The research for the Intelligent Reflecting Surface(IRS)which has the advantages of cost and energy efficiency has been studied.Channel capacity can be effectively increased by appropriately setting the phase value of...The research for the Intelligent Reflecting Surface(IRS)which has the advantages of cost and energy efficiency has been studied.Channel capacity can be effectively increased by appropriately setting the phase value of IRS elements according to the channel conditions.However,the problem of obtaining an appropriate phase value of IRs is difficult to solve due to the non-convex problem.This paper proposes an iterative algorithm for the alternating optimal solution in the Single User Multiple-Input-Multiple-Output(SU-MIMO)systems.The proposed iterative algorithm finds an alternating optimal solution that is the phase value of IRS one by one.The results show that the proposed method has better performance than that of the randomized IRS systems.The number of iterations for maximizing the performance of the proposed algorithm depends on the channel state between the IRS and the receiver.展开更多
In this paper,a novel precoding scheme based on the Gauss-Seidel(GS)method is proposed for downlink massive multiple-input multiple-output(MIMO)systems.The GS method iteratively approximates the matrix inversion and r...In this paper,a novel precoding scheme based on the Gauss-Seidel(GS)method is proposed for downlink massive multiple-input multiple-output(MIMO)systems.The GS method iteratively approximates the matrix inversion and reduces the overall complexity of the precoding process.In addition,the GS method shows a fast convergence rate to the Zero-forcing(ZF)method that requires an exact invertible matrix.However,to satisfy demanded error performance and converge to the error performance of the ZF method in the practical condition such as spatially correlated channels,more iterations are necessary for the GS method and increase the overall complexity.For efficient approximation with fewer iterations,this paper proposes a weighted GS(WGS)method to improve the approximation accuracy of the GS method.The optimal weights that accelerate the convergence rate by improved accuracy are computed by the least square(LS)method.After the computation of weights,the different weights are applied for each iteration of the GS method.In addition,an efficient method of weight computation is proposed to reduce the complexity of the LS method.The simulation results show that bit error rate(BER)performance for the proposed scheme with fewer iterations is better than the GS method in spatially correlated channels.展开更多
The sewer system plays an important role in protecting rainfall and treating urban wastewater.Due to the harsh internal environment and complex structure of the sewer,it is difficult to monitor the sewer system.Resear...The sewer system plays an important role in protecting rainfall and treating urban wastewater.Due to the harsh internal environment and complex structure of the sewer,it is difficult to monitor the sewer system.Researchers are developing different methods,such as the Internet of Things and Artificial Intelligence,to monitor and detect the faults in the sewer system.Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects.However,the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small,which can affect the robustness of the model in the constraint environment.As a result,this paper proposes a sewer condition monitoring framework based on deep learning,which can effectively detect and evaluate defects in sewer pipelines with high accuracy.We also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer pipeline.This study modified the original RegNet model by modifying the squeeze excitation(SE)block and adding the dropout layer and Leaky Rectified Linear Units(LeakyReLU)activation function in the Block structure of RegNet model.This study explored different deep learning methods such as RegNet,ResNet50,very deep convolutional networks(VGG),and GoogleNet to train on the sewer defect dataset.The experimental results indicate that the proposed system framework based on the modified-RegNet(RegNet+)model achieves the highest accuracy of 99.5 compared with the commonly used deep learning models.The proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications.展开更多
Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits ...Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from pictures.But,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing scale.Recently,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human life.An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts.CNN’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several domains.Considering the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)model.In the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the image.In addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature vectors.For fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of fruits.The performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit images.Extensive comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.展开更多
