The Experts below are selected from a list of 39123 Experts worldwide ranked by ideXlab platform
Sung Won Kim - One of the best experts on this subject based on the ideXlab platform.
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Deep Reinforcement Learning Paradigm for Performance Optimization of Channel Observation–Based MAC Protocols in Dense WLANs
IEEE Access, 2019Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Yousaf Bin Zikria, Sung Won KimAbstract:The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best Channel resources with the assistance of self-scrutinized wireless Channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of Channel Observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer Channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, Channel access delay, and fairness as performance metrics.
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deep reinforcement learning paradigm for performance optimization of Channel Observation based mac protocols in dense wlans
IEEE Access, 2019Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Yousaf Bin Zikria, Sung Won KimAbstract:The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best Channel resources with the assistance of self-scrutinized wireless Channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of Channel Observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer Channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, Channel access delay, and fairness as performance metrics.
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fair and efficient Channel Observation based listen before talk colbt for laa wifi coexistence in unlicensed lte
International Conference on Ubiquitous and Future Networks, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Arslan Musaddiq, Sung Won KimAbstract:License Assisted Access-WiFi (LAA-WiFi) coexistence allows the operations on the unlicensed spectrum for Long Term Evolution (LTE) along with existing unlicensed wireless local area networks (WLANs). The current spectrum access process of legacy WLANs uses clear Channel assessment (CCA) and carrier sense multiple access with collision avoidance (CSMA/CA), where the spectrum is sensed before use and a random binary exponential backoff (BEB) mechanism is employed for collision avoidance. While LAA uses a listen-before-talk (LBT) mechanism, moderately similar to the CCA CSMA/CA for Channel access. However, there is a fairness issue when these two technologies coexist. In this paper, we propose a Channel Observation-based LBT (CoLBT) mechanism for fairness in LAA-Wi-Fi coexistence scenarios. Specifically, we introduce a more realistic practical Channel Observation-based collision probability observed by the LAA evolved Node B (eNB) to adaptively scale-up and scale-down the backoff contention window for Channel contention, to reduce the waste of resources and improve LAA-WiFi coexistence performance. Simulation results validate that the proposed CoLBT mechanism is effective in LAA-WiFi coexistence scenario and can improve fairness performance, compared with the current mechanism of LBT.
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A Self-Scrutinized Backoff Mechanism for IEEE 802.11ax in 5G Unlicensed Networks
Sustainability, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Rojeena Bajracharya, Sung Won KimAbstract:The IEEE 802.11ax high-efficiency wireless local area network (HEW) is promising as a foundation for evolving the fifth-generation (5G) radio access network on unlicensed bands (5G-U). 5G-U is a continued effort toward rich ubiquitous communication infrastructures, promising faster and reliable services for the end user. HEW is likely to provide four times higher network efficiency even in highly dense network deployments. However, the current wireless local area network (WLAN) itself faces huge challenge of efficient radio access due to its contention-based nature. WLAN uses a carrier sense multiple access with collision avoidance (CSMA/CA) procedure in medium access control (MAC) protocols, which is based on a binary exponential backoff (BEB) mechanism. Blind increase and decrease of the contention window in BEB limits the performance of WLAN to a limited number of contenders, thus affecting end-user quality of experience. In this paper, we identify future use cases of HEW proposed for 5G-U networks. We use a self-scrutinized Channel Observation-based scaled backoff (COSB) mechanism to handle the high-density contention challenges. Furthermore, a recursive discrete-time Markov chain model (R-DTMC) is formulated to analyze the performance efficiency of the proposed solution. The analytical and simulation results show that the proposed mechanism can improve user experience in 5G-U networks.
