46 research outputs found

    Fractional frequency reused based interference mitigation in irregular geometry multicellular networks

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    Recent drastic growth in the mobile broadband services specifically with the proliferation of smart phones demands for higher spectrum capacity of wireless cellular systems. Due to the scarcity of the frequency spectrum, cellular systems are seeking aggressive frequency reuse, which improve the network capacity, however, at the expense of increased Inter Cell Interference (ICI). Fractional Frequency Reuse (FFR) scheme has been acknowledged as an effective ICI mitigation scheme, however, in literature FFR has been used mostly in perfect geometry network. In realistic deployment, the cellular geometry is irregular and each cell experiences varying ICI. The main objective of this thesis is to develop ICI mitigation scheme that improves spectrum efficiency and throughput for irregular geometry multicellular network. Irregular Geometry Sectored-Fractional Frequency Reuse (IGS-FFR) scheme is developed that comprises of cell partitioning and sectoring, and dynamic spectrum partitioning. The cell-partitioning and sectoring allows full frequency reuse within an irregular geometry cell. Nevertheless, the sub-regions in an irregular cell have varying coverage areas and thus demands diverse spectrum requirements. The IGSFFR scheme is designed to dynamically allocate the spectrum resources according to the traffic demands of each sub-region. An enhanced IGS-FFR has been developed to optimally allocate the spectrum resources to individual users of each sub-region. Enhanced IGS-FFR has been realized using two different approaches, Auction based Optimized IGS-FFR (AO-IGS-FFR) and Hungarian based Optimized IGS-FFR (HO-IGS-FFR). The results show that IGS-FFR has significantly improved the cell throughput by 89%, 45% and 18% and users’ satisfaction by 112%, 65.8% and 38% compared to Reuse-1, Strict-FFR and FFR-3 schemes, respectively. The findings show that the ICI mitigation in IGS-FFR is reinforced by users’ satisfaction. As the number of sectors in IGS-FFR increases from 3 to 4 and 6, the cell throughput increase by 21% and 33% because of spatial diversity exploitation along with orthogonal sub-band allocation. AO-IGS-FFR and HO-IGS-FFR have further improved the cell throughput of the basic FFR-3 by 65% and 72.2%, respectively. HO-IGS-FFR performs 7% better than the AO-IGS-FFR at the expense of 26.7% decrease in the users’ satisfaction and excessive complexity. Although, AO-IGS-FFR compromises sub-optimal bandwidth allocation, it is a low complexity scheme and can mitigate ICI with high users’ satisfaction. The enhanced IGS-FFR can be deployed in future heterogeneous irregular geometry multicellular OFDMA networks

    Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

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    Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

