The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods’ performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods.

中文翻译：

---

基于形态图像处理的无线电通信频谱图中增强的信号区域估计

信号区域 (SA) 的概念定义为频谱图中检测信号的矩形时频区域，在频谱使用测量中发挥着重要作用。信号区域估计 (SAE) 的需求是合理的，因为它在动态频谱接入系统中向二级用户分配空白频谱的过程中以及在其他有趣的应用中发挥着重要作用，例如频谱法规的合规性验证和执行、信号拦截、以及网络规划和优化。现有的 SAE 方法远非完美，因此需要能够提供更准确估计的新解决方案。在这项研究中，探索了一种基于图像处理技术的新方法。具体来说，探讨了使用形态学操作 (MO) 的可行性，以检验其在 SAE 背景下的有用性。通过广泛的模拟，研究了 SAE 背景下不同 MO（侵蚀、扩张、打开和关闭）在各种配置下的性能，包括结构元素 (SE) 的不同形状和大小（当两者单独使用时） SAE 方法并与文献中的其他 SAE 方法相结合。基于所获得的结果，基于每个特定 SNR 范围的最佳 MO 和 SE 配置制定了基于 MO 的 SAE 方法，当用作预处理或后处理技术时，可以大大提高其他提出的 SAE 方法的性能。具体来说，在低 SNR 范围内，F1 分数的准确度提高高达 40%，而在高 SNR 范围内则达到 100% 的完美准确度。实现这一目标不会对相关计算成本产生明显影响（甚至在高 SNR 时将计算成本降低多达 15%）。因此，性能改进在低 SNR 范围内尤其显着，其中大多数方法的性能都受到限制，因此所提出的 SAE 方法可以扩展现有 SAE 方法的操作 SNR 范围。