Joint Uplink and Downlink Coverage Analysis of Cellular-based RF-powered IoT Network (1705.06799v1)

Abstract: Ambient radio frequency (RF) energy harvesting has emerged as a promising solution for powering small devices and sensors in massive Internet of Things (IoT) ecosystem due to its ubiquity and cost efficiency. In this paper, we study joint uplink and downlink coverage of cellular-based ambient RF energy harvesting IoT where the cellular network is assumed to be the only source of RF energy. We consider a time division-based approach for power and information transmission where each time-slot is partitioned into three sub-slots: (i) charging sub-slot during which the cellular base stations (BSs) act as RF chargers for the IoT devices, which then use the energy harvested in this sub-slot for information transmission and/or reception during the remaining two sub-slots, (ii) downlink sub-slot during which the IoT device receives information from the associated BS, and (iii) uplink sub-slot during which the IoT device transmits information to the associated BS. For this setup, we characterize the joint coverage probability, which is the joint probability of the events that the typical device harvests sufficient energy in the given time slot and is under both uplink and downlink signal-to-interference-plus-noise ratio (SINR) coverage with respect to its associated BS. This metric significantly generalizes the prior art on energy harvesting communications, which usually focused on downlink or uplink coverage separately. The key technical challenge is in handling the correlation between the amount of energy harvested in the charging sub-slot and the information signal quality (SINR) in the downlink and uplink sub-slots. Dominant BS-based approach is developed to derive tight approximation for this joint coverage probability. Several system design insights including comparison with regularly powered IoT network and throughput-optimal slot partitioning are also provided.

• The paper analyzes the joint uplink and downlink coverage probability for cellular-based IoT devices powered solely by ambient RF energy harvested from the cellular network using stochastic geometry.
• A time-slotted approach models energy harvesting and communication, while a dominant BS-based approximation simplifies the coverage probability derivation.
• Key findings show the importance of optimizing time division parameters for throughput and provide thresholds for achieving performance comparable to regularly powered IoT systems through network design.

Joint Uplink and Downlink Coverage Analysis of Cellular-based RF-powered IoT Network

This paper investigates the potential of ambient RF energy harvesting as a sustainable power source for Internet of Things (IoT) devices in a cellular-based network. The authors propose a novel approach to characterize the joint uplink and downlink coverage for IoT devices, where the cellular network serves as the sole RF energy source. As IoT expands, optimizing energy consumption while maintaining reliable communication becomes crucial, especially for devices deployed in challenging environments.

Methodology and Theoretical Framework

The authors deploy stochastic geometry to model the cellular network and IoT devices as independent Poisson point processes (PPPs). The envisioned system partitions each time-slot into three sub-slots: charging, downlink, and uplink. This partitioning enables a coherent analysis of energy harvesting and communication performance. A dominant BS-based approach is employed to derive an approximation for joint coverage probability by emphasizing the roles of the closest and second-closest base stations (BSs). This approximation circumvents some complexities of power-law shot noise fields associated with energy harvesting.

Key Contributions:

1. Joint Coverage Probability: The paper defines the joint uplink-downlink coverage probability as the likelihood that a typical IoT device harvests enough energy and meets SINR thresholds for both uplink and downlink communications simultaneously.
2. Time Division Analysis: The introduction of time slot partitioning allows the examination of system performance under different configurations, identifying optimal parameter settings that maximize throughput.
3. Comparative Performance: Insights reveal system parameters' thresholds where RF-powered networks perform similarly to regularly powered ones.

Numerical Results and System Insights

Simulation results align well with theoretical predictions, verifying the dominant BS-based approach's effectiveness. The paper shows the importance of optimizing the time division parameters to enhance system throughput significantly. Threshold metrics, such as device-BS distances and BS density, guide network design to achieve regularly powered systems' performance. Moreover, practical implications include guidelines for tuning transmission power and network density to optimize energy harvesting efficiency.

Implications and Speculation

This research provides a solid foundation for the development of self-sustaining IoT networks. By effectively exploiting ambient RF energy, IoT devices can potentially reduce reliance on conventional batteries, making deployment in remote or hard-to-reach areas feasible. The findings advocate for incorporating stochastic geometry in energy harvesting models, which can be integral to designing next-gen IoT systems.

Looking forward, it will be intriguing to expand this framework to incorporate heterogeneous networks, alternative energy sources, or IoT devices with finite-sized batteries. As IoT networks evolve, integrating these models with dynamic environments and machine learning algorithms could become essential for adaptive energy management and reliable communication.

In summary, this paper presents a thorough analysis of RF energy harvesting systems in IoT, providing valuable insights into optimizing joint coverage and enhancing system performance. The research bridges gaps between theoretical model development and practical network application, paving the way for sustainable IoT deployments using cellular infrastructure.