The Experts below are selected from a list of 102870 Experts worldwide ranked by ideXlab platform
Tomoaki Ohtsuki - One of the best experts on this subject based on the ideXlab platform.
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Learning-Based Cell Selection Method for Femtocell Networks
2012 IEEE 75th Vehicular Technology Conference (VTC Spring), 2012Co-Authors: Chaima Dhahri, Tomoaki OhtsukiAbstract:In open-access non-stationary femtocell networks, cellular users (also known as macro users or MU) may join, through a handover procedure, one of the neighboring femtocells so as to enhance their communications/increase their respective channel capacities. To avoid frequent communication disruptions owing to effects such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell Selection Method. Traditionally, such Selection Method is usually a measured channel/cell quality metric such as the channel capacity, the load of the candidate cell, the received signal strength (RSS), etc. However, one problem with such approaches is that present measured performance does not necessarily reflect the future performance, thus the need for novel cell Selection that can predict the \textit{horizon}. Subsequently, we present in this paper a reinforcement learning (RL), i.e, Q- learning algorithm, as a generic solution for the cell Selection problem in a non-stationary femtocell network. After comparing our solution for cell Selection with different Methods in the literature (least loaded (LL), random and capacity-based), simulation results demonstrate the benefits of using learning in terms of the gained capacity and the number of handovers.
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VTC Spring - Learning-Based Cell Selection Method for Femtocell Networks
2012 IEEE 75th Vehicular Technology Conference (VTC Spring), 2012Co-Authors: Chaima Dhahri, Tomoaki OhtsukiAbstract:In open-access non-stationary femtocell networks, cellular users (also known as macro users or MU) may join, through a handover procedure, one of the neighboring femtocells so as to enhance their communications/increase their respective channel capacities. To avoid frequent communication disruptions owing to effects such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell Selection Method. Traditionally, such Selection Method is usually a measured channel/cell quality metric such as the channel capacity, the load of the candidate cell, the received signal strength (RSS), etc. However, one problem with such approaches is that present measured performance does not necessarily reflect the future performance, thus the need for novel cell Selection that can predict the \textit{horizon}. Subsequently, we present in this paper a reinforcement learning (RL), i.e, Q- learning algorithm, as a generic solution for the cell Selection problem in a non-stationary femtocell network. After comparing our solution for cell Selection with different Methods in the literature (least loaded (LL), random and capacity-based), simulation results demonstrate the benefits of using learning in terms of the gained capacity and the number of handovers.
Yasuyuki Oishi - One of the best experts on this subject based on the ideXlab platform.
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A receiver side antenna Selection Method for MIMO-OFDM system
IEEE Vehicular Technology Conference, 2006Co-Authors: Quoc Tuan Tran, Yuuta Nakaya, Ichirou Ida, Atsushi Honda, Shinsuke Hara, Yasuyuki OishiAbstract:This paper proposes an antenna Selection Method for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) receiver. The proposed Method selects a proper combination of polarization antenna elements at receive antenna branches to achieve lower bit error rate (BER) performance. Using the channel impulse responses measured in an indoor environment, numerical results show that the proposed Selection Method works effectively for the realistic channel and promises a great deal of decrease in computational complexity.
Chaima Dhahri - One of the best experts on this subject based on the ideXlab platform.
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Learning-Based Cell Selection Method for Femtocell Networks
2012 IEEE 75th Vehicular Technology Conference (VTC Spring), 2012Co-Authors: Chaima Dhahri, Tomoaki OhtsukiAbstract:In open-access non-stationary femtocell networks, cellular users (also known as macro users or MU) may join, through a handover procedure, one of the neighboring femtocells so as to enhance their communications/increase their respective channel capacities. To avoid frequent communication disruptions owing to effects such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell Selection Method. Traditionally, such Selection Method is usually a measured channel/cell quality metric such as the channel capacity, the load of the candidate cell, the received signal strength (RSS), etc. However, one problem with such approaches is that present measured performance does not necessarily reflect the future performance, thus the need for novel cell Selection that can predict the \textit{horizon}. Subsequently, we present in this paper a reinforcement learning (RL), i.e, Q- learning algorithm, as a generic solution for the cell Selection problem in a non-stationary femtocell network. After comparing our solution for cell Selection with different Methods in the literature (least loaded (LL), random and capacity-based), simulation results demonstrate the benefits of using learning in terms of the gained capacity and the number of handovers.
