论文标题
通过联合通道估计和数据检测来减轻MU-MIMO中的智能干扰器
Mitigating Smart Jammers in MU-MIMO via Joint Channel Estimation and Data Detection
论文作者
论文摘要
无线系统必须有弹性，可抵御干扰攻击。现有的缓解方法需要了解干扰器的传输特征。但是，这些知识可能很难获取，尤其是对于仅在传输过程中攻击特定瞬间才能避免缓解的智能干扰器。我们提出了一种新颖的方法，可以减轻智能干扰器对大型多用户多输入多输出（MU-MIMO）基座（BSS）的攻击。我们的方法基于联合渠道估计和数据检测（JED）的最新进展，并利用了干扰器在连贯间隔内无法更改其子空间的事实。我们的方法称为MAED（缓解，估计和检测的缩写），使用了一种新的问题制定，将干扰器估计和缓解，通道估计和数据检测结合在一起，而不是分开这些任务。我们通过有效的迭代算法大约解决了问题。我们的结果表明，MAED有效地减轻了广泛的智能干扰攻击，而无需对攻击类型有任何先验知识。
Wireless systems must be resilient to jamming attacks. Existing mitigation methods require knowledge of the jammer's transmit characteristics. However, this knowledge may be difficult to acquire, especially for smart jammers that attack only specific instants during transmission in order to evade mitigation. We propose a novel method that mitigates attacks by smart jammers on massive multi-user multiple-input multiple-output (MU-MIMO) basestations (BSs). Our approach builds on recent progress in joint channel estimation and data detection (JED) and exploits the fact that a jammer cannot change its subspace within a coherence interval. Our method, called MAED (short for MitigAtion, Estimation, and Detection), uses a novel problem formulation that combines jammer estimation and mitigation, channel estimation, and data detection, instead of separating these tasks. We solve the problem approximately with an efficient iterative algorithm. Our results show that MAED effectively mitigates a wide range of smart jamming attacks without having any a priori knowledge about the attack type.
