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المؤلفElsayed M.
المؤلفBadawy A.
المؤلفEl Shafie A.
المؤلفMohamed A.
المؤلفKhattab T.
تاريخ الإتاحة2022-04-21T08:58:24Z
تاريخ النشر2020
اسم المنشورCanadian Conference on Electrical and Computer Engineering
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/CCECE47787.2020.9255757
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30088
الملخصOne of the highly promising radio access strategies for enhancing performance in the next generation cellular communications is non-orthogonal multiple access (NOMA). NOMA offers a number of advantages including better spectrum efficiency. This paper focuses primarily on proposing an energy efficient system for transmitting medical data, such as electroencephalogram (EEG), collected from patients for the sake of continuous monitoring. The framework proposes the use of deep reinforcement learning (DRL) to provide smart data compression in uplink-NOMA protocol. DRL enforces the data compression ratios for the nodes in order to avoid outage constraints at any sensor node. Jointly, it optimizes the power consumption of these sensor nodes. The data compression for such sensor network is vital in order to minimize the power every sensor consumes to maximize its service lifetime. We minimize the expected distortion under practical channel realization and outage probability constraints using NOMA-uplink protocol. Meanwhile, we optimize the power efficiency of the user node in order to increase the battery lifetime. 2020 IEEE.
راعي المشروعQatar National Research Fund
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعCellular radio systems
Data compression ratio
Electroencephalography
Energy efficiency
Learning algorithms
Outages
Reinforcement learning
Sensor networks
Sensor nodes
Battery lifetime
Channel realizations
Continuous monitoring
Electro-encephalogram (EEG)
Energy efficient systems
Outage probability constraints
Power efficiency
Spectrum efficiency
Deep learning
العنوانDeep Reinforcement Learning Algorithm for Smart Data Compression under NOMA-Uplink Protocol
النوعConference Paper
رقم المجلد2020-August


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