Pub Date : 2019-10-24DOI: 10.1109/UCET.2019.8881849
Zubair Bashir, Muhammad Zahid, Naeem Abbas, M. Yousaf, S. Shoaib, Muhammad Adeel Asghar, Y. Amin
In this paper, a miniaturized high gain antenna was designed for biomedical applications. The designed antenna operates on the industrial, medical, and scientific (2.40 – 2.4835) GHz band. The proposed antenna consists of the radiating element having rectangular, and circular slots, and a ground plane with rectangular slots. The total volume of the designed antenna is $(7times 7times 0.2)mm^{3}$, and the thickness of the superstrate and substrate is 0.1 mm. The Rogers ULTRALAM $(varepsilon_{r}=2.9, tandelta=0.0025)$ material is used for substrate and superstrate. The proposed antenna is placed inside the different phantoms of the human body. The maximum gain achieved by the simulations of the proposed antenna is −12 dBi at 2.45 GHz. The designed antenna has better results than the antennas discussed in the literature in term of size, gain, and bandwidth.
{"title":"A Miniaturized Wide Band Implantable Antenna for Biomedical Application","authors":"Zubair Bashir, Muhammad Zahid, Naeem Abbas, M. Yousaf, S. Shoaib, Muhammad Adeel Asghar, Y. Amin","doi":"10.1109/UCET.2019.8881849","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881849","url":null,"abstract":"In this paper, a miniaturized high gain antenna was designed for biomedical applications. The designed antenna operates on the industrial, medical, and scientific (2.40 – 2.4835) GHz band. The proposed antenna consists of the radiating element having rectangular, and circular slots, and a ground plane with rectangular slots. The total volume of the designed antenna is $(7times 7times 0.2)mm^{3}$, and the thickness of the superstrate and substrate is 0.1 mm. The Rogers ULTRALAM $(varepsilon_{r}=2.9, tandelta=0.0025)$ material is used for substrate and superstrate. The proposed antenna is placed inside the different phantoms of the human body. The maximum gain achieved by the simulations of the proposed antenna is −12 dBi at 2.45 GHz. The designed antenna has better results than the antennas discussed in the literature in term of size, gain, and bandwidth.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125611796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-24DOI: 10.1109/UCET.2019.8881861
Yi Zhang, R. Ghannam, M. Valyrakis, H. Heidari
Sensor networks can detect and communicate information regarding the ambient environment using wireless and real-time methods. Consequently, sensor node design is of critical importance for monitoring water quality. This paper describes the design, fabrication and implementation process of a 3D-printed sensory ball that can remotely collect water flow parameters in real-time. A sensory ball that is 10-cm in diameter was used to measure water flow parameters. Data was then captured in real time and sent to a personal computer via wireless communications. Discussions regarding alternative applications of this device are provided in this manuscript.
{"title":"Design and Implementation of a 3D Printed Sensory Ball for Wireless Water Flow Monitoring","authors":"Yi Zhang, R. Ghannam, M. Valyrakis, H. Heidari","doi":"10.1109/UCET.2019.8881861","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881861","url":null,"abstract":"Sensor networks can detect and communicate information regarding the ambient environment using wireless and real-time methods. Consequently, sensor node design is of critical importance for monitoring water quality. This paper describes the design, fabrication and implementation process of a 3D-printed sensory ball that can remotely collect water flow parameters in real-time. A sensory ball that is 10-cm in diameter was used to measure water flow parameters. Data was then captured in real time and sent to a personal computer via wireless communications. Discussions regarding alternative applications of this device are provided in this manuscript.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114172256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-24DOI: 10.1109/UCET.2019.8881845
Syed Shah, Syed Yaseen Shah, Syed Aziz Shah
This paper presents design and implementation of a low-cost system solution. A light-weight wireless device is used for surveillance of suspicious activities at highly sensitive areas. The devices such as transceivers operating at 2.4 GHz are deployed in indoor settings. Finer-grained Channel State Information in conjunction with pair of leaky wave cable detects any intruder in the area. The intrusion detection is identified at a particular subcarrier frequency. The processing of the measured data over time is analyzed and used for reporting the disturbances. Deploying leaky wave cable as transmitter and receiver has benefits in terms of wider coverage area, covering blind and semi blind zones. The system fully exploits the amplitude and phase information of Channel State Information provided by Intel 5300 NIC and its associated driver. The experimental results demonstrate greater level of accuracy in a cluttered environment.
