170 research outputs found
Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory
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
Spectrum Leasing as an Incentive towards Uplink Macrocell and Femtocell Cooperation
The concept of femtocell access points underlaying existing communication
infrastructure has recently emerged as a key technology that can significantly
improve the coverage and performance of next-generation wireless networks. In
this paper, we propose a framework for macrocell-femtocell cooperation under a
closed access policy, in which a femtocell user may act as a relay for
macrocell users. In return, each cooperative macrocell user grants the
femtocell user a fraction of its superframe. We formulate a coalitional game
with macrocell and femtocell users being the players, which can take individual
and distributed decisions on whether to cooperate or not, while maximizing a
utility function that captures the cooperative gains, in terms of throughput
and delay.We show that the network can selforganize into a partition composed
of disjoint coalitions which constitutes the recursive core of the game
representing a key solution concept for coalition formation games in partition
form. Simulation results show that the proposed coalition formation algorithm
yields significant gains in terms of average rate per macrocell user, reaching
up to 239%, relative to the non-cooperative case. Moreover, the proposed
approach shows an improvement in terms of femtocell users' rate of up to 21%
when compared to the traditional closed access policy.Comment: 29 pages, 11 figures, accepted at the IEEE JSAC on Femtocell Network
Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting
We consider the distributed channel selection problem in the context of
device-to-device (D2D) communication as an underlay to a cellular network.
Underlaid D2D users communicate directly by utilizing the cellular spectrum but
their decisions are not governed by any centralized controller. Selfish D2D
users that compete for access to the resources construct a distributed system,
where the transmission performance depends on channel availability and quality.
This information, however, is difficult to acquire. Moreover, the adverse
effects of D2D users on cellular transmissions should be minimized. In order to
overcome these limitations, we propose a network-assisted distributed channel
selection approach in which D2D users are only allowed to use vacant cellular
channels. This scenario is modeled as a multi-player multi-armed bandit game
with side information, for which a distributed algorithmic solution is
proposed. The solution is a combination of no-regret learning and calibrated
forecasting, and can be applied to a broad class of multi-player stochastic
learning problems, in addition to the formulated channel selection problem.
Analytically, it is established that this approach not only yields vanishing
regret (in comparison to the global optimal solution), but also guarantees that
the empirical joint frequencies of the game converge to the set of correlated
equilibria.Comment: 31 pages (one column), 9 figure
A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions
The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network
Secrecy-Optimized Resource Allocation for Device-to-Device Communication Undelaying Cellular Networks
L’objectif principal de l’introduction de la communication de périphérique-à -périphérique «device-to-device» (D2D) sous-jacente aux systèmes de communication sans fil de cinquième génération (5G), est d’augmenter l’efficacité spectrale (ES). Cependant, la communication
D2D sous-jacente aux réseaux cellulaires peut entraîner une dégradation des performances causée par des co-interférences de canal sévères entre les liaisons cellulaires et D2D. De plus, en raison de la complexité du contrôle et de la gestion, les connexions directes entre les appareils à proximité sont vulnérables. En conséquence, la communication D2D n’est pas robuste contre les menaces de sécurité et l’écoute clandestine. Pourtant, les co-interférences
de canal peuvent être adoptées pour aider les utilisateurs cellulaires (UC) et les paires D2D afin d’empêcher l’écoute clandestine. Dans cette thèse, nous étudions différents scénarios de problèmes d’allocation de ressources en utilisant le concept de sécurité de couche physique
«physical layer security» (PLS) pour la communication D2D sous-jacente aux réseaux cellulaires, tout en satisfaisant les exigences minimales de qualité de service (QoS) des liaisons cellulaires et D2D. Dans le cas où PLS est pris en compte, l’interférence peut aider à réduire l’écoute clandestine. Premièrement, nous formulons un scénario d’allocation de ressources dans lequel chaque bloc de ressources (RB) temps-fréquence de multiplexage par répartition orthogonale en fréquence (OFDM) peut être partagé par une seule CU et une paire D2D dans un réseau
unicellulaire. Le problème formulé est réduit au problème de correspondance tridimensionnelle, qui est généralement NP-difficile, et la solution optimale peut être obtenue par des
méthodes compliquées, telles que la recherche par force brute et/ou l’algorithme de branchement et de liaison qui ont une complexité de calcul exponentielle. Nous proposons donc une méta-heuristique basée sur l’algorithme de recherche tabou «Tabu Search» (TS) avec une complexité de calcul réduite pour trouver globalement la solution d’allocation de ressources radio quasi-optimale.----------ABSTRACT: The primary goal of introducing device-to-device (D2D) communication underlying fifthgeneration (5G) wireless communication systems is to increase spectral efficiency (ES). However, D2D communication underlying cellular networks can lead to performance degradation caused by severe co-channel interference between cellular and D2D links. In addition, due to the complexity of control and management, direct connections between nearby devices
