Simulation of QoS based call admission schemes for DS-CDMA

Code division multiple-access (CDMA) has been receiving considerable attention as the core multiple-access technology in the development of upcoming ubiquitous personal communication service (PCS) system with flexible quality of service (QoS) requirements at any time and anywhere. Moreover, future wireless networks are expected to integrate different types of multimedia traffic, such as voice, data, and compressed images and video. This work aims to investigate the resource allocation problem for wireless direct sequence spread-spectrum CDMA cellular networks supporting heterogeneous multimedia applications where individual users have different QoS requirements. We propose optimal rate and power distribution approaches for source allocation to individual users, subject to a constraint on the total available bandwidth, to maximize the per-cell capacity and maintain the quality of service (QoS) for different multimedia services. Connection oriented admission control schemes based on the required QoS are proposed in this thesis. Several policies are introduced for use in CDMA network. Simulation results show that the proposed Radio Resource Management scheme can achieve both effective QoS guarantee and efficient resource utilization

Information Exchange Management as a Service for Network Function Virtualization Environments

The Internet landscape is gradually adopting new communication paradigms characterized by flexibility and adaptability to the resource constraints and service requirements, including network function virtualization (NFV), software-defined networks, and various virtualization and network slicing technologies. These approaches need to be realized from multiple management and network entities exchanging information between each other. We propose a novel information exchange management as a service facility as an extension to ETSI's NFV management and orchestration framework, namely the virtual infrastructure information service (VIS). VIS is characterized by the following properties: 1) it exhibits the dynamic characteristics of such network paradigms; 2) it supports information flow establishment, operation, and optimization; and 3) it provides a logically centralized control of the established information flows with respect to the diverse demands of the entities exchanging information elements. Our proposal addresses the information exchange management requirements of NFV environments and is information-model agnostic. This paper includes an experimental analysis of its main functional and non-functional characteristics

Efficient radio resource management in next generation wireless networks

The current decade has witnessed a phenomenal growth in mobile wireless communication networks and subscribers. In 2015, mobile wireless devices and connections were reported to have grown to about 7.9 billion, exceeding human population. The explosive growth in mobile wireless communication network subscribers has created a huge demand for wireless network capacity, ubiquitous wireless network coverage, and enhanced Quality of Service (QoS). These demands have led to several challenging problems for wireless communication networks operators and designers. The Next Generation Wireless Networks (NGWNs) will support high mobility communications, such as communication in high-speed rails. Mobile users in such high mobility environment demand reliable QoS, however, such users are plagued with a poor signal-tonoise ratio, due to the high vehicular penetration loss, increased transmission outage and handover information overhead, leading to poor QoS provisioning for the networks' mobile users. Providing a reliable QoS for high mobility users remains one of the unique challenges for NGWNs. The increased wireless network capacity and coverage of NGWNs means that mobile communication users at the cell-edge should have enhanced network performance. However, due to path loss (path attenuation), interference, and radio background noise, mobile communication users at the cell-edge can experience relatively poor transmission channel qualities and subsequently forced to transmit at a low bit transmission rate, even when the wireless communication networks can support high bit transmission rate. Furthermore, the NGWNs are envisioned to be Heterogeneous Wireless Networks (HWNs). The NGWNs are going to be the integration platform of diverse homogeneous wireless communication networks for a convergent wireless communication network. The HWNs support single and multiple calls (group calls), simultaneously. Decision making is an integral core of radio resource management. One crucial decision making in HWNs is network selection. Network selection addresses the problem of how to select the best available access network for a given network user connection. For the integrated platform of HWNs to be truly seamless and efficient, a robust and stable wireless access network selection algorithm is needed. To meet these challenges for the different mobile wireless communication network users, the NGWNs will have to provide a great leap in wireless network capacity, coverage, QoS, and radio resource utilization. Moving wireless communication networks (mobile hotspots) have been proposed as a solution to providing reliable QoS to high mobility users. In this thesis, an Adaptive Thinning Mobility Aware (ATMA) Call Admission Control (CAC) algorithm for improving the QoS and radio resource utilization of the mobile hotspot networks, which are of critical importance for communicating nodes in moving wireless networks is proposed. The performance of proposed ATMA CAC scheme is investigated and compare it with the traditional CAC scheme. The ATMA scheme exploits the mobility events in the highspeed mobility communication environment and the calls (new and handoff calls) generation pattern to enhance the QoS (new call blocking and handoff call dropping probabilities) of the mobile users. The numbers of new and handoff calls in wireless communication networks are dynamic random processes that can be effectively modeled by the Continuous Furthermore, the NGWNs are envisioned to be Heterogeneous Wireless Networks (HWNs). The NGWNs are going to be the integration platform of diverse homogeneous wireless communication networks for a convergent wireless communication network. The HWNs support single and multiple calls (group calls), simultaneously. Decision making is an integral core of radio resource management. One crucial decision making in HWNs is network selection. Network selection addresses the problem of how to select the best available access network for a given network user connection. For the integrated platform of HWNs to be truly seamless and efficient, a robust and stable wireless access network selection algorithm is needed. To meet these challenges for the different mobile wireless communication network users, the NGWNs will have to provide a great leap in wireless network capacity, coverage, QoS, and radio resource utilization. Moving wireless communication networks (mobile hotspots) have been proposed as a solution to providing reliable QoS to high mobility users. In this thesis, an Adaptive Thinning Mobility Aware (ATMA) Call Admission Control (CAC) algorithm for improving the QoS and radio resource utilization of the mobile hotspot networks, which are of critical importance for communicating nodes in moving wireless networks is proposed

