Link Scheduling in Wireless Powered Internet of Things Networks

Internet of Things (IoT) networks rely on devices with sensors and/or actuators to monitor critical targets/objects, and to control/manage an environment. Example devices include smart sensors and wearable devices that facilitate pandemic control, and management of smart homes and cities. In these applications, devices are required to sense their environment and forward sensed data or samples to a fusion centre for processing. Apart from that, these devices are likely to be powered by energy sources such as solar or Radio Frequency (RF) signals. The amount of RF energy harvested by a sensor device is time varying due to random channel gains. This in turn affects their transmit power during uplink data transmissions to a Hybrid Access Point (HAP). Hence, the amount of harvested RF energy has an impact on the sum-rate at a HAP. In addition, the sum-rate is also affected by the number of devices that transmit in a given time slot. In this respect, this thesis considers a HAP that is equipped with a Successive Interfer- ence Cancellation (SIC) radio. In addition, the HAP assigns one or more uplinks data transmission slots to these devices. In this regard, an important and challenging problem is to determine an uplink transmission schedule that maximises the throughput at the HAP. Moreover, a key consideration is that the HAP has imperfect Channel State Information (CSI). To this end, this thesis aims to develop an algorithm to cope with unknown channel gain that allows the HAP to learn the best transmission schedule.