Throughput Optimal Scheduling with Dynamic Channel Feedback

It is well known that opportunistic scheduling algorithms are throughput optimal under full knowledge of channel and network conditions. However, these algorithms achieve a hypothetical achievable rate region which does not take into account the overhead associated with channel probing and feedback required to obtain the full channel state information at every slot. We adopt a channel probing model where $\beta$ fraction of time slot is consumed for acquiring the channel state information (CSI) of a single channel. In this work, we design a joint scheduling and channel probing algorithm named SDF by considering the overhead of obtaining the channel state information. We first analytically prove SDF algorithm can support $1+\epsilon$ fraction of of the full rate region achieved when all users are probed where $\epsilon$ depends on the expected number of users which are not probed. Then, for homogenous channel, we show that when the number of users in the network is greater than 3, $\epsilon > 0$, i.e., we guarantee to expand the rate region. In addition, for heterogenous channels, we prove the conditions under which SDF guarantees to increase the rate region. We also demonstrate numerically in a realistic simulation setting that this rate region can be achieved by probing only less than 50% of all channels in a CDMA based cellular network utilizing high data rate protocol under normal channel conditions.Comment: submitte

Link Scheduling in UAV-Aided Networks

Unmanned Aerial Vehicles (UAVs) or drones are a type of low altitude aerial mobile vehicles. They can be integrated into existing networks; e.g., cellular, Internet of Things (IoT) and satellite networks. Moreover, they can leverage existing cellular or Wi-Fi infrastructures to communicate with one another. A popular application of UAVs is to deploy them as mobile base stations and/or relays to assist terrestrial wireless communications. Another application is data collection, whereby they act as mobile sinks for wireless sensor networks or sensor devices operating in IoT networks. Advantageously, UAVs are cost-effective and they are able to establish line-of-sight links, which help improve data rate. A key concern, however, is that the uplink communications to a UAV may be limited, where it is only able to receive from one device at a time. Further, ground devices, such as those in IoT networks, may have limited energy, which limit their transmit power. To this end, there are three promising approaches to address these concerns, including (i) trajectory optimization, (ii) link scheduling, and (iii) equipping UAVs with a Successive Interference Cancellation (SIC) radio. Henceforth, this thesis considers data collection in UAV-aided, TDMA and SICequipped wireless networks. Its main aim is to develop novel link schedulers to schedule uplink communications to a SIC-capable UAV. In particular, it considers two types of networks: (i) one-tier UAV communications networks, where a SIC-enabled rotary-wing UAV collects data from multiple ground devices, and (ii) Space-Air-Ground Integrated Networks (SAGINs), where a SIC-enabled rotary-wing UAV offloads collected data from ground devices to a swarm of CubeSats. A CubeSat then downloads its data to a terrestrial gateway. Compared to one-tier UAV communications networks, SAGINs are able to provide wide coverage and seamless connectivity to ground devices in remote and/or sparsely populated areas