QoE-Driven Video Transmission: Energy-Efficient Multi-UAV Network Optimization

This paper is concerned with the issue of improving video subscribers' quality of experience (QoE) by deploying a multi-unmanned aerial vehicle (UAV) network. Different from existing works, we characterize subscribers' QoE by video bitrates, latency, and frame freezing and propose to improve their QoE by energy-efficiently and dynamically optimizing the multi-UAV network in terms of serving UAV selection, UAV trajectory, and UAV transmit power. The dynamic multi-UAV network optimization problem is formulated as a challenging sequential-decision problem with the goal of maximizing subscribers' QoE while minimizing the total network power consumption, subject to some physical resource constraints. We propose a novel network optimization algorithm to solve this challenging problem, in which a Lyapunov technique is first explored to decompose the sequential-decision problem into several repeatedly optimized sub-problems to avoid the curse of dimensionality. To solve the sub-problems, iterative and approximate optimization mechanisms with provable performance guarantees are then developed. Finally, we design extensive simulations to verify the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can effectively improve the QoE of subscribers and is 66.75\% more energy-efficient than benchmarks

Ultra Dense Small Cell Networks: Turning Density into Energy Efficiency

In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit energy, in ultra dense small cell networks (UDNs). Due to severe coupling in interference, this problem is formulated as a dynamic stochastic game (DSG) between small cell base stations (SBSs). This game enables to capture the dynamics of both the queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a mean-field game (MFG) in which the MFG equilibrium is analyzed with the aid of low-complexity tractable partial differential equations. Exploiting the stochastic nature of the problem, user scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of Lyapunov optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean-field theory, the proposed solution yields an equilibrium control policy per SBS which maximizes the network utility while ensuring users' quality-of-service. Simulation results show that the proposed approach achieves up to 70.7% gains in EE and 99.5% reductions in the network's outage probabilities compared to a baseline model which focuses on improving EE while attempting to satisfy the users' instantaneous quality-of-service requirements.Comment: 15 pages, 21 figures (sub-figures are counted separately), IEEE Journal on Selected Areas in Communications - Series on Green Communications and Networking (Issue 2

Live Prefetching for Mobile Computation Offloading

The conventional designs of mobile computation offloading fetch user-specific data to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching is proposed in this paper that seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies enable real-time control of the prefetched-data sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based structure, selecting candidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design close-to-optimal prefetching policies to fast fading channels. Compared with fetching without prediction, live prefetching is shown theoretically to always achieve reduction on mobile energy consumption.Comment: To appear in IEEE Trans. on Wireless Communicatio

Scheduling of Multicast and Unicast Services under Limited Feedback by using Rateless Codes

Many opportunistic scheduling techniques are impractical because they require accurate channel state information (CSI) at the transmitter. In this paper, we investigate the scheduling of unicast and multicast services in a downlink network with a very limited amount of feedback information. Specifically, unicast users send imperfect (or no) CSI and infrequent acknowledgements (ACKs) to a base station, and multicast users only report infrequent ACKs to avoid feedback implosion. We consider the use of physical-layer rateless codes, which not only combats channel uncertainty, but also reduces the overhead of ACK feedback. A joint scheduling and power allocation scheme is developed to realize multiuser diversity gain for unicast service and multicast gain for multicast service. We prove that our scheme achieves a near-optimal throughput region. Our simulation results show that our scheme significantly improves the network throughput over schemes employing fixed-rate codes or using only unicast communications

A review of relay network on UAVS for enhanced connectivity

One of the best evolution in technology breakthroughs is the Unmanned Aerial Vehicle (UAV). This aerial system is able to perform the mission in an agile environment and can reach the hard areas to perform the tasks autonomously. UAVs can be used in post-disaster situations to estimate damages, to monitor and to respond to the victims. The Ground Control Station can also provide emergency messages and ad-hoc communication to the Mobile Users of the disaster-stricken community using this network. A wireless network can also extend its communication range using UAV as a relay. Major requirements from such networks are robustness, scalability, energy efficiency and reliability. In general, UAVs are easy to deploy, have Line of Sight options and are flexible in nature. However, their 3D mobility, energy constraints, and deployment environment introduce many challenges. This paper provides a discussion of basic UAV based multi-hop relay network architecture and analyses their benefits, applications, and tradeoffs. Key design considerations and challenges are investigated finding fundamental issues and potential research directions to exploit them. Finally, analytical tools and frameworks for performance optimizations are presented

