Optimization for Centralized and Decentralized Cognitive Radio Networks

Cognitive radio technology improves radio resource usage by reconfiguring the wireless connection settings according to the optimum decisions, which are made on the basis of the collected context information. This paper focuses on optimization algorithms for decision making to optimize radio resource usage in heterogeneous cognitive wireless networks. For networks with centralized management, we proposed a novel optimization algorithm whose solution is guaranteed to be exactly optimal. In order to avoid an exponential increase of computational complexity in large-scale wireless networks, we model the target optimization problem as a minimum cost-flow problem and find the solution of the problem in polynomial time. For the networks with decentralized management, we propose a distributed algorithm using the distributed energy minimization dynamics of the Hopfield-Tank neural network. Our algorithm minimizes a given objective function without any centralized calculation. We derive the decision-making rule for each terminal to optimize the entire network. We demonstrate the validity of the proposed algorithms by several numerical simulations and the feasibility of the proposed schemes by designing and implementing them on experimental cognitive radio network systems.

认知无线电技术通过根据最优决策重新配置无线连接设置来提高无线电资源的利用率，这些最优决策是基于收集到的环境信息做出的。本文重点研究用于决策的优化算法，以优化异构认知无线网络中的无线电资源使用。对于集中式管理的网络，我们提出了一种新的优化算法，其解保证是完全最优的。为了避免在大规模无线网络中计算复杂度呈指数增长，我们将目标优化问题建模为最小费用流问题，并在多项式时间内找到问题的解。对于分散式管理的网络，我们提出一种分布式算法，利用霍普菲尔德 - 坦克神经网络的分布式能量最小化动态。我们的算法在没有任何集中计算的情况下最小化给定的目标函数。我们推导出每个终端的决策规则以优化整个网络。我们通过若干数值模拟证明了所提出算法的有效性，并通过在实验性认知无线电网络系统上设计和实现所提出的方案证明了其可行性。