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Interference Graph Estimation for Resource Allocation in Multi-Cell Multi-Numerology Networks: A Power-Domain Approach

Daqian Ding, Haorui Li, Yibo Pi, Xudong Wang

TL;DR

This work tackles the challenge of estimating the interference graph in multi-cell multi-numerology networks to enable efficient resource allocation. It introduces a power-domain interference graph estimation method that leverages a linear relation between receive power and products of transmit powers and interference gains, enforcing a full-rank transmit-power matrix to guarantee unique channel gain recovery. The authors formulate a joint optimization that co-optimizes interference graph estimation and energy-efficient resource allocation, solved via a staged approach using SOCP/SDP relaxations and iterative refinements with proven convergence. The results show strong interference gains can be accurately estimated with low power overhead and robustness to timing and carrier frequency offsets, enabling simultaneous IGE and resource allocation with meaningful energy efficiency gains in realistic network scenarios.

Abstract

The interference graph, depicting the intra- and inter-cell interference channel gains, is indispensable for resource allocation in multi-cell networks.However, there lacks viable methods of interference graph estimation (IGE) for multi-cell multi-numerology (MN) networks. To fill this gap, we propose an efficient power-domain approach to IGE for the resource allocation in multi-cell MN networks. Unlike traditional reference signal-based approaches that consume frequency-time resources, our approach uses power as a new dimension for the estimation of channel gains. By carefully controlling the transmit powers of base stations, our approach is capable of estimating both intra- and inter-cell interference channel gains. As a power-domain approach, it can be seamlessly integrated with the resource allocation such that IGE and resource allocation can be conducted simultaneously using the same frequency-time resources. We derive the necessary conditions for the power-domain IGE and design a practical power control scheme. We formulate a multi-objective joint optimization problem of IGE and resource allocation, propose iterative solutions with proven convergence, and analyze the computational complexity. Our simulation results show that power-domain IGE can accurately estimate strong interference channel gains with low power overhead and is robust to carrier frequency and timing offsets.

Interference Graph Estimation for Resource Allocation in Multi-Cell Multi-Numerology Networks: A Power-Domain Approach

TL;DR

This work tackles the challenge of estimating the interference graph in multi-cell multi-numerology networks to enable efficient resource allocation. It introduces a power-domain interference graph estimation method that leverages a linear relation between receive power and products of transmit powers and interference gains, enforcing a full-rank transmit-power matrix to guarantee unique channel gain recovery. The authors formulate a joint optimization that co-optimizes interference graph estimation and energy-efficient resource allocation, solved via a staged approach using SOCP/SDP relaxations and iterative refinements with proven convergence. The results show strong interference gains can be accurately estimated with low power overhead and robustness to timing and carrier frequency offsets, enabling simultaneous IGE and resource allocation with meaningful energy efficiency gains in realistic network scenarios.

Abstract

The interference graph, depicting the intra- and inter-cell interference channel gains, is indispensable for resource allocation in multi-cell networks.However, there lacks viable methods of interference graph estimation (IGE) for multi-cell multi-numerology (MN) networks. To fill this gap, we propose an efficient power-domain approach to IGE for the resource allocation in multi-cell MN networks. Unlike traditional reference signal-based approaches that consume frequency-time resources, our approach uses power as a new dimension for the estimation of channel gains. By carefully controlling the transmit powers of base stations, our approach is capable of estimating both intra- and inter-cell interference channel gains. As a power-domain approach, it can be seamlessly integrated with the resource allocation such that IGE and resource allocation can be conducted simultaneously using the same frequency-time resources. We derive the necessary conditions for the power-domain IGE and design a practical power control scheme. We formulate a multi-objective joint optimization problem of IGE and resource allocation, propose iterative solutions with proven convergence, and analyze the computational complexity. Our simulation results show that power-domain IGE can accurately estimate strong interference channel gains with low power overhead and is robust to carrier frequency and timing offsets.

Paper Structure

This paper contains 29 sections, 5 theorems, 54 equations, 17 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. Multi-Cell Multi-Numerology Networks
  4. Channel Model
  5. Interference Graph Estimation: A Power-Domain Approach
  6. Estimating the Interference Graph with Power Control
  7. Practical Power Control Scheme
  8. Error Analysis for IGE
  9. Joint Optimization for Interference Graph Estimation and Resource Allocation
  10. Problem Formulation
  11. Proposed Solution
  12. Solution to $\mathcal{P}2$
  13. Transformation of the Fractional Objective
  14. Continuous Relaxation
  15. Convex Approximation
  16. ...and 14 more sections

Key Result

Lemma 1

For the downlink of the OFDMA-based multi-cell multi-numerology systems, if CP exceeds TO and $\mathbb{E}[X_k^{i}[m]] = 0$ for each subcarrier, the scaled expected receive power of UE $z$ on subcarrier $d$ is a linear combination of the products of the equivalent channel gains and the scaled expecte where $\hat{p}_{z,rx}^i[d] = \mathbb{E}[|Y_z^i[d]|^2]$ is the receive power of UE $z$ on the $d$-th

Figures (17)

  • Figure 1: A multi-cell multi-numerology network
  • Figure 2: High-level workflow of the power control scheme
  • Figure 3: Practical power control scheme for interference graph estimation
  • Figure 4: Speeding up IGE by neglecting weak interference between RBs
  • Figure 5: Inter-block interference when TO exceeds CP, where TO is mainly due to the time synchronization errors between BSs.
  • ...and 12 more figures

Theorems & Definitions (7)

  • Lemma 1
  • Corollary 1
  • Theorem 1
  • Theorem 2
  • proof
  • Theorem 3
  • proof