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Channel Observation-based scaled backoff mechanism for high-efficiency WLANs
Electronics Letters, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Young-tak Kim, Byung-seo Kim, Sung Won KimAbstract:A Channel Observation-based scaled backoff (COSB) mechanism for the carrier sense multiple access with collision avoidance of high efficiency wireless local area networks (WLANs) is devised. The proposed protocol modifies the blind scaling of contention window (W) in binary exponential backoff (BEB) scheme of currently deployed WLANs. COSB is employed to adaptively scale-up and scale-down the W size during the backoff mechanism for collided and successfully transmitted data frames, respectively. It can achieve higher throughput and shorter delay compared to the conventional BEB mechanism in highly dense WLANs.
Rashid Ali - One of the best experts on this subject based on the ideXlab platform.
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Deep Reinforcement Learning Paradigm for Performance Optimization of Channel Observation–Based MAC Protocols in Dense WLANs
IEEE Access, 2019Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Yousaf Bin Zikria, Sung Won KimAbstract:The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best Channel resources with the assistance of self-scrutinized wireless Channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of Channel Observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer Channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, Channel access delay, and fairness as performance metrics.
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deep reinforcement learning paradigm for performance optimization of Channel Observation based mac protocols in dense wlans
IEEE Access, 2019Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Yousaf Bin Zikria, Sung Won KimAbstract:The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best Channel resources with the assistance of self-scrutinized wireless Channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of Channel Observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer Channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, Channel access delay, and fairness as performance metrics.
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fair and efficient Channel Observation based listen before talk colbt for laa wifi coexistence in unlicensed lte
International Conference on Ubiquitous and Future Networks, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Arslan Musaddiq, Sung Won KimAbstract:License Assisted Access-WiFi (LAA-WiFi) coexistence allows the operations on the unlicensed spectrum for Long Term Evolution (LTE) along with existing unlicensed wireless local area networks (WLANs). The current spectrum access process of legacy WLANs uses clear Channel assessment (CCA) and carrier sense multiple access with collision avoidance (CSMA/CA), where the spectrum is sensed before use and a random binary exponential backoff (BEB) mechanism is employed for collision avoidance. While LAA uses a listen-before-talk (LBT) mechanism, moderately similar to the CCA CSMA/CA for Channel access. However, there is a fairness issue when these two technologies coexist. In this paper, we propose a Channel Observation-based LBT (CoLBT) mechanism for fairness in LAA-Wi-Fi coexistence scenarios. Specifically, we introduce a more realistic practical Channel Observation-based collision probability observed by the LAA evolved Node B (eNB) to adaptively scale-up and scale-down the backoff contention window for Channel contention, to reduce the waste of resources and improve LAA-WiFi coexistence performance. Simulation results validate that the proposed CoLBT mechanism is effective in LAA-WiFi coexistence scenario and can improve fairness performance, compared with the current mechanism of LBT.
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A Self-Scrutinized Backoff Mechanism for IEEE 802.11ax in 5G Unlicensed Networks
Sustainability, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Rojeena Bajracharya, Sung Won KimAbstract:The IEEE 802.11ax high-efficiency wireless local area network (HEW) is promising as a foundation for evolving the fifth-generation (5G) radio access network on unlicensed bands (5G-U). 5G-U is a continued effort toward rich ubiquitous communication infrastructures, promising faster and reliable services for the end user. HEW is likely to provide four times higher network efficiency even in highly dense network deployments. However, the current wireless local area network (WLAN) itself faces huge challenge of efficient radio access due to its contention-based nature. WLAN uses a carrier sense multiple access with collision avoidance (CSMA/CA) procedure in medium access control (MAC) protocols, which is based on a binary exponential backoff (BEB) mechanism. Blind increase and decrease of the contention window in BEB limits the performance of WLAN to a limited number of contenders, thus affecting end-user quality of experience. In this paper, we identify future use cases of HEW proposed for 5G-U networks. We use a self-scrutinized Channel Observation-based scaled backoff (COSB) mechanism to handle the high-density contention challenges. Furthermore, a recursive discrete-time Markov chain model (R-DTMC) is formulated to analyze the performance efficiency of the proposed solution. The analytical and simulation results show that the proposed mechanism can improve user experience in 5G-U networks.
