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A Baseline Mobility-Aware IRS-Assisted Uplink Framework With Energy-Detection-Based Channel Allocation

Ardavan Rahimian

Abstract

This paper develops a self-contained framework for studying a mobility-aware intelligent reflecting surface (IRS)-assisted multi-node uplink under simplified but explicit modeling assumptions. The considered system combines direct and IRS-assisted narrowband propagation, geometric IRS phase control with finite-bit phase quantization, adaptive IRS-user focusing based on inverse-rate priority weights, and sequential channel allocation guided by energy detection. The analytical development is restricted to a physics-based two-hop cascaded path-loss formulation with appropriate scaling, an expectation-level reflected-power characterization under the stated independence assumptions, and the exact chi-square threshold for energy detection, together with its large-sample Gaussian approximation. A MATLAB implementation is used to generate a sample run, which is interpreted as a numerical example. This work is intended as a consistent, practically-aligned baseline to support future extensions involving richer mobility models or more advanced scheduling policies.

A Baseline Mobility-Aware IRS-Assisted Uplink Framework With Energy-Detection-Based Channel Allocation

Abstract

This paper develops a self-contained framework for studying a mobility-aware intelligent reflecting surface (IRS)-assisted multi-node uplink under simplified but explicit modeling assumptions. The considered system combines direct and IRS-assisted narrowband propagation, geometric IRS phase control with finite-bit phase quantization, adaptive IRS-user focusing based on inverse-rate priority weights, and sequential channel allocation guided by energy detection. The analytical development is restricted to a physics-based two-hop cascaded path-loss formulation with appropriate scaling, an expectation-level reflected-power characterization under the stated independence assumptions, and the exact chi-square threshold for energy detection, together with its large-sample Gaussian approximation. A MATLAB implementation is used to generate a sample run, which is interpreted as a numerical example. This work is intended as a consistent, practically-aligned baseline to support future extensions involving richer mobility models or more advanced scheduling policies.
Paper Structure (16 sections, 2 theorems, 46 equations, 2 figures, 4 tables, 1 algorithm)

This paper contains 16 sections, 2 theorems, 46 equations, 2 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Channel Characterization
  3. Composite uplink channel
  4. Two-hop cascaded channel model
  5. Physics-based path loss
  6. IRS phase design
  7. Simplified mobility and Doppler model
  8. SINR Model and Energy-Based Channel Allocation
  9. Energy detector and exact threshold
  10. Sequential channel allocation rule
  11. Adaptive IRS Focus Scheduling
  12. Sliding-window rate and priority weights
  13. Illustrative Seeded Numerical Example
  14. Per-node statistics
  15. Network-level behavior
  16. ...and 1 more sections

Key Result

Proposition 1

Assume for a fixed user $k$ that, for each IRS element $n$, the small-scale coefficients $\tilde{g}_{k,n}(t)$ and $\tilde{g}_{n,b}$ are zero-mean, unit-variance, and mutually independent. Also, assume that the pairs $\{(\tilde{g}_{k,n}(t),\tilde{g}_{n,b})\}_{n=1}^{N}$ are independent across differen Consequently, if the per-element large-scale factors remain of comparable order, the mean reflected

Figures (2)

  • Figure 1: Per-node average SINR together with the minimum decode threshold, as well as the corresponding IRS focus-time allocation for the seeded adaptive run.
  • Figure 2: Temporal trajectories for the adaptive run: total network sum rate (top) and individual rates (bottom). It depicts strong temporal variability and persistent heterogeneity across users.

Theorems & Definitions (5)

  • Proposition 1: Mean reflected-power scaling
  • proof
  • Remark 1
  • Theorem 1: Exact energy-detection threshold
  • proof