This paper proposes the multiple-input multiple-output(MIMO)detection scheme by using the deep neural network(DNN)based ensemble machine learning for higher error performance in wireless communication systems.For the ...This paper proposes the multiple-input multiple-output(MIMO)detection scheme by using the deep neural network(DNN)based ensemble machine learning for higher error performance in wireless communication systems.For the MIMO detection based on the ensemble machine learning,all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models.In the offline learning,the received signals and channel coefficients are set to input data,and the labels which correspond to transmit symbols are set to output data.In the online learning,the perfectly learned models are used for signal detection where the models have fixed bias and weights.For performance improvement,the proposed scheme uses the majority vote and the maximum probability as the methods of the model combinations for obtaining diversity gains at the MIMO receiver.The simulation results show that the proposed scheme has improved symbol error rate(SER)performance without additional receive antennas.展开更多
In this paper,the supervised Deep Neural Network(DNN)based signal detection is analyzed for combating with nonlinear distortions efficiently and improving error performances in clipping based Orthogonal Frequency Divi...In this paper,the supervised Deep Neural Network(DNN)based signal detection is analyzed for combating with nonlinear distortions efficiently and improving error performances in clipping based Orthogonal Frequency Division Multiplexing(OFDM)ssystem.One of the main disadvantages for the OFDM is the high Peak to Average Power Ratio(PAPR).The clipping is a simple method for the PAPR reduction.However,an effect of the clipping is nonlinear distortion,and estimations for transmitting symbols are difficult despite a Maximum Likelihood(ML)detection at the receiver.The DNN based online signal detection uses the offline learning model where all weights and biases at fully-connected layers are set to overcome nonlinear distortions by using training data sets.Thus,this paper introduces the required processes for the online signal detection and offline learning,and compares error performances with the ML detection in the clipping-based OFDM systems.In simulation results,the DNN based signal detection has better error performance than the conventional ML detection in multi-path fading wireless channel.The performance improvement is large as the complexity of system is increased such as huge Multiple Input Multiple Output(MIMO)system and high clipping rate.展开更多
As the installation of small cells increases,the use of relay also increases.The relay operates as a base station as well as just an amplifier.As the roles and types of relays become more diverse,appropriate relay sel...As the installation of small cells increases,the use of relay also increases.The relay operates as a base station as well as just an amplifier.As the roles and types of relays become more diverse,appropriate relay selection technology is an effective way to improve communication performance.Many researches for relay selection have been studied to secure the reliability of relay communication.In this paper,the relay selection scheme is proposed for a cooperative system using decode-and-forward(DF)relaying scheme in the mobile communication system.To maintain the transmission rate,the proposed scheme classifies a candidate group considering the outage probability of multiple relays.For the applicable candidate group,the proposed scheme selects the relay considering the amount of data allocated to each user.Therefore,the proposed scheme defines the unit transmission time through each user’s data and relay capacity.Finally,the proposed scheme selects a relay that minimizes the total transmission time through the relay transmission time that calculates the unit transmission time for all users.With this adaptive relay selection scheme,an optimal relay can be assigned for each user.For the same transmission rate and the amount of data,the proposed scheme improves the performance of transmission time and reliability.Simulation results show that the proposed scheme reduces the total transmission time for the same amount of data and signal to noise ratio(SNR).展开更多
Massive multiple input multiple output(MIMO)has become essential for the increase of capacity as the millimeter-wave(mmWave)communication is considered.Also,hybrid beamforming systems have been studied since full-digi...Massive multiple input multiple output(MIMO)has become essential for the increase of capacity as the millimeter-wave(mmWave)communication is considered.Also,hybrid beamforming systems have been studied since full-digital beamforming is impractical due to high cost and power consumption of the radio frequency(RF)chains.This paper proposes a hybrid beamforming scheme to improve the spectral efciency for multi-user MIMO(MU-MIMO)systems.In a frequency selective fading environment,hybrid beamforming schemes suffer from performance degradation since the analog precoder performs the same precoding for all subcarriers.To mitigate performance degradation,this paper uses the average channel covariance matrix for all subcarriers and considers an iterative algorithm to design analog precoder using approximation techniques.The analog precoder is iteratively updated for each column until it converges.The proposed scheme can reduce errors in the approximating process of the overall spectral efciency.This scheme can be applied to fully-connected and partially-connected structures.The simulation results show that spectral efciency performance for the proposed scheme is better than the conventional schemes.The proposed scheme can achieve similar performance with full-digital beamforming by using a sufciently large number of RF chains.Also,this paper shows that the proposed scheme outperforms other schemes in the frequency selective fading environment.This performance improvement can be achieved in both structures.展开更多
