Development of a convolutional neural network joint detector for non-orthogonal multiple access uplink receivers

We present a novel approach to signal detection for Non-Orthogonal Multiple Access (NOMA) uplink receivers using Convolutional Neural Networks (CNNs) in a single-shot fashion. The defacto NOMA detection method is the so-called Successive Interference Cancellation which requires precise channel estimation and accurate successive detection of the user equipment with the higher powers. It is proposed converting incoming packets into 2D image-like streams. These images are fed to a CNN-based deep learning network commonly used in the image processing literature for image classification. The classification label for each packet converted to an image is the transmitted symbols by all user equipment joined together. CNN network is trained using uniformly distributed samples of incoming packets at different signals to noise ratios. Furthermore, let’s performed hyperparameter optimization using the exhaustive search method. Our approach is tested using a modeled system of two user equipment systems in a 64-subcarrier Orthogonal Frequency Division Multiplexing (OFDM) and Rayleigh channel. It is found that a three-layer CNN with 32 filters of size 7×7 has registered the highest training and testing accuracy of about 81. In addition, our result showed significant improvement in Symbol Error Rate (SER) vs. Signal to Noise Ratio (SNR) compared to other state-of-the-art approaches such as least square, minimum mean square error, and maximum likelihood under various channel conditions. When the channel length is fixed at 20, our approach is at least one significant Figure better than the maximum likelihood method at (SNR) of 2 dB. Finally, the channel length to 12 is varied and it is registered about the same performance. Hence, our approach is more robust to joint detection in NOMA receivers, particularly in low signal-to-noise environment

Exploiting One-Dimensional Convolutional Neural Networks for Joint Channel Estimation and Signal Detection in Non-Orthogonal Multiple Access Systems

الوصول المتعدد غير المتعامد (NOMA) هو تقنية واعدة للجيل الخامس و الاجيال المستقبلية من شبكات الاتصالات اللاسلكية ، مما يزيد من كفاءة الطيف ويقلل من زمن الوصول. ومع ذلك، يمكن أن يتأثر أداء NOMA بإلغاء التداخل المتتالي غير المثالي (SIC). تم اقتراح تقنيات الذكاء الاصطناعي للمساعدة في الكشف عن الإشارات وتقدير القنوات في أنظمة NOMA. في هذه الدراسة ، نقترح نهجًا جديدًا باستخدام الشبكات العصبية التلافيفية أحادية البعد (1D CNN) لمعالجة قيود المحددة لأنظمة الذكاء الاصطناعي الحالية. على عكس طرق الذكاء الاصطناعي الأخرى التي تعتمد على تبعيات الوقت لتصنيف البيانات ، تستخدم 1D CNN طبقة التفاف أحادية البعد لاستخراج الميزات، مما يؤدي إلى موثوقية عالية. تظهر نتائج المحاكاة أن طريقتنا المقترحة تتفوق على تقنيات التعلم العميق الحالية من حيث معدل الخطأ في العينة (SER). علاوة على ذلك ، يؤدي تقليل معلمة البادئة الدورية (CP) إلى زيادة التداخل بين العينات (ISI) ، ولكن طريقتنا لا تزال تحقق تحسينًا بمقدار 6 ديسيبل على النهوج في (11،13) وتقنيات تقدير القنوات التقليدية مثل الاحتمال الأقصى (ML) عند إشارة منخفضة إلى- نسب الضوضاء (SNR).Non-Orthogonal Multiple Access (NOMA) is a promising technology for the fifth and future generations of wireless communication networks, which increases spectral efficiency and reduces latency. However, NOMA performance can be affected by imperfect successive interference cancellation (SIC). Deep learning techniques have been proposed to aid in signal detection and channel estimation in NOMA systems. In this study, we propose a new approach using one-dimensional convolutional neural networks (1D CNN) to address the limitations of current deep learning methods. Unlike other deep learning methods that rely on time dependencies for data classification, 1D CNN uses a 1-dimensional convolution layer for feature extraction, resulting in high reliability. Simulation results demonstrate that our proposed method outperforms existing deep learning techniques in terms of sample error rate (SER) by 7dB. Moreover, reducing the cyclic prefix (CP) parameter increases inter-sample interference (ISI), but our method still achieves a 6 dB improvement over approaches in [11,13] and traditional channel estimation techniques like maximum likelihood (ML) at low signal-to-noise ratios (SNR)