Mathematical optimization and game theoretic techniques for multicell beamforming

The main challenge in mobile wireless communications is the incompatibility between limited wireless resources and increasing demand on wireless services. The employment of frequency reuse technique has effectively increased the capacity of the network and improved the efficiency of frequency utilization. However, with the emergence of smart phones and even more data hungry applications such as interactive multimedia, higher data rate is demanded by mobile users. On the other hand, the interference induced by spectrum sharing arrangement has severely degraded the quality of service for users and restricted further reduction of cell size and enhancement of frequency reuse factor. Beamforming technique has great potential to improve the network performance. With the employment of multiple antennas, a base station is capable of directionally transmitting signals to desired users through narrow beams rather than omnidirectional waves. This will result users suffer less interference from the signals transmitted to other co-channel users. In addition, with the combination of beamforming technique and appropriate power control schemes, the resources of the wireless networks can be used more efficiently. In this thesis, mathematical optimization and game theoretic techniques have been exploited for beamforming designs within the context of multicell wireless networks. Both the coordinated beamforming and the coalitional game theoretic based beamforming techniques have been proposed. Initially, coordinated multicell beamforming algorithms for mixed design criteria have been developed, in which some users are allowed to achieve target signal-to-interference- plus-noise ratios (SINRs) while the SINRs of rest of the users in all cells will be balanced to a maximum achievable SINR. An SINR balancing based coordinated multicell beamforming algorithm has then been proposed which is capable of balancing users in different cells to different SINR levels. Finally, a coalitional game based multicell beamforming has been considered, in which the proposed coalition formation algorithm can reach to stable coalition structures. The performances of all the proposed algorithms have been demonstrated using MATLAB based simulations

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

Allocations de ressources dans les réseaux sans fils énergétiquement efficaces.

In this thesis, we investigate two techniques used for enhancing the energy orspectral efficiency of the network. In the first part of the thesis, we propose tocombine the network future context prediction capabilities with the well-knownlatency vs. energy efficiency tradeoff. In that sense, we consider a proactivedelay-tolerant scheduling problem. In this problem, the objective consists ofdefining the optimal power strategies of a set of competing users, which minimizesthe individual power consumption, while ensuring a complete requestedtransmission before a given deadline. We first investigate the single user versionof the problem, which serves as a preliminary to the concepts of delay tolerance,proactive scheduling, power control and optimization, used through the first halfof this thesis. We then investigate the extension of the problem to a multiusercontext. The conducted analysis of the multiuser optimization problem leads toa non-cooperative dynamic game, which has an inherent mathematical complexity.In order to address this complexity issue, we propose to exploit the recenttheoretical results from the Mean Field Game theory, in order to transitionto a more tractable game with lower complexity. The numerical simulationsprovided demonstrate that the power strategies returned by the Mean FieldGame closely approach the optimal power strategies when it can be computed(e.g. in constant channels scenarios), and outperform the reference heuristicsin more complex scenarios where the optimal power strategies can not be easilycomputed.In the second half of the thesis, we investigate a dual problem to the previousoptimization problem, namely, we seek to optimize the total spectral efficiencyof the system, in a constant short-term power configuration. To do so, we proposeto exploit the recent advances in interference classification. the conductedanalysis reveals that the system benefits from adapting the interference processingtechniques and spectral efficiencies used by each pair of Access Point (AP) and User Equipment (UE). The performance gains offered by interferenceclassification can also be enhanced by considering two improvements. First, wepropose to define the optimal groups of interferers: the interferers in a samegroup transmit over the same spectral resources and thus interfere, but can processinterference according to interference classification. Second, we define theconcept of ’Virtual Handover’: when interference classification is considered,the optimal Access Point for a user is not necessarily the one providing themaximal SNR. For this reason, defining the AP-UE assignments makes sensewhen interference classification is considered. The optimization process is thenthreefold: we must define the optimal i) interference processing technique andspectral efficiencies used by each AP-UE pair in the system; ii) the matching ofinterferers transmitting over the same spectral resources; and iii) define the optimalAP-UE assignments. Matching and interference classification algorithmsare extensively detailed in this thesis and numerical simulations are also provided,demonstrating the performance gain offered by the threefold optimizationprocedure compared to reference scenarios where interference is either avoidedwith orthogonalization or treated as noise exclusively.Dans le cadre de cette thèse, nous nous intéressons plus particulièrement àdeux techniques permettant d’améliorer l’efficacité énergétique ou spectrale desréseaux sans fil. Dans la première partie de cette thèse, nous proposons de combinerles capacités de prédictions du contexte futur de transmission au classiqueet connu tradeoff latence - efficacité énergétique, amenant à ce que l’on nommeraun réseau proactif tolérant à la latence. L’objectif dans ce genre de problèmesconsiste à définir des politiques de transmissions optimales pour un ensembled’utilisateur, qui garantissent à chacun de pouvoir accomplir une transmissionavant un certain délai, tout en minimisant la puissance totale consommée auniveau de chaque utilisateur. Nous considérons dans un premier temps le problèmemono-utilisateur, qui permet alors d’introduire les concepts de tolérance àla latence, d’optimisation et de contrôle de puissance qui sont utilisés dans lapremière partie de cette thèse. L’extension à un système multi-utilisateurs estensuite considérée. L’analyse révèle alors que l’optimisation multi-utilisateurpose problème du fait de sa complexité mathématique. Mais cette complexitépeut néanmoins être contournée grâce aux récentes avancées dans le domainede la théorie des jeux à champs moyens, théorie qui permet de transiter d’unjeu multi-utilisateur, vers un jeu à champ moyen, à plus faible complexité. Lessimulations numériques démontrent que les stratégies de puissance retournéespar l’approche jeu à champ moyen approchent notablement les stratégies optimaleslorsqu’elles peuvent être calculées, et dépassent les performances desheuristiques communes, lorsque l’optimum n’est plus calculable, comme c’est lecas lorsque le canal varie au cours du temps.Dans la seconde partie de cettethèse, nous investiguons un possible problème dual au problème précédent. Plusspécifiquement, nous considérons une approche d’optimisation d’efficacité spectrale,à configuration de puissance constante. Pour ce faire, nous proposonsalors d’étudier l’impact sur le réseau des récentes avancées en classification d’interférence.L’analyse conduite révèle que le système peut bénéficier d’uneadaptation des traitements d’interférence faits à chaque récepteur. Ces gainsobservés peuvent également être améliorés par deux altérations de la démarched’optimisation. La première propose de redéfinir les groupes d’interféreurs decellules concurrentes, supposés transmettre sur les mêmes ressources spectrales.L’objectif étant alors de former des paires d’interféreurs “amis”, capables detraiter efficacement leurs interférences réciproques. La seconde altération portele nom de “Virtual Handover” : lorsque la classification d’interférence est considérée,l’access point offrant le meilleur SNR n’est plus nécessairement le meilleuraccess point auquel assigner un utilisateur. Pour cette raison, il est donc nécessairede laisser la possibilité au système de pouvoir choisir par lui-même la façondont il procède aux assignations des utilisateurs. Le processus d’optimisationse décompose donc en trois parties : i) Définir les coalitions d’utilisateurs assignésà chaque access point ; ii) Définir les groupes d’interféreurs transmettantsur chaque ressource spectrale ; et iii) Définir les stratégies de transmissionet les traitements d’interférences optimaux. L’objectif de l’optimisationest alors de maximiser l’efficacité spectrale totale du système après traitementde l’interférence. Les différents algorithmes utilisés pour résoudre, étape parétape, l’optimisation globale du système sont détaillés. Enfin, des simulationsnumériques permettent de mettre en évidence les gains de performance potentielsofferts par notre démarche d’optimisation

