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Smart Resource Allocation at mmWave/THz Frequencies with Cooperative Rate-Splitting

Hyesang Cho, Junil Choi

TL;DR

A novel efficiency constrained optimization (ECO) algorithm assuming instantaneous CSI, and a simple but effective even data transmission (EDT) algorithm that promotes steady transmission along the time blocks is proposed.

Abstract

In this paper, we propose algorithms to minimize the energy consumption in millimeter wave/terahertz multi-user downlink communication systems. To ensure coverage in blockage-vulnerable high frequency systems, we consider cooperative rate-splitting (CRS) and transmission over multiple time blocks, where via CRS, multiple users cooperate to assist a blocked user. Moreover, we show that transmission over multiple time blocks provides benefits through smart resource allocation. We first propose a communication framework named improved distinct extraction-based CRS (iDeCRS) that utilizes the benefits of rate-splitting. With our transmission framework, we derive a performance benchmark assuming genie channel state information (CSI), i.e., the channels of the present and future time blocks are known, denoted as GENIE. Using the results from GENIE, we derive a novel efficiency constrained optimization (ECO) algorithm assuming instantaneous CSI. In addition, a simple but effective even data transmission (EDT) algorithm that promotes steady transmission along the time blocks is proposed. Simulation results show that ECO and EDT have satisfactory performances compared to GENIE. The results also show that ECO outperforms EDT when many users are cooperating, and vise versa.

Smart Resource Allocation at mmWave/THz Frequencies with Cooperative Rate-Splitting

TL;DR

A novel efficiency constrained optimization (ECO) algorithm assuming instantaneous CSI, and a simple but effective even data transmission (EDT) algorithm that promotes steady transmission along the time blocks is proposed.

Abstract

In this paper, we propose algorithms to minimize the energy consumption in millimeter wave/terahertz multi-user downlink communication systems. To ensure coverage in blockage-vulnerable high frequency systems, we consider cooperative rate-splitting (CRS) and transmission over multiple time blocks, where via CRS, multiple users cooperate to assist a blocked user. Moreover, we show that transmission over multiple time blocks provides benefits through smart resource allocation. We first propose a communication framework named improved distinct extraction-based CRS (iDeCRS) that utilizes the benefits of rate-splitting. With our transmission framework, we derive a performance benchmark assuming genie channel state information (CSI), i.e., the channels of the present and future time blocks are known, denoted as GENIE. Using the results from GENIE, we derive a novel efficiency constrained optimization (ECO) algorithm assuming instantaneous CSI. In addition, a simple but effective even data transmission (EDT) algorithm that promotes steady transmission along the time blocks is proposed. Simulation results show that ECO and EDT have satisfactory performances compared to GENIE. The results also show that ECO outperforms EDT when many users are cooperating, and vise versa.
Paper Structure (14 sections, 1 theorem, 34 equations, 7 figures, 1 algorithm)

This paper contains 14 sections, 1 theorem, 34 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Improved DeCRS (iDeCRS)
  4. Channel Model
  5. Genie CSI Case
  6. Instantaneous CSI Case
  7. Efficiency Constrained Optimization (ECO)
  8. Even Data Transmission (EDT)
  9. Initialization and Analyses
  10. Initialization
  11. Convergence Analysis
  12. Complexity Analysis
  13. Simulation Results
  14. Conclusion

Key Result

Proposition 1

For a single UE AWGN channel with the noise distribution as ${\mathcal{C}} {\mathcal{N}} (0,1)$, even data transmission is the optimal energy consumption minimization strategy.

Figures (7)

  • Figure 1: Two-phase cooperative communication system over $T$ time blocks.
  • Figure 2: Block diagram of the iDeCRS framework.
  • Figure 3: The energy consumption with respect to the number of mUEs, where $T = 20, D_k = 10, D_\mathrm{d} = 2,$ and $s = 0.7$.
  • Figure 4: The energy consumption with respect to the hyper parameter $s$, where $T = 20, D_k = 10, D_\mathrm{d} = 2,$ and $K = 4$.
  • Figure 5: The energy consumption with respect to the number of time blocks, where $s = 0.7, D_k = 10, D_\mathrm{d} = 2,$ and $K = 4$.
  • ...and 2 more figures

Theorems & Definitions (2)

  • Proposition 1
  • proof