Low PAPR waveform design for OFDM SYSTEM based on Convolutional Auto-Encoder

• URL: http://arxiv.org/abs/2011.06349v1
• Date: Thu, 12 Nov 2020 12:44:30 GMT
• Title: Low PAPR waveform design for OFDM SYSTEM based on Convolutional Auto-Encoder
• Authors: Yara Huleihel and Eilam Ben-Dror and Haim H. Permuter
• Abstract summary: This paper introduces the architecture of a convolutional autoencoder (CAE) for the task of peak-to-average power ratio (PAPR) reduction and waveform design. The proposed architecture integrates a PAPR reduction block and a non-linear high power amplifier (HPA) model. We analyze the models performance by examining the bit error rate (BER), the PAPR and the spectral response, and comparing them with common PAPR reduction algorithms.
• Score: 17.390856495666316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces the architecture of a convolutional autoencoder (CAE) for the task of peak-to-average power ratio (PAPR) reduction and waveform design, for orthogonal frequency division multiplexing (OFDM) systems. The proposed architecture integrates a PAPR reduction block and a non-linear high power amplifier (HPA) model. We apply gradual loss learning for multi-objective optimization. We analyze the models performance by examining the bit error rate (BER), the PAPR and the spectral response, and comparing them with common PAPR reduction algorithms.

Related papers

• Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFDM [13.870974874382025]
  We propose a machine learning-based framework to determine the FDSS filter, optimizing a tradeoff between the symbol error rate (SER), the PAPR, and the spectral flatness requirements. numerical results show that learned FDSS filters lower the PAPR compared to conventional baselines, with minimal SER degradation.
  arXiv  Detail & Related papers  (2024-04-24T18:50:56Z)
• Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves [69.9104427437916]
  Multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves. These complex devices need controllers with multiple objectives of energy capture efficiency, reduction of structural stress to limit maintenance, and proactive protection against high waves. In this paper, we explore different function approximations for the policy and critic networks in modeling the sequential nature of the system dynamics.
  arXiv  Detail & Related papers  (2024-04-17T02:04:10Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals [104.11663769306566]
  We study the single-channel source separation problem involving frequency-division multiplexing (OFDM) signals. We propose critical domain-informed modifications to the network parameterization, based on insights from OFDM structures.
  arXiv  Detail & Related papers  (2023-03-11T16:29:13Z)
• Adversarial Audio Synthesis with Complex-valued Polynomial Networks [60.231877895663956]
  Time-frequency (TF) representations in audio have been increasingly modeled real-valued networks. We introduce complex-valued networks called APOLLO, that integrate such complex-valued representations in a natural way. APOLLO results in $17.5%$ improvement over adversarial methods and $8.2%$ over the state-of-the-art diffusion models on SC09 in audio generation.
  arXiv  Detail & Related papers  (2022-06-14T12:58:59Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• Learning OFDM Waveforms with PAPR and ACLR Constraints [15.423422040627331]
  We propose a learning-based method to design OFDM-based waveforms that satisfy selected constraints while maximizing an achievable information rate. We show that the end-to-end system is able to satisfy target PAPR and ACLR constraints and allows significant throughput gains.
  arXiv  Detail & Related papers  (2021-10-21T08:58:59Z)
• End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints [15.423422040627331]
  We propose to use a neural network (NN) at the transmitter to learn a high-dimensional modulation scheme allowing to control the PAPR and adjacent channel leakage ratio (ACLR) The two NNs operate on top of OFDM, and are jointly optimized in and end-to-end manner using a training algorithm that enforces constraints on the PAPR and ACLR.
  arXiv  Detail & Related papers  (2021-06-30T13:09:30Z)
• Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping [7.0349768355860895]
  We investigate and compare several well-known methods to reduce the number of parameters in neural networks. We show that we can achieve high-performing low-complexity models by applying specific restrictions on the Receptive Field. We propose a filter-damping technique for regularizing the RF of models, without altering their architecture.
  arXiv  Detail & Related papers  (2020-11-05T16:34:11Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.