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

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

Coexistence of RF-powered IoT and a Primary Wireless Network with Secrecy Guard Zones

This paper studies the secrecy performance of a wireless network (primary network) overlaid with an ambient RF energy harvesting IoT network (secondary network). The nodes in the secondary network are assumed to be solely powered by ambient RF energy harvested from the transmissions of the primary network. We assume that the secondary nodes can eavesdrop on the primary transmissions due to which the primary network uses secrecy guard zones. The primary transmitter goes silent if any secondary receiver is detected within its guard zone. Using tools from stochastic geometry, we derive the probability of successful connection of the primary network as well as the probability of secure communication. Two conditions must be jointly satisfied in order to ensure successful connection: (i) the SINR at the primary receiver is above a predefined threshold, and (ii) the primary transmitter is not silent. In order to ensure secure communication, the SINR value at each of the secondary nodes should be less than a predefined threshold. Clearly, when more secondary nodes are deployed, more primary transmitters will remain silent for a given guard zone radius, thus impacting the amount of energy harvested by the secondary network. Our results concretely show the existence of an optimal deployment density for the secondary network that maximizes the density of nodes that are able to harvest sufficient amount of energy. Furthermore, we show the dependence of this optimal deployment density on the guard zone radius of the primary network. In addition, we show that the optimal guard zone radius selected by the primary network is a function of the deployment density of the secondary network. This interesting coupling between the two networks is studied using tools from game theory. Overall, this work is one of the few concrete works that symbiotically merge tools from stochastic geometry and game theory

Q-learning Channel Access Methods for Wireless Powered Internet of Things Networks

The Internet of Things (IoT) is becoming critical in our daily life. A key technology of interest in this thesis is Radio Frequency (RF) charging. The ability to charge devices wirelessly creates so called RF-energy harvesting IoT networks. In particular, there is a hybrid access point (HAP) that provides energy in an on-demand manner to RF-energy harvesting devices. These devices then collect data and transmit it to the HAP. In this respect, a key issue is ensuring devices have a high number of successful transmissions. There are a number of issues to consider when scheduling the transmissions of devices in the said network. First, the channel gain to/from devices varies over time. This means the efficiency to deliver energy to devices and to transmit the same amount of data is different over time. Second, during channel access, devices are not aware of the energy level of other devices nor whether they will transmit data. Third, devices have non-causal knowledge of their energy arrivals and channel gain information. Consequently, they do not know whether they should delay their transmissions in hope of better channel conditions or less contention in future time slots or doing so would result in energy overflow