{"title":"Intrusion Detection through Leaky Wave Cable in Conjunction with Channel State Information","authors":"Syed Shah, Syed Yaseen Shah, Syed Aziz Shah","doi":"10.1109/UCET.2019.8881845","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881845","url":null,"abstract":"This paper presents design and implementation of a low-cost system solution. A light-weight wireless device is used for surveillance of suspicious activities at highly sensitive areas. The devices such as transceivers operating at 2.4 GHz are deployed in indoor settings. Finer-grained Channel State Information in conjunction with pair of leaky wave cable detects any intruder in the area. The intrusion detection is identified at a particular subcarrier frequency. The processing of the measured data over time is analyzed and used for reporting the disturbances. Deploying leaky wave cable as transmitter and receiver has benefits in terms of wider coverage area, covering blind and semi blind zones. The system fully exploits the amplitude and phase information of Channel State Information provided by Intel 5300 NIC and its associated driver. The experimental results demonstrate greater level of accuracy in a cluttered environment.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115394142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-24DOI: 10.1109/UCET.2019.8881872
Fehaid Alqahtani, Stamos Katsigiannis, N. Ramzan
Intelligent tutoring Systems (ITS) have emerged as an attractive solution for providing personalised learning experiences on a large scale. Traditional ITS are able to adapt the learning process according to the capabilities and needs of their users, but lack the capability to adapt to their affective/emotional state. In this work, we examine the use of electrocardiography (ECG) signals for detecting the affective state of ITS users. Features, extracted from ECG signals acquired while users undertook a computerised English language test, were used for the prediction of the self-reported difficulty level of the test's questions. Supervised classification experiments demonstrated the potential of this approach, achieving a classification F1-score of 61.22% for the prediction of the self-assessed difficulty level of the questions.
{"title":"ECG-based affective computing for difficulty level prediction in Intelligent Tutoring Systems","authors":"Fehaid Alqahtani, Stamos Katsigiannis, N. Ramzan","doi":"10.1109/UCET.2019.8881872","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881872","url":null,"abstract":"Intelligent tutoring Systems (ITS) have emerged as an attractive solution for providing personalised learning experiences on a large scale. Traditional ITS are able to adapt the learning process according to the capabilities and needs of their users, but lack the capability to adapt to their affective/emotional state. In this work, we examine the use of electrocardiography (ECG) signals for detecting the affective state of ITS users. Features, extracted from ECG signals acquired while users undertook a computerised English language test, were used for the prediction of the self-reported difficulty level of the test's questions. Supervised classification experiments demonstrated the potential of this approach, achieving a classification F1-score of 61.22% for the prediction of the self-assessed difficulty level of the questions.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131109817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-21DOI: 10.1109/UCET.2019.8881850
M. Saeed, M. Rehman
Interest in wearable applications providing early medical diagnostics and reliable communication to the remote observation station is ever increasing. A flexible microstrip patch antenna array designed on a liquid crystal polymer (LCP) substrate is presented in this paper. The designed antenna array consists of five radiating patches fed by a combination of series transmission lines. The simulated results show that the antenna resonates at 52 GHz with −10 dB impedance bandwidth of 1.34 GHz. It offers a gain of 3.5 dBi and 3-dB angular width of 72.3°at the operating frequency along with low weight, low cost and ease of fabrication. Good impedance and radiation characteristics with small size and low profile make this antenna ideally suited for low latency wearable applications in 5G/Beyond 5G networks.
