[Paper Review] Analysis of RF Energy Harvesting in Uplink-NOMA IoT-based Network

Zhou Ni, Ziru Chen+2 more|arXiv (Cornell University)|Jul 25, 2019

Energy Harvesting in Wireless Networks15 references4 citations

TL;DR

This paper proposes a stochastic geometry-based model for uplink non-orthogonal multiple access (NOMA) in IoT networks where devices harvest RF energy from downlink transmissions to power uplink data transmission. By optimizing the energy harvesting time slot $T$, the system maximizes total throughput while accounting for inter-cell interference, revealing an optimal $T$ that balances energy collection and transmission time, with throughput degrading at high base station densities due to interference.

ABSTRACT

Internet of Things (IoT) systems in general consist of a lot of devices with massive connectivity. Those devices are usually constrained with limited energy supply and can only operate at low power and low rate. In this paper, we investigate a cellular-based IoT system combined with energy harvesting and NOMA. We consider all base stations (BS) and IoT devices follow the Poisson Point Process (PPP) distribution in a given area. The unit time slot is divided into two phases, energy harvesting phase in downlink (DL) and data transmission phase in uplink (uplink). That is, IoT devices will first harvest energy from all BS transmissions and then use the harvested energy to do the NOMA information transmission. We define an energy harvesting circle within which all IoT devices can harvest enough energy for NOMA transmission. The design objective is to maximize the total throughput in uplink within the circle by varying the duration T of energy harvesting phase. In our work, we also consider the inter-cell interference in the throughput calculation. The analysis of Probability Mass Function (PMF) for IoT devices in the energy harvesting circle is also compared with simulation results. It is shown that the BS density needs to be carefully set so that the IoT devices in the energy harvesting circle receive relatively smaller interference and energy circles overlap only with a small probability. Our simulations show that there exists an optimal T to achieve the maximum throughput. When the BSs are densely deployed consequently the total throughput will decrease because of the interference.

Motivation & Objective

• To model a cellular-based IoT network where IoT devices are powered solely by downlink RF energy harvesting.
• To analyze uplink NOMA transmission performance in a Poisson point process (PPP)-based network with inter-cell interference.
• To maximize total system throughput by optimizing the duration $T$ of the energy harvesting phase.
• To evaluate the impact of base station density and energy harvesting circle overlap on system performance.

Proposed method

• Model the network using a Poisson Point Process (PPP) for both base stations (BSs) and IoT devices.
• Divide each time slot into two phases: energy harvesting in downlink and uplink NOMA transmission.
• Define an energy harvesting circle around each BS where devices can collect sufficient energy for transmission.
• Use the Laplace transform to model and analyze inter-cell interference in the uplink NOMA system.
• Derive the Probability Mass Function (PMF) for the number of IoT devices within the energy harvesting circle and compare with simulations.
• Formulate the total system throughput as a function of $T$ and BS density $\lambda_b$, incorporating interference and energy availability.

Experimental results

Research questions

• RQ1 What is the optimal duration $T$ of the energy harvesting phase that maximizes uplink NOMA system throughput in a stochastic geometry-based IoT network?
• RQ2 How does base station density $\lambda_b$ affect the size of the energy harvesting circle and the overlap probability between circles from different cells?
• RQ3 What is the impact of inter-cell interference on the total system throughput in uplink NOMA with RF energy harvesting?
• RQ4 How accurate is the analytical PMF of the number of IoT devices within the energy harvesting circle compared to simulation results?
• RQ5 How does the trade-off between energy harvesting time and transmission time affect overall system performance?

Key findings

• An optimal energy harvesting time $T = 0.15$ maximizes system throughput, with performance degrading when $T$ exceeds this value due to increased inter-cell interference.
• Higher base station density ($\lambda_b$) increases the energy harvesting range but also raises inter-cell interference, reducing throughput beyond a certain point.
• When $\lambda_b$ reaches 300 BS/km², the overlap probability of energy harvesting circles reaches 100%, significantly affecting system analysis accuracy.
• The analytical PMF of the number of IoT devices within the energy harvesting circle closely matches simulation results for $\lambda_b$ between 20 and 40 BS/km².
• Inter-cell interference has a dramatic negative impact on system throughput, making it a critical factor in performance evaluation.
• System throughput decreases with higher BS density due to increased interference, even though more devices can harvest energy.