DeepSHARQ: hybrid error coding using deep learning

Cyber-physical systems operate under changing environments and on resource-constrained devices. Communication in these environments must use hybrid error coding, as pure pro- or reactive schemes cannot always fulfill application demands or have suboptimal performance. However, finding optimal coding configurations that fulfill application constraints—e.g., tolerate loss and delay—under changing channel conditions is a computationally challenging task. Recently, the systems community has started addressing these sorts of problems using hybrid decomposed solutions, i.e., algorithmic approaches for wellunderstood formalized parts of the problem and learning-based approaches for parts that must be estimated (either for reasons of uncertainty or computational intractability). For DeepSHARQ, we revisit our own recent work and limit the learning problem to block length prediction, the major contributor to inference time (and its variation) when searching for hybrid error coding configurations. The remaining parameters are found algorithmically, and hence we make individual contributions with respect to finding close-to-optimal coding configurations in both of these areas—combining them into a hybrid solution. DeepSHARQ applies block length regularization in order to reduce the neural networks in comparison to purely learningbased solutions. The hybrid solution is nearly optimal concerning the channel efficiency of coding configurations it generates, as it is trained so deviations from the optimum are upper bound by a configurable percentage. In addition, DeepSHARQ is capable of reacting to channel changes in real time, thereby enabling cyber-physical systems even on resource-constrained platforms. Tightly integrating algorithmic and learning-based approaches allows DeepSHARQ to react to channel changes faster and with a more predictable time than solutions that rely only on either of the two approaches

A hybrid packet loss recovery technique in wireless ad hoc networks

TCP utilization in wireless networks poses certain problems due to its inability to distinguish packet losses caused by congestion from those caused by frequent wireless errors, leading to degraded network performance. To avoid these problems and to minimize the effect of intensive channel contention in wireless networks, this work presents a new Hybrid ARQ technique for reliable and efficient packets transfer in static wireless ad hoc network. It is a combination of recent FEC based Raptor coding technique with ARQ based selective retransmission method, which outperforms purely ARQ based method. In contrast to most Hybrid ARQ techniques, which usually employ a byte level FEC, we mostly use packet level FEC in our simulations for the data transfer, on top of less frequent ARQ to recover the residual errors. Existing packet level FEC methods are mostly based on simple parity check codes or Reed Solomon codes with erasure decoding; in this work we use the recent raptor codes. We also introduce the notion of adaptive redundancy which helps to achieve better average network performance and to further improve the redundancy efficiency

Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

Packet scheduling algorithms in LTE systems

University of Technology Sydney. Faculty of Engineering and Information Technology.There has been a huge increase in demand towards improving the Quality of Service (QoS) of wireless services. Long Term Evolution (LTE) is a development of the Third-Generation Partnership Project (3GPP) with the aim to meet the needs of International Telecommunication Union (ITU). Some of its aspects are highlighted as follows: increase in data rate, scalable bandwidth, reduced latency and increase in coverage and capacity that result in better quality of service in communication. LTE employs Orthogonal Frequency Division Multiple Access (OFDMA) to simultaneously deliver multimedia services at a high speed rate. Packet switching is used by LTE to support different media services. To meet the QoS requirements for LTE networks, packet scheduling has been employed. Packet scheduling decides when and how different packets are delivered to the receiver. It is responsible for smart user packet selection to allocate radio resources appropriately. Therefore, packet scheduling should be cleverly designed to achieve QoS that is similar to fixed line services. eNodeB is a node in LTE network which is responsible for radio resource management that involves packet scheduling. There are two main categories of application in multimedia services: RT (Real Time) and NRT (None Real Time) services. RT services are either delay sensitive (e.g. voice over IP), loss sensitive (e.g. Buffered Video) or both (delay &loss sensitive) for example video conferencing. Best effort users are an example of NRT services that do not have exact requisites and have been allocated to spare resources. Reaching higher throughput has sometimes resulted in unfair allocation to users who are located far from the base station or users who suffer from bad channel conditions. Therefore, a sufficient trade-off between throughput and fairness is essential. The scarce bandwidth, fading radio channels and the QoS requirement of the users, makes resource allocation a demanding issue. Different scheduling approaches have been suggested for different service demands described briefly throughout the thesis. Initially, a comprehensive literature review of existing work on the packet scheduling topic has been accomplished in this thesis to realize the characteristics of packet scheduling and the resource allocation for the wireless network. Many packet scheduling algorithms developed to provide satisfactory QoS for multimedia services in downlink LTE systems. Several algorithms considered in this thesis include time and frequency domain algorithms and their way of approach has been investigated. The next objective of this thesis is to improve the performance of packet scheduling in LTE downlink systems. A new packet scheduling algorithm has been introduced in this thesis. A study on VoLTE (Voice over LTE), video streaming and best effort traffic under three different scheduling algorithms has been conducted. Heterogeneous traffic based on precise modelling of packets has been used in the simulation. The main resource allocation and assignment technique used in this work namely Dynamic Subcarrier Allocation scheme is shown to provide a solution to solve the cross layer optimisation problem. It depends on Channel Quality Information (CQI) and has been broadly investigated for single carrier and multicarrier wireless networks. The problem is based on the maximisation of average utility functions. Different scheduling algorithms in this method consider to be utility functions. The throughput, fairness and Packet Loss Ratio have been considered as the requirements for examining the performance of algorithms. Simulation results show that the proposed algorithm significantly increases the performance of streaming and best effort users in terms of PLR and throughput. Fairness has also been improved with less computational complexity compared to previous algorithms that have been introduced in this thesis