{"title":"Design of an LCP-based Antenna Array for 5G/B5G Wearable Applications","authors":"M. Saeed, M. Rehman","doi":"10.1109/UCET.2019.8881850","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881850","url":null,"abstract":"Interest in wearable applications providing early medical diagnostics and reliable communication to the remote observation station is ever increasing. A flexible microstrip patch antenna array designed on a liquid crystal polymer (LCP) substrate is presented in this paper. The designed antenna array consists of five radiating patches fed by a combination of series transmission lines. The simulated results show that the antenna resonates at 52 GHz with −10 dB impedance bandwidth of 1.34 GHz. It offers a gain of 3.5 dBi and 3-dB angular width of 72.3°at the operating frequency along with low weight, low cost and ease of fabrication. Good impedance and radiation characteristics with small size and low profile make this antenna ideally suited for low latency wearable applications in 5G/Beyond 5G networks.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121606799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-21DOI: 10.1109/UCET.2019.8881834
Tarek Elouaret, Stéphane Zuckerman, L. Kessal, Yoan Espada, N. Cuperlier, Guillaume Bresson, F. Ouezdou, Olivier Romain
One important feature required by autonomous vehicles is the ability to perform a localization task in order to navigate in both known (urban, suburban, and highways) and unknown environments. Instead of relying on LIDAR technology, we propose to leverage a bio-inspired algorithm relying on more conventional cameras and a large neural network (NN) [2]. Yet, this approach must be able to scale. We propose to investigate the development of an FPGA-based solution. Due to the size of NN, dynamic partial reconfiguration will be required, and an efficient (software-based) scheduler must place the hardware tasks on multiple FPGA chip. We intend to implement this algorithm using a unique custom board, Wizarde, which embeds a 3 × 3 matrix of Zynq SoCs with high-end FPGAs to prototype a possible solution.
{"title":"Position Paper: Prototyping Autonomous Vehicles Applications with Heterogeneous Multi-FpgaSystems","authors":"Tarek Elouaret, Stéphane Zuckerman, L. Kessal, Yoan Espada, N. Cuperlier, Guillaume Bresson, F. Ouezdou, Olivier Romain","doi":"10.1109/UCET.2019.8881834","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881834","url":null,"abstract":"One important feature required by autonomous vehicles is the ability to perform a localization task in order to navigate in both known (urban, suburban, and highways) and unknown environments. Instead of relying on LIDAR technology, we propose to leverage a bio-inspired algorithm relying on more conventional cameras and a large neural network (NN) [2]. Yet, this approach must be able to scale. We propose to investigate the development of an FPGA-based solution. Due to the size of NN, dynamic partial reconfiguration will be required, and an efficient (software-based) scheduler must place the hardware tasks on multiple FPGA chip. We intend to implement this algorithm using a unique custom board, Wizarde, which embeds a 3 × 3 matrix of Zynq SoCs with high-end FPGAs to prototype a possible solution.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115043576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/UCET.2019.8881867
Daniyal Haider, O. Romain, J. Kernec, Syed Yaseen Shah, Malik Muhammad Umer Farooq, Zunaira Qadus
The modern wireless technology exploiting the full potential of 5G IoT is the future for healthcare sector. In the healthcare sector, the 5G technology will maximize the performance and will reduce the chances of damage to the patient by providing careful and advance activity monitoring scenarios. We have proposed the idea of monitoring different body posture in Huntington disease by exploiting the low cost wireless devices operating at 4.8 GHz frequency. The system captures the wireless channel information for three body motions and classification of these motions was performed by using support vector machine. The SVM used 10 time-domain features for the classification process by using three main kernel functions, such as, Linear, Polynomial and Radial basis function. The system minimizes all the external noise by using the microwave absorbing materials. This increases the performance and feasibility of sensing body motions.
