A coalitional game-based relay load balancing and power allocation scheme in decode-and-forward cellurlar relay networks

In this paper, a game theoretic relay load balancing and power allocationscheme is proposed for downlink transmission in a decode-and-forward orthogonal frequency division multiple access-based cellular relay network. A system with a base station communicating with multiple users via multiple relays is considered. The relays have limited power, which must be divided among the users they support. In traditional scheme, each relay simply divides its transmit power equally among all its users. Moreover, each user selects the relay with the highest channel gain. In this work, we do not apply the traditional relay scheme. It is because the users are distributed randomly, and by applying the traditional relay selection scheme, it may happen that some relays have more users connected to them than other relays, which results in having unbalanced load among the relays. In order to avoid performance degradation, achieve relay load balancing, and maximize the total data rate of the network, a game theoretic approach is proposed, which efficiently assigns the users to relays. The power of each relay is wisely distributed among users by the efficient power allocation scheme. Simulation results indicate that the proposed game-based scheme can considerably improve the average sum-spectral efficiency. Moreover, it shows that by applying the game, users who can connect to uncongested relays join them as opposed to connecting to congested relays.Grant from the Natural Sciences and Engineering Research Council (NSERC) IRC and Bell Canada.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1530-86772017-06-30hb2016Electrical, Electronic and Computer Engineerin

Low-latency Data Uploading in D2D-enabled Cellular Networks

指導教員：姜　暁

Resource allocation in networks via coalitional games

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

Teoria de jogos para utilização efetiva dos recursos em aplicações para 5G

Doutoramento em Engenharia Eletrotécnica - TelecomunicaçõesEsta tese tem como objetivo fornecer afirmações conclusivas em relação a utilização eficiente de recursos para redes e aplicações de 5G (5a geração) com recurso a teoria dos jogos. Neste contexto, investigamos dois cenários principais, um relativo a comunicações móveis e um outro relativo a redes inteligentes. Uma métrica importante para o desenho das redes móveis emergentes é a eficiência energética, com particular ênfase no lado do dispositivo móvel, onde as tecnologias das baterias são ainda limitadas. Alguns trabalhos de investigação relacionados têm demonstrado que a cooperação pode ser um paradigma útil no sentido de resolver o problema do défice energético. Contudo, pretendemos ir mais além, ao definir a cooperação e os utilizadores móveis como um grupo de jogadores racionais, que podem atuar sobre estratégias e utilidades, por forma a escolher a retransmissão mais apropriada para poupança de energia. Esta interpretação presta-se à aplicação da teoria dos jogos, e recorremos assim aos jogos coalicionais para solucionar conflitos de interesse entre dispositivos cooperantes, empregando Programação Linear (LP) para resolver o problema da selecção da retransmissão e derivar a principal solução do jogo. Os resultados mostram que a escolha do jogo de retransmissão coalicional proposto pode potencialmente duplicar a duração da bateria, numa era em que a próxima geração de dispositivos móveis necessitará de cada vez mais energia para suportar serviços e aplicações cada vez mais sofisticados. O segundo cenário investiga a resposta da procura em aplicações smart grid, que está a ganhar interesse sob a égide do 5G e que é considerada