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Exact Resource Allocation for Fair Wireless Relay

Edgar Arribas, Vicent Cholvi, Vincenzo Mancuso

TL;DR

The paper tackles exact downlink resource allocation in relay-enabled cellular networks under backhaul bottlenecks with the goal of $max$--$min$ fairness across mobile users. It introduces LinEx, an algorithm that delivers the exact optimum with linear complexity $\mathcal{O}( |\mathcal{U}_g \cup \mathcal{U}_g^*| \cdot |\mathcal{R}_g| )$ per gNB by balancing relay- and user-side allocations while respecting wired and wireless backhaul limits. Compared to a generic convex solver and a legacy water-filling approach, LinEx achieves optimal fairness with much lower computation and substantial performance gains in simulations, including near-identical results to the solver and large improvements over the baseline. The work also demonstrates practicality through realistic channel models for aerial relays and explicit backhaul-beamforming assumptions, and discusses specialization to single-base-station scenarios. Overall, LinEx offers a scalable, real-time capable method to achieve $max$--$min$ fairness in dynamic heterogeneous networks with relays and backhaul constraints.

Abstract

In relay-enabled cellular networks, the intertwined nature of network agents calls for complex schemes to allocate wireless resources. Resources need to be distributed among mobile users while considering how relay resources are allocated, and constrained by the traffic rate achievable by base stations and over backhaul links. In this letter, we derive an exact resource allocation scheme that achieves max-min fairness across mobile users, found with a linear complexity with respect to the number of mobile users and relays. The results reveal that the proposed scheme remarkably outperforms current solutions.

Exact Resource Allocation for Fair Wireless Relay

TL;DR

The paper tackles exact downlink resource allocation in relay-enabled cellular networks under backhaul bottlenecks with the goal of -- fairness across mobile users. It introduces LinEx, an algorithm that delivers the exact optimum with linear complexity per gNB by balancing relay- and user-side allocations while respecting wired and wireless backhaul limits. Compared to a generic convex solver and a legacy water-filling approach, LinEx achieves optimal fairness with much lower computation and substantial performance gains in simulations, including near-identical results to the solver and large improvements over the baseline. The work also demonstrates practicality through realistic channel models for aerial relays and explicit backhaul-beamforming assumptions, and discusses specialization to single-base-station scenarios. Overall, LinEx offers a scalable, real-time capable method to achieve -- fairness in dynamic heterogeneous networks with relays and backhaul constraints.

Abstract

In relay-enabled cellular networks, the intertwined nature of network agents calls for complex schemes to allocate wireless resources. Resources need to be distributed among mobile users while considering how relay resources are allocated, and constrained by the traffic rate achievable by base stations and over backhaul links. In this letter, we derive an exact resource allocation scheme that achieves max-min fairness across mobile users, found with a linear complexity with respect to the number of mobile users and relays. The results reveal that the proposed scheme remarkably outperforms current solutions.
Paper Structure (16 sections, 23 equations, 7 figures, 2 tables, 3 algorithms)

This paper contains 16 sections, 23 equations, 7 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. The Resource Allocation Problem
  4. The Exact $\max$--$\min$ Resource Allocation
  5. Performance Evaluation
  6. Conclusions
  7. The Exact Allocation for Scenarios with One Base Station Without Relays
  8. Linear Complexity of the Exact Resource Allocation of Algorithm \ref{['alg:BHmanaged']}
  9. Algorithm \ref{['alg:CPMaxMinEquiv']}: Resource allocation of one base station without relays
  10. Algorithm \ref{['alg:MaxMinThrReduction']}: $\max$--$\min$ throughput reduction
  11. Overall complexity of Algorithm \ref{['alg:BHmanaged']}: The exact $\max$--$\min$ resource allocation
  12. Channel Models and Parameters Used in the Performance Evaluation
  13. Channel Modelling
  14. Air-to-Ground Channels
  15. Ground-to-Ground Channels.
  16. ...and 1 more sections

Figures (7)

  • Figure 1: Reference scenario.
  • Figure 2: Flowchart diagram of the L i n E x scheme operation.
  • Figure 3: Wireless relay network with $3$$g\!N\!B$s and $U\!=\!600$ users.
  • Figure 4: Wireless relay network with $3$ relays per $g\!N\!B$ and $U\!\!=\!600$.
  • Figure : L i n E x: The linear and exact$\max$--$\min$ allocation.
  • ...and 2 more figures