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Energy Efficiency in Rate-Splitting Multiple Access with Mixed Criticality

Robert-Jeron Reifert, Stefan Roth, Alaa Alameer Ahmad, Aydin Sezgin

TL;DR

Numerical results validate that there is a trade-off between the QoS fulfillment and power minimization, and the energy efficiency of the proposed rate-splitting algorithm is larger than in comparative schemes, especially with mixed criticality.

Abstract

Future sixth generation (6G) wireless communication networks face the need to similarly meet unprecedented quality of service (QoS) demands while also providing a larger energy efficiency (EE) to minimize their carbon footprint. Moreover, due to the diverseness of network participants, mixed criticality QoS levels are assigned to the users of such networks. In this work, with a focus on a cloud-radio access network (C-RAN), the fulfillment of desired QoS and minimized transmit power use is optimized jointly within a rate-splitting paradigm. Thereby, the optimization problem is non-convex. Hence, a low-complexity algorithm is proposed based on fractional programming. Numerical results validate that there is a trade-off between the QoS fulfillment and power minimization. Moreover, the energy efficiency of the proposed rate-splitting algorithm is larger than in comparative schemes, especially with mixed criticality.

Energy Efficiency in Rate-Splitting Multiple Access with Mixed Criticality

TL;DR

Numerical results validate that there is a trade-off between the QoS fulfillment and power minimization, and the energy efficiency of the proposed rate-splitting algorithm is larger than in comparative schemes, especially with mixed criticality.

Abstract

Future sixth generation (6G) wireless communication networks face the need to similarly meet unprecedented quality of service (QoS) demands while also providing a larger energy efficiency (EE) to minimize their carbon footprint. Moreover, due to the diverseness of network participants, mixed criticality QoS levels are assigned to the users of such networks. In this work, with a focus on a cloud-radio access network (C-RAN), the fulfillment of desired QoS and minimized transmit power use is optimized jointly within a rate-splitting paradigm. Thereby, the optimization problem is non-convex. Hence, a low-complexity algorithm is proposed based on fractional programming. Numerical results validate that there is a trade-off between the QoS fulfillment and power minimization. Moreover, the energy efficiency of the proposed rate-splitting algorithm is larger than in comparative schemes, especially with mixed criticality.
Paper Structure (11 sections, 3 theorems, 14 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 11 sections, 3 theorems, 14 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Optimization Metric
  3. Transmit Signal and Successive Decoding
  4. Considered MSE and EE Metric
  5. Problem Formulation and Algorithm
  6. Quadratic Transform-based Solution
  7. Numerical Simulation
  8. Impact of Interference Management Schemes
  9. Impact of QoS Targets
  10. Rate MSE vs. Transmit Power Minimization
  11. Conclusion

Key Result

Lemma 1

The optimization problem eq:Opt1 can be rewritten as Thereby, the introduced optimization variable $\bm{\gamma}$ covers all possible non-zero elements of $\bm{\gamma}'=[{\gamma}_{1}^p,\ldots,{\gamma}_{K}^p,{\gamma}_{1,1}^c,{\gamma}_{1,2}^c,\ldots,{\gamma}_{K,K}^c]^T$. Note that ${\gamma}^c_{i,k} = 0$$\forall i\notin\mathcal{I}_k, k \in \mathcal{K}$ as

Figures (4)

  • Figure 1: System model of a C-RAN consisting of $2$ BSs and $3$ users, where private and common messages are transmitted.
  • Figure 2: EE as a function of fronthaul capacity and SNR for the considered schemes.
  • Figure 3: Objective function over sum target rate comparing various schemes.
  • Figure 4: Rate MSE and transmit power over $\alpha$.

Theorems & Definitions (8)

  • Remark 1
  • Lemma 1
  • proof
  • Remark 2
  • Lemma 2
  • proof
  • Lemma 3
  • proof