Learning algorithms for the control of routing in integrated service communication networks

There is a high degree of uncertainty regarding the nature of traffic on future integrated service networks. This uncertainty motivates the use of adaptive resource allocation policies that can take advantage of the statistical fluctuations in the traffic demands. The adaptive control mechanisms must be 'lightweight', in terms of their overheads, and scale to potentially large networks with many traffic flows. Adaptive routing is one form of adaptive resource allocation, and this thesis considers the application of Stochastic Learning Automata (SLA) for distributed, lightweight adaptive routing in future integrated service communication networks. The thesis begins with a broad critical review of the use of Artificial Intelligence (AI) techniques applied to the control of communication networks. Detailed simulation models of integrated service networks are then constructed, and learning automata based routing is compared with traditional techniques on large scale networks. Learning automata are examined for the 'Quality-of-Service' (QoS) routing problem in realistic network topologies, where flows may be routed in the network subject to multiple QoS metrics, such as bandwidth and delay. It is found that learning automata based routing gives considerable blocking probability improvements over shortest path routing, despite only using local connectivity information and a simple probabilistic updating strategy. Furthermore, automata are considered for routing in more complex environments spanning issues such as multi-rate traffic, trunk reservation, routing over multiple domains, routing in high bandwidth-delay product networks and the use of learning automata as a background learning process. Automata are also examined for routing of both 'real-time' and 'non-real-time' traffics in an integrated traffic environment, where the non-real-time traffic has access to the bandwidth 'left over' by the real-time traffic. It is found that adopting learning automata for the routing of the real-time traffic may improve the performance to both real and non-real-time traffics under certain conditions. In addition, it is found that one set of learning automata may route both traffic types satisfactorily. Automata are considered for the routing of multicast connections in receiver-oriented, dynamic environments, where receivers may join and leave the multicast sessions dynamically. Automata are shown to be able to minimise the average delay or the total cost of the resulting trees using the appropriate feedback from the environment. Automata provide a distributed solution to the dynamic multicast problem, requiring purely local connectivity information and a simple updating strategy. Finally, automata are considered for the routing of multicast connections that require QoS guarantees, again in receiver-oriented dynamic environments. It is found that the distributed application of learning automata leads to considerably lower blocking probabilities than a shortest path tree approach, due to a combination of load balancing and minimum cost behaviour

DYNAMIC RESOURCE ALLOCATION FOR MULTIUSER VIDEO STREAMING

With the advancement of video compression technology and wide deployment of wired/wireless networks, there is an increasing demand of multiuser video communication services. A multiuser video transmission system should consider not only the reconstructed video quality in the individual-user level but also the service objectives among all users on the network level. There are many design challenges to support multiuser video communication services, such as fading channels, limited radio resources of wireless networks, heterogeneity of video content complexity, delay and decoding dependency constraints of video bitstreams, and mixed integer optimization. To overcome these challenges, a general strategy is to dynamically allocate resources according to the changing environments and requirements, so as to improve the overall system performance and ensure quality of service (QoS) for each user. In this dissertation, we address the aforementioned design challenges from a resource-allocation point of view and two aspects of system and algorithm designs, namely, a cross-layer design that jointly optimizes resource utilization from physical layer to application layer, and multiuser diversity that explores the source and channel heterogeneity among different users. We also address the impacts on systems caused by dynamic environment along time domain and consider the time-heterogeneity of video sources and time-varying characteristics of channel conditions. To achieve the desired service objectives, a general resource allocation framework is formulated in terms of constrained optimization problems to dynamically allocate resources and control the quality of multiple video bitstreams. Based on the design methodology of multiuser cross-layer optimization, we propose several systems to efficiently transmit multiple video streams, encoded by current and emerging video codecs, over major types of wireless networks such as 3G cellular system, Wireless Local Area Network, 4G cellular system, and future Wireless Metropolitan Area Networks. Owing to the integer nature of some system parameters, the formulated optimization problems are often integer or mixed integer programming problem and involve high computation to search the optimal solutions. Fast algorithms are proposed to provide real-time services. We demonstrate the advantages of dynamic and joint resource allocation for multiple video sources compared to static strategy. We also show the improvement of exploring diversity on frequency, time, and transmission path, and the benefits from multiuser cross-layer optimization

