认知无线电网络中的动态波束形成

认知无线电技术能够大幅度提升频谱利用率，具有十分广阔的前景. 有效的波束形成算法能够在避免对主用户造成干扰的同时，保障认知用户的通信质量. 通常的认知波束形成算法均针对静止目标，而无线认知网络节点随时可能处于移动状态. 为此，研究了认知网络中机动目标的波束形成问题. 针对最大化信噪比、最小化主用户干扰和均衡认知用户信噪比3个不同的优化目标，分别提出了相应的波束形成解决方法. 利用粒子滤波对运动目标的DOA进行跟踪估计，并根据得到的DOA估计值来建模基站与认知用户之间的信道. 对于最大化信噪比的优化问题，将其转化为瑞利熵形式，得出问题的闭式解；对于最小化主用户干扰和均衡认知用户信噪比的优化问题，均采用凸优化方法将两个问题转化为二阶锥规划形式，通过内点法求解. 仿真实验结果证明了动态波束形成方法的有效性.

Dynamic Beamforming in Cognitive Radio Networks

Cognitive radio network has the potential of enhancing spectrum utilization.An effective beamforming algorithm can protect primary user from excessive interference while ensuring a meaningful QoS to the secondary system. Conventional beamforming algorithms focus on stationary targets, whereas the cognitive radio nodes are mobile. Dynamic beamforming is investigated in this paper. The direction of arrival (DOA) of a moving target is tracked by a particle filter algorithm. The derived DOA estimation is then used to model the channel between the base station and the users. Three different beamforming
schemes are proposed aiming at maximizing signal-to-noise ratio (SNR), minimizing inference, and SNR balancing, respectively. SNR maximization is transformed to Rayleigh-Ritz quotient, resulting in a closed-form solution. Interference minimization and SNR balancing are reformulated as secondary-order cone programming (SOCP) problems. These problems can be solved efficiently using interior point methods. Several numerical simulation examples
are provided to illustrate effectiveness of our approaches.