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DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things

Yulin Shao

TL;DR

DEP-IoT is presented, a novel communication paradigm poised to redefine how IoT devices communicate that challenges and transforms the traditional transmitter (IoT devices)-centric communication model to one where the receiver (the access point) play a pivotal role, thereby cutting down energy use and boosting device longevity.

Abstract

At the heart of the Internet of Things (IoT) -- a domain witnessing explosive growth -- the imperative for energy efficiency and the extension of device lifespans has never been more pressing. This paper presents DEEP-IoT, an innovative communication paradigm poised to redefine how IoT devices communicate. Through a pioneering feedback channel coding strategy, DEEP-IoT challenges and transforms the traditional transmitter (IoT devices)-centric communication model to one where the receiver (the access point) play a pivotal role, thereby cutting down energy use and boosting device longevity. We not only conceptualize DEEP-IoT but also actualize it by integrating deep learning-enhanced feedback channel codes within a narrow-band system. Simulation results show a significant enhancement in the operational lifespan of IoT cells -- surpassing traditional systems using Turbo and Polar codes by up to 52.71%. This leap signifies a paradigm shift in IoT communications, setting the stage for a future where IoT devices boast unprecedented efficiency and durability.

DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things

TL;DR

DEP-IoT is presented, a novel communication paradigm poised to redefine how IoT devices communicate that challenges and transforms the traditional transmitter (IoT devices)-centric communication model to one where the receiver (the access point) play a pivotal role, thereby cutting down energy use and boosting device longevity.

Abstract

At the heart of the Internet of Things (IoT) -- a domain witnessing explosive growth -- the imperative for energy efficiency and the extension of device lifespans has never been more pressing. This paper presents DEEP-IoT, an innovative communication paradigm poised to redefine how IoT devices communicate. Through a pioneering feedback channel coding strategy, DEEP-IoT challenges and transforms the traditional transmitter (IoT devices)-centric communication model to one where the receiver (the access point) play a pivotal role, thereby cutting down energy use and boosting device longevity. We not only conceptualize DEEP-IoT but also actualize it by integrating deep learning-enhanced feedback channel codes within a narrow-band system. Simulation results show a significant enhancement in the operational lifespan of IoT cells -- surpassing traditional systems using Turbo and Polar codes by up to 52.71%. This leap signifies a paradigm shift in IoT communications, setting the stage for a future where IoT devices boast unprecedented efficiency and durability.
Paper Structure (23 sections, 4 theorems, 56 equations, 10 figures, 1 table, 1 algorithm)

This paper contains 23 sections, 4 theorems, 56 equations, 10 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. DEEP-IoT System Overview
  4. DEEP-IoT Physical Layer
  5. Signal flow
  6. Feedback channel coding and modulation
  7. UL feedback coding
  8. SC-FDMA
  9. DL feedback coding and OFDMA
  10. Decoding
  11. Performance
  12. PER
  13. Average transmit power
  14. DEEP-IoT MAC Layer
  15. Feedback channel allocation
  16. ...and 8 more sections

Key Result

Proposition 1

In Rayleigh fading channels, the average transmit power of DEEP-IoT is where the probability density function (PDF) of the transmit power $F'_{P}(p)$ is detailed in eq:pdf_p.

Figures (10)

  • Figure 1: The system model and duplexing mode of DEEP-IoT.
  • Figure 2: Feedback channel coding/decoding and modulation/demodulation at the IoT devices and the AP in DEEP-IoT.
  • Figure 3: The composition of feature matrix $\bm{M}_F$ for UL feedback coding.
  • Figure 4: The PER performance of DEEP-IoT physical layer benchmarked against Polar code and Turbo code under various feedback SNR and available number of subcarriers for feedback.
  • Figure 5: Illustration of the logistic function approximation for the required UL SNR in DEEP-IoT to meet a target PER, highlighting the dependency on DL feedback channel SNR and the allocation of feedback subcarriers to the IoT device, as defined in \ref{['eq:fit']}.
  • ...and 5 more figures

Theorems & Definitions (9)

  • Remark 1
  • Proposition 1
  • Remark 2
  • Lemma 2: monotonicity of the value function
  • Definition 1: index of an action
  • Theorem 3: properties of the indexes
  • Remark 3
  • Corollary 4
  • Remark 4