This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that estimates future interference values by filtering measured interference at a given location. The predictor's parameterization is done offline by translating the autocorrelation of interference into an autoregressive moving average (ARMA) representation. This ARMA model is inserted into a steady-state Kalman filter enabling nodes to predict with low computational effort. Results show a good accuracy of predicted values versus true values for relevant time horizons. Although the predictor is parameterized for Poisson-distributed nodes, Rayleigh fading, and fixed message lengths, a sensitivity analysis shows that it also tends to work well in more general network scenarios. Numerical examples for underlay device-to-device communications, a common wireless sensor technology, and coexistence scenarios of Wi-Fi and LTE illustrate its broad applicability. The predictor can be applied as part of interference management to improve medium access, scheduling, and radio resource allocation.
翻译:本条提出并评价一种技术,以预测无线网络的干扰程度。 我们设计了一个循环预测器, 通过过滤特定地点的测量干扰来估计未来干扰值。 预测器的参数化是通过将干扰的自动相关关系转换成自动递减移动平均( ARMA) 表示而脱线的。 这个ARMA 模型被插入一个稳定状态的 Kalman 过滤器, 以便以低计算努力来预测无线网络的干扰程度。 结果显示预测值相对于相关时平线的真实值的准确性。 尽管预测器是Poisson分布节点、 Rayleg 淡化和固定电文长度的参数化参数化的, 但敏感度分析显示它也往往在更一般的网络情景下运作良好。 内置装置对设备通信、通用无线传感器技术以及Wi-Fi和LTE的共存情景的数值示例说明了其广泛适用性。 预测器可以作为干扰管理的一部分用于改进中位访问、 和无线电资源分配。