This paper presents a practical example where generative adversarial networks (GANs) can be employed as an accommodative memory unit (AMU). An array of such units can memorize/learn any algorithm’s results. This kind of memory can accommodate their response to new unseen scenarios by traversing the GAN’s latent space and finding the best answer. Accordingly, accommodative memory (AM) can be viewed as a generalization of look-up tables (LUT), in which writing and reading operations are equivalent to training and inference of an AMU or traversing its latent space.

We explore cognitive radar waveform synthesis to showcase a practical application of the proposed AM concept. In this regard, a Wasserstein GAN (WGAN) is trained as an AMU for a particular ambiguity function (AF) shaping scenario.

Here, retrieving information for the most frequent scenarios, called input basis scenarios (IBSs), involves only the inference of the generator. For more complicated input scenarios, the memory accommodates the input by traversing the latent space using ADAM optimization. Compared to redesigning the AF, the AM can remember or accommodate new scenarios several orders of magnitude faster at the expense of more memory hardware.

As an auxiliary result, we also demonstrate that traditional algorithms can be defeated in terms of suppression level by penalizing the loss function according to desired AF.