Batched Sparse Codes

Network coding can significantly improve the transmission rate of communication networks with packet loss compared with routing. However, using network coding usually incurs high computational and storage costs in the network devices and terminals. For example, some network coding schemes require the computational and/or storage capacities of an intermediate network node to increase linearly with the number of packets for transmission, making such schemes difficult to be implemented in a router-like device that has only constant computational and storage capacities. In this paper, we introduce BATched Sparse code (BATS code), which enables a digital fountain approach to resolve the above issue. BATS code is a coding scheme that consists of an outer code and an inner code. The outer code is a matrix generation of a fountain code. It works with the inner code that comprises random linear coding at the intermediate network nodes. BATS codes preserve such desirable properties of fountain codes as ratelessness and low encoding/decoding complexity. The computational and storage capacities of the intermediate network nodes required for applying BATS codes are independent of the number of packets for transmission. Almost capacity-achieving BATS code schemes are devised for unicast networks, two-way relay networks, tree networks, a class of three-layer networks, and the butterfly network. For general networks, under different optimization criteria, guaranteed decoding rates for the receiving nodes can be obtained.Comment: 51 pages, 12 figures, submitted to IEEE Transactions on Information Theor

Characterisation and performance analysis of random linear network coding for reliable and secure communication

In this thesis, we develop theoretical frameworks to characterize the performance of Random Linear Network Coding (RLNC), and propose novel communication schemes for the achievement of both reliability and security in wireless networks. In particular, (i) we present an analytical model to evaluate the performance of practical RLNC schemes suitable for low-complexity receivers, prioritized (i.e., layered) coding and multi-hop communications, (ii) investigate the performance of RLNC in relay assisted networks and propose a new cross-layer RLNC-aided cooperative scheme for reliable communication, (iii) characterize the secrecy feature of RLNC and propose a new physical-application layer security technique for the purpose of achieving security and reliability in multi-hope communications. At first, we investigate random block matrices and derive mathematical expressions for the enumeration of full-rank matrices that contain blocks of random entries arranged in a diagonal, lower-triangular or tri-diagonal structure. The derived expressions are then used to model the probability that a receiver will successfully decode a source message or layers of a service, when RLNC based on non-overlapping, expanding or sliding generations is employed. Moreover, the design parameters of these schemes allow to adjust the desired decoding performance. Next, we evaluate the performance of Random Linear Network Coded Cooperation (RLNCC) in relay assisted networks, and propose a cross-layer cooperative scheme which combines the emerging Non-Orthogonal Multiple Access (NOMA) technique and RLNCC. In this regard, we first consider the multiple-access relay channel in a setting where two source nodes transmit packets to a destination node, both directly and via a relay node. Secondly, we consider a multi-source multi-relay network, in which relay nodes employ RLNC on source packets and generate coded packets. For each network, we build our analysis on fundamental probability expressions for random matrices over finite fields and we derive theoretical expressions of the probability that the destination node will successfully decode the source packets. Finally, we consider a multi-relay network comprising of two groups of source nodes, where each group transmits packets to its own designated destination node over single-hop links and via a cluster of relay nodes shared by both groups. In an effort to boost reliability without sacrificing throughput, a scheme is proposed whereby packets at the relay nodes are combined using two methods; packets delivered by different groups are mixed using non-orthogonal multiple access principles, while packets originating from the same group are mixed using RLNC. An analytical framework that characterizes the performance of the proposed scheme is developed, and benchmarked against a counterpart scheme that is based on orthogonal multiple access. Finally, we quantify and characterize the intrinsic security feature of RLNC and design a joint physical-application layer security technique. For this purpose, we first consider a network comprising a transmitter, which employs RLNC to encode a message, a legitimate receiver, and a passive eavesdropper. Closed-form analytical expressions are derived to evaluate the intercept probability of RLNC, and a resource allocation model is presented to further minimize the intercept probability. Afterward, we propose a joint RLNC and opportunistic relaying scheme in a multi relay network to transmit confi- dential data to a destination in the presence of an eavesdropper. Four relay selection protocols are studied covering a range of network capabilities, such as the availability of the eavesdropper’s channel state information or the possibility to pair the selected relay with a jammer node that intentionally generates interference. For each case, expressions of the probability that a coded packet will not be decoded by a receiver, which can be either the destination or the eavesdropper, are derived. Based on those expressions, a framework is developed that characterizes the probability of the eavesdropper intercepting a sufficient number of coded packets and partially or fully decoding the confidential data. We observe that the field size over which RLNC is performed at the application layer as well as the adopted modulation and coding scheme at the physical layer can be modified to fine-tune the trade-off between security and reliability

Network-coded NOMA with antenna selection for the support of two heterogeneous groups of users

