Random linear network coding is widely proposed as the solution for practical network coding applications due to the robustness to random packet loss, packet delays as well as network topology and capacity changes. In order to implement random linear network coding in practical scenarios where the encoding and decoding methods perform efficiently, the computational complex coding algorithms associated with random linear network coding must be overcome. This research contributes to the field of practical random linear network coding by presenting new, low complexity coding algorithms with low decoding delay. In this thesis we contribute to this research field by building on the current solutions available in the literature through the utilisation of familiar coding schemes combined with methods from other research areas, as well as developing innovative coding methods. We show that by transmitting source symbols in predetermined and constrained patterns from the source node, the causality of the random linear network coding network can be used to create structure at the receiver nodes. This structure enables us to introduce an innovative decoding scheme of low decoding delay. This decoding method also proves to be resilient to the effects of packet loss on the structure of the received packets. This decoding method shows a low decoding delay and resilience to packet erasures, that makes it an attractive option for use in multimedia multicasting. We show that fountain codes can be implemented in RLNC networks without changing the complete coding structure of RLNC networks. By implementing an adapted encoding algorithm at strategic intermediate nodes in the network, the receiver nodes can obtain encoded packets that approximate the degree distribution of encoded packets required for successful belief propagation decoding. Previous work done showed that the redundant packets generated by RLNC networks can be used for error detection at the receiver nodes. This error detection method can be implemented without implementing an outer code; thus, it does not require any additional network resources. We analyse this method and show that this method is only effective for single error detection, not correction. In this thesis the current body of knowledge and technology in practical random linear network coding is extended through the contribution of effective decoding techniques in practical network coding networks. We present both analytical and simulation results to show that the developed techniques can render low complexity coding algorithms with low decoding delay in RLNC networks. / Thesis (PhD (Computer Engineering))--North-West University, Potchefstroom Campus, 2013

Random linear network coding is widely proposed as the solution for practical network coding applications due to the robustness to random packet loss, packet delays as well as network topology and capacity changes. In order to implement random linear network coding in practical scenarios where the encoding and decoding methods perform efficiently, the computational complex coding algorithms associated with random linear network coding must be overcome. This research contributes to the field of practical random linear network coding by presenting new, low complexity coding algorithms with low decoding delay. In this thesis we contribute to this research field by building on the current solutions available in the literature through the utilisation of familiar coding schemes combined with methods from other research areas, as well as developing innovative coding methods. We show that by transmitting source symbols in predetermined and constrained patterns from the source node, the causality of the random linear network coding network can be used to create structure at the receiver nodes. This structure enables us to introduce an innovative decoding scheme of low decoding delay. This decoding method also proves to be resilient to the effects of packet loss on the structure of the received packets. This decoding method shows a low decoding delay and resilience to packet erasures, that makes it an attractive option for use in multimedia multicasting. We show that fountain codes can be implemented in RLNC networks without changing the complete coding structure of RLNC networks. By implementing an adapted encoding algorithm at strategic intermediate nodes in the network, the receiver nodes can obtain encoded packets that approximate the degree distribution of encoded packets required for successful belief propagation decoding. Previous work done showed that the redundant packets generated by RLNC networks can be used for error detection at the receiver nodes. This error detection method can be implemented without implementing an outer code; thus, it does not require any additional network resources. We analyse this method and show that this method is only effective for single error detection, not correction. In this thesis the current body of knowledge and technology in practical random linear network coding is extended through the contribution of effective decoding techniques in practical network coding networks. We present both analytical and simulation results to show that the developed techniques can render low complexity coding algorithms with low decoding delay in RLNC networks. / Thesis (PhD (Computer Engineering))--North-West University, Potchefstroom Campus, 2013

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No / Abstract: Error control techniques are designed to ensure reliable data transfer over unreliable communication channels that are frequently subjected to channel errors. In this paper, the effect of applying a convolution code to the Scattered Random Network Coding (SRNC) scheme over a multi-hop wireless channel was studied. An interleaver was implemented for bit scattering in the SRNC with the purpose of dividing the encoded data into protected blocks and vulnerable blocks to achieve error diversity in one modulation symbol while randomising errored bits in both blocks. By combining the interleaver with the convolution encoder, the network decoder in the receiver would have enough number of correctly received network coded blocks to perform the decoding process efficiently. Extensive simulations were carried out to study the performance of three systems: 1) SRNC with convolutional encoding, 2) SRNC; and 3) A system without convolutional encoding nor interleaving. Simulation results in terms of block error rate for a 2-hop wireless transmission scenario over an Additive White Gaussian Noise (AWGN) channel were presented. Results showed that the system with interleaving and convolutional code achieved better performance with coding gain of at least 1.29 dB and 2.08 dB on average when the block error rate is 0.01 when compared with system II and system III respectively.