{"title":"Monitoring Body Motions Related To Huntington Disease by Exploiting the 5G Paradigm","authors":"Daniyal Haider, O. Romain, J. Kernec, Syed Yaseen Shah, Malik Muhammad Umer Farooq, Zunaira Qadus","doi":"10.1109/UCET.2019.8881867","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881867","url":null,"abstract":"The modern wireless technology exploiting the full potential of 5G IoT is the future for healthcare sector. In the healthcare sector, the 5G technology will maximize the performance and will reduce the chances of damage to the patient by providing careful and advance activity monitoring scenarios. We have proposed the idea of monitoring different body posture in Huntington disease by exploiting the low cost wireless devices operating at 4.8 GHz frequency. The system captures the wireless channel information for three body motions and classification of these motions was performed by using support vector machine. The SVM used 10 time-domain features for the classification process by using three main kernel functions, such as, Linear, Polynomial and Radial basis function. The system minimizes all the external noise by using the microwave absorbing materials. This increases the performance and feasibility of sensing body motions.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114258763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/UCET.2019.8881841
Ruiyu Wang, Oluwakayode Onireti, Lei Zhang, M. Imran, Guangmei Ren, Jing Qiu, Tingjian Tian
With the rapid growth of mobile data demand, the fifth generation (5G) mobile network must exploit the large amount of spectrum in the millimeter wave (mmWave) band to increase the network capacity. Due to the limitation of propagation distance, line-of-sight (LOS) link is highly desirable for mmWave systems. However, LOS channel is not feasible all the time and mmWave is also impacted significantly by the surrounding environment. The LOS signal can be easily blocked by surrounding buildings. Based on this issue, in this paper, we propose to use reinforcement learning to manage the non line of sight (NLOS) scenario. Specifically, we build a model simulating blocked LOS signal for the user equipment (UE) with only NLOS channel available for the UE. Q-Learning is used to select the NLOS beam that meets the UE's quality of service requirements. Simulation results show that Q-Learning can be used to manage the beam selection. In particular, at initial training stage the Q-Learning explores in the environment. However, with the training process, Q-Learning learns from experience and the received power increases significantly and converges to an excellent level.
{"title":"Reinforcement Learning Method for Beam Management in Millimeter-Wave Networks","authors":"Ruiyu Wang, Oluwakayode Onireti, Lei Zhang, M. Imran, Guangmei Ren, Jing Qiu, Tingjian Tian","doi":"10.1109/UCET.2019.8881841","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881841","url":null,"abstract":"With the rapid growth of mobile data demand, the fifth generation (5G) mobile network must exploit the large amount of spectrum in the millimeter wave (mmWave) band to increase the network capacity. Due to the limitation of propagation distance, line-of-sight (LOS) link is highly desirable for mmWave systems. However, LOS channel is not feasible all the time and mmWave is also impacted significantly by the surrounding environment. The LOS signal can be easily blocked by surrounding buildings. Based on this issue, in this paper, we propose to use reinforcement learning to manage the non line of sight (NLOS) scenario. Specifically, we build a model simulating blocked LOS signal for the user equipment (UE) with only NLOS channel available for the UE. Q-Learning is used to select the NLOS beam that meets the UE's quality of service requirements. Simulation results show that Q-Learning can be used to manage the beam selection. In particular, at initial training stage the Q-Learning explores in the environment. However, with the training process, Q-Learning learns from experience and the received power increases significantly and converges to an excellent level.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117032695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/UCET.2019.8881848
I. S. Bayram
Per-capita electricity consumption in the Gulf region is one of the highest in the world due to high disposable incomes, year-long need to air conditioning, and energy subsidies. Electrical consumption data and load profiles of major household appliances are crucial elements for demand response programs which aim to reduce electricity consumption. In this study, we choose three typical villa-type accommodation in the State of Qatar and deployed multiple smart energy monitors to gain insights about energy consumption patterns. Measurement study covered the first five months of 2019 and the data is recorded at every five minutes. Electricity bills of all houses are subsidized by their employees, hence, results provide an opportunity to reveal the impacts of subsidies on consumption patterns. The analyses show that air conditioning (AC) and water heater loads are the major electricity consumers and significant demand savings can be achieved by introducing applications for them. To the best of author's knowledge, this is the first study conducted in the Gulf region that presents the measurement of appliance-level electricity consumption.