A number of requirements for 5G mobile communication are satisfied by adopting multiple input multiple output(MIMO)systems.The inter user interference(IUI)which is an inevitable problem in MIMO systems becomes control...A number of requirements for 5G mobile communication are satisfied by adopting multiple input multiple output(MIMO)systems.The inter user interference(IUI)which is an inevitable problem in MIMO systems becomes controllable when the precoding scheme is used.In this paper,the horizontal Gauss-Seidel(HGS)method is proposed as precoding scheme in massive MIMO systems.In massive MIMO systems,the exact inversion of channel matrix is impractical due to the severe computational complexity.Therefore,the conventionalGauss-Seidel(GS)method is used to approximate the inversion of channel matrix.The GS has good performance by using previous calculation results as feedback.However,the required time for obtaining the precoding symbols is too long due to the sequential process of GS.Therefore,the HGS with parallel calculation is proposed in this paper to reduce the required time.The rows of channel matrix are eliminated for parallel calculation inHGSmethod.In addition,HGSuses the ordered channelmatrix to prevent performance degradation which is occurred by parallel calculation.The HGS with proper number of parallelly computed symbols has better performance and reduced required time compared to the traditional GS.展开更多
With the rapid evolution of Internet of things(IoT),many edge devices require simultaneous connection in 5G communication era.To afford massive data of IoT devices,multiple input multiple output non-orthogonal multipl...With the rapid evolution of Internet of things(IoT),many edge devices require simultaneous connection in 5G communication era.To afford massive data of IoT devices,multiple input multiple output non-orthogonal multiple access(MIMO-NOMA)method has been considered as a promising technology.However,there are numerous drawbacks due to error propagation and inter-user interferences.Therefore,proposed scheme aims to improve the reliability of the MIMO-NOMA system with digital beamforming and intracluster cooperative multi point(CoMP)to efficiently support IoT system.In the conventional MIMO-NOMA system,user entities are grouped into clusters.Block diagonalization(BD)is applied to efficiently eliminate the inter-cluster interference of the MIMO-NOMA system.However,since the channel path of the data stream from a single antenna to a single cluster doesn’t hold other cluster’s data,the system can’t fully utilize the selective subcarrier channel states.It indicates that there can be better channel paths for a data stream at a certain subcarrier index.Therefore,proposed scheme allocates data streams to antennas adaptively considering selective channel states.Additionally,intra-cluster CoMP method is adjusted to enhance the reliability of the system in the clusters.The simulation results show that the proposed scheme improves BER and throughput performance compared to the conventional MIMO-NOMA system.展开更多
This paper proposes an improved hybrid beamforming system based on multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system.The proposed beamforming system improves energy efficiency ...This paper proposes an improved hybrid beamforming system based on multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system.The proposed beamforming system improves energy efficiency compared to the conventional hybrid beamforming system.Both sub-connected and full-connected structure are considered to apply the proposed algorithm.In the conventional hybrid beamforming,the usage of radio frequency(RF)chains and phase shifter(PS)gives high power and hardware complexity.In this paper,the phase over sampling(POS)with switches(SW)is used in hybrid beamforming system to improve the energy efficiency.The POS-SW structure samples the value of analog beamformer to make lower resolution than conventional system.The number of output data in POS is decided by the resolution of POS system.The limited number of POS decides the resolution of antenna array and the values of POSs are designed from maximum and minimum phase angle antenna array.Energy efficiency without the phase shifter is high although channel capacity is nearly similar with conventional system.Also,the amplifier with POS-SW system is proposed to improve the BER performance.According to the data bits,the output signals of POS are decided.The system with 2,3 and 4 bits is simulated to prove the proposed algorithm.In order to overcome the loss of low-resolution system,the amplifier with POS-SW system using channel information is proposed.The average sum-rate of 4 bits system shows the similar performance with the conventional hybrid beamforming system.This structure can play an important role by increasing the energy efficiency of the wireless communication system that many antennas are used.It is shown that the BER,average sum rate and energy efficiency of the proposed scheme are more improved than the conventional hybrid beamforming system.展开更多
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2022-2018-0-01423)supervised by the ITP(Institute for Information&Communications Technology Planning&Evaluation)supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2020R1A6A1A03038540).