Resource allocation for heterogeneous wireless networks

Demand for high volumes of mobile data traffic with better quality-of-service (QoS) support and seamless network coverage is ever increasing, due to growth of the number of smart mobile devices and the applications that run on these devices. Also, most of these high volumes of data traffic demanding areas are covered by heterogeneous wireless networks, such as cellular networks and wireless local area networks (WLANs). Therefore, interworking mechanisms can be used in these areas to enhance the network capacity, QoS support and coverage. Interworking enhances network capacity and QoS support by jointly allocating resources of multiple networks and enabling user multi-homing, where multi-homing allows users to simultaneously communicate over multiple networks. It widens network coverage by merging coverage of individual networks. However, there are areas where interworking cannot improve network capacity or QoS support, such as the areas with coverage of only one networks. Therefore, to achieve network-wide uniform capacity and QoS support enhancements, interworking can be integrated with device-to-device (D2D) communication and small cell deployment techniques. One of the challenging issues that need to be solved before these techniques can be applied in practical networks is the efficient resource allocation, as it has a direct impact on the network capacity and QoS support. Therefore, this thesis focuses on studying and developing efficient resource allocation schemes for interworking heterogeneous wireless networks which apply D2D communication and small cell deployment techniques. First, uplink resource allocation for cellular network and WLAN interworking to provide multi-homing voice and data services is investigated. The main technical challenge, which makes the resource allocation for this system complicated, is that resource allocation decisions need to be made capturing multiple physical layer (PHY) and medium access control layer (MAC) technologies of the two networks. This is essential to ensure that the decisions are feasible and can be executed at the lower layers. Thus, the resource allocation problem is formulated based on PHY and MAC technologies of the two networks. The optimal resource allocation problem is a multiple time-scale Markov decision process (MMDP) as the two networks operate at different time-scales, and due to voice and data service requirements. A resource allocation scheme consisting of decision policies for the upper and the lower levels of the MMDP is derived. To reduce the time complexity, a heuristic resource allocation algorithm is also proposed. Second, resource allocation for D2D communication underlaying cellular network and WLAN interworking is investigated. Enabling D2D communication within the interworking system further enhances the spectrum efficiency, especially at areas where only one network is available. In addition to the technical challenges encountered in the first interworking system, interference management and selection of users' communication modes for multiple networks to maximize hop and reuse gains complicate resource allocation for this system. To address these challenges, a semi-distributed resource allocation scheme that performs mode selection, allocation of WLAN resources, and allocation of cellular network resources in three different time-scales is proposed. Third, resource allocation for interworking macrocell and hyper-dense small cell networks is studied. Such system is particularly useful for interference prone and high capacity demanding areas, such as busy streets and city centers, as it uses license frequency bands and provides a high spectrum efficiency through frequency reuse and bringing network closer to the users. The key challenge for allocating resources for this system is high complexity of the resource allocation scheme due to requirement to jointly allocate resources for a large number of small cells to manage co-channel interference (CCI) in the system. Further, the resource allocation scheme should minimize the computational burden for low-cost small cell base stations (BSs), be able to adapt to time-varying network load conditions, and reduce signaling overhead in the small cell backhauls with limited capacity. To this end, a resource allocation scheme which operates on two time-scales and utilizes cloud computing to determine resource allocation decisions is proposed. Resource allocation decisions are made at the cloud in a slow time-scale, and are further optimized at the BSs in a fast time-scale in order to adapt the decisions to fast varying wireless channel conditions. Achievable throughput and QoS improvements using the proposed resource allocation schemes for all three systems are demonstrated via simulation results. In summary, designing of the proposed resource allocation schemes provides valuable insights on how to efficiently allocate resources considering PHY and MAC technologies of the heterogeneous wireless networks, and how to utilize cloud computing to assist executing a complex resource allocation scheme. Furthermore, it also demonstrates how to operate a resource allocation scheme over multiple time-scales. This is particularly important if the scheme is complex and requires a long time to execute, yet the resource allocation decisions are needed to be made within a short interval

Competitive MA-DRL for Transmit Power Pool Design in Semi-Grant-Free NOMA Systems

