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Connecting the Unconnectable through Feedback

Yimeng Li, Yulin Shao

TL;DR

IoT devices at cell edges face uplink reliability challenges due to limited power and single-antenna hardware. The paper proposes a feedback-aided uplink framework that leverages real-time AP decoding feedback and feedback channel codes to induce dual-channel coupling, effectively lowering the uplink SNR requirement to a logistic threshold $\Omega_f$ and expanding connectable APs without increasing device power. An analytical model based on Gauss-Laguerre quadrature yields tractable expressions for the connectable probability $\varphi_f(R)$ and the average number of connectable APs $M_f(D)$, contrasted against forward-mode benchmarks with $\varphi_c(R)$ and $M_c(D)$. Numerical results show up to 51% more connectable APs and a 24% range extension at the cell edge, validating the approach and highlighting trade-offs in downlink bandwidth and latency due to feedback. The work demonstrates a robust, energy-efficient pathway to connect the unconnectable IoT devices in challenging environments, with practical applicability to modern edge networks.

Abstract

Reliable uplink connectivity remains a persistent challenge for IoT devices, particularly those at the cell edge, due to their limited transmit power and single-antenna configurations. This paper introduces a novel framework aimed at connecting the unconnectable, leveraging real-time feedback from access points (APs) to enhance uplink coverage without increasing the energy consumption of IoT devices. At the core of this approach are feedback channel codes, which enable IoT devices to dynamically adapt their transmission strategies based on AP decoding feedback, thereby reducing the critical uplink SNR required for successful communication. Analytical models are developed to quantify the coverage probability and the number of connectable APs, providing a comprehensive understanding of the system's performance. Numerical results validate the proposed method, demonstrating substantial improvements in coverage range and connectivity, particularly for devices at the cell edge, with up to a 51% boost in connectable APs. Our approach offers a robust and energy-efficient solution to overcoming uplink coverage limitations, enabling IoT networks to connect devices in challenging environments.

Connecting the Unconnectable through Feedback

TL;DR

IoT devices at cell edges face uplink reliability challenges due to limited power and single-antenna hardware. The paper proposes a feedback-aided uplink framework that leverages real-time AP decoding feedback and feedback channel codes to induce dual-channel coupling, effectively lowering the uplink SNR requirement to a logistic threshold and expanding connectable APs without increasing device power. An analytical model based on Gauss-Laguerre quadrature yields tractable expressions for the connectable probability and the average number of connectable APs , contrasted against forward-mode benchmarks with and . Numerical results show up to 51% more connectable APs and a 24% range extension at the cell edge, validating the approach and highlighting trade-offs in downlink bandwidth and latency due to feedback. The work demonstrates a robust, energy-efficient pathway to connect the unconnectable IoT devices in challenging environments, with practical applicability to modern edge networks.

Abstract

Reliable uplink connectivity remains a persistent challenge for IoT devices, particularly those at the cell edge, due to their limited transmit power and single-antenna configurations. This paper introduces a novel framework aimed at connecting the unconnectable, leveraging real-time feedback from access points (APs) to enhance uplink coverage without increasing the energy consumption of IoT devices. At the core of this approach are feedback channel codes, which enable IoT devices to dynamically adapt their transmission strategies based on AP decoding feedback, thereby reducing the critical uplink SNR required for successful communication. Analytical models are developed to quantify the coverage probability and the number of connectable APs, providing a comprehensive understanding of the system's performance. Numerical results validate the proposed method, demonstrating substantial improvements in coverage range and connectivity, particularly for devices at the cell edge, with up to a 51% boost in connectable APs. Our approach offers a robust and energy-efficient solution to overcoming uplink coverage limitations, enabling IoT networks to connect devices in challenging environments.
Paper Structure (6 sections, 2 theorems, 35 equations, 10 figures)

This paper contains 6 sections, 2 theorems, 35 equations, 10 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Uplink Coverage Analysis with Feedback
  4. Numerical Results
  5. Conclusions
  6. Sensitive Analysis of Approximations

Key Result

Proposition 1

In feedback mode, the probability that an AP at a distance $R$ is connectable, denoted by $\varphi_{f}(R)\triangleq\Pr(\eta_{U}(R)\geq\Omega_f)$, can be approximated as where the approximation is based on a Gauss-Laguerre quadrature of order ${L}$, with $x_k$ denoting the roots of the Laguerre polynomials and $w_k$ the corresponding weights ioakimidis1993gauss. The coefficients $J_{1,k}$ and $J_{

Figures (10)

  • Figure 1: (a) Typical asymmetric communication scenario, where downlink is successful while uplink fails. (b) Enhanced uplink with real-time feedback, where IoT devices leverage feedback to improve uplink coverage.
  • Figure 2: An IoT Device connects with distributed APs. The target scenario of our feedback-aided coverage extension scheme includes both fixed and mobile IoT devices.
  • Figure 3: Comparison of analytical and simulation results to validate the coverage analysis in the feedback mode with the DEEP-IoT feedback coding scheme.
  • Figure 4: The coverage probability gains of the feedback mode versus the forward mode with Polar and Turbo codes.
  • Figure 5: The gains in the number of connectable APs achieved by the feedback mode versus the forward mode using Polar and Turbo codes.
  • ...and 5 more figures

Theorems & Definitions (6)

  • Proposition 1
  • proof
  • Proposition 2
  • proof
  • Remark
  • Remark