文摘The research for the Intelligent Reflecting Surface(IRS)which has the advantages of cost and energy efficiency has been studied.Channel capacity can be effectively increased by appropriately setting the phase value of IRS elements according to the channel conditions.However,the problem of obtaining an appropriate phase value of IRs is difficult to solve due to the non-convex problem.This paper proposes an iterative algorithm for the alternating optimal solution in the Single User Multiple-Input-Multiple-Output(SU-MIMO)systems.The proposed iterative algorithm finds an alternating optimal solution that is the phase value of IRS one by one.The results show that the proposed method has better performance than that of the randomized IRS systems.The number of iterations for maximizing the performance of the proposed algorithm depends on the channel state between the IRS and the receiver.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2019-2018-0-01423)supervised by the IITP(Institute for Information&communications Technology Promotion)supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2020R1A6A1A03038540).
文摘In this paper,a novel precoding scheme based on the Gauss-Seidel(GS)method is proposed for downlink massive multiple-input multiple-output(MIMO)systems.The GS method iteratively approximates the matrix inversion and reduces the overall complexity of the precoding process.In addition,the GS method shows a fast convergence rate to the Zero-forcing(ZF)method that requires an exact invertible matrix.However,to satisfy demanded error performance and converge to the error performance of the ZF method in the practical condition such as spatially correlated channels,more iterations are necessary for the GS method and increase the overall complexity.For efficient approximation with fewer iterations,this paper proposes a weighted GS(WGS)method to improve the approximation accuracy of the GS method.The optimal weights that accelerate the convergence rate by improved accuracy are computed by the least square(LS)method.After the computation of weights,the different weights are applied for each iteration of the GS method.In addition,an efficient method of weight computation is proposed to reduce the complexity of the LS method.The simulation results show that bit error rate(BER)performance for the proposed scheme with fewer iterations is better than the GS method in spatially correlated channels.
基金supported by Basic ScienceResearch Program through the National Research Foundation ofKorea(NRF)funded by the Ministry of Education(2020R1A6A1A03038540)by Korea Institute of Planning and Evaluation for Technology in Food,Agriculture,Forestry and Fisheries(IPET)through Digital Breeding Transformation Technology Development Program,funded by Ministry of Agriculture,Food and Rural Affairs(MAFRA)(322063-03-1-SB010)by the Technology development Program(RS-2022-00156456)funded by the Ministry of SMEs and Startups(MSS,Korea).
文摘The sewer system plays an important role in protecting rainfall and treating urban wastewater.Due to the harsh internal environment and complex structure of the sewer,it is difficult to monitor the sewer system.Researchers are developing different methods,such as the Internet of Things and Artificial Intelligence,to monitor and detect the faults in the sewer system.Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects.However,the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small,which can affect the robustness of the model in the constraint environment.As a result,this paper proposes a sewer condition monitoring framework based on deep learning,which can effectively detect and evaluate defects in sewer pipelines with high accuracy.We also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer pipeline.This study modified the original RegNet model by modifying the squeeze excitation(SE)block and adding the dropout layer and Leaky Rectified Linear Units(LeakyReLU)activation function in the Block structure of RegNet model.This study explored different deep learning methods such as RegNet,ResNet50,very deep convolutional networks(VGG),and GoogleNet to train on the sewer defect dataset.The experimental results indicate that the proposed system framework based on the modified-RegNet(RegNet+)model achieves the highest accuracy of 99.5 compared with the commonly used deep learning models.The proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications.
基金supported by Basic Science Research Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Education (2020R1A6A1A03038540)by Korea Institute of Planning and Evaluation for Technology in Food,Agriculture,Forestry and Fisheries (IPET)through Digital Breeding Transformation Technology Development Program,funded by Ministry of Agriculture,Food and Rural Affairs (MAFRA) (322063-03-1-SB010)by the Technology development Program (RS-2022-00156456)funded by the Ministry of SMEs and Startups (MSS,Korea).
文摘Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from pictures.But,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing scale.Recently,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human life.An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts.CNN’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several domains.Considering the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)model.In the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the image.In addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature vectors.For fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of fruits.The performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit images.Extensive comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C2005777)was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2020R1A6A1A03038540)。
文摘This paper proposes the multiple-input multiple-output(MIMO)detection scheme by using the deep neural network(DNN)based ensemble machine learning for higher error performance in wireless communication systems.For the MIMO detection based on the ensemble machine learning,all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models.In the offline learning,the received signals and channel coefficients are set to input data,and the labels which correspond to transmit symbols are set to output data.In the online learning,the perfectly learned models are used for signal detection where the models have fixed bias and weights.For performance improvement,the proposed scheme uses the majority vote and the maximum probability as the methods of the model combinations for obtaining diversity gains at the MIMO receiver.The simulation results show that the proposed scheme has improved symbol error rate(SER)performance without additional receive antennas.
