DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction

• URL: http://arxiv.org/abs/2603.23544v1
• Date: Sun, 15 Mar 2026 10:31:15 GMT
• Title: DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction
• Abstract summary: Peak-to-average power ratio (PAPR) is a major limitation of multicarrier modulation schemes.<n>We propose DeepOFW, a deep learning-driven OFDM-flexible waveform modulation framework.
• Score: 9.446392400546364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Peak-to-average power ratio (PAPR) remains a major limitation of multicarrier modulation schemes such as orthogonal frequency-division multiplexing (OFDM), reducing power amplifier efficiency and limiting practical transmit power. In this work, we propose DeepOFW, a deep learning-driven OFDM-flexible waveform modulation framework that enables data-driven waveform design while preserving the low-complexity hardware structure of conventional transceivers. The proposed architecture is fully differentiable, allowing end-to-end optimization of waveform generation and receiver processing under practical physical constraints. Unlike neural transceiver approaches that require deep learning inference at both ends of the link, DeepOFW confines the learning stage to an offline or centralized unit, enabling deployment on standard transmitter and receiver hardware without additional computational overhead. The framework jointly optimizes waveform representations and detection parameters while explicitly incorporating PAPR constraints during training. Extensive simulations over 3GPP multipath channels demonstrate that the learned waveforms significantly reduce PAPR compared with classical OFDM while simultaneously improving bit error rate (BER) performance relative to state-of-the-art transmission schemes. These results highlight the potential of data-driven waveform design to enhance multicarrier communication systems while maintaining hardware-efficient implementations. An open-source implementation of the proposed framework is released to facilitate reproducible research and practical adoption.

Related papers

• Neural Beamforming with Doppler-Aware Sparse Attention for High Mobility Environments [35.90167049594104]
  Deep learning-based beamforming offers an alternative with nonlinear mappings from channel state information to beamforming weights.<n> Transformer-based models are particularly effective due to their ability to model long-range dependencies across time and frequency.<n>Recent studies address this issue through sparse attention mechanisms that reduce complexity while maintaining, yet often employ patterns that disregard channel dynamics.
  arXiv  Detail & Related papers  (2025-11-05T16:51:26Z)
• Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systems [18.330923801141886]
  We propose an adaptive end-to-end (E2E) transceiver architecture tailored for pilot-free and CP-free wireless systems.<n>The architecture combines AI-driven constellation shaping and a neural receiver through joint training.<n>We show that the proposed framework delivers superior bit error rate (BER), throughput, and resilience across diverse channel scenarios.
  arXiv  Detail & Related papers  (2025-10-29T11:34:09Z)
• PASS-Enhanced MEC: Joint Optimization of Task Offloading and Uplink PASS Beamforming [67.78883135636657]
  pinching-antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated.<n>PASS establishes short-distance line-of-sight (LoS) links while effectively mitigating the significant path loss and potential signal blockage.<n>We formulate a network latency minimization problem to joint optimize uplink PASS beamforming and task offloading.
  arXiv  Detail & Related papers  (2025-10-27T03:04:46Z)
• Deep Learning Optimization of Two-State Pinching Antennas Systems [48.70043547158868]
  Pinching antennas (PAs) can dynamically control electromagnetic wave propagation through binary activation states.<n>In this work, we investigate the problem of optimally selecting a subset of fixed-position PAs to activate in a waveguide, when the aim is to maximize the communication rate at a user terminal.
  arXiv  Detail & Related papers  (2025-07-08T17:55:54Z)
• Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based? [89.05848771674773]
  A novel antenna system ()-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed.<n>It consists of multiple waveguides, which equip numerous low-cost antennas, named (PAs)<n>The positions of PAs can be reconfigured to both spanning large-scale path and space.
  arXiv  Detail & Related papers  (2025-02-12T18:54:10Z)
• 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)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples [6.378498479725599]
  We propose a low-intercept waveform(LIW) generation method that can reduce the probability of the modulation being recognized by a third party without affecting the reliable communication of the friendly party. Our LIW exhibits significant low-interception performance even in the physical hardware experiment, decreasing the accuracy of the state of the art model to approximately $15%$ with small perturbations.
  arXiv  Detail & Related papers  (2022-01-20T08:13:04Z)
• 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)
• 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.