In this paper, we exploit the capability of multi-agent deep reinforcement learning (MA-DRL) technique to generate a transmit power pool (PP) for Internet of things (IoT) networks with semi-grant-free non-orthogonal multiple access (SGF-NOMA). The PP is mapped with each resource block (RB) to achieve distributed transmit power control (DPC). We first formulate the resource (sub-channel and transmit power) selection problem as stochastic Markov game, and then solve it using two competitive MA-DRL algorithms, namely double deep Q network (DDQN) and Dueling DDQN. Each GF user as an agent tries to find out the optimal transmit power level and RB to form the desired PP. With the aid of dueling processes, the learning process can be enhanced by evaluating the valuable state without considering the effect of each action at each state. Therefore, DDQN is designed for communication scenarios with a small-size action-state space, while Dueling DDQN is for a large-size case. Our results show that the proposed MA-Dueling DDQN based SGF-NOMA with DPC outperforms the SGF-NOMA system with the fixed-power-control mechanism and networks with pure GF protocols with 17.5% and 22.2% gain in terms of the system throughput, respectively. Moreover, to decrease the training time, we eliminate invalid actions (high transmit power levels) to reduce the action space. We show that our proposed algorithm is computationally scalable to massive IoT networks. Finally, to control the interference and guarantee the quality-of-service requirements of grant-based users, we find the optimal number of GF users for each sub-channel

Multicast Aware Virtual Network Embedding in Software Defined Networks

The Software Defined Networking (SDN) provides not only a higher level abstraction of lower level functionalities, but also flexibility to create new multicast framework. SDN decouples the low level network elements (forwarding/data plane) from the control/management layer (control plane), where a centralized controller can access and modify the configuration of each distributed network element. The centralized framework allows to develop more network functionalities that can not be easily achieved in the traditional network architecture. Similarly, Network Function Virtualization (NFV) enables the decoupling of network services from the underlying hardware infrastructure to allow the same Substrate (Physical) Network (SN) shared by multiple Virtual Network (VN) requests. With the network virtualization, the process of mapping virtual nodes and links onto a shared SN while satisfying the computing and bandwidth constraints is referred to as Virtual Network Embedding (VNE), an NP-Hard problem. The VNE problem has drawn a lot of attention from the research community. In this dissertation, we motivate the importance of characterizing the mode of communication in VN requests, and we focus our attention on the problem of embedding VNs with one-to-many (multicast) communication mode. Throughout the dissertation, we highlight the unique properties of multicast VNs and explore how to efficiently map a given Virtual Multicast Tree/Network (VMT) request onto a substrate IP Network or Elastic Optical Networks (EONs). The major objective of this dissertation is to study how to efficiently embed (i) a given virtual request in IP or optical networks in the form of a multicast tree while minimizing the resource usage and avoiding the redundant multicast tranmission, (ii) a given virtual request in optical networks while minimizing the resource usage and satisfying the fanout limitation on the multicast transmission. Another important contribution of this dissertation is how to efficiently map Service Function Chain (SFC) based virtual multicast request without prior constructed SFC while minimizing the resource usage and satisfying the SFC on the multicast transmission

Power Control and Resource Allocation for QoS-Constrained Wireless Networks

Developments such as machine-to-machine communications and multimedia services are placing growing demands on high-speed reliable transmissions and limited wireless spectrum resources. Although multiple-input multiple-output (MIMO) systems have shown the ability to provide reliable transmissions in fading channels, it is not practical for single-antenna devices to support MIMO system due to cost and hardware limitations. Cooperative communication allows single-antenna devices to share their spectrum resources and form a virtual MIMO system where their quality of service (QoS) may be improved via cooperation. Most cooperative communication solutions are based on fixed spectrum access schemes and thus cannot further improve spectrum efficiency. In order to support more users in the existing spectrum, we consider dynamic spectrum access schemes and cognitive radio techniques in this dissertation. Our work includes the modelling, characterization and optimization of QoS-constrained cooperative networks and cognitive radio networks. QoS constraints such as delay and data rate are modelled. To solve power control and channel resource allocation problems, dynamic power control, matching theory and multi-armed bandit algorithms are employed in our investigations. In this dissertation, we first consider a cluster-based cooperative wireless network utilizing a centralized cooperation model. The dynamic power control and optimization problem is analyzed in this scenario. We then consider a cooperative cognitive radio network utilizing an opportunistic spectrum access model. Distributed spectrum access algorithms are proposed to help secondary users utilize vacant channels of primary users in order to optimize the total utility of the network. Finally, a noncooperative cognitive radio network utilizing the opportunistic spectrum access model is analyzed. In this model, primary users do not communicate with secondary users. Therefore, secondary users are required to find vacant channels on which to transmit. Multi-armed bandit algorithms are proposed to help secondary users predict the availability of licensed channels. In summary, in this dissertation we consider both cooperative communication networks and cognitive radio networks with QoS constraints. Efficient power control and channel resource allocation schemes have been proposed for optimization problems in different scenarios.Cambridge Overseas Trust; China Scholarship Counci