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VTC Spring - Learning-Based Cell Selection Method for Femtocell Networks
2012 IEEE 75th Vehicular Technology Conference (VTC Spring), 2012Co-Authors: Chaima Dhahri, Tomoaki OhtsukiAbstract:In open-access non-stationary femtocell networks, cellular users (also known as macro users or MU) may join, through a handover procedure, one of the neighboring femtocells so as to enhance their communications/increase their respective channel capacities. To avoid frequent communication disruptions owing to effects such as the ping-pong effect, it is necessary to ensure the effectiveness of the cell Selection Method. Traditionally, such Selection Method is usually a measured channel/cell quality metric such as the channel capacity, the load of the candidate cell, the received signal strength (RSS), etc. However, one problem with such approaches is that present measured performance does not necessarily reflect the future performance, thus the need for novel cell Selection that can predict the \textit{horizon}. Subsequently, we present in this paper a reinforcement learning (RL), i.e, Q- learning algorithm, as a generic solution for the cell Selection problem in a non-stationary femtocell network. After comparing our solution for cell Selection with different Methods in the literature (least loaded (LL), random and capacity-based), simulation results demonstrate the benefits of using learning in terms of the gained capacity and the number of handovers.
Qing Bi - One of the best experts on this subject based on the ideXlab platform.
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ROBIO - Research on EEG Channel Selection Method for Emotion Recognition
2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2019Co-Authors: Hongpei Xu, Xingbo Wang, Weimin Li, Haibin Wang, Qing BiAbstract:In this paper, based on the DEAP dataset, we studied the effects of different EEG channel Selection Methods on the accuracy of emotion recognition for different frequency bands. First, the discrete wavelet transform Method is used to divide the EEG signals into four bands of gamma, beta, alpha and theta, and extract the entropy and energy of each band as classification features. Then, the following three channel Selection Methods are compared to select the best EEG channel combination for the four emotions classification susing, the channel Selection Method based on experience, the indirect channel Selection Method based on the mRMR feature Selection algorithm, and the direct channel Selection Method based on the mRMR feature Selection algorithm. Finally, the extreme learning machine with kernel is used to verify the effectiveness of the channel Selection Method. The results show that based on the mRMR feature Selection algorithm, the channel Selection Method taking each channel as a whole is more powerful in balancing the number of channels and classification accuracy. In the beta band, the number of channels is reduced from 32 to 22, which is only 1.37% (from 80.83% to 79.37%) lower than the best classification accuracy, and the emotion recognition performance remains at a high level. Compared with the results of others, this paper can use less channels to achieve similar or higher emotional recognition performance than others, which further proves the effectiveness of the Method. In addition, we also found that high frequencies (gamma and beta bands) are better for emotional recognition. This study provides a reference for channel and band Selection in EEG-based emotion recognition.
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Research on EEG Channel Selection Method for Emotion Recognition
2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2019Co-Authors: Hongpei Xu, Xingbo Wang, Weimin Li, Haibin Wang, Qing BiAbstract:In this paper, based on the DEAP dataset, we studied the effects of different EEG channel Selection Methods on the accuracy of emotion recognition for different frequency bands. First, the discrete wavelet transform Method is used to divide the EEG signals into four bands of gamma, beta, alpha and theta, and extract the entropy and energy of each band as classification features. Then, the following three channel Selection Methods are compared to select the best EEG channel combination for the four emotions classification susing, the channel Selection Method based on experience, the indirect channel Selection Method based on the mRMR feature Selection algorithm, and the direct channel Selection Method based on the mRMR feature Selection algorithm. Finally, the extreme learning machine with kernel is used to verify the effectiveness of the channel Selection Method. The results show that based on the mRMR feature Selection algorithm, the channel Selection Method taking each channel as a whole is more powerful in balancing the number of channels and classification accuracy. In the beta band, the number of channels is reduced from 32 to 22, which is only 1.37% (from 80.83% to 79.37%) lower than the best classification accuracy, and the emotion recognition performance remains at a high level. Compared with the results of others, this paper can use less channels to achieve similar or higher emotional recognition performance than others, which further proves the effectiveness of the Method. In addition, we also found that high frequencies (gamma and beta bands) are better for emotional recognition. This study provides a reference for channel and band Selection in EEG-based emotion recognition.
Quoc Tuan Tran - One of the best experts on this subject based on the ideXlab platform.
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A receiver side antenna Selection Method for MIMO-OFDM system
IEEE Vehicular Technology Conference, 2006Co-Authors: Quoc Tuan Tran, Yuuta Nakaya, Ichirou Ida, Atsushi Honda, Shinsuke Hara, Yasuyuki OishiAbstract:This paper proposes an antenna Selection Method for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) receiver. The proposed Method selects a proper combination of polarization antenna elements at receive antenna branches to achieve lower bit error rate (BER) performance. Using the channel impulse responses measured in an indoor environment, numerical results show that the proposed Selection Method works effectively for the realistic channel and promises a great deal of decrease in computational complexity.
