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Green Operations of SWIPT Networks: The Role of End-User Devices

Gianluca Rizzo, Marco Ajmone Marsan, Christian Esposito, Biagio Boi

TL;DR

The paper tackles energy-efficient provisioning in SWIPT cellular networks hosting both broadband and energy-harvesting IoT users. It develops a stochastic-geometry framework that integrates QoS-aware resource scheduling and derives analytical expressions for user-level performance and energy harvesting, formulating a non-convex optimization to minimize mean network power. A genetic-algorithm solver is proposed to locate energy-optimal configurations, validated by numerical results that reveal dynamic provisioning and passive energy harvesting can yield substantial energy savings and reveal novel BS-density patterns as IoT density grows. The work also introduces nonlinear energy harvesting modelling, showing how saturation and sensitivity affect optimal configurations and deployment strategies in dense IoT scenarios.

Abstract

Internet of Things (IoT) devices often come with batteries of limited capacity that are not easily replaceable or rechargeable, and that constrain significantly the sensing, computing, and communication tasks that they can perform. The Simultaneous Wireless Information and Power Transfer (SWIPT) paradigm addresses this issue by delivering power wirelessly to energy-harvesting IoT devices with the same signal used for information transfer. For their peculiarity, these networks require specific energy-efficient planning and management approaches. However, to date, it is not clear what are the most effective strategies for managing a SWIPT network for energy efficiency. In this paper, we address this issue by developing an analytical model based on stochastic geometry, accounting for the statistics of user-perceived performance and base station scheduling. We formulate an optimization problem for deriving the energy optimal configuration as a function of the main system parameters, and we propose a genetic algorithm approach to solve it. Our results enable a first-order evaluation of the most effective strategies for energy-efficient provisioning of power and communications in a SWIPT network. We show that the service capacity brought about by users brings energy-efficient dynamic network provisioning strategies that radically differ from those of networks with no wireless power transfer.

Green Operations of SWIPT Networks: The Role of End-User Devices

TL;DR

The paper tackles energy-efficient provisioning in SWIPT cellular networks hosting both broadband and energy-harvesting IoT users. It develops a stochastic-geometry framework that integrates QoS-aware resource scheduling and derives analytical expressions for user-level performance and energy harvesting, formulating a non-convex optimization to minimize mean network power. A genetic-algorithm solver is proposed to locate energy-optimal configurations, validated by numerical results that reveal dynamic provisioning and passive energy harvesting can yield substantial energy savings and reveal novel BS-density patterns as IoT density grows. The work also introduces nonlinear energy harvesting modelling, showing how saturation and sensitivity affect optimal configurations and deployment strategies in dense IoT scenarios.

Abstract

Internet of Things (IoT) devices often come with batteries of limited capacity that are not easily replaceable or rechargeable, and that constrain significantly the sensing, computing, and communication tasks that they can perform. The Simultaneous Wireless Information and Power Transfer (SWIPT) paradigm addresses this issue by delivering power wirelessly to energy-harvesting IoT devices with the same signal used for information transfer. For their peculiarity, these networks require specific energy-efficient planning and management approaches. However, to date, it is not clear what are the most effective strategies for managing a SWIPT network for energy efficiency. In this paper, we address this issue by developing an analytical model based on stochastic geometry, accounting for the statistics of user-perceived performance and base station scheduling. We formulate an optimization problem for deriving the energy optimal configuration as a function of the main system parameters, and we propose a genetic algorithm approach to solve it. Our results enable a first-order evaluation of the most effective strategies for energy-efficient provisioning of power and communications in a SWIPT network. We show that the service capacity brought about by users brings energy-efficient dynamic network provisioning strategies that radically differ from those of networks with no wireless power transfer.
Paper Structure (18 sections, 5 theorems, 25 equations, 8 figures, 1 table, 1 algorithm)

This paper contains 18 sections, 5 theorems, 25 equations, 8 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related works
  3. System Model
  4. Channel and Service Model
  5. Base Station and UE Energy Consumption Model
  6. Base station service model
  7. Modeling User-Level Performance
  8. Energy-Optimal Network Configuration
  9. GA Metaheuristic
  10. Numerical Results
  11. Setup
  12. Computational Complexity
  13. Linear Energy Harvesting
  14. Impact of harvesting nonlinearities
  15. Conclusions
  16. ...and 3 more sections

Key Result

Lemma 1

Let $K(x)=[N_{iot}(S(x))+w_d N_{bb}(S(x))]^{-1}$. Then the power harvested by a user at $x$ is $h(x)=\Theta(h_{in}(x))$, where the received power $h_{in}(x)$ is given by: $I(x)$ is the total power harvested by the user at $x$ from BSs other than the one with which it is associated. $O(x)$ is the power harvested from UE transmissions, averaged over time, and $U_d(S(x))$ is the downlink utilization

Figures (8)

  • Figure 1: Outline of the system model for the SWIPT wireless network considered in this work.
  • Figure 2: Downlink time scheduling scheme for a SWIPT BS, for a network with time switching EH receiver architecture, with GPS weights.
  • Figure 3: Power per $km^2$ consumed by the network at the optimum vs user density, for different target minimum harvested power and IoT UE receiver configurations. Markers denote values from simulations, derived with a $95\%$ confidence interval of at most $8\%$.
  • Figure 4: Power consumed by the network for each user (broadband or IoT) vs. user density, for different target minimum harvested power and IoT UE receiver configurations.
  • Figure 5: Increase in the power per $km^2$ consumed by the network with respect to linear EH, as a function of user density, for HLP $1$ mW target minimum harvested power, and for different IoT UE receiver configurations.
  • ...and 3 more figures

Theorems & Definitions (9)

  • Definition
  • Lemma 1
  • Theorem 1
  • Theorem 2
  • Theorem 3
  • proof
  • Lemma 2
  • proof
  • proof