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Universidad de Zaragoza

Academic institutioneurope · es
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Research library167linked papers
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Selected work

Representative Papers

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

Aug 01, 2012Ad hoc networks

To address the challenges of joint channel and power allocation, lack of global information, and high coordination overhead in multi-user dynamic spectrum sharing for cognitive radio networks, this paper proposes a distributed resource allocation algorithm based on game-theoretic learning. The method integrates non-cooperative game modeling with online reinforcement learning, enabling rapid convergence to Nash equilibrium without a central controller and adaptive optimization of spectrum access policies. Compared with conventional approaches, the proposed algorithm achieves significant performance gains: an average 18.7% increase in total system capacity, a 22.3% improvement in spectrum utilization, and a 35.1% reduction in channel collision rate. Moreover, it features low signaling overhead, strong robustness to dynamic environments, and excellent scalability—providing an efficient and practical autonomous coordination solution for distributed cognitive networks.

22 citations1 influentialRead paper

Performance analysis of turbo decoding algorithms in wireless OFDM systems

Aug 01, 2009IEEE transactions on consumer electronics

Turbo decoding in wireless OFDM systems faces a fundamental trade-off between error-rate performance and computational complexity. Method: This study systematically compares two low-complexity iterative decoding algorithms—Max-Log-MAP and the Soft-Output Viterbi Algorithm (SOVA)—under realistic OFDM channel conditions, evaluating both bit-error rate (BER) performance and computational overhead within a unified simulation framework across representative cellular and broadcast receiver scenarios. Contribution/Results: Results demonstrate that SOVA achieves BER performance comparable to Max-Log-MAP in the medium SNR regime while reducing computational complexity by approximately 40%. Based on this empirical analysis, we propose a practical decoding algorithm selection criterion tailored for cost- and power-constrained terminal chips. The findings provide actionable, implementation-oriented guidance for real-time Turbo decoding in 5G/6G mobile devices and digital television receivers.

15 citationsRead paper

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

Sep 01, 2010IEEE Communications Letters

To address the high computational complexity of maximum-likelihood (ML) decoding for tail-biting convolutional codes (TBCCs), this paper proposes a two-stage A* graph-search decoding framework. In the first stage, the Viterbi algorithm is executed to obtain reliable survivor paths and state-level statistics. In the second stage, these statistics are leveraged to construct a high-accuracy heuristic function for A*, and a path-confidence-based early-termination mechanism is introduced. This work is the first to exploit Viterbi path statistics explicitly to enhance the precision of the A* heuristic, thereby substantially reducing the search space. Simulation results demonstrate that the proposed decoder achieves strict ML performance while significantly lowering computational complexity compared to standard A* and BCJR decoders—offering marked complexity advantages without compromising optimality.

12 citationsRead paper

Joint cell selection and resource allocation games with backhaul constraints

Feb 01, 2017Pervasive and Mobile Computing

To address the coupled optimization of base station association and radio resource allocation in ultra-dense networks (UDNs), this paper pioneers the integration of hard backhaul bandwidth constraints into a multi-cell access game model, establishing a non-cooperative game framework that explicitly accounts for backhaul capacity limitations—enabling joint distributed optimization of user association, transmit power, and subchannel allocation. We propose an asynchronous iterative algorithm with rigorously proven convergence guarantees, overcoming resource allocation distortion caused by conventional approaches that neglect backhaul bottlenecks. Under typical dense deployment scenarios, the proposed scheme achieves a 32% increase in system throughput, a 41% reduction in edge-user outage probability, and backhaul constraint satisfaction exceeding 98%, outperforming benchmark schemes. Key contributions include: (i) backhaul-aware coupled modeling of access and resource allocation; (ii) a provably convergent distributed solution mechanism; and (iii) substantial performance gains in both spectral efficiency and reliability.

11 citations2 influentialRead paper

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

Mar 12, 2012IEEE Communications Letters

To address the high computational complexity and excessive control signaling overhead in resource allocation—leading to throughput degradation in multihop cognitive radio networks—this paper proposes a distributed end-to-end joint channel and power allocation mechanism grounded in the physical interference model. The core contribution is a localized cooperative link-level game framework that integrates potential game structure with link-wise coordination strategies, enabling efficient decision-making using only partial state information from neighboring nodes. This design substantially reduces both computational complexity and signaling overhead. Theoretical analysis and simulations demonstrate that the proposed scheme achieves flow admission count and network throughput close to those attainable under centralized, global-information-based game-theoretic optimization. These results validate the effectiveness and feasibility of lightweight distributed optimization for cognitive radio networks.

9 citationsRead paper
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