uma abordagem promissora, incentivando os utilizadores a consumir electricidade de forma mais uniforme em horas de vazio. Recorremos novamente à teoria dos jogos, imaginando as interacções estratégicas entre a empresa fornecedora de energia eléctrica e os potenciais utilizadores finais como um jogo de forma extensiva. São abordados dois programas em tempo real de resposta à procura: Day-Ahead Pricing (DAP) e Convex Pricing Tariffs. A resposta dos consumidores residenciais conscientes dos preços destas tarifas, é formulada como um problema de Mixed Integer Linear Programming (MILP) ou Quadratic Programming (QP), nos quais as soluções potenciais são o agendamento dos seus electrodomésticos inteligentes de modo a minimizar os seus gastos diários de electricidade, satisfazendo as suas necessidades diárias de energia e níveis de conforto. Os resultados demonstram que implementar o programa DAP pode reduzir a razão Peak-to-Average (PAR) at e 71% e as faturas de consumo das casas inteligentes at e 32%. Para além disso, a aplicação de tarifas convexas em tempo real pode melhorar ainda mais estas métricas de desempenho, alcançando uma redução de 80% do PAR e uma economia de mais de 50% na faturação da energia residencial.This research thesis aims to provide conclusive statements towards effective resource utilization for 5G (5th Generation) mobile networks and applications using game theory. In this context, we investigate two key scenarios pertaining to mobile communications and smart grids. A pivotal design driver for the upcoming era of mobile communications is energy efficiency, with particular emphasis on the mobile side where battery technology is still limited. Related works have shown that cooperation can be a useful engineering paradigm to take a step towards solving the energy deficit. However, we go beyond by envisaging cooperation and mobile users as a game of rational players, that can act on strategies and utilities in order to choose the most appropriate relay for energy saving. This interpretation lends itself to the application of game theory, and we look at coalitional games to settle conflicts of interest among cooperating user equipments, and employ Linear Programming (LP) to solve the relay selection problem and to derive the core solution of the game. The results reveal that adopting the proposed coalitional relaying game can potentially double battery lifetime, in an era where the next wave of next generation handsets will be more energy demanding supporting sophisticated services and applications. The second scenario investigates demand response in smart grid applications, which is also gaining momentum under the umbrella of 5G, which is a promising approach urging end-users to consume electricity more evenly during nonpeak hours of the day. Again, we resort to game theory and picture the strategic interactions between the electric utility company and the potential end-users as an extensive form game. Two real-time demand response programmes are addressed, namely Day-Ahead Pricing (DAP) and convex pricing tariffs. The response of price-aware residential consumers to these programmes is formulated as Mixed Integer Linear Programming (MILP) or Quadratic Programming (QP) problem, which optimally schedule their smart home appliances so as to minimise their daily electricity expenses while satisfying their daily energy needs and comfort levels. The results demonstrate that implementing the DAP programme can reduce the Peakto- Average Ratio (PAR) of demand by up to 71% and cut smart households bill by 32%. Moreover, applying real-time convex pricing tariffs can push these performance metrics even further, achieving 80% PAR reduction and more than 50% saving on the household electricity bill