Hybrid Radio Resource Management for Heterogeneous Wireless Access Network

Heterogeneous wireless access network (HWAN) is composed of fifth-generation (5G) and fourth-generation (4G) cellular systems and IEEE 802.11-based wireless local area networks (WLANs). These diverse and dense wireless networks have different data rates, coverage, capacity, cost, and QoS. Furthermore, user devices are multi-modal devices that allow users to connect to more than one network simultaneously. This thesis presents radio resource management for RAT selection, radio resource allocation, load balancing, congestion control mechanism, and user device (UD) energy management that can effectively utilize the available resources in the heterogeneous wireless networks and enhance the quality-of-service (QoS) and user quality-of-experience (QoE). Recent studies on radio resource management in HWAN lead to two broad categories, 1) centralized architecture and 2) distributed model. In the centralized model, all the decision making power confines to a centralized controller and user devices are assumed as passive transceivers. In contrast, user devices actively participate in radio resource management in the distributed model, resulting in poor resource utilization and maximum call blocking and call dropping probabilities. In this thesis, we present a novel hybrid radio resource management model for HWAN that is composed of OFDMA based system and WLAN. In this model, both the centralized controller and the user device take part in resource management. Our hybrid mechanism considers attributes related to both user and network. However, these attributes are conflicting in nature. Moreover, a single RAT selection is performed based on user location and available networks, whereas UD with a multi-homing call receives the radio resource share from each network to fulfil its minimum data rate requirement. A novel approach is proposed for load balancing where an equal load ratio is maintained across all the available networks in HWAN. Performance evaluation through call blocking probability and network utilization will reveal the effectiveness of the proposed scheme. The demand for more data rates is on the rise. The 5G heterogeneous wireless access network is a potential solution to tackle the high data rate demand. The 5GHWAN is composed of 5G new radio (NR) and 4G long-term evolution (LTE) base stations (BSs). In a practical system, the channel conditions fluctuate due to user mobility. We, therefore, investigate radio resource allocation and congestion control mechanism along with network-assisted distributive RAT selection in a time-varying 5GHWAN. This joint problem of radio resource allocation and congestion control management has signalling overhead and computational complexity limitations. Therefore, we use the Lyapunov optimization to convert the offline problem into an online optimization problem based on channel state information (CSI) and queue state information (QSI). The theoretical and simulation results evaluate the performance of our proposed approach under the assumption of network stability. In addition, simulation results are presented to depict our proposed scheme’s effectiveness. Furthermore, our proposed RAT selection scheme performs better than the traditional centralized and distributive mechanisms. Recently an increase in the usage of video applications has been observed. Therefore, we explore hybrid radio resource management video streaming over time-varying HWAN. Using the Lyapunov optimization technique, we decompose our two-time scale stochastic optimization problem into two main sub-problems. One of the sub-problems is related to radio resource allocation that operates at a scheduling time interval. The radio resource allocation policy is implemented at a centralized control node responsible for allocating radio resources from the available wireless networks using Lagrange dual method. The other sub-problem is related to the quality rate adaptation policy that works at a chunk time scale. Each user selects the appropriate quality level of the video chunks adaptively in a distributive way based on buffer state and channel state information. We analyze and compare the QoE of our proposed approach over an arbitrary sample path of channel state information with an optimal T-slot algorithm. Finally, we evaluate the performance analysis of our proposed scheme for video streaming over a time-varying heterogeneous wireless access network through simulation results

Efficient and Secure Resource Allocation in Mobile Edge Computing Enabled Wireless Networks

To support emerging applications such as autonomous vehicles and smart homes and to build an intelligent society, the next-generation internet of things (IoT) is calling for up to 50 billion devices connected world wide. Massive devices connection, explosive data circulation, and colossal data processing demand are driving both the industry and academia to explore new solutions. Uploading this vast amount of data to the cloud center for processing will significantly increase the load on backbone networks and cause relatively long latency to time-sensitive applications. A practical solution is to deploy the computing resource closer to end-users to process the distributed data. Hence, Mobile Edge Computing (MEC) emerged as a promising solution to providing high-speed data processing service with low latency. However, the implementation of MEC networks is handicapped by various challenges. For one thing, to serve massive IoT devices, dense deployment of edge servers will consume much more energy. For another, uploading sensitive user data through a wireless link intro-duces potential risks, especially for those size-limited IoT devices that cannot implement complicated encryption techniques. This dissertation investigates problems related to Energy Efficiency (EE) and Physical Layer Security (PLS) in MEC-enabled IoT networks and how Non-Orthogonal Multiple Access (NOMA), prediction-based server coordination, and Intelligent Reflecting Surface (IRS) can be used to mitigate them. Employing a new spectrum access method can help achieve greater speed with less power consumption, therefore increasing system EE. We first investigated NOMA-assisted MEC networks and verified that the EE performance could be significantly improved. Idle servers can consume unnecessary power. Proactive server coordination can help relieve the tension of increased energy consumption in MEC systems. Our next step was to employ advanced machine learning algorithms to predict data workload at the server end and adaptively adjust the system configuration over time, thus reducing the accumulated system cost. We then introduced the PLS to our system and investigated the long-term secure EE performance of the MEC-enabled IoT network with NOMA assistance. It has shown that NOMA can improve both EE and PLS for the network. Finally, we switch from the single antenna scenario to a multiple-input single-output (MISO) system to exploit space diversity and beam forming techniques in mmWave communication. IRS can be used simultaneously to help relieve the pathloss and reconfigure multi-path links. In the final part, we first investigated the secure EE performance of IRS-assisted MISO networks and introduced a friendly jammer to block the eavesdroppers and improve the PLS rate. We then combined the IRS with the NOMA in the MEC network and showed that the IRS can further enhance the system EE