The development of Unmanned Aerial Vehicles (UAVs) and fifth-generation (5G) wireless technology provides more possibilities for wireless networks. The application of UAVs is gradually evolving from individual UAVs performing tasks to UAV swarm performing tasks in concert. A UAV swarm network is when many drones work cooperatively in a swarm mode to achieve a particular goal. Due to the UAV swarm's easy deployment, self-organization, self-management, and high flexibility, it can provide robust and efficient wireless communications in some unique scenarios, such as emergency communications, hotspot region coverage, sensor networks, and vehicular networks. Therefore, UAV networks have attracted more and more attention from commercial and military; however, many problems need to be resolved before UAV cellular communications become a reality. One of the most challenging core components is the routing protocol design in the UAV swarm network. Due to the high mobility of UAVs, the position of each UAV changes dynamically, so problems such as high latency, high packet loss rate, and even loss of connection arise when UAVs are far apart. These problems dramatically reduce the transmission rate and data integrity for traditional routing protocols based on path discovery. This thesis focuses on developing, emulating, and optimizing a flooding-based routing protocol for UAV swarm using Random Linear Network Coding (RLNC) to improve the latency and bit rate and solve the packet loss problem without routing information and network topology. RLNC can reduce the number of packets demand in some hops. Due to this feature of RLNC, when relay transmitter UAVs or the destination receiver UAV receive sufficient encoded packets from any transmitter UAVs, the raw data can be decoded. For those relay transmitter UAVs in the UAV swarm network that already received some encoded packets in previous hops but not enough to decode the raw data, only need to receive the rest of the different encoded packets needed for decoding. Thus, flooding-based routing protocol significantly improves transmission efficiency in the UAV swarm network. / Master of Science / People are used to using fiber, 4G, and Wi-Fi in the city, but numerous people still live in areas without Internet access. Moreover, in some particular scenarios like large-scale activities, remote areas, and military operations, when the cellular network cannot provide enough bandwidth or good signal, UAV wireless network would be helpful and provide stable Internet access. Successful UAV test flights can last for several weeks, and researchers' interest in high-altitude long-endurance (HALE) UAVs are booming. HALE UAVs will create Wi-Fi or other network signals for remote areas, including polar regions, which will allow millions of people to enter the information society and connect to the Internet. The development of UAV and 5G provides more possibilities for wireless networks. UAV applications have evolved from individual UAV performing tasks to UAV swarm performing tasks. A UAV swarm network is where multiple drones work in tandem to achieve a particular goal. It can provide robust and efficient wireless communications in unique scenarios. As a result, UAVs are receiving attention from both commercial and military. However, there are still many problems that need to be resolved before the actual use of UAVs. One of the biggest challenges is routing protocol which is how UAVs communicate with each other and select routes. As the location of UAVs is constantly changing, this leads to delays, data loss, or complete loss of connectivity. Ultimately these issues can lead to slow transmission speed and lack of data integrity for traditional routing protocols based on path discovery. This thesis focuses on developing, emulating, and optimizing a flooding-based routing protocol for the UAV swarm. Specifically, this protocol uses RLNC, which can reduce the number of packets demand in some hops so that the latency and transmission speed will be improved, and the data loss problem will also be solved. Due to this feature of RLNC, when any receiver receives enough encoded packets from any transmitter, the original data can be decoded. Some receivers that already received some encoded packets in the previous transmission only need to receive the rest of the different encoded packets needed for decoding. Therefore, flooding-based routing protocol significantly improves transmission efficiency for UAV swarm networks.

