Energy-Efficient Resource Allocation in Cloud and Fog Radio Access Networks

PhD ThesisWith the development of cloud computing, radio access networks (RAN) is migrating to fully or partially centralised architecture, such as Cloud RAN (C- RAN) or Fog RAN (F-RAN). The novel architectures are able to support new applications with the higher throughput, the higher energy e ciency and the better spectral e ciency performance. However, the more complex energy consumption features brought by these new architectures are challenging. In addition, the usage of Energy Harvesting (EH) technology and the computation o oading in novel architectures requires novel resource allocation designs.This thesis focuses on the energy e cient resource allocation for Cloud and Fog RAN networks. Firstly, a joint user association (UA) and power allocation scheme is proposed for the Heterogeneous Cloud Radio Access Networks with hybrid energy sources where Energy Harvesting technology is utilised. The optimisation problem is designed to maximise the utilisation of the renewable energy source. Through solving the proposed optimisation problem, the user association and power allocation policies are derived together to minimise the grid power consumption. Compared to the conventional UAs adopted in RANs, green power harvested by renewable energy source can be better utilised so that the grid power consumption can be greatly reduced with the proposed scheme. Secondly, a delay-aware energy e cient computation o oading scheme is proposed for the EH enabled F-RANs, where for access points (F-APs) are supported by renewable energy sources. The uneven distribution of the harvested energy brings in dynamics of the o oading design and a ects the delay experienced by users. The grid power minimisation problem is formulated. Based on the solutions derived, an energy e cient o oading decision algorithm is designed. Compared to SINR-based o oading scheme, the total grid power consumption of all F-APs can be reduced signi cantly with the proposed o oading decision algorithm while meeting the latency constraint. Thirdly, an energy-e cient computation o oading for mobile applications with shared data is investigated in a multi-user fog computing network. Taking the advantage of shared data property of latency-critical applications such as virtual reality (VR) and augmented reality (AR) into consideration, the energy minimisation problem is formulated. Then the optimal computation o oading and communications resources allocation policy is proposed which is able to minimise the overall energy consumption of mobile users and cloudlet server. Performance analysis indicates that the proposed policy outperforms other o oading schemes in terms of energy e ciency. The research works conducted in this thesis and the thorough performance analysis have revealed some insights on energy e cient resource allocation design in Cloud and Fog RANs

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224x224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102x faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.Comment: This manuscript is the accepted version for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015. See Changelo

Deep Residual Learning for Image Recognition

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.Comment: Tech repor

Design, fabrication and optimization of III-nitride micro light emitting diodes for optical communication

The work presented in this thesis focuses on the development and optimization of III-nitride micro-structured light emitting diodes (µLEDs) for optical communications in free space. The main body of this work is divided into two parts. The first part is focused on the development and optimization of blue and violet series-biased µLEDs and blue µLEDs arrays with individually addressable n-electrodes for free space visible light communication (VLC). The second part demonstrates the development of deep ultraviolet (UV) µLED and its application in free space deep UV communication. In this work, a new series-biased µLED is introduced, enabling high optical power without sacrificing too much 6-dB electrical modulation bandwidth. Over 10 Gbps data transmission rates are achieved using such µLEDs in long distance VLC. Furthermore, a new n-type metal-oxide-semiconductor (NMOS) driver controlled µLED array is introduced along with its VLC application. The design and fabrication process of this device are given in detail. The performances of the µLED array with an integrated NMOS driver are presented. Based on the novel III-nitride deep UV µLEDs developed in this work, a record deep UV data transmission rate of 3.36 Gbps is achieved at a bit error rate (BER) of 3.8 x 10⁻³ under orthogonal frequency division multiplexing (OFDM) modulation schemes.The work presented in this thesis focuses on the development and optimization of III-nitride micro-structured light emitting diodes (µLEDs) for optical communications in free space. The main body of this work is divided into two parts. The first part is focused on the development and optimization of blue and violet series-biased µLEDs and blue µLEDs arrays with individually addressable n-electrodes for free space visible light communication (VLC). The second part demonstrates the development of deep ultraviolet (UV) µLED and its application in free space deep UV communication. In this work, a new series-biased µLED is introduced, enabling high optical power without sacrificing too much 6-dB electrical modulation bandwidth. Over 10 Gbps data transmission rates are achieved using such µLEDs in long distance VLC. Furthermore, a new n-type metal-oxide-semiconductor (NMOS) driver controlled µLED array is introduced along with its VLC application. The design and fabrication process of this device are given in detail. The performances of the µLED array with an integrated NMOS driver are presented. Based on the novel III-nitride deep UV µLEDs developed in this work, a record deep UV data transmission rate of 3.36 Gbps is achieved at a bit error rate (BER) of 3.8 x 10⁻³ under orthogonal frequency division multiplexing (OFDM) modulation schemes