Question: How is the transfer setting formulated in section 5.3?
Evidence: We implemented the mixture density network and communication autoencoder models using TensorFlow (Abadi et al., 2015) and TensorFlow Probability. We used the BFGS optimizer implementation available in TensorFlow Probability. The code base for our work can be found at https: //anonymous.4open.science/r/ domain_adaptation-7C0D/. All the experiments were run on a Macbook Pro laptop with 16 GB memory and 8 CPU cores. Table 4 summarizes the architecture of the encoder, MDN (channel model), and decoder neural networks. Note that the output layer of the MDN is a concatenation (denoted by ⊕) of three fully-connected layers predicting the means, variances, and mixing prior logit parameters of the Gaussian mixture. The size of the hidden layers nh was set to 100. The parameters ψ of the proposed adaptation method are initialized as follows for each component i: Ai = Id, bi = 0, Ci = Id, βi = 1, γi = 0, where Id is the d × d identity matrix. This initialization ensures that the target Gaussian mixture is always initialized with the source Gaussian mixture. The regularization constant λ in the adaptation objective was varied over 16 equally-spaced values on the log-scale (base 10) with range 10−5 to 100. The λ value and ψ corresponding to the smallest validation metric are selected as the final solution. We used the Adam optimizer (Kingma & Ba, 2015) with a fixed learning rate of 0.001, batch size of 128, and 100 epochs for training the MDN. For adaptation of the MDN using the baseline methods transfer and transfer-last-layer, Adam is used with the same learning rate for 200 epochs. The batch size is set as b = max{10, 0.1 N (t) c }, where N (t) c is number of adaptation samples in the target domain. For training the autoencoder using Algorithm 1, we found that stochastic gradient descent (SGD) with Nesterov momentum (constant 0.9), and an exponential learning rate schedule between 0.1 and 0.005 works better than Adam
Answer: A large part of our experimental evaluation is on simulated channel variations that are commonly used for modeling communication channels. Also, these distribution changes (of varying SNR) can be quite challenging for adaptation in the small-sample setting. To the best of our knowledge, there are no real-world, publicly-available datasets suitable for this domain. Therefore, we have performed real over-the-air data collection and evaluation using a mmWave FPGA testbed (details in Appendix D.1). We plan to conduct more extensive experiments using this setup as future work for a communication-focused paper.
In section 5.3, we introduced distribution changes via IQ imbalance based distortions to the symbol constellation, and evaluated the adaptation performance as a function of the level of imbalance. The source domain would be the original channel, the over-the-air link between the transmitter and receiver on which the training data is collected. This source domain data is used for training the channel and the autoencoder. The target domain would be a modification of the source domain where the symbols used by the transmitter (e.g., 16-QAM) are distorted by modifying the in-phase and quadrature-phase (IQ) components of the RF signal. This causes a change in the distribution observed by the decoder, leading to a drop in performance without any adaptation. Due to the page limit, we have added details clarifying the above points in Appendix D.1 of the revised paper.