In this paper a novel dynamic spectrum management scheme for 5G Non Orthogonal Multiple Access (NOMA) wireless networks is proposed, where users are offered the option to transmit via licensed and unlicensed bands. Users are enabled to determine the optimal allocation of their transmission power in each one of the bands, while the unlicensed band is treated as a Common Pool Resource (CPR) - being non-excludable and rivalrous - which may collapse due to over-exploitation. Towards providing a pragmatic modeling approach for decision making under a realistic setting of probabilistic uncertainty, while properly capturing user risk perceptions, we model the corresponding optimization problem under the principles of Prospect Theory, removing the common assumption that subjects are behaving as neutral utility maximizers, a concept that does not reflect user risk behavior peculiarities. The corresponding problem is formulated as a CPR game, while the existence and uniqueness of its Pure Nash Equilibrium point are proven, and a user centric distributed algorithm is devised that obtains the corresponding solution. Detailed evaluation results are presented, highlighting the operation and superiority of the proposed framework against conventional Expected Utility Theory based approaches, while providing useful insights about user optimal decisions under realistic behaviors.

The evolution of communication systems in the direction of heterogeneity and mass connectivity through the deployment of 5G compatible technologies, is posing significant challenges to Wireless Internet Service Providers (WISPs)towards enhancing spectral efficiency and ensuring steady and uninterrupted operation. In this paper, we introduce a holistic framework that dynamically combines multiple access technologies while accounting for the users' Quality of Service (QoS)prerequisites and risk preferences. In emerging future wireless networks with flexible access technology interfaces, both bands operating over Orthogonal Frequency Division Multiple Access (OFDMA)and Non-Orthogonal Multiple Access (NOMA)become simultaneously available to the users as potential options of communications and usage. OFDMA technology, due to organizing the available spectrum into distinct resource blocks, provides free of interference but of relatively limited bandwidth service to the users, whereas NOMA has the potential to provide superior spectral capacity by accommodating all users in a single carrier. However, the latter comes at the expense of resource fragility and potential failure from over-exploitation, due to its fully shared nature and excessive competition among users. Considering users' diverse behavioral patterns when probabilistic uncertainty of the shared system's resources is assumed, we model the resource control problem under the principles of Prospect Theory, and solve it as a Fragile Common Pool of Resources (CPR)game converging to a unique Pure Nash Equilibrium (PNE)point. Decentralization of users' decisions under the proposed pragmatic approach enhances the stability and network's performance, which are confirmed by a series of comprehensive numerical results.

In this paper an analytical framework for the joint allocation of multiple physical resources under a dynamic personalized pricing setting, in a NOMA wireless network, is designed. Under our proposed user-centric paradigm, price is treated as a resource itself rather than simply being a control parameter and represents the user willingness to pay towards obtaining certain QoS levels. Each user expresses her satisfaction with respect to her and other users choices through a specifically designed utility function based on her unique characteristics and preferences. The resource allocation problem under consideration, becomes a distributed utility maximization problem, where each user updates her controllable parameters in an autonomous manner targeting at her satisfaction maximization. The problem is modeled and solved as a multivariable non cooperative game, admitting a unique Nash Equilibrium (NE), whose convergence is reached via a distributed and low complexity algorithm. Detailed numerical results, clearly demonstrate that the proposed framework allows the users to better exploit the system's resources, and therefore improve their overall satisfaction, while achieving significant improvements in the system operation in terms of power savings and achievable data rate.

In this paper, the problem of joint users' uplink transmission power and rate allocation in NOMA wireless networks is studied, under the scenario that each user is simultaneously requesting multiple services. Each user is associated with a two-variable utility function that represents his satisfaction from his allocated resources (i.e., power and rate). In order to appropriately reflect the combined needs of the user for bundling different types of services under the same common umbrella, user's utility function consists of two parts while different percentages are adopted by each user for each part in order to express his need for real and non-real time services. The joint resource allocation problem is directly confronted as a two-variable optimization problem and formulated as a non-cooperative game. The theory of S-modular multivariable games is adopted towards determining the Nash equilibrium point of the game. A distributed, iterative and low complexity algorithm for computing game's Nash equilibrium is introduced, while updating user's uplink transmission power and rate at the same step. Detailed numerical results exhibit the ability of the proposed framework to simultaneously satisfy diverse multiple services requested by the same user.

This paper tackles the problem of joint users' uplink transmission power and data rate allocation in multi-service two-tier femtocell networks. Each user-either macrocell (MUE) or femtocell user equipment (FUE)-is associated with a two-variable utility function that represents his perceived satisfaction with respect to his allocated resources (i.e., power and rate). User's utility function is differentiated based both on the tier that the user belongs to and the service he requests. The joint resource allocation problem is directly confronted as a two-variable optimization problem and formulated as a non-cooperative game. The theory of supermodular games is utilized towards treating the two-variable optimization problem and the inherent multidimensional competition that arises among the users. The existence of proposed game's Nash Equilibrium (NE) point is analytically shown, while game's convergence to its NE point is proven. A distributed and iterative algorithm for computing the desired NE is introduced, where the optimal values of each user's uplink transmission power and data rate are simultaneously updated at the same step. The performance of the proposed approach is evaluated via modeling and simulation and its superiority compared to other state of the art approaches is illustrated.

Joint power and rate control is a key element to the efficient use of wireless system resources especially when considering heterogeneous services with various transmission rates and requirements. Although several game-theoretic approaches have appeared to solve this problem their stable outcome is extracted independently or semi-jointly, and the corresponding equilibrium solutions introduce several inefficiencies. To eliminate some of them and achieve a more socially desirable energy efficient operational point, in this paper we combine pricing mechanisms with a joint utility-based uplink transmission power and rate allocation non-cooperative game formulation. The existence of a unique Nash equilibrium is shown, and the respective game's convergence is proven. A distributed, iterative and low-complexity algorithm for computing the desired equilibrium point is presented while the performance effectiveness of the proposed approach is evaluated via modeling and simulation considering both linear and nonlinear pricing.