Single-signal detection in orthogonal frequency-divisionmultiplexing(OFDM)systems presents a challenge due to the time-varying nature of wireless channels.Although conventional methods have limitations,particularly in...Single-signal detection in orthogonal frequency-divisionmultiplexing(OFDM)systems presents a challenge due to the time-varying nature of wireless channels.Although conventional methods have limitations,particularly inmulti-inputmultioutput orthogonal frequency divisionmultiplexing(MIMO-OFDM)systems,this paper addresses this problem by exploring advanced deep learning approaches for combined channel estimation and signal detection.Specifically,we propose two hybrid architectures that integrate a convolutional neural network(CNN)with a recurrent neural network(RNN),namely,CNN-long short-term memory(CNN-LSTM)and CNN-bidirectional-LSTM(CNNBi-LSTM),designed to enhance signal detection performance in MIMO-OFDM systems.The proposed CNN-LSTM and CNN-Bi-LSTM architectures are evaluated and compared with both traditional methods and standalone deep learning models.Training was conducted offline using a dataset generated from a 2×2 MIMO-OFDM system with a 3GPP 5G channel model.The trained models are evaluated using accuracy,loss,and computational time,and further analysis of signal detection performance is based on bit error rate,optimal cyclic prefix length,and optimal pilot subcarrier configurations under various noise conditions and channel uncertainty scenarios.The results demonstrate that the proposed CNN-based architectures,particularly the CNN-Bi-LSTM trained model,significantly reduce the need for pilot and cyclic prefix symbols while delivering superior performance,especially at SNRs.All the hybrid deep learning architectures(CNN-LSTM,CNN-Bi-LSTM)demonstrated greater robustness and adaptability under dynamic channel conditions,outperforming conventional methods and benchmark deep learning architectures.These results indicate the effectiveness of CNN-based feature extractors in learning generalized spatial patterns,positioning these hybrid models as highly efficient and reliable solutions for MIMO-OFDM signal detection in 5G and future wireless communication systems.展开更多
The demand for group communication using smart devices in campus environment is increasing rapidly. In this paper, we design an architecture for a mobile group communication system （MGCS） on campus by using Wi-Fi ne...The demand for group communication using smart devices in campus environment is increasing rapidly. In this paper, we design an architecture for a mobile group communication system （MGCS） on campus by using Wi-Fi networks and smart devices. The architecture is composed of a web-based system and a smart device based mobile system. Through the systems, users on campus create community/mobile group, maintain dynamic group membership, and reliably deliver the message to other users. We use the common features of many smart devices to develop a prototype that works on off-the-shelf hardware. In the experimental section, we demonstrate our system using various real scenarios which can occur in university campuses.展开更多
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2022-00156299).
文摘Single-signal detection in orthogonal frequency-divisionmultiplexing(OFDM)systems presents a challenge due to the time-varying nature of wireless channels.Although conventional methods have limitations,particularly inmulti-inputmultioutput orthogonal frequency divisionmultiplexing(MIMO-OFDM)systems,this paper addresses this problem by exploring advanced deep learning approaches for combined channel estimation and signal detection.Specifically,we propose two hybrid architectures that integrate a convolutional neural network(CNN)with a recurrent neural network(RNN),namely,CNN-long short-term memory(CNN-LSTM)and CNN-bidirectional-LSTM(CNNBi-LSTM),designed to enhance signal detection performance in MIMO-OFDM systems.The proposed CNN-LSTM and CNN-Bi-LSTM architectures are evaluated and compared with both traditional methods and standalone deep learning models.Training was conducted offline using a dataset generated from a 2×2 MIMO-OFDM system with a 3GPP 5G channel model.The trained models are evaluated using accuracy,loss,and computational time,and further analysis of signal detection performance is based on bit error rate,optimal cyclic prefix length,and optimal pilot subcarrier configurations under various noise conditions and channel uncertainty scenarios.The results demonstrate that the proposed CNN-based architectures,particularly the CNN-Bi-LSTM trained model,significantly reduce the need for pilot and cyclic prefix symbols while delivering superior performance,especially at SNRs.All the hybrid deep learning architectures(CNN-LSTM,CNN-Bi-LSTM)demonstrated greater robustness and adaptability under dynamic channel conditions,outperforming conventional methods and benchmark deep learning architectures.These results indicate the effectiveness of CNN-based feature extractors in learning generalized spatial patterns,positioning these hybrid models as highly efficient and reliable solutions for MIMO-OFDM signal detection in 5G and future wireless communication systems.
文摘The demand for group communication using smart devices in campus environment is increasing rapidly. In this paper, we design an architecture for a mobile group communication system （MGCS） on campus by using Wi-Fi networks and smart devices. The architecture is composed of a web-based system and a smart device based mobile system. Through the systems, users on campus create community/mobile group, maintain dynamic group membership, and reliably deliver the message to other users. We use the common features of many smart devices to develop a prototype that works on off-the-shelf hardware. In the experimental section, we demonstrate our system using various real scenarios which can occur in university campuses.
