Panagiotis Vamvakas

Joint utility-based uplink power and rate allocation in wireless networks: A non-cooperative game theoretic framework

Abstract In this paper a novel utility-based game theoretic framework is proposed to address the problem of joint transmission power and rate allocation in the uplink of a cellular wireless network. Initially, each user is associated with a generic utility function, capable of properly expressing and representing mobile user’s degree of satisfaction, in relation to the allocated system’s resources for heterogeneous services with various transmission rates. Then, a Joint Utility-based uplink Power and Rate Allocation (JUPRA) game is formulated, where each user aims selfishly at maximizing his utility-based performance under the imposed physical limitations, and its unique Nash equilibrium is determined with respect to both variables, i.e. uplink transmission power and rate. The JUPRA game’s convergence to its unique Nash equilibrium is proven and a distributed, iterative and low complexity algorithm for computing JUPRA game’s equilibrium is introduced. The performance of the proposed approach is evaluated in detail and its superiority compared to various state of the art approaches is illustrated, while the contribution of each component of the proposed framework in its performance is quantified and analyzed.

Supermodular Game-Based Distributed Joint Uplink Power and Rate Allocation in Two-Tier Femtocell Networks

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). Users 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 games Nash Equilibrium (NE) point is analytically shown, while games 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 users 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 Customized Price and Power Control for Energy-Efficient Multi-Service Wireless Networks via S-Modular Theory

In this paper, the problem of joint utility-based customized price and power control in multi-service wireless networks is addressed via S-modular theory. Each user is associated with a generic two-variable net utility function. The latter takes a different form based on the type of the requested service, and depends on both user’s uplink transmission power and the price he is willing to pay to achieve his quality of service prerequisites. The joint customized price and power control problem is formulated as a two-variable optimization problem and confronted as a non-cooperative distributed game. The S-modular theory is adopted to solve the corresponding optimization problem. The existence of the game’s Nash equilibrium point with respect to both user’s uplink transmission power and price is analytically shown, while game convergence is also proven. A distributed and iterative algorithm for computing the two-variable Nash equilibrium point is also presented. The optimal price and uplink transmission power are both determined simultaneously in a distributed manner while the control intelligence and the decision making process lie at the user. 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 especially in terms of energy efficiency.

Efficient uplink power control in multi-service two-tier femtocell networks via a game theoretic approach

In this paper the problem of efficient power allocation in the uplink of two-tier closed-access femtocell networks is addressed. The modeling and design approach we follow considers two different dimensions, along with the corresponding constraints and properties that stem from each one of them: a) the two tier architecture, and b) the simultaneous support of both real-time and non-real time services with various and diverse Quality of Service (QoS) prerequisites. To treat the above problem under a common framework, network utility maximization theory is used, where each mobile user, either in a femtocell or in the macrocell, is associated with a properly defined QoS-aware utility function. To further mitigate the intercell and cross-tier interference and give higher priority to the macrocell users (MUs), the femtocell users (FUs) are also “penalized” through a convex cost function with respect to uplink transmission power. Due to its nature, the overall resulting problem is formulated as a non-cooperative game, and is solved using the supermodularity theory towards determining a Nash equilibrium point. A distributed iterative algorithm is also proposed in order to obtain the games equilibrium point. Finally, the operational effectiveness of the proposed approach is evaluated through modeling and simulation, while its superiority against other power control frameworks in two-tier femtocell networks proposed in the literature, is illustrated.

Dynamic Provider Selection & Power Resource Management in Competitive Wireless Communication Markets

In this paper the combined problem of Wireless Internet Service Provider (WISP) selection by the mobile customers and corresponding power allocation is treated, in order to meet user expectations and satisfaction in a competitive wireless communication market with several co-existing WISPs. Each WISP is characterized by a price and service-based reputation, formed based on its adopted pricing policy and its success to satisfy customers’ Quality of Service (QoS) prerequisites, the latter implicitly characterizing the specific WISP’s market penetration factor. The customers who act as learning automata selecting the most appropriate WISP adopt a machine learning based mechanism. The optimal power allocation is concluded from the maximization problem of each user’s utility function, which is confronted as a non-cooperative game among users and its Nash equilibrium is determined. The output of the resource allocation problem feeds the learning system in order to build knowledge and conclude to the optimal provider selection. A two-stage iterative algorithm is proposed in order to realize the machine learning provider selection and the distributed resource allocation. The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated.

Resource Allocation in Visible Light Communication Networks: NOMA vs OFDMA Transmission Techniques

Energy-efficiency, high transmission data rates and Quality of Service (QoS) awareness are the major challenges for resource management in the uplink of Visible Light Communication Personal Area Networks (VPANs). This paper investigates the problem of Optical Access Point (OAP) selection and resource allocation in the uplink of VPANs under two different transmission techniques, namely Orthogonal Frequency Division Multiple Access (OFDMA) and Non-Orthogonal Multiple Access (NOMA). Each user is associated with a generic utility function, which represents his perceived satisfaction with respect to the overall resource allocation problem. OAP selection adopts Maximum Gain Selection (MGS) policy, i.e. users select an OAP to connect to based on the highest path gain. A distributed resource allocation problem in VPANs is formulated and solved as an optimization problem. Following this analysis, a decentralized iterative and low-complexity algorithm for determining OAP selection and resource allocation is proposed, while the overall approach’s efficiency is illustrated via modeling and simulation, highlighting and assessing the advantages and drawbacks of each adopted transmission technique, i.e. OFDMA and NOMA.

Energy efficient uplink joint resource allocation non-cooperative game with pricing

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 games 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.