Waveform Learning for Next-Generation Wireless Communication Systems

• URL: http://arxiv.org/abs/2109.00998v1
• Date: Thu, 2 Sep 2021 14:51:16 GMT
• Title: Waveform Learning for Next-Generation Wireless Communication Systems
• Authors: Fay\c{c}al Ait Aoudia and Jakob Hoydis
• Abstract summary: We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-based detector. The method maximizes an achievable information rate, while simultaneously satisfying constraints on the adjacent channel leakage ratio (ACLR) and peak-to-average power ratio (PAPR)
• Score: 16.26230847183709
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-based detector. The method maximizes an achievable information rate, while simultaneously satisfying constraints on the adjacent channel leakage ratio (ACLR) and peak-to-average power ratio (PAPR). This allows control of the tradeoff between spectral containment, peak power, and communication rate. Evaluation on an additive white Gaussian noise (AWGN) channel shows significant reduction of ACLR and PAPR compared to a conventional baseline relying on quadrature amplitude modulation (QAM) and root-raised-cosine (RRC), without significant loss of information rate. When considering a 3rd Generation Partnership Project (3GPP) multipath channel, the learned waveform and neural receiver enable competitive or higher rates than an orthogonal frequency division multiplexing (OFDM) baseline, while reducing the ACLR by 10 dB and the PAPR by 2 dB. The proposed method incurs no additional complexity on the transmitter side and might be an attractive tool for waveform design of beyond-5G systems.

Related papers

• Deep Learning-Based Synchronization for Uplink NB-IoT [72.86843435313048]
  We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT) The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications.
  arXiv  Detail & Related papers  (2022-05-22T12:16:43Z)
• 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)
• 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)
• End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping [16.26230847183709]
  Communication systems are foreseen to enable new services such as joint communication and sensing. We present in this work an end-to-end learning approach to design waveforms through joint learning of pulse shaping and constellation geometry.
  arXiv  Detail & Related papers  (2021-06-29T08:22:05Z)
• Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems [61.78590389147475]
  This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for millimeter-wave (mmWave) systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains, we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-04-22T13:34:53Z)
• Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis [65.31074639627226]
  Principal component analysis (PCA) is a technique for extracting the linear structure of a dataset. We propose the deployment of PCA over a multi-access channel based on the algorithm of gradient descent. Over-the-air aggregation is adopted to reduce the multi-access latency, giving the name over-the-air PCA.
  arXiv  Detail & Related papers  (2021-04-20T16:28:33Z)
• Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV [119.97450366894718]
  A novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed. In order to maximize the downlink sum-rate, the optimal precoding matrix (at the base station) and reflection coefficient (at the IR) are jointly derived.
  arXiv  Detail & Related papers  (2020-11-03T16:50:37Z)
• End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication [12.325545487629297]
  We explore the gains of end-to-end learning over a frequency- and time-selective fading channel. With imperfect channel knowledge at the receiver, the shaping gains observed on AWGN channels vanish. We identify two other sources of performance improvements.
  arXiv  Detail & Related papers  (2020-09-11T07:33:00Z)
• Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces [99.34306447202546]
  This paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems. We first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. The complete channel matrix can be reconstructed from the limited measurements based on compressive sensing.
  arXiv  Detail & Related papers  (2020-06-03T12:18:57Z)

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.