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)

Spectrum Sensing and Signal Identification with Deep Learning based on Spectral Correlation Function

Spectrum sensing is one of the means of utilizing the scarce source of wireless spectrum efficiently. In this paper, a convolutional neural network (CNN) model employing spectral correlation function which is an effective characterization of cyclostationarity property, is proposed for wireless spectrum sensing and signal identification. The proposed method classifies wireless signals without a priori information and it is implemented in two different settings entitled CASE1 and CASE2. In CASE1, signals are jointly sensed and classified. In CASE2, sensing and classification are conducted in a sequential manner. In contrary to the classical spectrum sensing techniques, the proposed CNN method does not require a statistical decision process and does not need to know the distinct features of signals beforehand. Implementation of the method on the measured overthe-air real-world signals in cellular bands indicates important performance gains when compared to the signal classifying deep learning networks available in the literature and against classical sensing methods. Even though the implementation herein is over cellular signals, the proposed approach can be extended to the detection and classification of any signal that exhibits cyclostationary features. Finally, the measurement-based dataset which is utilized to validate the method is shared for the purposes of reproduction of the results and further research and development