A Deep Learning-Aided Detection Method for FTN-Based NOMA
المؤلفون المشاركون
Li, Xiangming
Wang, Aihua
Ye, Neng
Pan, Jianxiong
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-01-29
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency.
Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively.
In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses.
Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity.
To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology.
The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency.
Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Pan, Jianxiong& Ye, Neng& Wang, Aihua& Li, Xiangming. 2020. A Deep Learning-Aided Detection Method for FTN-Based NOMA. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1214439
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Pan, Jianxiong…[et al.]. A Deep Learning-Aided Detection Method for FTN-Based NOMA. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1214439
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Pan, Jianxiong& Ye, Neng& Wang, Aihua& Li, Xiangming. A Deep Learning-Aided Detection Method for FTN-Based NOMA. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1214439
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1214439
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر