Analysis of the PAPR Behavior of the OFDM Passband Signal

Fa, Eras; Ia, Carreño; T, Borja; Dj, Reinoso; Urquiza-Aguiar, Luis; Paredes, Paredes

doi:10.20944/preprints201912.0255.v1

Cited by 1 publication

(2 citation statements)

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“…By focusing the available energy on these subcarriers, the neural transmitter jointly minimizes the probability of peaks in the analog waveform and decreases the number of FBSs that have a significant impact on them. Note that since the carrier frequency is typically much higher than the bandwidth of each subcarrier, the average power of the passband and baseband signals differs by a factor two but their maximum are approximately equal, which amounts to a roughly 3 dB difference between the passband and baseband PAPR [32]. Finally, the mean variance of the clusters on each subcarrier are plotted in Fig.…”

Section: Simulations Results and Insightsmentioning

confidence: 99%

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Learning OFDM Waveforms with PAPR and ACLR Constraints

Goutay¹,

Aoudia²,

Hoydis³

et al. 2021

Preprint

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An attractive research direction for future communication systems is the design of new waveforms that can both support high throughputs and present advantageous signal characteristics. Although most modern systems use orthogonal frequency-division multiplexing (OFDM) for its efficient equalization, this waveform suffers from multiple limitations such as a high adjacent channel leakage ratio (ACLR) and high peak-to-average power ratio (PAPR). In this paper, we propose a learning-based method to design OFDM-based waveforms that satisfy selected constraints while maximizing an achievable information rate. To that aim, we model the transmitter and the receiver as convolutional neural networks (CNNs) that respectively implement a high-dimensional modulation scheme and perform the detection of the transmitted bits. This leads to an optimization problem that is solved using the augmented Lagrangian method. Evaluation results show that the end-to-end system is able to satisfy target PAPR and ACLR constraints and allows significant throughput gains compared to a tone reservation (TR) baseline. An additional advantage is that no dedicated pilots are needed.

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Section: Simulations Results and Insightsmentioning

confidence: 99%

“…Each training iteration comprises multiples steps of SGD on the augmented Lagrangian (32) followed by an update of the hyperparameters. The optimization procedure is detailed in in other related works [15], [26], [27].…”

Section: Learning a High Dimensional Modulationmentioning

confidence: 99%