In networks, using large buffers tend to increase end-to-end packet delay and its deviations, conflicting with real-time applications such as online gaming, audio-video services, IPTV, and VoIP. Further, large buffers complicate the design of high speed routers, leading to more power consumption and board space. According to Moore's law, switching speeds double every 18 months while memory access speeds double only every 10 years. Hence, as memory requirements increasingly become a limiting aspect of router design, studying networks in finite-buffer regime seems necessary for network engineers. This work focuses on both practical and theoretical aspects of finite-buffer networks. In Chapters 1-7, we investigate the effects of finite buffer sizes on the throughput and packet delay in different networks. These performance measures are shown to be linked to the stationary distribution of an underlying irreducible Markov chain that exactly models the changes in the network. An iterative scheme is proposed to approximate the steady-state distribution of buffer occupancies by decoupling the exact chain to smaller chains. These approximate solutions are used to analytically characterize network throughput and packet delay, and are also applied to some network performance optimization problems. Further, using simulations, it is confirmed that the proposed framework yields accurate estimates of the throughput and delay performance measures and captures the vital trends and tradeoffs in these networks. In Chapters 8-10, we address the problem of modeling and analysis of the performance of finite-memory random linear network coding in erasure networks. When using random linear network coding, the content of buffers creates dependencies which cannot be captured directly using the classical queueing theoretical models. A careful derivation of the buffer occupancy states and their transition rules are presented as well as decodability conditions when random linear network coding is performed on a stream of arriving packets.

Optimising the throughput performance for wireless networks is one of the challenging tasks in the objectives of communication engineering, since wireless channels are prone to errors due to path losses, random noise, and fading phenomena. The transmission errors will be worse in a multihop scenario due to its accumulative effects. Network Coding (NC) is an elegant technique to improve the throughput performance of a communication network. There is the fact that the bit error rates over one modulation symbol of 16- and higher order- Quadrature Amplitude Modulation (QAM) scheme follow a certain pattern. The Scattered Random Network Coding (SRNC) system was proposed in the literature to exploit the error pattern of 16-QAM by using bit-scattering to improve the throughput of multihop network to which is being applied the Random Linear Network Coding (RLNC). This thesis aims to improve further the SRNC system by using Forward Error Correction (FEC) code; the proposed system is called Joint RLNC and FEC with interleaving. The first proposed system (System-I) uses Convolutional Code (CC) FEC. The performances analysis of System-I with various CC rates of 1/2, 1/3, 1/4, 1/6, and 1/8 was carried out using the developed simulation tools in MATLAB and compared to two benchmark systems: SRNC system (System-II) and RLNC system (System- III). The second proposed system (System-IV) uses Reed-Solomon (RS) FEC code. Performance evaluation of System IV was carried out and compared to three systems; System-I with 1/2 CC rate, System-II, and System-III. All simulations were carried out over three possible channel environments: 1) AWGN channel, 2) a Rayleigh fading channel, and 3) a Rician fading channel, where both fading channels are in series with the AWGN channel. The simulation results show that the proposed system improves the SRNC system. How much improvement gain can be achieved depends on the FEC type used and the channel environment.

Optimising the throughput performance for wireless networks is one of the challenging tasks in the objectives of communication engineering, since wireless channels are prone to errors due to path losses, random noise, and fading phenomena. The transmission errors will be worse in a multihop scenario due to its accumulative effects. Network Coding (NC) is an elegant technique to improve the throughput performance of a communication network. There is the fact that the bit error rates over one modulation symbol of 16- and higher order- Quadrature Amplitude Modulation (QAM) scheme follow a certain pattern. The Scattered Random Network Coding (SRNC) system was proposed in the literature to exploit the error pattern of 16-QAM by using bit-scattering to improve the throughput of multihop network to which is being applied the Random Linear Network Coding (RLNC). This thesis aims to improve further the SRNC system by using Forward Error Correction (FEC) code; the proposed system is called Joint RLNC and FEC with interleaving. The first proposed system (System-I) uses Convolutional Code (CC) FEC. The performances analysis of System-I with various CC rates of 1/2, 1/3, 1/4, 1/6, and 1/8 was carried out using the developed simulation tools in MATLAB and compared to two benchmark systems: SRNC system (System-II) and RLNC system (System- III). The second proposed system (System-IV) uses Reed-Solomon (RS) FEC code. Performance evaluation of System IV was carried out and compared to three systems; System-I with 1/2 CC rate, System-II, and System-III. All simulations were carried out over three possible channel environments: 1) AWGN channel, 2) a Rayleigh fading channel, and 3) a Rician fading channel, where both fading channels are in series with the AWGN channel. The simulation results show that the proposed system improves the SRNC system. How much improvement gain can be achieved depends on the FEC type used and the channel environment. / Indonesian Government and the University of Bradford