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.Comment: Accepted by IEEE JSA

Enable Reliable and Secure Data Transmission in Resource-Constrained Emerging Networks

The increasing deployment of wireless devices has connected humans and objects all around the world, benefiting our daily life and the entire society in many aspects. Achieving those connectivity motivates the emergence of different types of paradigms, such as cellular networks, large-scale Internet of Things (IoT), cognitive networks, etc. Among these networks, enabling reliable and secure data transmission requires various resources including spectrum, energy, and computational capability. However, these resources are usually limited in many scenarios, especially when the number of devices is considerably large, bringing catastrophic consequences to data transmission. For example, given the fact that most of IoT devices have limited computational abilities and inadequate security protocols, data transmission is vulnerable to various attacks such as eavesdropping and replay attacks, for which traditional security approaches are unable to address. On the other hand, in the cellular network, the ever-increasing data traffic has exacerbated the depletion of spectrum along with the energy consumption. As a result, mobile users experience significant congestion and delays when they request data from the cellular service provider, especially in many crowded areas. In this dissertation, we target on reliable and secure data transmission in resource-constrained emerging networks. The first two works investigate new security challenges in the current heterogeneous IoT environment, and then provide certain countermeasures for reliable data communication. To be specific, we identify a new physical-layer attack, the signal emulation attack, in the heterogeneous environment, such as smart home IoT. To defend against the attack, we propose two defense strategies with the help of a commonly found wireless device. In addition, to enable secure data transmission in large-scale IoT network, e.g., the industrial IoT, we apply the amply-and-forward cooperative communication to increase the secrecy capacity by incentivizing relay IoT devices. Besides security concerns in IoT network, we seek data traffic alleviation approaches to achieve reliable and energy-efficient data transmission for a group of users in the cellular network. The concept of mobile participation is introduced to assist data offloading from the base station to users in the group by leveraging the mobility of users and the social features among a group of users. Following with that, we deploy device-to-device data offloading within the group to achieve the energy efficiency at the user side while adapting to their increasing traffic demands. In the end, we consider a perpendicular topic - dynamic spectrum access (DSA) - to alleviate the spectrum scarcity issue in cognitive radio network, where the spectrum resource is limited to users. Specifically, we focus on the security concerns and further propose two physical-layer schemes to prevent spectrum misuse in DSA in both additive white Gaussian noise and fading environments

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