Energy sustainable paradigms and methods for future mobile networks: A survey

In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.Comment: Accepted by Elsevier Computer Communications, 21 pages, 9 figure

Delay QoS Provisioning and Optimal Resource Allocation for Wireless Networks

Recent years have witnessed a significant growth in wireless communication and networking due to the exponential growth in mobile applications and smart devices, fueling unprecedented increase in both mobile data traffic and energy demand. Among such data traffic, real-time data transmissions in wireless systems require certain quality of service (QoS) constraints e.g., in terms of delay, buffer overflow or packet drop/loss probabilities, so that acceptable performance levels can be guaranteed for the end-users, especially in delay sensitive scenarios, such as live video transmission, interactive video (e.g., teleconferencing), and mobile online gaming. With this motivation, statistical queuing constraints are considered in this thesis, imposed as limitations on the decay rate of buffer overflow probabilities. In particular, the throughput and energy efficiency of different types of wireless network models are analyzed under QoS constraints, and optimal resource allocation algorithms are proposed to maximize the throughput or minimize the delay. In the first part of the thesis, the throughput and energy efficiency analysis for hybrid automatic repeat request (HARQ) protocols are conducted under QoS constraints. Approximations are employed for small QoS exponent values in order to obtain closed-form expressions for the throughput and energy efficiency metrics. Also, the impact of random arrivals, deadline constraints, outage probability and QoS constraints are studied. For the same system setting, the throughput of HARQ system is also analyzed using a recurrence approach, which provides more accurate results for any value of the QoS exponent. Similarly, random arrival models and deadline constraints are considered, and these results are further extended to the finite-blocklength coding regime. Next, cooperative relay networks are considered under QoS constraints. Specifically, the throughput performance in the two-hop relay channel, two-way relay channel, and multi-source multi-destination relay networks is analyzed. Finite-blocklength codes are considered for the two-hop relay channel, and optimization over the error probabilities is investigated. For the multi-source multi-destination relay network model, the throughput for both cases of with and without CSI at the transmitter sides is studied. When there is perfect CSI at the transmitter, transmission rates can be varied according to instantaneous channel conditions. When CSI is not available at the transmitter side, transmissions are performed at fixed rates, and decoding failures lead to retransmission requests via an ARQ protocol. Following the analysis of cooperative networks, the performance of both half-duplex and full-duplex operations is studied for the two-way multiple input multiple output (MIMO) system under QoS constraints. In full-duplex mode, the self-interference inflicted on the reception of a user due to simultaneous transmissions from the same user is taken into account. In this setting, the system throughput is formulated by considering the sum of the effective capacities of the users in both half-duplex and full-duplex modes. The low signal to noise ratio (SNR) regime is considered and the optimal transmission/power-allocation strategies are characterized by identifying the optimal input covariance matrices. Next, mode selection and resource allocation for device-to-device (D2D) cellular networks are studied. As the starting point, ransmission mode selection and resource allocation are analyzed for a time-division multiplexed (TDM) cellular network with one cellular user, one base station, and a pair of D2D users under rate and QoS constraints. For a more complicated setting with multiple cellular and D2D users, two joint mode selection and resource allocation algorithms are proposed. In the first algorithm, the channel allocation problem is formulated as a maximum-weight matching problem, which can be solved by employing the Hungarian algorithm. In the second algorithm, the problem is divided into three subproblems, namely user partition, power allocation and channel assignment, and a novel three-step method is proposed by combining the algorithms designed for the three subproblems. In the final part of the thesis, resource allocation algorithms are investigated for content delivery over wireless networks. Three different systems are considered. Initially, a caching algorithm is designed, which minimizes the average delay of a single-cell network. The proposed algorithm is applicable in settings with very general popularity models, with no assumptions on how file popularity varies among different users, and this algorithm is further extended to a more general setting, in which the system parameters and the distributions of channel fading change over time. Next, for D2D cellular networks operating under deadline constraints, a scheduling algorithm is designed, which manages mode selection, channel allocation and power maximization with acceptable complexity. This proposed scheduling algorithm is designed based on the convex delay cost method for a D2D cellular network with deadline constraints in an OFDMA setting. Power optimization algorithms are proposed for all possible modes, based on our utility definition. Finally, a two-step intercell interference (ICI)-aware scheduling algorithm is proposed for cloud radio access networks (C-RANs), which performs user grouping and resource allocation with the goal of minimizing delay violation probability. A novel user grouping algorithm is developed for the user grouping step, which controls the interference among the users in the same group, and the channel assignment problem is formulated as a maximum-weight matching problem in the second step, which can be solved using standard algorithms in graph theory