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Channel Observation-based scaled backoff mechanism for high-efficiency WLANs
Electronics Letters, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Young-tak Kim, Byung-seo Kim, Sung Won KimAbstract:A Channel Observation-based scaled backoff (COSB) mechanism for the carrier sense multiple access with collision avoidance of high efficiency wireless local area networks (WLANs) is devised. The proposed protocol modifies the blind scaling of contention window (W) in binary exponential backoff (BEB) scheme of currently deployed WLANs. COSB is employed to adaptively scale-up and scale-down the W size during the backoff mechanism for collided and successfully transmitted data frames, respectively. It can achieve higher throughput and shorter delay compared to the conventional BEB mechanism in highly dense WLANs.
Byung-seo Kim - One of the best experts on this subject based on the ideXlab platform.
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Deep Reinforcement Learning Paradigm for Performance Optimization of Channel Observation–Based MAC Protocols in Dense WLANs
IEEE Access, 2019Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Yousaf Bin Zikria, Sung Won KimAbstract:The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best Channel resources with the assistance of self-scrutinized wireless Channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of Channel Observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer Channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, Channel access delay, and fairness as performance metrics.
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deep reinforcement learning paradigm for performance optimization of Channel Observation based mac protocols in dense wlans
IEEE Access, 2019Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Yousaf Bin Zikria, Sung Won KimAbstract:The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best Channel resources with the assistance of self-scrutinized wireless Channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of Channel Observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer Channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, Channel access delay, and fairness as performance metrics.
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fair and efficient Channel Observation based listen before talk colbt for laa wifi coexistence in unlicensed lte
International Conference on Ubiquitous and Future Networks, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Arslan Musaddiq, Sung Won KimAbstract:License Assisted Access-WiFi (LAA-WiFi) coexistence allows the operations on the unlicensed spectrum for Long Term Evolution (LTE) along with existing unlicensed wireless local area networks (WLANs). The current spectrum access process of legacy WLANs uses clear Channel assessment (CCA) and carrier sense multiple access with collision avoidance (CSMA/CA), where the spectrum is sensed before use and a random binary exponential backoff (BEB) mechanism is employed for collision avoidance. While LAA uses a listen-before-talk (LBT) mechanism, moderately similar to the CCA CSMA/CA for Channel access. However, there is a fairness issue when these two technologies coexist. In this paper, we propose a Channel Observation-based LBT (CoLBT) mechanism for fairness in LAA-Wi-Fi coexistence scenarios. Specifically, we introduce a more realistic practical Channel Observation-based collision probability observed by the LAA evolved Node B (eNB) to adaptively scale-up and scale-down the backoff contention window for Channel contention, to reduce the waste of resources and improve LAA-WiFi coexistence performance. Simulation results validate that the proposed CoLBT mechanism is effective in LAA-WiFi coexistence scenario and can improve fairness performance, compared with the current mechanism of LBT.
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A Self-Scrutinized Backoff Mechanism for IEEE 802.11ax in 5G Unlicensed Networks
Sustainability, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Rojeena Bajracharya, Sung Won KimAbstract:The IEEE 802.11ax high-efficiency wireless local area network (HEW) is promising as a foundation for evolving the fifth-generation (5G) radio access network on unlicensed bands (5G-U). 5G-U is a continued effort toward rich ubiquitous communication infrastructures, promising faster and reliable services for the end user. HEW is likely to provide four times higher network efficiency even in highly dense network deployments. However, the current wireless local area network (WLAN) itself faces huge challenge of efficient radio access due to its contention-based nature. WLAN uses a carrier sense multiple access with collision avoidance (CSMA/CA) procedure in medium access control (MAC) protocols, which is based on a binary exponential backoff (BEB) mechanism. Blind increase and decrease of the contention window in BEB limits the performance of WLAN to a limited number of contenders, thus affecting end-user quality of experience. In this paper, we identify future use cases of HEW proposed for 5G-U networks. We use a self-scrutinized Channel Observation-based scaled backoff (COSB) mechanism to handle the high-density contention challenges. Furthermore, a recursive discrete-time Markov chain model (R-DTMC) is formulated to analyze the performance efficiency of the proposed solution. The analytical and simulation results show that the proposed mechanism can improve user experience in 5G-U networks.