{"title":"Non-intrusive Electricity Sub-metering in Selected Households in Qatar","authors":"I. S. Bayram","doi":"10.1109/UCET.2019.8881848","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881848","url":null,"abstract":"Per-capita electricity consumption in the Gulf region is one of the highest in the world due to high disposable incomes, year-long need to air conditioning, and energy subsidies. Electrical consumption data and load profiles of major household appliances are crucial elements for demand response programs which aim to reduce electricity consumption. In this study, we choose three typical villa-type accommodation in the State of Qatar and deployed multiple smart energy monitors to gain insights about energy consumption patterns. Measurement study covered the first five months of 2019 and the data is recorded at every five minutes. Electricity bills of all houses are subsidized by their employees, hence, results provide an opportunity to reveal the impacts of subsidies on consumption patterns. The analyses show that air conditioning (AC) and water heater loads are the major electricity consumers and significant demand savings can be achieved by introducing applications for them. To the best of author's knowledge, this is the first study conducted in the Gulf region that presents the measurement of appliance-level electricity consumption.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129330748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1109/UCET.2019.8881888
Narengerile, J. Thompson
This paper presents an initial investigation of deep learning (DL) for multi-user detection in non-orthogonal multiple access (NOMA) wireless systems. In NOMA systems, the successive interference cancellation (SIC) process is usually performed at the receiver, where multiple users are decoded in a sequential fashion. Due to error propagation effects, the detection accuracy will largely depend on the correct detection of previous users. A DL-based NOMA receiver is designed to decode messages for multiple users in a one-shot process, without estimating channels explicitly. The DL-based NOMA receiver is represented by a deep neural network (DNN), which performs channel estimation and signal detection in a joint manner. The DNN is first trained offline using simulation data based on channel statistics and then used to recover the transmitted symbols directly in the online deployment stage. Initial results show that the DL approach can outperform the conventional pilot-based channel estimation methods and is more robust to the number of pilot symbols. The DNN is shown to be capable of mitigating the potential error propagation effects that occur in the SIC detector. Furthermore, when the inter-symbol interference is severe, the DL approach can achieve better performance than a maximum likelihood detector that does not account for interference effects.
{"title":"Deep Learning for Signal Detection in Non-Orthogonal Multiple Access Wireless Systems","authors":"Narengerile, J. Thompson","doi":"10.1109/UCET.2019.8881888","DOIUrl":"https://doi.org/10.1109/UCET.2019.8881888","url":null,"abstract":"This paper presents an initial investigation of deep learning (DL) for multi-user detection in non-orthogonal multiple access (NOMA) wireless systems. In NOMA systems, the successive interference cancellation (SIC) process is usually performed at the receiver, where multiple users are decoded in a sequential fashion. Due to error propagation effects, the detection accuracy will largely depend on the correct detection of previous users. A DL-based NOMA receiver is designed to decode messages for multiple users in a one-shot process, without estimating channels explicitly. The DL-based NOMA receiver is represented by a deep neural network (DNN), which performs channel estimation and signal detection in a joint manner. The DNN is first trained offline using simulation data based on channel statistics and then used to recover the transmitted symbols directly in the online deployment stage. Initial results show that the DL approach can outperform the conventional pilot-based channel estimation methods and is more robust to the number of pilot symbols. The DNN is shown to be capable of mitigating the potential error propagation effects that occur in the SIC detector. Furthermore, when the inter-symbol interference is severe, the DL approach can achieve better performance than a maximum likelihood detector that does not account for interference effects.","PeriodicalId":169373,"journal":{"name":"2019 UK/ China Emerging Technologies (UCET)","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123681481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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