Generative Adversarial Networks as an Accommodative Memory for Cognitive Waveform Synthesis

Abstract

This paper introduces a new concept of Generative Accommodative Memory (GAM) by showcasing a practical example of using Generative Adversarial Networks (GANs) as Accommodative Memory Basic Units (AMBUs). The GAM can memorize and learn the results of any algorithm and adapt its response to new unseen scenarios by exploring the latent space. This memory is a generalization of look-up tables (LUT), where writing and reading operations correspond to the training and inference of an AMBU or traversing its latent space. To demonstrate the practical application of GAM, we use it in cognitive radar waveform synthesis. Here, a Wasserstein GAN is trained as an AMBU for a specific ambiguity function shaping scenario. The memory can retrieve information for frequent basic scenarios (called input basis scenarios) through the inference of the generator, i.e., generative read. For more complex inputs, the memory accommodates the input by optimizing output over the latent space, i.e., accommodation read. In this light, the GAM can accommodate new scenarios much faster than traditional methods, but at the cost of more memory hardware. As an additional result, we show that traditional algorithms can be outperformed in terms of suppression level by penalizing the loss function according to the desired ambiguity function.