are vulnerable. Thus, D2D communication is not robust against security threats and eavesdropping. On the other hand, the co-channel interference can be adopted to help cellular users (CUs) and D2D pairs to prevent eavesdropping. In this thesis, we investigate different resource allocation problem scenarios using the physical layer security (PLS) concept for the D2D communication underlying cellular networks, while satisfying the minimum quality of service (QoS) requirements of cellular and D2D link. If the PLS is taken into account, the interference can help reduce eavesdropping. First, we formulate a resource allocation scenario in which each orthogonal frequency-division
multiplexing (OFDM) time-frequency resource block (RB) can be shared by one single CU and one D2D pair in a single-cell network. The formulated problem is reduced to the threedimensional matching problem, which is generally NP-hard, and the optimal solution can be obtained through the complicated methods, such as brute-force search and/or branch-andbound algorithm that have exponential computational complexity. We, therefore, propose a meta-heuristic based on Tabu Search (TS) algorithm with a reduced computational complexity to globally find the near-optimal radio resource allocation solution
Reliable and Efficient Cognitive Radio Communications Using Directional Antennas
Cognitive Radio (CR) is a promising solution that enhances spectrum utilization by allowing an unlicensed or Secondary User (SU) to access licensed bands in a such way that its imposed interference on a license holder Primary User (PU) is limited, and hence fills the spectrum holes in time and/or frequency domains. Resource allocation, which involves scheduling of available time and transmit power, represents a crucial problem for the performance evaluation of CR systems. In this dissertation, we study the spectral efficiency maximization problem in an opportunistic CR system. Specifically, in the first part of the dissertation, we consider an opportunistic CR system where the SU transmitter (SUtx) is equipped to a Reconfigurable Antenna (RA). RA, with the capabilities of dynamically modifying their characteristics can improve the spectral efficiency, via beam steering and utilizing the spectrum white spaces in spatial (angular) domain. In our opportunistic CR system, SUtx relies on the beam steering capability of RA to detect the direction of PU\u27s activity and also to select the strongest beam for data transmission to SU receiver (SUrx). We study the combined effects of spectrum sensing error and channel training error as well as the beam detection error and beam selection error on the achievable rates of an opportunistic CR system with a RA at SUtx. We also find the best duration for spectrum sensing and channel training as well as the best transmit power at SUtx such that the throughput of our CR system is maximized subject to the Average Transmit Power Constraint (ATPC) and Average Interference Constraint (AIC). In the second part of the dissertation, we consider an opportunistic Energy Harvesting (EH)-enabled CR network, consisting of multiple SUs and an Access Point (AP), that can access a wideband spectrum licensed to a primary network. Assuming that each SU is equipped with a finite size rechargeable battery, we study how the achievable sum-rate of SUs is impacted by the combined effects of spectrum sensing error and imperfect Channel State Information (CSI) of SUs–AP links. We also design an energy management strategy that maximizes the achievable sum-rate of SUs, subject to a constraint on the average interference that SUs can impose on the PU
Resource Allocation for D2D Communications Based on Matching Theory
PhDDevice-to-device (D2D) communications underlaying a cellular infrastructure takes advantage
of the physical proximity of communicating devices and increasing resource utilisation.
However, adopting D2D communications in complex scenarios poses substantial
challenges for the resource allocation design. Meanwhile, matching theory has emerged
as a promising framework for wireless resource allocation which can overcome some limitations
of game theory and optimisation. This thesis focuses on the resource allocation
optimisation for D2D communications based on matching theory.
First, resource allocation policy is designed for D2D communications underlaying cellular
networks. A novel spectrum allocation algorithm based on many-to-many matching
is proposed to improve system sum rate. Additionally, considering the quality-of-service
(QoS) requirements and priorities of di erent applications, a context-aware resource allocation
algorithm based on many-to-one matching is proposed, which is capable of providing
remarkable performance enhancement in terms of improved data rate, decreased
packet error rate (PER) and reduced delay.
Second, to improve resource utilisation, joint subchannel and power allocation problem
for D2D communications with non-orthogonal multiple access (NOMA) is studied. For
the subchannel allocation, a novel algorithm based on the many-to-one matching is
proposed for obtaining a suboptimal solution. Since the power allocation problem is
non-convex, sequential convex programming is adopted to transform the original power
allocation problem to a convex one. The proposed algorithm is shown to enhance the
network sum rate and number of accessed users.
Third, driven by the trend of heterogeneity of cells, the resource allocation problem for
NOMA-enhanced D2D communications in heterogeneous networks (HetNets) is investigated. In such a scenario, the proposed resource allocation algorithm is able to closely
approach the optimal solution within a limited number of iterations and achieves higher
sum rate compared to traditional HetNets schemes.
Thorough theoretical analysis is conducted in the development of all proposed algorithms,
and performance of proposed algorithm is evaluated via comprehensive simulations.
This thesis concludes that matching theory based resource allocation for D2D communications
achieves near-optimal performance with acceptable complexity. In addition,
the application of D2D communications in NOMA and HetNets can improve system
performance in terms of sum rate and users connectivity
Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends
Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios
- …