Interference is an important limitation in many communication systems. It has been shown in many situations that the popular Gaussian approximation is not adequate and interference exhibits an impulsive behavior. This paper surveys the different statistical models proposed for such an interference, that can generally be unified using the class of sub-exponential family of distributions, and its impact on the receiver design. Visualizing the optimal decision boundaries allows one to show the non linear effect induced by impulsive noise models, which explains the significant loss in receiver performance designed under the standard Gaussian approximation. This motivates the need to develop new receivers. We propose a framework to design receivers robust to a variety of interference types, both Gaussian and non-Gaussian. We explore three ways of thinking about such receiver designs: a linear approach; by approximating the noise plus interference distribution; and by mimicking the decision rule distribution directly. Except for the linear approach, the other designs are capable of replicating the non-trivial optimal decision regions to different extents. The new detection algorithms are evaluated via Monte Carlo simulations. We focus on four efficient architectures, including the parameter estimations: Myriad, Normal Inverse Gaussian, p-norm and a direct estimation of the likelihood ratio function. They exhibit good performance, close to the optimal, in a large range of situations demonstrating they may be considered as robust decision rules in the presence of heavy tailed or impulsive interference environments.

干扰是许多通信系统中的重要限制。在许多情况下，已经证明流行的高斯近似是不充分的，并且干涉表现出脉冲行为。本文调查了针对这种干扰建议的不同统计模型，这些统计模型通常可以使用次指数分布族的类别统一，并且可以影响接收器设计。可视化最佳决策边界，可以显示由脉冲噪声模型引起的非线性效应，这解释了在标准高斯近似下设计的接收机性能的重大损失。这激发了开发新接收器的需求。我们提出了一个框架来设计对各种干扰类型（高斯和非高斯）均具有鲁棒性的接收机。我们探索了关于这种接收器设计的三种思考方式：线性方式；通过近似噪声加干扰分布；并直接模仿决策规则的分配。除线性方法外，其他设计都能够在不同程度上复制非平凡的最优决策区域。通过蒙特卡洛仿真评估了新的检测算法。我们专注于四种有效的架构，包括参数估计：无数，正态逆高斯，通过蒙特卡洛仿真评估了新的检测算法。我们专注于四种有效的架构，包括参数估计：无数，正态逆高斯，通过蒙特卡洛仿真评估了新的检测算法。我们专注于四种有效的架构，包括参数估计：无数，正态逆高斯，p-范数和似然比函数的直接估计。它们在大范围的情况下表现出接近最佳的良好性能，表明在存在重尾或脉冲干扰环境的情况下，它们可以被视为可靠的决策规则。