基金This work was supported by Institute for Information&communications Technology Promotion(IITP)grant funded by the Korea government(MSIT)(No.2017-0-00217,Development of Immersive Signage Based on Variable Transparency and Multiple Layers)was supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2019-2018-0-01423)supervised by the IITP(Institute for Information&communications Technology Promotion).
文摘In this paper,the supervised Deep Neural Network(DNN)based signal detection is analyzed for combating with nonlinear distortions efficiently and improving error performances in clipping based Orthogonal Frequency Division Multiplexing(OFDM)ssystem.One of the main disadvantages for the OFDM is the high Peak to Average Power Ratio(PAPR).The clipping is a simple method for the PAPR reduction.However,an effect of the clipping is nonlinear distortion,and estimations for transmitting symbols are difficult despite a Maximum Likelihood(ML)detection at the receiver.The DNN based online signal detection uses the offline learning model where all weights and biases at fully-connected layers are set to overcome nonlinear distortions by using training data sets.Thus,this paper introduces the required processes for the online signal detection and offline learning,and compares error performances with the ML detection in the clipping-based OFDM systems.In simulation results,the DNN based signal detection has better error performance than the conventional ML detection in multi-path fading wireless channel.The performance improvement is large as the complexity of system is increased such as huge Multiple Input Multiple Output(MIMO)system and high clipping rate.
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2019-2018-0-01423)supervised by the IITP(Institute for Information&communications Technology Promotion)was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2020R1A6A1A03038540).
文摘As the installation of small cells increases,the use of relay also increases.The relay operates as a base station as well as just an amplifier.As the roles and types of relays become more diverse,appropriate relay selection technology is an effective way to improve communication performance.Many researches for relay selection have been studied to secure the reliability of relay communication.In this paper,the relay selection scheme is proposed for a cooperative system using decode-and-forward(DF)relaying scheme in the mobile communication system.To maintain the transmission rate,the proposed scheme classifies a candidate group considering the outage probability of multiple relays.For the applicable candidate group,the proposed scheme selects the relay considering the amount of data allocated to each user.Therefore,the proposed scheme defines the unit transmission time through each user’s data and relay capacity.Finally,the proposed scheme selects a relay that minimizes the total transmission time through the relay transmission time that calculates the unit transmission time for all users.With this adaptive relay selection scheme,an optimal relay can be assigned for each user.For the same transmission rate and the amount of data,the proposed scheme improves the performance of transmission time and reliability.Simulation results show that the proposed scheme reduces the total transmission time for the same amount of data and signal to noise ratio(SNR).
基金supported in part by Institute for Information&communications Technology Promotion(IITP)grant funded by the Korea government(MSIT)(No.2017-0-00217,Development of Immersive Signage Based on Variable Transparency and Multiple Layers)in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant 2020R1A6A1A03038540.
文摘Massive multiple input multiple output(MIMO)has become essential for the increase of capacity as the millimeter-wave(mmWave)communication is considered.Also,hybrid beamforming systems have been studied since full-digital beamforming is impractical due to high cost and power consumption of the radio frequency(RF)chains.This paper proposes a hybrid beamforming scheme to improve the spectral efciency for multi-user MIMO(MU-MIMO)systems.In a frequency selective fading environment,hybrid beamforming schemes suffer from performance degradation since the analog precoder performs the same precoding for all subcarriers.To mitigate performance degradation,this paper uses the average channel covariance matrix for all subcarriers and considers an iterative algorithm to design analog precoder using approximation techniques.The analog precoder is iteratively updated for each column until it converges.The proposed scheme can reduce errors in the approximating process of the overall spectral efciency.This scheme can be applied to fully-connected and partially-connected structures.The simulation results show that spectral efciency performance for the proposed scheme is better than the conventional schemes.The proposed scheme can achieve similar performance with full-digital beamforming by using a sufciently large number of RF chains.Also,this paper shows that the proposed scheme outperforms other schemes in the frequency selective fading environment.This performance improvement can be achieved in both structures.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2020R1A6A1A03038540)was supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(2021R1A2C2005777).