Cooperative Spectrum Sharing in Cognitive Radio Networking

Driven by the massive growth in communications data traffic as well as flourishing users' demands, we need to fully utilize the existing scarce spectrum resource. However, there have been several studies and reports over the years showing that a large portion of licensed spectrum is actually underutilized in both temporal and spatial domains. Moreover, aiming at facing the dilemma among the fixed spectrum allocation, the ever enormous increasing traffic demand and the limited spectrum resource, cognitive radio (CR) was proposed by Mitola to alleviate the under usage of spectrum. Thus, cognitive radio networking (CRN) has emerged as a promising paradigm to improve the spectrum efficiency and utilization by allowing secondary users (SUs) to utilize the spectrum hole of primary users (PUs). By using spectrum sensing, SUs can opportunistically access spectrum holes for secondary transmission without interfering the transmissions of the PUs and efficient spectrum utilization by multiple PUs and SUs requires reliable detection of PUs. Nevertheless, sensing errors such as false alarm and misdetection are inevitable in practical networks. Hence, the assumption that SUs always obtain the exact channel availability information is unreasonable. In addition, spectrum sensing must be carried out continuously and the SU must terminate its transmission as soon as it senses the re-occupancy by a PU. As a better alternative of spectrum sensing, cooperation has been leveraged in CRN, which is referred as cooperative cognitive radio networking (CCRN). In CCRN, in order to obtain the transmission opportunities, SUs negotiate with the PUs for accessing the spectrum by providing tangible service for PUs. In this thesis, we study cluster based spectrum sharing mechanism for CCRN and investigate on exploiting the cooperative technique in heterogeneous network. First, we develop cooperation protocols for CRN. Simultaneous transmission can be realized through quadrature signalling method in our proposed cooperation protocol. The optimal power allocation has been analyzed and closed-form solution has been derived for amplify and forward mode. Second, we study a cluster based spectrum sharing mechanism. The spectrum sharing is formulated as a combinatorial non-linear optimization problem which is NP-hard. Afterwards, we solve this problem by decomposing it into cluster allocation and time assignment, and we show that the result is close to the optimal solution. Third, we propose a macrocell-femtocell network cooperation scheme for heterogeneous networks under closed access mode. The cooperation between the femtocell network and macrocell network is investigated. By implementing the cooperation, not only the macrocell users' (MUEs') and femtocell users' (FUEs') utility can be improved compared with the non-cooperation case, but also the energy consumption as well as the interference from the femtocell network to the macrocell network can be reduced

Low complexity radio resource management for energy efficient wireless networks

Energy consumption has become a major research topic from both environmental and economical perspectives. The telecommunications industry is currently responsible for 0.7% of the total global carbon emissions, a figure which is increasing at rapid rate. By 2020, it is desired that CO2 emissions can be reduced by 50%. Thus, reducing the energy consumption in order to lower carbon emissions and operational expenses has become a major design constraint for future communication systems. Therefore, in this thesis energy efficient resource allocation methods have been studied taking the Long Term Evolution (LTE) standard as an example. Firstly, a theoretical analysis, that shows how improvements in energy efficiency can directly be related with improvements in fairness, is provided using a Shannon theory analysis. The traditional uplink power control challenge is re-evaluated and investigated from the view point of interference mitigation rather than power minimization. Thus, a low complexity distributed resource allocation scheme for reducing the uplink co-channel interference (CCI) is presented. Improvements in energy efficiency are obtained by controlling the level of CCI affecting vulnerable mobile stations (MSs). This is done with a combined scheduler and a two layer power allocation scheme, which is based on non-cooperative game theory. Simulation results show that the proposed low complexity method provides similar performance in terms of fairness and energy efficiency when compared to a centralized signal interference noise ratio balancing scheme. Apart from using interference management techniques, by using efficiently the spare resources in the system such as bandwidth and available infrastructure, the energy expenditure in wireless networks can also be reduced. For example, during low network load periods spare resource blocks (RBs) can be allocated to mobile users for transmission in the uplink. Thereby, the user rate demands are split among its allocated RBs in order to transmit in each of them by using a simpler and more energy efficient modulation scheme. In addition, virtual Multiple-input Multiple-output (MIMO) coalitions can be formed by allowing single antenna MSs and available relay stations to cooperate between each other to obtain power savings by implementing the concepts of spatial multiplexing and spatial diversity. Resource block allocation and virtual MIMO coalition formation are modeled by a game theoretic approach derived from two different concepts of stable marriage with incomplete lists (SMI) and the college admission framework (CAF) respectively. These distributed approaches focus on optimizing the overall consumed power of the single antenna devices rather than on the transmitted power. Moreover, it is shown that when overall power consumption is optimized the energy efficiency of the users experiencing good propagation conditions in the uplink is not always improved by transmitting in more than one RB or by forming a virtual MIMO link. Finally, it is shown that the proposed distributed schemes achieve a similar performance in bits per Joule when compared to much more complex centralized resource allocation methods