Swarm Unmanned Aerial Vehicle Networks in Wireless Communications: Routing Protocol, Multicast, and Data Exchange

Unmanned aerial vehicle (UAV) networks, a flying platform, are a promising wireless communications infrastructure with wide-ranging applications in both commercial and military domain. Owing to the appealing characteristics, such as high mobility, high feasibility, and low cost, UAV networks can be applied in various scenarios, such as emergency communications, cellular networks, device-to-device (D2D) networks, and sensor networks, regardless of infrastructure and spatial constraints. To handle complicated missions, provide wireless coverage for a large range, and have a long lifetime, a UAV network may consist of a large amount of UAVs, working cooperatively as a swarm, also referred to as swarm UAV networks. Although high mobility and numerous UAVs offer high flexibility, high scalability, and performance enhancement for swarm UAV networks, they also incur some technical challenges. One of the major challenges is the routing protocol design. With high mobility, a dynamic network topology may be encountered. As a result, traditional routing protocols based on routing path discovery are not applicable in swarm UAV networks, as the discovered routing path may be outdated especially when the amount of UAVs is large causing considerable routing path discovery delay. Multicast is an essential and key technology in the scenarios, where swarm UAV networks are employed as aerial small base station (BSs), like relay or micro BS. Swarm UAV networks consisting of a large amount of UAVs will encounter severe multicast delay with existing multicast methods using acknowledgement (ACK) feedback and retransmissions. This issue will be deteriorated when a swarm UAV network is deployed far away from BSs, causing high packet loss. Data exchange is another major technical challenge in swarm UAV networks, where UAVs exchange data packets with each other, such as requesting and retrieving lost packets. Due to numerous UAVs, data exchange between UAVs can cause message and signaling storm, resulting in a long data exchange delay and severe ovehead. In this dissertation, I focus on developing novel routing protocols, multicast schemes, and data exchange schemes, enabling efficient, robust, and high-performance routing, multicast, and data exchange in swarm UAV networks. To be specific, two novel flooding-based routing protocols are designed in this dissertation, where random network coding (RNC) is utilized to improve the efficiency of the flooding-based routing in swarm UAV networks without relying on network topology information and routing path discovery. Using the property of RNC that as long as sufficient different versions of encoded packets/generations are accumulated, original packets could be decoded, RNC is naturally able to accelerate the routing process. This is because the use of RNC can reduce the number of encoded packets that are required to be delivered in some hop. In a hop, the receiver UAV may have already overheard some generations in previous hops, so that it only needs to receive fewer generations from the transmitter UAV in the current hop. To further expedite the flooding-based routing, the second flooding-based routing protocol is designed, where each forwarding UAV creates a new version of generation by linearly combining received generations rather than by decode original packets. Despite the flooding-based routing significantly hastened by RNC, the inherent drawback of the flooding-based routing is still unsolved, namely numerous hops. Aiming at reducing the amount of hops, a novel enhanced flooding-based routing protocol leveraging clustering is designed, where the whole UAV network will be partitioned into multiple clusters and in each cluster only one UAV will be selected as the representative of this cluster, participating in the flooding-based routing process. By this way, the number of hops is restricted by the number of representatives, since packets are only flooded between limited representatives rather than numerous UAVs. To address the multicast issue in swarm UAV networks, a novel multicast scheme is proposed based on clustering, where a UAV experiencing packet loss will retrieve the lost packets by requesting other UAVs in the same cluster without depending on retransmissions of BSs. In this way, the lost packet retrieval is carried out through short-distance data exchange between UAVs with reliable transmissions and a short delay. Tractable stochastic geometry tools are used to model swarm UAV networks with a dynamic network topology, based on which comprehensive analytical performance analysis is given. To enable efficient data exchange between UAVs in swarm UAV networks, a data exchange scheme is proposed utilizing unsupervised learning. With the proposed scheme, all UAVs are assigned to multiple clusters and a UAV can only carry out data exchange within its cluster. By this way, UAVs in different clusters perform data exchange in a parallel fashion to expedite data exchange. The agglomerative hierarchical clustering, a type of unsupervised learning, is used to conduct clustering in order to guarantee that UAVs in the same cluster are able to supply and supplement each other's lost packets. Additionally, a data exchange mechanism, including a novel random backoff procedure, is designed, where the priorities of UAVs in data exchange determined by the number of their lost packets or requested packets that they can provide. As a result, each request-reply process would be taken fully advantage, maximally supplying lost packets not only to the UAV sending request, but also to other UAVs in the same cluster. For all the developed technologies in this dissertation, their technical details and the corresponding system procedures are designed based on low-complexity and well-developed technologies, such as the carrier sense multiple access/collision avoidance (CSMA/CA), for practicability in practice and without loss of generality. Moreover, extensive simulation studies are conducted to demonstrate the effectiveness and superiority of the proposed and developed technologies. Additionally, system design insights are also explored and revealed through simulations.