Abstract:We consider a trainable point-to-point communication system, where both transmitter and receiver are implemented as neural networks (NNs), and demonstrate that training on the bit-wise mutual information (BMI) allows seamless integration with practical bit-metric decoding (BMD) receivers, as well as joint optimization of constellation shaping and labeling. Moreover, we present a fully differentiable neural iterative demapping and decoding (IDD) structure which achieves significant gains on additive white Gauss… Show more
“…As previously mentioned, the idea of this autoencoder setup is to maximize the BMI at the receiver's output, which is shown in [12] to be closely related to minimizing the total binary CE, and leads to the following loss definition 1…”
Section: A Training Approachesmentioning
confidence: 99%
“…Contrary to [9], [10], the wireless channel can become an attractive subject of investigation once multipath and, thus, frequency-selectivity becomes part of the transmission. We utilize the orthogonal frequency division multiplex (OFDM)-autoencoder structure from [11] and optimize the autoencoder for bit-wise information transmission as introduced in [12]. Further, we utilize Wasserstein generative adversarial networks (WGANs) [13] for improved convergence and training stability.…”
Section: Introductionmentioning
confidence: 99%
“…[14]). Most of the previously proposed fully end-to-end trained autoencoder-based communication systems rely on optimizing the mutual information between channel input and channel output by minimizing the symbol-wise categorical crossentropy (CE) (see [12] for a detailed derivation). The big drawback of this symbol-wise architecture is that it cannot be scaled to practical (bit) sequence lengths, as it suffers from the curse of dimensionality [15].…”
Section: Introductionmentioning
confidence: 99%
“…To reduce this complexity, practical systems usually rely on bitinterleaved coded modulation (BICM) and bit-metric decoding (BMD). We follow the approach of [12] and combine the autoencoder NN in the BICM framework with an outer channel code which can be decoded by a fully differentiable belief propagation (BP) decoder. Such an autoencoder system can then be trained in an end-to-end manner to maximize the bit-wise mutual information (BMI) at its output, which is also the decoder's input, and, thereby, inherently learns the optimal constellation shaping and bit labeling.…”
Section: Introductionmentioning
confidence: 99%
“…Such an autoencoder system can then be trained in an end-to-end manner to maximize the bit-wise mutual information (BMI) at its output, which is also the decoder's input, and, thereby, inherently learns the optimal constellation shaping and bit labeling. Throughout this work, we use the bit-wise iterative autoencoder architecture as described in [12] with an outer IEEE 802.11n WLANirregular low-density parity-check (LDPC) code of rate r = 1 /2, length n = 1296 bit and 40 iterations of iterative demapping and decoding (IDD) between the autoencoder receiver and the differentiable BP decoder.…”
The practical realization of end-to-end training of communication systems is fundamentally limited by its accessibility of the channel gradient. To overcome this major burden, the idea of generative adversarial networks (GANs) that learn to mimic the actual channel behavior has been recently proposed in the literature. Contrarily to handcrafted classical channel modeling, which can never fully capture the real world, GANs promise, in principle, the ability to learn any physical impairment, enabled by the data-driven learning algorithm. In this work, we verify the concept of GAN-based autoencoder training in actual over-theair (OTA) measurements. To improve training stability, we first extend the concept to conditional Wasserstein GANs and embed it into a state-of-the-art autoencoder-architecture, including bitwise estimates and an outer channel code. Further, in the same framework, we compare the existing three different training approaches: model-based pre-training with receiver finetuning, reinforcement learning (RL) and GAN-based channel modeling. For this, we show advantages and limitations of GAN-based endto-end training. In particular, for non-linear effects, it turns out that learning the whole exploration space becomes prohibitively complex. Finally, we show that the training strategy benefits from a simpler (training) data acquisition when compared to RL-based training, which requires continuous transmitter weight updates. This becomes an important practical bottleneck due to limited bandwidth and latency between transmitter and training algorithm that may even operate at physically different locations.
“…As previously mentioned, the idea of this autoencoder setup is to maximize the BMI at the receiver's output, which is shown in [12] to be closely related to minimizing the total binary CE, and leads to the following loss definition 1…”
Section: A Training Approachesmentioning
confidence: 99%
“…Contrary to [9], [10], the wireless channel can become an attractive subject of investigation once multipath and, thus, frequency-selectivity becomes part of the transmission. We utilize the orthogonal frequency division multiplex (OFDM)-autoencoder structure from [11] and optimize the autoencoder for bit-wise information transmission as introduced in [12]. Further, we utilize Wasserstein generative adversarial networks (WGANs) [13] for improved convergence and training stability.…”
Section: Introductionmentioning
confidence: 99%
“…[14]). Most of the previously proposed fully end-to-end trained autoencoder-based communication systems rely on optimizing the mutual information between channel input and channel output by minimizing the symbol-wise categorical crossentropy (CE) (see [12] for a detailed derivation). The big drawback of this symbol-wise architecture is that it cannot be scaled to practical (bit) sequence lengths, as it suffers from the curse of dimensionality [15].…”
Section: Introductionmentioning
confidence: 99%
“…To reduce this complexity, practical systems usually rely on bitinterleaved coded modulation (BICM) and bit-metric decoding (BMD). We follow the approach of [12] and combine the autoencoder NN in the BICM framework with an outer channel code which can be decoded by a fully differentiable belief propagation (BP) decoder. Such an autoencoder system can then be trained in an end-to-end manner to maximize the bit-wise mutual information (BMI) at its output, which is also the decoder's input, and, thereby, inherently learns the optimal constellation shaping and bit labeling.…”
Section: Introductionmentioning
confidence: 99%
“…Such an autoencoder system can then be trained in an end-to-end manner to maximize the bit-wise mutual information (BMI) at its output, which is also the decoder's input, and, thereby, inherently learns the optimal constellation shaping and bit labeling. Throughout this work, we use the bit-wise iterative autoencoder architecture as described in [12] with an outer IEEE 802.11n WLANirregular low-density parity-check (LDPC) code of rate r = 1 /2, length n = 1296 bit and 40 iterations of iterative demapping and decoding (IDD) between the autoencoder receiver and the differentiable BP decoder.…”
The practical realization of end-to-end training of communication systems is fundamentally limited by its accessibility of the channel gradient. To overcome this major burden, the idea of generative adversarial networks (GANs) that learn to mimic the actual channel behavior has been recently proposed in the literature. Contrarily to handcrafted classical channel modeling, which can never fully capture the real world, GANs promise, in principle, the ability to learn any physical impairment, enabled by the data-driven learning algorithm. In this work, we verify the concept of GAN-based autoencoder training in actual over-theair (OTA) measurements. To improve training stability, we first extend the concept to conditional Wasserstein GANs and embed it into a state-of-the-art autoencoder-architecture, including bitwise estimates and an outer channel code. Further, in the same framework, we compare the existing three different training approaches: model-based pre-training with receiver finetuning, reinforcement learning (RL) and GAN-based channel modeling. For this, we show advantages and limitations of GAN-based endto-end training. In particular, for non-linear effects, it turns out that learning the whole exploration space becomes prohibitively complex. Finally, we show that the training strategy benefits from a simpler (training) data acquisition when compared to RL-based training, which requires continuous transmitter weight updates. This becomes an important practical bottleneck due to limited bandwidth and latency between transmitter and training algorithm that may even operate at physically different locations.
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