文摘A number of requirements for 5G mobile communication are satisfied by adopting multiple input multiple output(MIMO)systems.The inter user interference(IUI)which is an inevitable problem in MIMO systems becomes controllable when the precoding scheme is used.In this paper,the horizontal Gauss-Seidel(HGS)method is proposed as precoding scheme in massive MIMO systems.In massive MIMO systems,the exact inversion of channel matrix is impractical due to the severe computational complexity.Therefore,the conventionalGauss-Seidel(GS)method is used to approximate the inversion of channel matrix.The GS has good performance by using previous calculation results as feedback.However,the required time for obtaining the precoding symbols is too long due to the sequential process of GS.Therefore,the HGS with parallel calculation is proposed in this paper to reduce the required time.The rows of channel matrix are eliminated for parallel calculation inHGSmethod.In addition,HGSuses the ordered channelmatrix to prevent performance degradation which is occurred by parallel calculation.The HGS with proper number of parallelly computed symbols has better performance and reduced required time compared to the traditional GS.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2020R1A6A1A 03038540)was supported by Institute for Information&communications Technology Promotion(IITP)grant funded by the Korea government(MSIT)(No.2017-0-00217,Development of Immersive Signage Based on Variable Transparency and Multiple Layers)。
文摘With the rapid evolution of Internet of things(IoT),many edge devices require simultaneous connection in 5G communication era.To afford massive data of IoT devices,multiple input multiple output non-orthogonal multiple access(MIMO-NOMA)method has been considered as a promising technology.However,there are numerous drawbacks due to error propagation and inter-user interferences.Therefore,proposed scheme aims to improve the reliability of the MIMO-NOMA system with digital beamforming and intracluster cooperative multi point(CoMP)to efficiently support IoT system.In the conventional MIMO-NOMA system,user entities are grouped into clusters.Block diagonalization(BD)is applied to efficiently eliminate the inter-cluster interference of the MIMO-NOMA system.However,since the channel path of the data stream from a single antenna to a single cluster doesn’t hold other cluster’s data,the system can’t fully utilize the selective subcarrier channel states.It indicates that there can be better channel paths for a data stream at a certain subcarrier index.Therefore,proposed scheme allocates data streams to antennas adaptively considering selective channel states.Additionally,intra-cluster CoMP method is adjusted to enhance the reliability of the system in the clusters.The simulation results show that the proposed scheme improves BER and throughput performance compared to the conventional MIMO-NOMA system.
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2020R1A6A1A03038540)in part by R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(NRF2020M3C1C1A02086427).
文摘This paper proposes an improved hybrid beamforming system based on multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system.The proposed beamforming system improves energy efficiency compared to the conventional hybrid beamforming system.Both sub-connected and full-connected structure are considered to apply the proposed algorithm.In the conventional hybrid beamforming,the usage of radio frequency(RF)chains and phase shifter(PS)gives high power and hardware complexity.In this paper,the phase over sampling(POS)with switches(SW)is used in hybrid beamforming system to improve the energy efficiency.The POS-SW structure samples the value of analog beamformer to make lower resolution than conventional system.The number of output data in POS is decided by the resolution of POS system.The limited number of POS decides the resolution of antenna array and the values of POSs are designed from maximum and minimum phase angle antenna array.Energy efficiency without the phase shifter is high although channel capacity is nearly similar with conventional system.Also,the amplifier with POS-SW system is proposed to improve the BER performance.According to the data bits,the output signals of POS are decided.The system with 2,3 and 4 bits is simulated to prove the proposed algorithm.In order to overcome the loss of low-resolution system,the amplifier with POS-SW system using channel information is proposed.The average sum-rate of 4 bits system shows the similar performance with the conventional hybrid beamforming system.This structure can play an important role by increasing the energy efficiency of the wireless communication system that many antennas are used.It is shown that the BER,average sum rate and energy efficiency of the proposed scheme are more improved than the conventional hybrid beamforming system.