A simulation-based algorithm for solving the resource-assignment problem in satellite telecommunication networks

This paper proposes an heuristic for the scheduling of capacity requests and the periodic assignment of radio resources in geostationary (GEO) satellite networks with star topology, using the Demand Assigned Multiple Access (DAMA) protocol in the link layer, and Multi-Frequency Time Division Multiple Access (MF-TDMA) and Adaptive Coding and Modulation (ACM) in the physical layer.En este trabajo se propone una heurística para la programación de las solicitudes de capacidad y la asignación periódica de los recursos de radio en las redes de satélites geoestacionarios (GEO) con topología en estrella, con la demanda de acceso múltiple de asignación (DAMA) de protocolo en la capa de enlace, y el Multi-Frequency Time Division (Acceso múltiple por MF-TDMA) y codificación y modulación Adaptable (ACM) en la capa física.En aquest treball es proposa una heurística per a la programació de les sol·licituds de capacitat i l'assignació periòdica dels recursos de ràdio en les xarxes de satèl·lits geoestacionaris (GEO) amb topologia en estrella, amb la demanda d'accés múltiple d'assignació (DAMA) de protocol en la capa d'enllaç, i el Multi-Frequency Time Division (Accés múltiple per MF-TDMA) i codificació i modulació Adaptable (ACM) a la capa física

A two-stage approach for robust HEVC coding and streaming

The increased compression ratios achieved by the High Efficiency Video Coding (HEVC) standard lead to reduced robustness of coded streams, with increased susceptibility to network errors and consequent video quality degradation. This paper proposes a method based on a two-stage approach to improve the error robustness of HEVC streaming, by reducing temporal error propagation in case of frame loss. The prediction mismatch that occurs at the decoder after frame loss is reduced through the following two stages: (i) at the encoding stage, the reference pictures are dynamically selected based on constraining conditions and Lagrangian optimisation, which distributes the use of reference pictures, by reducing the number of prediction units (PUs) that depend on a single reference; (ii) at the streaming stage, a motion vector (MV) prioritisation algorithm, based on spatial dependencies, selects an optimal sub-set of MVs to be transmitted, redundantly, as side information to reduce mismatched MV predictions at the decoder. The simulation results show that the proposed method significantly reduces the effect of temporal error propagation. Compared to the reference HEVC, the proposed reference picture selection method is able to improve the video quality at low packet loss rates (e.g., 1%) using the same bitrate, achieving quality gains up to 2.3 dB for 10% of packet loss ratio. It is shown, for instance, that the redundant MVs are able to boost the performance achieving quality gains of 3 dB when compared to the reference HEVC, at the cost using 4% increase in total bitrate