认知网络中的容量最优机会干扰对齐接入

在传统认知网络中，认知用户只能接入主要用户的空闲频带，而不能同时与主要用户共享带宽. 为了更有效地利用频谱资源，提出一种容量最优机会干扰对齐接入算法. 在保证主要用户信道容量的基础上，认知用户与主要用户可以占用相同的频带同时接入网络，且认知用户对主要用户的干扰为零. 所提出的算法由混合网络基站估计信道状态信息，并计算主要用户的信道容量和系统总信道容量. 根据容量最优的准则确定预编码向量及解码向量. 当主要用户的信道容量与未接入认知用户相比几乎不下降，且总信道容量大于未接入认知用户的总容量时，认知用户以容量最优机会干扰对齐的方式接入网络. 仿真结果表明：在相同信道状态条件下，所提出的算法较现有算法具有更优的信道容量并可提高认知网络频带利用率.

Opportunistic Interference Alignment with Optimal Capacity in Cognitive Networks

Cognitive users can only access idle bandwidth in traditional cognitive networks, but they cannot share the same bandwidth with the primary user simultaneously. In order to make full use of the spectrum, we propose a capacity optimal opportunistic interference alignment algorithm. By ensuring the channel capacity of primary users, cognitive users and primary users can share the same bandwidth, and the cognitive users cause no interference to the primary user. In the proposed algorithm, the hybrid network base station estimates channel state information and calculates channel capacity. It then determines the pre-coding vectors and decoding vectors according to the capacity optimal rule. Cognitive users access the networks by capacity optimal opportunistic interference alignment when the primary user’s channel capacity almost does not decline and the whole network can still obtain good channel capacity. Simulation results show that the proposed algorithm can obtain greater channel capacity and increase the utilization ratio of spectrum than other existing algorithms under the same condition of channel state information.