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Channel Observation-based scaled backoff mechanism for high-efficiency WLANs
Electronics Letters, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Young-tak Kim, Byung-seo Kim, Sung Won KimAbstract:A Channel Observation-based scaled backoff (COSB) mechanism for the carrier sense multiple access with collision avoidance of high efficiency wireless local area networks (WLANs) is devised. The proposed protocol modifies the blind scaling of contention window (W) in binary exponential backoff (BEB) scheme of currently deployed WLANs. COSB is employed to adaptively scale-up and scale-down the W size during the backoff mechanism for collided and successfully transmitted data frames, respectively. It can achieve higher throughput and shorter delay compared to the conventional BEB mechanism in highly dense WLANs.
Nurullah Shahin - One of the best experts on this subject based on the ideXlab platform.
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Deep Reinforcement Learning Paradigm for Performance Optimization of Channel Observation–Based MAC Protocols in Dense WLANs
IEEE Access, 2019Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Yousaf Bin Zikria, Sung Won KimAbstract:The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best Channel resources with the assistance of self-scrutinized wireless Channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of Channel Observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer Channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, Channel access delay, and fairness as performance metrics.
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deep reinforcement learning paradigm for performance optimization of Channel Observation based mac protocols in dense wlans
IEEE Access, 2019Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Yousaf Bin Zikria, Sung Won KimAbstract:The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best Channel resources with the assistance of self-scrutinized wireless Channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of Channel Observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer Channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, Channel access delay, and fairness as performance metrics.
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fair and efficient Channel Observation based listen before talk colbt for laa wifi coexistence in unlicensed lte
International Conference on Ubiquitous and Future Networks, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Arslan Musaddiq, Sung Won KimAbstract:License Assisted Access-WiFi (LAA-WiFi) coexistence allows the operations on the unlicensed spectrum for Long Term Evolution (LTE) along with existing unlicensed wireless local area networks (WLANs). The current spectrum access process of legacy WLANs uses clear Channel assessment (CCA) and carrier sense multiple access with collision avoidance (CSMA/CA), where the spectrum is sensed before use and a random binary exponential backoff (BEB) mechanism is employed for collision avoidance. While LAA uses a listen-before-talk (LBT) mechanism, moderately similar to the CCA CSMA/CA for Channel access. However, there is a fairness issue when these two technologies coexist. In this paper, we propose a Channel Observation-based LBT (CoLBT) mechanism for fairness in LAA-Wi-Fi coexistence scenarios. Specifically, we introduce a more realistic practical Channel Observation-based collision probability observed by the LAA evolved Node B (eNB) to adaptively scale-up and scale-down the backoff contention window for Channel contention, to reduce the waste of resources and improve LAA-WiFi coexistence performance. Simulation results validate that the proposed CoLBT mechanism is effective in LAA-WiFi coexistence scenario and can improve fairness performance, compared with the current mechanism of LBT.
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A Self-Scrutinized Backoff Mechanism for IEEE 802.11ax in 5G Unlicensed Networks
Sustainability, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Byung-seo Kim, Rojeena Bajracharya, Sung Won KimAbstract:The IEEE 802.11ax high-efficiency wireless local area network (HEW) is promising as a foundation for evolving the fifth-generation (5G) radio access network on unlicensed bands (5G-U). 5G-U is a continued effort toward rich ubiquitous communication infrastructures, promising faster and reliable services for the end user. HEW is likely to provide four times higher network efficiency even in highly dense network deployments. However, the current wireless local area network (WLAN) itself faces huge challenge of efficient radio access due to its contention-based nature. WLAN uses a carrier sense multiple access with collision avoidance (CSMA/CA) procedure in medium access control (MAC) protocols, which is based on a binary exponential backoff (BEB) mechanism. Blind increase and decrease of the contention window in BEB limits the performance of WLAN to a limited number of contenders, thus affecting end-user quality of experience. In this paper, we identify future use cases of HEW proposed for 5G-U networks. We use a self-scrutinized Channel Observation-based scaled backoff (COSB) mechanism to handle the high-density contention challenges. Furthermore, a recursive discrete-time Markov chain model (R-DTMC) is formulated to analyze the performance efficiency of the proposed solution. The analytical and simulation results show that the proposed mechanism can improve user experience in 5G-U networks.
