On the Throughput of Large-but-Finite MIMO Networks using Schedulers

This paper studies the sum throughput of the {multi-user} multiple-input-single-output (MISO) networks in the cases with large but finite number of transmit antennas and users. Considering continuous and bursty communication scenarios with different users' data request probabilities, we derive quasi-closed-form expressions for the maximum achievable throughput of the networks using optimal schedulers. The results are obtained in various cases with different levels of interference cancellation. Also, we develop an efficient scheduling scheme using genetic algorithms (GAs), and evaluate the effect of different parameters, such as channel/precoding models, number of antennas/users, scheduling costs and power amplifiers' efficiency, on the system performance. Finally, we use the recent results on the achievable rates of finite block-length codes to analyze the system performance in the cases with short packets. As demonstrated, the proposed GA-based scheduler reaches (almost) the same throughput as in the exhaustive search-based optimal scheduler, with substantially less implementation complexity. Moreover, the power amplifiers' inefficiency and the scheduling delay affect the performance of the scheduling-based systems significantly

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

Cross-layer Design for Wireless Mesh Networks with Advanced Physical and Network Layer Techniques

Cross-layer optimization is an essential tool for designing wireless network protocols. We present a cross-layer optimization framework for wireless networks where at each node, various smart antenna techniques such as beam-forming, spatial division multiple access and spatial division multiplexing are employed. These techniques provide interference suppression, capability for simultaneous communication with several nodes and transmission with higher data rates, respectively. By integrating different combinations of these multi-antenna techniques in physical layer with various constraints from MAC and network layers, three Mixed Integer Linear Programming models are presented to minimize the scheduling period. Since these optimization problems are combinatorially complex, the optimal solution is approached by a Column Generation (CG) decomposition method. Our numerical results show that the resulted directive, multiple access and multiplexing gains combined with scheduling, effectively increase both the spatial reuse and the capacity of the links and therefore enhance the achievable system throughput. The introduced cross-layer approach is also extended to consider heterogeneous networks where we present a multi-criteria optimization framework to model the design problem with an objective of jointly minimizing the cost of deployment and the scheduling period. Our results reveal the significant benefits of this joint design method. We also investigate the achievable performance gain that network coding (with opportunistic listening) when combined with Successive Interference Cancellation (SIC) brings to a multi-hop wireless network. We develop a cross-layer formulation in which SIC enables concurrent receptions from multiple transmitters and network coding reduces the transmission time-slot for minimizing the scheduling time. To solve this combinatorially complex non-linear problem, we decompose it to two linear sub-problems; namely opportunistic network coding aware routing, and scheduling sub-problems. Our results affirm our expectation for a remarkable performance improvement when both techniques are jointly used. Further, we develop an optimization model for combining SIC with power control (PC). Our model optimally adjusts the transmission power of nodes to avoid interference on unintended receivers and properly embraces undesired interference through SIC. Therefore, it provides a balance between usage of PC and SIC at the transmitting and receiving sides, respectively. Our results show considerable throughput improvement in dense and heavily loaded networks