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Scheduling for Downlink OFDMA With IRS Reconfiguration Constraints

Alberto Rech, Leonardo Badia, Stefano Tomasin

TL;DR

The paper addresses downlink OFDMA scheduling in IRS-assisted networks under practical reconfiguration and control overhead constraints. It formulates the sum-rate maximization as an NP-hard generalized multi-knapsack problem and proposes a Greedy Maximum-Rate Scheduler (GMAX) that decomposes the problem into configuration and RB allocation, supplemented by a cell-specific codebook to reduce signaling. A two-step decomposition, complexity-aware greedy solution, and clustering-based codebooks yield substantial overhead reductions while maintaining near-optimal performance, as demonstrated by simulations against baselines and GA-based methods. The work provides a practical, scalable framework for IRS-enabled cellular networks with realistic reconfiguration limits and demonstrates significant gains in average sum rate.

Abstract

The technical limitations of the intelligent reflecting surface (IRS) (re)configurations in terms of both communication overhead and energy efficiency must be considered when IRSs are used in cellular networks. In this paper, we investigate the downlink time-frequency scheduling of an IRS-assisted multi-user system in the orthogonal frequency-division multiple access (OFDMA) framework wherein both the set of possible IRS configurations and the number of IRS reconfigurations within a time frame are limited. We formulate the sum rate maximization problem as a non-polynomial (NP)-complete generalized multi-knapsack problem. A heuristic greedy algorithm for the joint IRS configuration and time-frequency scheduling is also proposed. Numerical simulations prove the effectiveness of our greedy solution.

Scheduling for Downlink OFDMA With IRS Reconfiguration Constraints

TL;DR

The paper addresses downlink OFDMA scheduling in IRS-assisted networks under practical reconfiguration and control overhead constraints. It formulates the sum-rate maximization as an NP-hard generalized multi-knapsack problem and proposes a Greedy Maximum-Rate Scheduler (GMAX) that decomposes the problem into configuration and RB allocation, supplemented by a cell-specific codebook to reduce signaling. A two-step decomposition, complexity-aware greedy solution, and clustering-based codebooks yield substantial overhead reductions while maintaining near-optimal performance, as demonstrated by simulations against baselines and GA-based methods. The work provides a practical, scalable framework for IRS-enabled cellular networks with realistic reconfiguration limits and demonstrates significant gains in average sum rate.

Abstract

The technical limitations of the intelligent reflecting surface (IRS) (re)configurations in terms of both communication overhead and energy efficiency must be considered when IRSs are used in cellular networks. In this paper, we investigate the downlink time-frequency scheduling of an IRS-assisted multi-user system in the orthogonal frequency-division multiple access (OFDMA) framework wherein both the set of possible IRS configurations and the number of IRS reconfigurations within a time frame are limited. We formulate the sum rate maximization problem as a non-polynomial (NP)-complete generalized multi-knapsack problem. A heuristic greedy algorithm for the joint IRS configuration and time-frequency scheduling is also proposed. Numerical simulations prove the effectiveness of our greedy solution.
Paper Structure (11 sections, 10 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 11 sections, 10 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Configuration and User Scheduling
  4. Optimization Problem Decomposition
  5. Greedy Maximum-Rate Scheduler (GMAX)
  6. Codebook Design And Control Overhead
  7. Computational Complexity
  8. Numerical Results
  9. Compared Solutions
  10. Performance Results
  11. Conclusions

Figures (5)

  • Figure 1: ofdma scheduling for irs-assisted multi-ue communication.
  • Figure 2: Average sum rate versus the number of clusters, for $K = 90$ue, $F = 5$ carriers. Between brackets is the number $b_{\rm q}$ of bits in the codebook.
  • Figure 3: ECDF of the per-user rate, for $K = 90$ue, $F = 5$ carrier frequencies, and $Z\in\{7,14\}$. Between brackets is the number $b_{\rm q}$ of bits in the codebook.
  • Figure 4: Average sum rate versus the number of ue, for $F = 3$ carrier frequencies, and $Z\in\{4,8\}$. Between brackets is the number $b_{\rm q}$ of bits in the codebook.
  • Figure 5: Average sum rate versus $ZF$, for $K = 90$ue, $F \in \{1, 3, 5, 10\}$ carrier frequencies. Between brackets is the number $b_{\rm q}$ of bits in the codebook.