A lightweight hybrid deep learning-based OFDM receiver for enhanced coverage and efficiency

IF 1.9 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-01-31 DOI:10.1016/j.phycom.2026.103020

R. Anil Kumar , Sarala Patchala , R. Prakash Kumar , Ummiti Sreenivasulu , Shaik Fairooz

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引用次数: 0

Abstract

In modern wireless communication systems, Orthogonal Frequency Division Multiplexing (OFDM) is widely used in modulation technology. However, inter-subcarrier interference, along with nonlinear distortion, results in OFDM signals with a shorter cyclic prefix (CP) length and restricted pilot usages having lower performance in Channel Estimation (CE) or signal detection. Moreover, in conventional frequency or angle-domain based CE, performance degradation may occur due to natural sparsity in practical wireless communication channels. This study proposes an innovative Hybrid Polar and Frequency Domain Multi-Scale Depthwise Separable ResNet based Receiver (HPF-MSDSRRxr), whose aim is to overcome these limitations in recovering distorted OFDM signals due to non-linearities. Specifically, two new deep learning (DL) receiver architectures are formulated by combining the frequency domain and polar domain signal processing. To alleviate the performance degradation introduced by CE methods involving the angular domain, a Multi-Scale Depthwise Separable ResNet model called Polar Domain MSDSR (P-MSDSR) is first constructed. This model helps effectively exploit the channel sparseness property inherent to the polar domain. The results from the MSDSR model operating in the frequency domain, called F-MSDSR, which uses Demodulation Reference Signals (DMRS) along with accurate channel estimation and soft-bit detection, are then combined by seamlessly blending polar domain features. The introduced multi-scale idea, which efficiently detects both fine and coarse channel characteristics, enables dependable performance, including large nonlinear impairments and high error vector magnitude in the proposed architectural design. Simulation results verify that the HPF-MSDSRRxr scheme typically gains a constant Signal-to-Noise Ratio (SNR) margin of 2–3.5 dB with tolerable computing complexity over existing linear LMMSE and current DL baselines.

查看原文本刊更多论文

一种基于深度学习的轻型混合OFDM接收机，增强了覆盖和效率

在现代无线通信系统中，正交频分复用（OFDM）是一种广泛使用的调制技术。然而，子载波间干扰以及非线性失真导致循环前缀（CP）长度较短和导频使用受限的OFDM信号在信道估计（CE）或信号检测方面的性能较低。此外，在传统的基于频率或角域的CE中，由于实际无线通信信道的自然稀疏性，可能会导致性能下降。本研究提出了一种创新的基于混合极频域多尺度深度可分离ResNet的接收机（HPF-MSDSRRxr），其目的是克服这些由于非线性而导致的失真OFDM信号恢复的限制。具体而言，通过结合频域和极域信号处理，制定了两种新的深度学习（DL）接收器架构。为了缓解CE方法在角域引起的性能下降，首先构建了一个多尺度深度可分ResNet模型，称为极域MSDSR （P-MSDSR）。该模型有助于有效地利用极域固有的信道稀疏性。在频域中工作的MSDSR模型的结果称为F-MSDSR，它使用解调参考信号（DMRS）以及精确的信道估计和软位检测，然后通过无缝混合极域特征进行组合。引入的多尺度思想可以有效地检测细信道和粗信道特性，使所提出的结构设计具有可靠的性能，包括大的非线性损伤和高的误差矢量幅度。仿真结果验证了HPF-MSDSRRxr方案通常比现有的线性LMMSE和当前DL基线获得2-3.5 dB的恒定信噪比（SNR）裕度，计算复杂度可容忍。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physical Communication ENGINEERING, ELECTRICAL & ELECTRONICTELECO-TELECOMMUNICATIONS

CiteScore

5.00

自引率

9.10%

发文量

212

审稿时长

55 days

期刊介绍： PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published. Topics of interest include but are not limited to: Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.