OFDMA femtocell is a promising technology to improve indoor cellular network coverage cost-effectively. Large-scale deployment of femtocells in the urban area is expected to be realized in the near future. However, inter-femtocell interference significantly limits the achievable throughput of an OFDMA femtocell system, which calls for interference management tailored for femtocell networks. A typical approach to mitigate inter-femtocell interference is known as resource isolation, which aims at assigning non-overlapping resources to interfering femtocells. One of the main challenges for interference mitigation in femtocell networks is that end consumers often install the femtocells. Very limited information about the femtocells is available, making it hard to decipher the inter-femtocell interference. Previous studies either take time to resolve collisions online or adopt a conservative approach to identify interferers. Although the latter approach avoids wasting time on resolving collisions, it may result in resource underutilization. In this paper, we propose an efficient method to identify inter-femtocell interference by analyzing the received patterns observed by mobile stations. We conducted experiments on GNU Radio/USRP to demonstrate that the proposed interference identification method can successfully identify real interferers while excluding non-interfering femtocells from suspect femtocells. Based on the proposed interference identification, we propose a weighted vertex-coloring based resource assignment algorithm to allocate resources with better fairness and higher throughput.

中文翻译：

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毫微微蜂窝间干扰识别和资源管理

OFDMA femtocell 是一种很有前途的技术，可以经济有效地改善室内蜂窝网络覆盖。预计在不久的将来，femtocell 将在市区实现大规模部署。然而，毫微微蜂窝间干扰极大地限制了OFDMA毫微微蜂窝系统可实现的吞吐量，这需要为毫微微蜂窝网络量身定制的干扰管理。减轻毫微微小区间干扰的典型方法被称为资源隔离，其旨在将非重叠资源分配给干扰毫微微小区。在毫微微蜂窝网络中减轻干扰的主要挑战之一是终端消费者经常安装毫微微蜂窝。关于毫微微蜂窝的信息非常有限，因此很难破译毫微微蜂窝之间的干扰。以前的研究要么需要时间在线解决冲突，要么采用保守的方法来识别干扰。虽然后一种方法避免了在解决冲突上浪费时间，但它可能会导致资源利用不足。在本文中，我们提出了一种通过分析移动站观察到的接收模式来识别毫微微小区间干扰的有效方法。我们在 GNU Radio/USRP 上进行了实验，以证明所提出的干扰识别方法可以成功识别真正的干扰源，同时从可疑的 femtocell 中排除非干扰 femtocell。基于提出的干扰识别，我们提出了一种基于加权顶点着色的资源分配算法，以更好的公平性和更高的吞吐量分配资源。