The combination of Non-Orthogonal Multiple Access (NOMA) and Transmit Antenna Selection (TAS) techniques has recently attracted significant attention due to the low cost, low complexity and high diversity gains. Meanwhile, Random Linear Coding (RLC) is considered to be a promising technique for achieving high reliability and low latency in multicast communications. In this paper, we consider a downlink system with a multi-antenna base station and two multicast groups of single-antenna users, where one group can afford to be served opportunistically, while the other group consists of comparatively low power devices with limited processing capabilities that have strict Quality of Service (QoS) requirements. In order to boost reliability and satisfy the QoS requirements of the multicast groups, we propose a cross-layer framework including NOMAbased TAS at the physical layer and RLC at the application layer. In particular, two low complexity TAS protocols for NOMA are studied in order to exploit the diversity gain and meet the QoS requirements. In addition, RLC analysis aims to facilitate heterogeneous users, such that, sliding window based sparse RLC is employed for computational restricted users, and conventional RLC is considered for others. Theoretical expressions that characterize the performance of the proposed framework are derived and verified through simulation results

Dynamic and Dual Streaming Methods for H.264 Video and Parallel Performance Modeling

Traditional approaches to streaming H.264 video over a network typically rely on a single method of transport (i.e., reliable or unreliable) and/or use static values for parameters that can have a significant negative impact on the perceptual quality of the received video. This dissertation presents a dynamic method for wireless channel selection during video streaming, and explores the latency and QoE improvements yielded by the FDSP dual streaming method. The increased workload that results from these dynamic methods can lead to a counterproductive impairment of streaming performance, and therefore requires efficient use of the multiple cores typically present in both sender and receiver (or server and client). This dissertation therefore presents a performance cost model which can be used to guide the parallelization of specific types of client or server-side streaming components -- specifically, programs containing non-DOALL loops that have inter-iteration data dependences which constrain their parallelism

Adaptive sequence learning: a role for basal ganglia targets of accessory hyperdirect pathways

Thesis Abstract Numerous actions in sequence are often required to meet our demands, though the precise number and timing of these actions may not initially be evident; in such cases, adaptive learning processes must be implemented to discover appropriate solutions. By training mice in a revised tandem lever-press protocol and implementing novel behavioural analysis methods, the number of constituent actions and the timing of action sequences were quantified and categorised according to their successful or unsuccessful outcomes. Successful adaptations to action sequences following failed attempts reliably involved the addition of multiple constituent actions over less time. We next sought to investigate the neurobiological basis of these adaptive action sequences, focusing on motor cortical targets to the dorsal striatum (DStr) and external segment of the globus pallidus (GPe). Guided by the subthalamic nucleus' comparable motor cortex afferents of the hyperdirect pathway, we identified accessory projections of the hyperdirect pathway that densely innervate the DStr and GPe. Based on this newly characterized circuitry, we addressed the function of the post-synaptic DLS and GPe target structures using pathway-specific genetic ablation according to: motor cortical connectivity, DLS and GPe interconnectivity, and intra-striatal sub-circuitry. This revealed a unique relationship between post-synaptic targets of the motor cortex in the DLS and the timing of rewarded sequences, while further downstream reciprocal DLS-GPe connections and indirect pathway striatal neurons appear to modulate preparatory action sequence timing. This project revealed key adaptive strategies used by mice to successfully update the number and timing of actions in sequence

Zero-padding Network Coding and Compressed Sensing for Optimized Packets Transmission

Ubiquitous Internet of Things (IoT) is destined to connect everybody and everything on a never-before-seen scale. Such networks, however, have to tackle the inherent issues created by the presence of very heterogeneous data transmissions over the same shared network. This very diverse communication, in turn, produces network packets of various sizes ranging from very small sensory readings to comparatively humongous video frames. Such a massive amount of data itself, as in the case of sensory networks, is also continuously captured at varying rates and contributes to increasing the load on the network itself, which could hinder transmission efficiency. However, they also open up possibilities to exploit various correlations in the transmitted data due to their sheer number. Reductions based on this also enable the networks to keep up with the new wave of big data-driven communications by simply investing in the promotion of select techniques that efficiently utilize the resources of the communication systems. One of the solutions to tackle the erroneous transmission of data employs linear coding techniques, which are ill-equipped to handle the processing of packets with differing sizes. Random Linear Network Coding (RLNC), for instance, generates unreasonable amounts of padding overhead to compensate for the different message lengths, thereby suppressing the pervasive benefits of the coding itself. We propose a set of approaches that overcome such issues, while also reducing the decoding delays at the same time. Specifically, we introduce and elaborate on the concept of macro-symbols and the design of different coding schemes. Due to the heterogeneity of the packet sizes, our progressive shortening scheme is the first RLNC-based approach that generates and recodes unequal-sized coded packets. Another of our solutions is deterministic shifting that reduces the overall number of transmitted packets. Moreover, the RaSOR scheme employs coding using XORing operations on shifted packets, without the need for coding coefficients, thus favoring linear encoding and decoding complexities. Another facet of IoT applications can be found in sensory data known to be highly correlated, where compressed sensing is a potential approach to reduce the overall transmissions. In such scenarios, network coding can also help. Our proposed joint compressed sensing and real network coding design fully exploit the correlations in cluster-based wireless sensor networks, such as the ones advocated by Industry 4.0. This design focused on performing one-step decoding to reduce the computational complexities and delays of the reconstruction process at the receiver and investigates the effectiveness of combined compressed sensing and network coding

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Information handling: Concepts which emerged in practical situations and are analysed cybernetically

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University