Unified Offloading Decision Making and Resource Allocation in ME-RAN

In order to support communication and computation cooperation, we propose a mobile edge cloud-radio access network (ME-RAN) architecture, which consists of mobile edge cloud (ME) as the computation provision platform and radio access network (RAN) as the communication interface. A cooperative offloading framework is proposed to achieve the following tasks: first, to increase user equipment' (UE') computing capacity by triggering offloading action, especially for the UE, which cannot complete the computation locally; second, to reduce the energy consumption for all the UEs by considering limited computing and communication resources. Based on abovementioned objectives, we formulate the energy consumption minimization problem, which is shown to be a non-convex mixed-integer programming. First, decentralized local decision algorithm is proposed for each UE to estimate the possible local resource consumption and decide if offloading is in its interest. This operation will reduce the overhead and signalling in the later stage. Then, centralized decision and resource allocation algorithm (CAR) is proposed to conduct the decision making and resource allocation in ME-RAN. Moreover, two low-complexity algorithms, i.e., UE with largest saved energy consumption accepted first (CAR-E) and UE with smallest required data rate accepted first (CAR-D) are proposed. Simulations show that the performance of the proposed algorithms is very close to the exhaustive search but with much less complexity

Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive h -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research

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

On the Merits of Deploying TDM-based Next-Generation PON Solutions in the Access Arena As Multiservice, All Packet-Based 4G Mobile Backhaul RAN Architecture

The phenomenal growth of mobile backhaul capacity required to support the emerging fourth-generation (4G) traffic including mobile WiMAX, cellular Long-Term Evolution (LTE), and LTE-Advanced (LTE-A) requires rapid migration from today\u27s legacy circuit switched T1/E1 wireline and microwave backhaul technologies to a new fiber-supported, all-packet-based mobile backhaul infrastructure. Clearly, a cost effective fiber supported all-packet-based mobile backhaul radio access network (RAN) architecture that is compatible with these inherently distributed 4G RAN architectures is needed to efficiently scale current mobile backhaul networks. However, deploying a green fiber-based mobile backhaul infrastructure is a costly proposition mainly due to the significant cost associated with digging the trenches in which the fiber is to be laid. These, along with the inevitable trend towards all-IP/Ethernet transport protocols and packet switched networks, have prompted many carriers around the world to consider the potential of utilizing the existing fiber-based Passive Optical Network (PON) access infrastructure as an all-packet-based converged fixed-mobile optical access networking transport architecture to backhaul both mobile and typical wireline traffic. Passive Optical Network (PON)-based fiber-to-the-curb/home (FTTC/FTTH) access networks are being deployed around the globe based on two Time-Division Multiplexed (TDM) standards: ITU G.984 Gigabit PON (GPON) and IEEE 802.ah Ethernet PON (EPON). A PON connects a group of Optical Network Units (ONUs) located at the subscriber premises to an Optical Line Terminal (OLT) located at the service provider\u27s facility. It is the purpose of this thesis to examine the technological requirements and assess the performance analysis and feasibility for deploying TDM-based next-generation (NG) PON solutions in the access arena as multiservice, all packet-based 4G mobile backhaul RAN and/or converged fixed-mobile optical networking architecture. Specifically, this work proposes and devises a simple and cost-effective 10G-EPON-based 4G mobile backhaul RAN architecture that efficiently transports and supports a wide range of existing and emerging fixed-mobile advanced multimedia applications and services along with the diverse quality of service (QoS), rate, and reliability requirements set by these services. The techno-economics merits of utilizing PON-based 4G RAN architecture versus that of traditional 4G (mobile WiMAX and LTE) RAN will be thoroughly examine and quantified. To achieve our objective, we utilize the existing fiber-based PON access infrastructure with novel ring-based distribution access network and wireless-enabled OLT and ONUs as the multiservice packet-based 4G mobile backhaul RAN infrastructure. Specifically, to simplify the implementation of such a complex undertaking, this work is divided into two sequential phases. In the first phase, we examine and quantify the overall performance of the standalone ring-based 10G-EPON architecture (just the wireline part without overlaying/incorporating the wireless part (4G RAN)) via modeling and simulations. We then assemble the basic building blocks, components, and sub-systems required to build up a proof-of-concept prototype testbed for the standalone ring-based EPON architecture. The testbed will be used to verify and demonstrate the performance of the standalone architecture, specifically, in terms of power budget, scalability, and reach. In the second phase, we develop an integrated framework for the efficient interworking between the two wireline PON and 4G mobile access technologies, particularly, in terms of unified network control and management (NCM) operations. Specifically, we address the key technical challenges associated with tailoring a typically centralized PON-based access architecture to interwork with and support a distributed 4G RAN architecture and associated radio NCM operations. This is achieved via introducing and developing several salient-networking innovations that collectively enable the standalone EPON architecture to support a fully distributed 4G mobile backhaul RAN and/or a truly unified NG-PON-4G access networking architecture. These include a fully distributed control plane that enables intercommunication among the access nodes (ONUs/BSs) as well as signaling, scheduling algorithms, and handoff procedures that operate in a distributed manner. Overall, the proposed NG-PON architecture constitutes a complete networking paradigm shift from the typically centralized PON\u27s architecture and OLT-based NCM operations to a new disruptive fully distributed PON\u27s architecture and NCM operations in which all the typically centralized OLT-based PON\u27s NCM operations are migrated to and independently implemented by the access nodes (ONUs) in a distributed manner. This requires migrating most of the typically centralized wireline and radio control and user-plane functionalities such as dynamic bandwidth allocation (DBA), queue management and packet scheduling, handover control, radio resource management, admission control, etc., typically implemented in today\u27s OLT/RNC, to the access nodes (ONUs/4G BSs). It is shown that the overall performance of the proposed EPON-based 4G backhaul including both the RAN and Mobile Packet Core (MPC) {Evolved Packet Core (EPC) per 3GPP LTE\u27s standard} is significantly augmented compared to that of the typical 4G RAN, specifically, in terms of handoff capability, signaling overhead, overall network throughput and latency, and QoS support. Furthermore, the proposed architecture enables redistributing some of the intelligence and NCM operations currently centralized in the MPC platform out into the access nodes of the mobile RAN. Specifically, as this work will show, it enables offloading sizable fraction of the mobile signaling as well as actual local upstream traffic transport and processing (LTE bearers switch/set-up, retain, and tear-down and associated signaling commands from the BSs to the EPC and vice-versa) from the EPC to the access nodes (ONUs/BSs). This has a significant impact on the performance of the EPC. First, it frees up a sizable fraction of the badly needed network resources as well as processing on the overloaded centralized serving nodes (AGW) in the MPC. Second, it frees up capacity and sessions on the typically congested mobile backhaul from the BSs to the EPC and vice-versa

