Farshad Shams

A survey on resource allocation techniques in OFDM(A) networks

Abstract In this contribution, we present a survey on the radio resource allocation techniques in orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA) systems. This problem goes back to 1960s and that is related to properly and efficiently allocate the radio resources, namely subcarriers and power. We start by overviewing the main open issues in OFDM. Then, we describe the problem formulation in OFDMA, and we review the existing solutions to allocate the radio resources. The goal is to discuss the fundamental concepts and relevant features of different radio resource management criteria, including water-filling, max–min fairness, proportional fairness, cross-layer optimization, utility maximization, and game theory, also including a toy example with two terminals to compare the performance of the different schemes. We conclude the survey with a review of the state-of-the-art in resource allocation for next-generation wireless networks, including multicellular systems, cognitive radio, and relay-assisted communications, and we summarize advantages and common problems of the existing solutions available in the literature. The distinguishing feature of this contribution is a tutorial-style introduction to the fundamental problems in this area of research, intended for beginners on this topic.

Energy-Efficient Power Control for Multiple-Relay Cooperative Networks Using

In this paper, we investigate the power control problem in a cooperative network with multiple wireless transmitters, multiple amplify-and-forward relays, and one destination. The relay communication can be either full duplex or half-duplex, and all source nodes interfere with each other at every intermediate relay node, and all active nodes (transmitters and relay nodes) interfere with each other at the base station. A game-theory-based power control algorithm is devised to allocate the powers among all active nodes. The source nodes aim at maximizing their energy efficiency (in bits per Joule per Hertz), whereas the relays aim at maximizing the network sum rate. We show that the proposed game admits multiple pure/mixed-strategy Nash equilibrium points. A Q-learning-based algorithm is then formulated to let the active players converge to the best Nash equilibrium point that combines good performance in terms of both energy efficiency and overall data rate. Numerical results show that the full-duplex scheme outperforms half-duplex configuration, Nash bargaining solution, the max-min fairness, and the max-rate optimization schemes in terms of energy efficiency, and outperforms the half-duplex mode, Nash bargaining system, and the max-min fairness scheme in terms of network sum rate.

Q

This work investigates a fair adaptive resource management criterion (in terms of transmit powers and subchannel assignment) for the uplink of an orthogonal frequency-division multiple access network, populated by mobile users with constraints in terms of target data rates. The inherent optimization problem is tackled with the analytical tools of coalitional game theory, and a practical algorithm based on Markov modeling is introduced. The proposed scheme allows the mobile devices to fulfill their rate demands exactly with a minimum utilization of network resources. Simulation results show that the average number of operations of the proposed iterative algorithm are much lower than K · N, where N and K are the number of allocated subcarriers and of mobile terminals.

-Learning

The 2010 earthquake in Haiti is often referred to as the turning point that changed the way social media can be used during disasters. The development of strategies, technologies and tools to enhance user collaboration around disasters has become an emergent field, and their integration with appropriate sensor networks presents itself as an effective solution to drive decision making in emergency management. In this paper, we present a review of existing disaster management systems and their underlying strategies and technologies, and identify the limitations of the tools in which they are implemented. We then propose an architecture for disaster management that integrates the mining of social networks and the use of sensor networks as two complementary technologies to overcome the limitations of the current emergency management tools.

An OFDMA resource allocation algorithm based on coalitional games

Game theory is the study of decision making in an interactive environment. Coalitional games fulfill the promise of group efficient solutions to problems involving strategic actions. Formulation of optimal player behavior is a fundamental element in this theory. This paper comprises a self-instructive didactic means to study basics of coalitional games indicating how coalitional game theory tools can provide a framework to tackle different problems in communications and networking. We show that coalitional game approaches achieve an improved performance compare to non-cooperative game theoretical solutions.

On Integrating Social and Sensor Networks for Emergency Management

This work investigates the problem of resource allocation (in terms of transmit powers and subchannel assignment) in the uplink channel of an orthogonal frequency division multiple access (OFDMA) network, populated by mobile users with constraints in terms of target transmit data rates. The optimization problem is tackled with the analytical tools of coalitional game theory, and a simple and practical algorithm based on Markov modeling is introduced. The proposed algorithm allows the mobile devices to fulfill their data rate demands with minimum utilization of the network resources. Simulation results are provided to validate the theoretical analysis for practical OFDMA network parameters.

Basics of coalitional games with applications to communications and networking

This paper studies a resource allocation problem for a cooperative network with multiple wireless transmitters, multiple full-duplex amplify-and-forward relays, and one destination. A game-theoretic model is used to devise a power control algorithm among all active nodes, wherein the sources aim at maximizing their energy efficiency, and the relays aim at maximizing the network sum-rate. To this end, we formulate a low-complexity Q-learning-based algorithm to let the active players converge to the best mixed-strategy Nash equilibrium point, that combines good performance in terms of energy efficiency and overall data rate. Numerical results show that the proposed scheme outperforms Nash bargaining, max-min fairness, and max-rate optimization schemes.