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Channel Observation-based scaled backoff mechanism for high-efficiency WLANs
Electronics Letters, 2018Co-Authors: Rashid Ali, Nurullah Shahin, Young-tak Kim, Byung-seo Kim, Sung Won KimAbstract:A Channel Observation-based scaled backoff (COSB) mechanism for the carrier sense multiple access with collision avoidance of high efficiency wireless local area networks (WLANs) is devised. The proposed protocol modifies the blind scaling of contention window (W) in binary exponential backoff (BEB) scheme of currently deployed WLANs. COSB is employed to adaptively scale-up and scale-down the W size during the backoff mechanism for collided and successfully transmitted data frames, respectively. It can achieve higher throughput and shorter delay compared to the conventional BEB mechanism in highly dense WLANs.
Christos N. Capsalis - One of the best experts on this subject based on the ideXlab platform.
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Indoor environments propagation simulation using a hybrid MoM and UTD electromagnetic method
Annales Des Télécommunications, 2005Co-Authors: Stelios A. Mitilineos, Stylianos C. Panagiotou, Pantelis K. Varlamos, Christos N. CapsalisAbstract:Radio Channel Observation and characterization is indispensable in mobile communications systems development. Efficient propagation prediction is crucial for rapid and cost-effective systems deployment. On-site measurements, statistical models, propagation prediction and deterministic modeling are widely used for the qualification of radio Channels. In this paper, a simulation tool for deterministic Channel modeling in indoor environments is evolved. A hybrid combination of the Method-Of-Moments (MoM) and the Uniform Theory of Diffraction (Utd) is used. Statistical characteristics of a simple office room are calculated and the results are in excellent agreement with corresponding results of relative research on the field.
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Indoor environments propagation simulation using a hybrid MoM and UTD electromagnetic method
Annales Des Télécommunications, 2005Co-Authors: Stelios A. Mitilineos, Stylianos C. Panagiotou, Pantelis K. Varlamos, Christos N. CapsalisAbstract:Radio Channel Observation and characterization is indispensable in mobile communications systems development. Efficient propagation prediction is crucial for rapid and cost-effective systems deployment. On-site measurements, statistical models, propagation prediction and deterministic modeling are widely used for the qualification of radio Channels. In this paper, a simulation tool for deterministic Channel modeling in indoor environments is evolved. A hybrid combination of the Method-Of-Moments (MoM) and the Uniform Theory of Diffraction ( Utd ) is used. Statistical characteristics of a simple office room are calculated and the results are in excellent agreement with corresponding results of relative research on the field. L’Observation et la caractérisation du canal de propagation sont indispensables au développement des systèmes de radiocommunication avec les mobiles. Une bonne estimation de la propagation est critique pour le déploiement rapide et à faible coût des systèmes. Des mesures in situ, des modèles statistiques et déterministes de propagation radioélectrique sont largement utilisés pour la qualification du canal de propagation. Dans l’article présenté, un outil d’ingénierie radioélectrique de modélisation déterministe du canal de propagation a été développé. Il est basé sur une méthode hydride combinant la Méthode des Moments et la Théorie Uniforme de la Diffraction. Les caractéristiques statistiques en environnement bureau sont évaluées et les résultats sont conformes aux résultats des recherches correspondantes.