order) by operating optimization algorithms based on mutually connected photonic neural networks. The performance of our proposed method was evaluated using a simulation model of the CIM. We compared the performance of our proposed method to simulated annealing, a conventional-NOMA pairing scheme, deep Q learning based scheme, and an exhaustive search scheme. Simulation results indicate that our proposed method is superior in terms of speed and the attained optimal solutions.

High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems

TL;DR

The proposed coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems is compared to simulated annealing, a conventional-NOMA pairing scheme, deep Q learning based scheme, and an exhaustive search scheme to show superior results.

Abstract

Non-orthogonal multiple access (NOMA) technique is important for achieving a high data rate in next-generation wireless communications. A key challenge to fully utilizing the effectiveness of the NOMA technique is the optimization of the resource allocation (RA), e.g., channel and power. However, this RA optimization problem is NP-hard, and obtaining a good approximation of a solution with a low computational complexity algorithm is not easy. To overcome this problem, we propose the coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems. The CIM is an Ising system that can deliver fair approximate solutions to combinatorial optimization problems at high speed (millisecond order) by operating optimization algorithms based on mutually connected photonic neural networks. The performance of our proposed method was evaluated using a simulation model of the CIM. We compared the performance of our proposed method to simulated annealing, a conventional-NOMA pairing scheme, deep Q learning based scheme, and an exhaustive search scheme. Simulation results indicate that our proposed method is superior in terms of speed and the attained optimal solutions.
Paper Structure (38 sections, 30 equations, 10 figures, 2 tables, 1 algorithm)

This paper contains 38 sections, 30 equations, 10 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Heuristic RA Approaches
  4. ML Based RA Approaches
  5. Hardware-based RA
  6. System Model and Problem Formulation
  7. Coherent Ising Machine
  8. Ising Hamiltonian
  9. Measurement Feedback Coherent Ising Machine
  10. Operation Process of the Measurement Feedback CIM
  11. Applying the CIM to the RA Problem in NOMA
  12. Mutually Connected Neural Network
  13. Mutually Connected Neural Network to Solve RA in NOMA System
  14. CIM Used to Solve RA in NOMA System
  15. Complexity Analysis of the CIM
  16. ...and 23 more sections

Figures (10)

  • Figure 1: System model.
  • Figure 2: Coherent Ising Machine 1011.
  • Figure 3: Overview of our proposed CIM-based RA method
  • Figure 4: Convergence of in-phase components of DOPO pulse (left) and the corresponding Ising Hamiltonian at that time (center) and comparison of the total data rate obtained by the CIM-based RA with the optimal and the OMA methods at that time (right) in the NOMA system with $N_u=12$ users and $N_c=6$ sub-channels.
  • Figure 5: Total data rate versus number of users $N_u$ in the NOMA system with total transmit power $P_T=12$ from the BS.
  • ...and 5 more figures