认知异构蜂窝网络中基于极化的跨层干扰对齐

Xiaofang Gao, Caili Guo, Shuo Chen
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引用次数: 1

摘要

为了消除下行底层认知异构蜂窝网络的跨层干扰,优化小蜂窝的误码率性能,利用两层网络的极化资源,提出了一种基于极化的跨层干扰对齐方案。利用干扰功率约束下最小总均方误差优化模型的类干涉准直特性,设计了小蜂窝的发射和接收极化状态。给出了该模型的解析非合作封闭解。仿真结果表明,该方案在保持整个系统最大和速率的前提下,在小小区的误码率性能上优于现有方案。并对宏小区和小小区的误码率性能进行了权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Polarization-based Cross-tier Interference Alignment in Cognitive Heterogeneous Cellular Network
To eliminate the cross-tier interference and optimize the bit-error-rate performance of small cell for downlink underlay cognitive heterogeneous cellular network, a novel polarization-based cross-tier interference alignment scheme is proposed in this paper by exploiting the polarization resource of two-tier network. The transmitting and receiving polarization states of small cell are designed by interference alignment-like behaviors of the minimum total mean squared error optimization model under interference power constraint. An analytical and noncooperative closed form solution is provided for the model. Simulation results show the proposed scheme outperforms the existing schemes in bit-error-rate performance of small cell while keeping the maximum sum rate of overall system. And the tradeoff between bit-error-rate performances of macro cell and small cell is also evaluated.
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