A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning

In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov Decision Process (MDP) approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity and implementation issues. For each of the approaches, the problem setup, the general solution and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multi-hop routing designs in general multi-hop networks are elaborated. Finally, the delay performance of the various approaches are compared through simulations using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201

Packet scheduling in satellite LTE networks employing MIMO technology.

Doctor of Philosophy in Electronic Engineering. University of KwaZulu-Natal, Durban 2014.Rapid growth in the number of mobile users and ongoing demand for different types of telecommunication services from mobile networks, have driven the need for new technologies that provide high data rates and satisfy their respective Quality of Service (QoS) requirements, irrespective of their location. The satellite component will play a vital role in these new technologies, since the terrestrial component is not able to provide global coverage due to economic and technical limitations. This has led to the emergence of Satellite Long Term Evolution (LTE) networks which employ Multiple-In Multiple-Out (MIMO) technology. In order to achieve the set QoS targets, required data rates and fairness among various users with different traffic demands in the satellite LTE network, it is crucial to design an effective scheduling and a sub-channel allocation scheme that will provide an optimal balance of all these requirements. It is against this background that this study investigates packet scheduling in satellite LTE networks employing MIMO technology. One of the main foci of this study is to propose new cross-layer based packet scheduling schemes, tagged Queue Aware Fair (QAF) and Channel Based Queue Sensitive (CBQS) scheduling schemes. The proposed schemes are designed to improve both fairness and network throughput without compromising users’ QoS demands, as they provide a good trade-off between throughput, QoS demands and fairness. They also improve the performance of the network in comparison with other scheduling schemes. The comparison is determined through simulations. Due to the fact that recent schedulers provide a trade-off among major performance indices, a new performance index to evaluate the overall performance of each scheduler is derived. This index is tagged the Scheduling Performance Metric (SPM). The study also investigates the impact of the long propagation delay and different effective isotropic radiated powers on the performance of the satellite LTE network. The results show that both have a significant impact on network performance. In order to actualize an optimal scheduling scheme for the satellite LTE network, the scheduling problem is formulated as an optimization function and an optimal solution is obtained using Karush-Kuhn-Tucker multipliers. The obtained Near Optimal Scheduling Scheme (NOSS), whose aim is to maximize the network throughput without compromising users’ QoS demands and fairness, provides better throughput and spectral efficiency performance than other schedulers. The comparison is determined through simulations. Based on the new SPM, the proposed NOSS1 and NOSS2 outperform other schedulers. A stability analysis is also presented to determine whether or not the proposed scheduler will provide a stable network. A fluid limit technique is used for the stability analysis. Finally, a sub-channel allocation scheme is proposed, with the aim of providing a better sub-channel or Physical Resource Block (PRB) allocation method, tagged the Utility Auction Based (UAB) subchannel allocation scheme that will improve the system performance of the satellite LTE network. The results show that the proposed method performs better than the other scheme. The comparison is obtained through simulations

Efficient radio resource management for the fifth generation slice networks

It is predicted that the IMT-2020 (5G network) will meet increasing user demands and, hence, it is therefore, expected to be as flexible as possible. The relevant standardisation bodies and academia have accepted the critical role of network slicing in the implementation of the 5G network. The network slicing paradigm allows the physical infrastructure and resources of the mobile network to be “sliced” into logical networks, which are operated by different entities, and then engineered to address the specific requirements of different verticals, business models, and individual subscribers. Network slicing offers propitious solutions to the flexibility requirements of the 5G network. The attributes and characteristics of network slicing support the multi-tenancy paradigm, which is predicted to drastically reduce the operational expenditure (OPEX) and capital expenditure (CAPEX) of mobile network operators. Furthermore, network slices enable mobile virtual network operators to compete with one another using the same physical networks but customising their slices and network operation according to their market segment's characteristics and requirements. However, owing to scarce radio resources, the dynamic characteristics of the wireless links, and its capacity, implementing network slicing at the base stations and the access network xix becomes an uphill task. Moreover, an unplanned 5G slice network deployment results in technical challenges such as unfairness in radio resource allocation, poor quality of service provisioning, network profit maximisation challenges, and rises in energy consumption in a bid to meet QoS specifications. Therefore, there is a need to develop efficient radio resource management algorithms that address the above mentioned technical challenges. The core aim of this research is to develop and evaluate efficient radio resource management algorithms and schemes that will be implemented in 5G slice networks to guarantee the QoS of users in terms of throughput and latency while ensuring that 5G slice networks are energy efficient and economically profitable. This thesis mainly addresses key challenges relating to efficient radio resource management. First, a particle swarm-intelligent profit-aware resource allocation scheme for a 5G slice network is proposed to prioritise the profitability of the network while at the same time ensuring that the QoS requirements of slice users are not compromised. It is observed that the proposed new radio swarm-intelligent profit-aware resource allocation (NR-SiRARE) scheme outperforms the LTE-OFDMA swarm-intelligent profit-aware resource (LO-SiRARE) scheme. However, the network profit for the NR-SiRARE is greatly affected by significant degradation of the path loss associated with millimetre waves. Second, this thesis examines the resource allocation challenge in a multi-tenant multi-slice multi-tier heterogeneous network. To maximise the total utility of a multi-tenant multislice multi-tier heterogeneous network, a latency-aware dynamic resource allocation problem is formulated as an optimisation problem. Via the hierarchical decomposition method for heterogeneous networks, the formulated optimisation problem is transformed to reduce the computational complexities of the proposed solutions. Furthermore, a genetic algorithmbased latency-aware resource allocation scheme is proposed to solve the maximum utility problem by considering related constraints. It is observed that GI-LARE scheme outperforms the static slicing (SS) and an optimal resource allocation (ORA) schemes. Moreover, the GI-LARE appears to be near optimal when compared with an exact solution based on spatial branch and bound. Third, this thesis addresses a distributed resource allocation problem in a multi-slice multitier multi-domain network with different players. A three-level hierarchical business model comprising InPs, MVNOs, and service providers (SP) is examined. The radio resource allocation problem is formulated as a maximum utility optimisation problem. A multi-tier multi-domain slice user matching game and a distributed backtracking multi-player multidomain games schemes are proposed to solve the maximum utility optimisation problem. The distributed backtracking scheme is based on the Fisher Market and Auction theory principles. The proposed multi-tier multi-domain scheme outperforms the GI-LARE and the SS schemes. This is attributed to the availability of resources from other InPs and MVNOs; and the flexibility associated with a multi-domain network. Lastly, an energy-efficient resource allocation problem for 5G slice networks in a highly dense heterogeneous environment is investigated. A mathematical formulation of energy-efficient resource allocation in 5G slice networks is developed as a mixed-integer linear fractional optimisation problem (MILFP). The method adopts hierarchical decomposition techniques to reduce complexities. Furthermore, the slice user association, QoS for different slice use cases, an adapted water filling algorithm, and stochastic geometry tools are employed to xxi model the global energy efficiency (GEE) of the 5G slice network. Besides, neither stochastic geometry nor a three-level hierarchical business model schemes have been employed to model the global energy efficiency of the 5G slice network in the literature, making it the first time such method will be applied to 5G slice network. With rigorous numerical simulations based on Monte-Carlo numerical simulation technique, the performance of the proposed algorithms and schemes was evaluated to show their adaptability, efficiency and robustness for a 5G slice network

Cross-layer scheduling and transmission strategies for energy-constrained wireless networks

Ph.DDOCTOR OF PHILOSOPH