Introduction to the Computation Offloading from Mobile Devices to the Edge of Mobile Network

This paper introduces the concept of Small Cell Cloud (SCC) composed of multiple Cloud-enabled Small Cells (CeSCs), which provide radio connection for mobile User Equipment (UE) such as smart-phones or wearables such as smart glasses. Moreover, CeSCs host computations offloaded from UEs in a way similar to centralized cloud, yet different in its proximity to users. Proposed client-server architecture of SCC con-veys mechanisms for moving offloaded computations from the UEs to CeSCs. Real-life implementation of the SCC architecture relies on custom-developed Of-floading Framework which is responsible for low-level communication between the UE and the SCC. The Of-floading Framework is accompanied by an Augmented Reality (AR) app, which employs intensive computa-tions for discovery of places of interest. Such app is latency-sensitive, a criterion which makes computation offloading beneficial due to its ability to decrease la-tency. The combination of the O˜oading Framework and the AR app makes up an SCC testbed used for fur-ther performance evaluation. Numerous measurements are carried out to examine the impact of various pa-rameters. Based on Proof-of-concept implementation and thorough measurements, it has been revealed that computation offloading can decrease overall latency as much as to 47 % and energy consumption on the UE side to 56

Cloud-aided wireless systems: communications and radar applications

This dissertation focuses on cloud-assisted radio technologies for communication, including mobile cloud computing and Cloud Radio Access Network (C-RAN), and for radar systems. This dissertation first concentrates on cloud-aided communications. Mobile cloud computing, which allows mobile users to run computationally heavy applications on battery limited devices, such as cell phones, is considered initially. Mobile cloud computing enables the offloading of computation-intensive applications from a mobile device to a cloud processor via a wireless interface. The interplay between offloading decisions at the application layer and physical-layer parameters, which determine the energy and latency associated with the mobile-cloud communication, motivates the inter-layer optimization of fine-grained task offloading across both layers. This problem is modeled by using application call graphs, and the joint optimization of application-layer and physical-layer parameters is carried out via a message passing algorithm by minimizing the total energy expenditure of the mobile user. The concept of cloud radio is also being considered for the development of two cellular architectures known as Distributed RAN (D-RAN) and C-RAN, whereby the baseband processing of base stations is carried out in a remote Baseband Processing Unit (BBU). These architectures can reduce the capital and operating expenses of dense deployments at the cost of increasing the communication latency. The effect of this latency, which is due to the fronthaul transmission between the Remote Radio Head (RRH) and the BBU, is then studied for implementation of Hybrid Automatic Repeat Request (HARQ) protocols. Specifically, two novel solutions are proposed, which are based on the control-data separation architecture. The trade-offs involving resources such as the number of transmitting and receiving antennas, transmission power and the blocklength of the transmitted codeword, and the performance of the proposed solutions is investigated in analysis and numerical results. The detection of a target in radar systems requires processing of the signal that is received by the sensors. Similar to cloud radio access networks in communications, this processing of the signals can be carried out in a remote Fusion Center (FC) that is connected to all sensors via limited-capacity fronthaul links. The last part of this dissertation is dedicated to exploring the application of cloud radio to radar systems. In particular, the problem of maximizing the detection performance at the FC jointly over the code vector used by the transmitting antenna and over the statistics of the noise introduced by quantization at the sensors for fronthaul transmission is investigated by adopting the information-theoretic criterion of the Bhattacharyya distance and information-theoretic bounds on the quantization rate