    Distributed optimisation techniques for wireless networks

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    Alongside the ever increasing traffic demand, the fifth generation (5G) cellular network architecture is being proposed to provide better quality of service, increased data rate, decreased latency, and increased capacity. Without any doubt, the 5G cellular network will comprise of ultra-dense networks and multiple input multiple output technologies. This will make the current centralised solutions impractical due to increased complexity. Moreover, the amount of coordination information that needs to be transported over the backhaul links will be increased. Distributed or decentralised solutions are promising to provide better alternatives. This thesis proposes new distributed algorithms for wireless networks which aim to reduce the amount of system overheads in the backhaul links and the system complexity. The analysis of conflicts amongst transmitters, and resource allocation are conducted via the use of game theory, convex optimisation, and auction theory. Firstly, game-theoretic model is used to analyse a mixed quality of service (QoS) strategic non-cooperative game (SNG), for a two-user multiple-input single-output (MISO) interference channel. The players are considered to have different objectives. Following this, the mixed QoS SNG is extended to a multicell multiuser network in terms of signal-to-interference-and-noise ratio (SINR) requirement. In the multicell multiuser setting, each transmitter is assumed to be serving real time users (RTUs) and non-real time users (NRTUs), simultaneously. A novel mixed QoS SNG algorithm is proposed, with its operating point identified as the Nash equilibrium-mixed QoS (NE-mixed QoS). Nash, Kalai-Smorodinsky, and Egalitarian bargain solutions are then proposed to improve the performance of the NE-mixed QoS. The performance of the bargain solutions are observed to be comparable to the centralised solutions. Secondly, user offloading and user association problems are addressed for small cells using auction theory. The main base station wishes to offload some of its users to privately owned small cell access points. A novel bid-wait-auction (BWA) algorithm, which allows single-item bidding at each auction round, is designed to decompose the combinatorial mathematical nature of the problem. An analysis on the existence and uniqueness of the dominant strategy equilibrium is conducted. The BWA is then used to form the forward BWA (FBWA) and the backward BWA (BBWA). It is observed that the BBWA allows more users to be admitted as compared to the FBWA. Finally, simultaneous multiple-round ascending auction (SMRA), altered SMRA (ASMRA), sequential combinatorial auction with item bidding (SCAIB), and repetitive combinatorial auction with item bidding (RCAIB) algorithms are proposed to perform user offloading and user association for small cells. These algorithms are able to allow bundle bidding. It is then proven that, truthful bidding is individually rational and leads to Walrasian equilibrium. The performance of the proposed auction based algorithms is evaluated. It is observed that the proposed algorithms match the performance of the centralised solutions when the guest users have low target rates. The SCAIB algorithm is shown to be the most preferred as it provides high admission rate and competitive revenue to the bidders

    Efficient Traffic Management Algorithms for the Core Network using Device-to-Device Communication and Edge Caching

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    Exponentially growing number of communicating devices and the need for faster, more reliable and secure communication are becoming major challenges for current mobile communication architecture. More number of connected devices means more bandwidth and a need for higher Quality of Service (QoS) requirements, which bring new challenges in terms of resource and traffic management. Traffic offload to the edge has been introduced to tackle this demand-explosion that let the core network offload some of the contents to the edge to reduce the traffic congestion. Device-to-Device (D2D) communication and edge caching, has been proposed as promising solutions for offloading data. D2D communication refers to the communication infrastructure where the users in proximity communicate with each other directly. D2D communication improves overall spectral efficiency, however, it introduces additional interference in the system. To enable D2D communication, efficient resource allocation must be introduced in order to minimize the interference in the system and this benefits the system in terms of bandwidth efficiency. In the first part of this thesis, low complexity resource allocation algorithm using stable matching is proposed to optimally assign appropriate uplink resources to the devices in order to minimize interference among D2D and cellular users. Edge caching has recently been introduced as a modification of the caching scheme in the core network, which enables a cellular Base Station (BS) to keep copies of the contents in order to better serve users and enhance Quality of Experience (QoE). However, enabling BSs to cache data on the edge of the network brings new challenges especially on deciding on which and how the contents should be cached. Since users in the same cell may share similar content-needs, we can exploit this temporal-spatial correlation in the favor of caching system which is referred to local content popularity. Content popularity is the most important factor in the caching scheme which helps the BSs to cache appropriate data in order to serve the users more efficiently. In the edge caching scheme, the BS does not know the users request-pattern in advance. To overcome this bottleneck, a content popularity prediction using Markov Decision Process (MDP) is proposed in the second part of this thesis to let the BS know which data should be cached in each time-slot. By using the proposed scheme, core network access request can be significantly reduced and it works better than caching based on historical data in both stable and unstable content popularity

    Resource allocation in networks via coalitional games

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    The main goal of this dissertation is to manage resource allocation in network engineering problems and to introduce efficient cooperative algorithms to obtain high performance, ensuring fairness and stability. Specifically, this dissertation introduces new approaches for resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA) wireless networks and in smart power grids by casting the problems to the coalitional game framework and by providing a constructive iterative algorithm based on dynamic learning theory.  Software Engineering (Software)Algorithms and the Foundations of Software technolog
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