Jorge Ortin

Distributed resource allocation in cognitive radio networks with a game learning approach to improve aggregate system capacity

This paper presents a game theoretic solution for joint channel allocation and power control in cognitive radio networks analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the network utility, defined with different criteria, with limited information. The problem is addressed through a non-cooperative game based on local information. Although the existence of a pure Nash Equilibrium cannot be assured for this game, simulation results show that it exists with high probability and with a performance similar to that of a potential game, where each player requires overall network information. The obtained results are compared with a centralized heuristic genetic algorithm to show the correctness of the proposals. From this point, utility functions for the local game are modified to restrict the transmitted power to drive the solution to a more cooperative approach. To overcome the convergence limitations of the local game, no-regret learning algorithms are used to perform the joint channel and power allocation. These algorithms provide stable mixed strategies in any scenario with even better global performance. This opens an interesting perspective to develop realistic protocols based on the modeled interactions and increases the adaptability to perform efficient opportunistic spectrum access.

A* Based Algorithm for Reduced Complexity ML Decoding of Tailbiting Codes

The A* algorithm is a graph search algorithm which has shown good results in terms of computational complexity for Maximum Likelihood (ML) decoding of tailbiting convolutional codes. The decoding of tailbiting codes with this algorithm is performed in two phases. In the first phase, a typical Viterbi decoding is employed to collect information regarding the trellis. The A* algorithm is then applied in the second phase, using the information obtained in the first one to calculate the heuristic function. The improvements proposed in this work decrease the computational complexity of the A* algorithm using further information from the first phase of the algorithm. This information is used for obtaining a more accurate heuristic function and finding early terminating conditions for the A* algorithm. Simulation results show that the proposed modifications decrease the complexity of ML decoding with the A* algorithm in terms of the performed number of operations.

On optimal resource allocation in virtual sensor networks

Sensor network virtualization is a promising paradigm to move away from highly-customized, application-specific wireless sensor network deployment by opening up to the possibility of dynamically assigning general purpose physical resources to multiple stakeholder applications. In this field, this paper introduces an optimization framework to perform the allocation of physical shared resources of wireless sensor networks to multiple requesting applications. The proposed optimization framework aims to maximize the total number of applications which can share a common physical network, while accounting for the distinguishing characteristics and limitations of the wireless sensor environment (limited storage, limited processing power, limited bandwidth, tight energy consumption requirements). Due to the complexity of the optimization problem, a heuristic algorithm is also proposed. The proposed framework is finally evaluated by simulation considering realistic parameters from actual sensor nodes and deployed applications to provide a detailed performance evaluation and to assess the gain involved in letting multiple applications share a common physical network with respect to one-application, one-network vertical design approaches.

Performance analysis of turbo decoding algorithms in wireless OFDM systems

Turbo codes are well known to be one of the error correction techniques which achieve closer results to the Shannon limit. Nevertheless, the specific performance of the code highly depends on the particular decoding algorithm used at the receiver. In this sense, the election of the decoding algorithm involves a trade off between the gain introduced by the code and the complexity of the decoding process. In this work we perform a thorough analysis of the different iterative decoding techniques and analyze their suitability for being implemented in the user terminals of new cellular and broadcast systems which are based on orthogonal frequency division multiplexing (OFDM). The analyzed iterative decoding algorithms are the max-log-MAP and the soft output Viterbi algorithm (SOVA), since both of them have a relative low computational complexity, simplifying their implementation in cost efficient terminals. Simulation results have been obtained for different encoder structures, block sizes and considering realistic channel conditions (an OFDM transmission over a wireless channel).

Game Theoretic Approach for End-to-End Resource Allocation in Multihop Cognitive Radio Networks

This paper presents a game theoretic solution for end-to-end channel and power allocation in multihop cognitive radio networks analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the number of flows that can be established in the network. The problem is addressed through three different games: a local flow game which uses complete information about the links of the flow, a potential flow game requiring global network knowledge and a cooperative link game based on partial information regarding the links of the flow. Results show that the proposed link game highly decreases the complexity of the channel and power allocation problem in terms of computational load, reducing the information shared between the links forming each flow with a performance similar to that of the more complex flow games.

Flow allocation with joint channel and power assignment in multihop cognitive radio networks using game theory

This paper presents a game theoretic solution for end-to-end flow allocation with joint channel and power assignment in multihop cognitive radio networks under the physical interference model. The objective is to find a distributed solution that maximizes the network utility with limited information. We consider two different criteria to define this network utility: the number of flows that can be established in the network and the aggregate capacity in bps of the established flows. We propose three different games to address this problem: a local flow game based on complete information about the links of the flow, a potential flow game which needs overall network information and a cooperative link game only requiring partial information about the links of the flow. We introduce the required modifications in the local flow and the cooperative link game definitions to ensure their convergence. Results show that the proposed games adapt to the considered network utility definitions. In addition, the cooperative link game highly decreases the complexity of the flow allocation problems with a performance similar to that of the local and potential flow games.

Two step SOVA-based decoding algorithm for tailbiting codes

In this work we propose a novel decoding algorithm for tailbiting convolutional codes and evaluate its performance over different channels. The proposed method consists on a fixed two-step Viterbi decoding of the received data. In the first step, an estimation of the most likely state is performed based on a SOVA decoding. The second step consists of a conventional Viterbi decoding that employs the state estimated in the previous step as the initial and final states of the trellis. Simulations results show a performance close to that of maximum-likelihood decoding.

An Optimization Framework for Resource Allocation in Virtual Sensor Networks

We propose an optimization framework to perform resource allocation in virtual sensor networks. Sensor network virtualization is a promising paradigm to improve flexibility of wireless sensor networks which allows to dynamically assign physical resources to multiple stakeholder applications. The proposed optimization framework aims at maximizing the total number of applications which can share a common physical network, while accounting for the distinguishing characteristics and limitations of the wireless sensor environment (limited storage, limited processing power, limited bandwidth, tight energy consumption requirements). The proposed framework is finally applied to realistic network topologies to assess the gain involved in letting multiple applications share a common physical network with respect to one-application, one-Network vertical design approaches.

Channel Independent Precoder for OFDM-Based Systems Over Fading Channels

In this paper we propose an independent channel precoder for orthogonal frequency division multiplexing (OFDM) systems over fading channels. The design of the precoder is based on the information redistribution of the input modulated symbols among the output precoded symbols. The proposed precoder decreases the variance of the instantaneous noise power at the receiver produced by the channel variability. The employment of an interleaver together with a precoding matrix whose size does not depend on the number of data carriers in an OFDM symbol allows different configurations of time-frequency diversity which can be easily adapted to the channel conditions. The precoder is evaluated with a modified Zero Forcing (ZF) equalizer whose maximum gain is constrained by means of a clipping factor. Thus, the clipping factor limits the noise power transfer in the receiver deprecoding block in low SNR conditions.

Joint cell selection and resource allocation games with backhaul constraints

Abstract In this work we study the problem of user association and resource allocation to maximize the proportional fairness of a wireless network with limited backhaul capacity. The optimal solution of this problem requires solving a mixed integer non-linear programming problem which generally cannot be solved in real time. We propose instead to model the problem as a potential game, which decreases dramatically the computational complexity and obtains a user association and resource allocation close to the optimal solution. Additionally, the use of a game-theoretic approach allows an efficient distribution of the computational burden among the computational resources of the network.