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Guodong Zhao - One of the best experts on this subject based on the ideXlab platform.

  • primary Channel Gain estimation for spectrum sharing in cognitive radio networks
    Wireless Communications and Networking Conference, 2017
    Co-Authors: Lin Zhang, Guodong Zhao, Wenli Zhou, Yingchang Liang
    Abstract:

    In cognitive radio networks, the Channel Gain between primary transceivers, namely, primary Channel Gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary Channel Gain is estimated in the primary system, and thus unavailable at the CT. To deal with the issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signal, an ML estimator is first developed. To reduce the computational complexity of the ML estimator, a median-based (MB) estimator is then proposed. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Simulation results show that the estimation errors of both estimators can be as small as 0.015. Meanwhile, the ML estimator outperforms the MB estimator in terms of the estimation accuracy if the sensed primary signal at the CT is weak. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and the estimation accuracy.

  • WCNC - Proactive Cross-Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks
    2017 IEEE Wireless Communications and Networking Conference (WCNC), 2017
    Co-Authors: Lin Zhang, Ming Xiao, Guodong Zhao
    Abstract:

    In an underlay cognitive radio network, the cross- Channel Gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-Channel Gain. In particular, the CT proactively acts as a full-duplex amplify-and- forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the instantaneous cross-Channel Gain by observing the power adaption. Numerical results show that the estimation error of the proactive estimation scheme can be as small as $1.7\%$ with success estimation probability around $91\%$. By comparing with the state of art, we show the advantages of the proposed proactive estimator.

  • WCNC - Primary Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks
    IEEE Transactions on Communications, 2017
    Co-Authors: Lin Zhang, Guodong Zhao, Wenli Zhou, Yingchang Liang
    Abstract:

    In cognitive radio networks, the Channel Gain between primary transceivers, namely, primary Channel Gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary Channel Gain is estimated in the primary system, and thus unavailable at the CT. To deal with the issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signal, an ML estimator is first developed. To reduce the computational complexity of the ML estimator, a median-based (MB) estimator is then proposed. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Simulation results show that the estimation errors of both estimators can be as small as 0.015. Meanwhile, the ML estimator outperforms the MB estimator in terms of the estimation accuracy if the sensed primary signal at the CT is weak. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and the estimation accuracy.

  • proactive cross Channel Gain estimation for spectrum sharing in cognitive radio
    IEEE Journal on Selected Areas in Communications, 2016
    Co-Authors: Lin Zhang, Guodong Zhao, Ming Xiao, Yingchang Liang
    Abstract:

    In an underlay cognitive radio network, the cross-Channel Gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-Channel Gain. Specifically, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the cross-Channel Gain by observing the power adaption. To demonstrate the accuracy of the estimation, we analytically characterize both an upper bound and a lower bound of the estimation performance. Furthermore, we study the impact of CT’s relaying on the primary transmission and observe that the impact is related to the CT’s location. By introducing a factor $\phi~(0\leq \phi \leq 1)$ to denote the probability that the CT’s relaying improves the primary transmission instead of causes interference, we design the CT location as a function of $\phi $ . Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of the art, we show the advantages of the proposed estimator.

  • primary Channel Gain estimation for spectrum sharing in cognitive radio networks
    arXiv: Information Theory, 2016
    Co-Authors: Lin Zhang, Guodong Zhao, Wenli Zhou, Yingchang Liang
    Abstract:

    In cognitive radio networks, the Channel Gain between primary transceivers, namely, primary Channel Gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary Channel Gain is estimated in the primary system and thus unavailable at the CT. To deal with this issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signals, a ML estimator is first developed. After demonstrating the high computational complexity of the ML estimator, a median based (MB) estimator with proved low complexity is then proposed. Furthermore, the estimation accuracy of the MB estimation is theoretically characterized. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Numerical results show that the estimation errors of the ML estimator and the MB estimator can be as small as $0.6$ dB and $0.7$ dB, respectively.

Lin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • primary Channel Gain estimation for spectrum sharing in cognitive radio networks
    Wireless Communications and Networking Conference, 2017
    Co-Authors: Lin Zhang, Guodong Zhao, Wenli Zhou, Yingchang Liang
    Abstract:

    In cognitive radio networks, the Channel Gain between primary transceivers, namely, primary Channel Gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary Channel Gain is estimated in the primary system, and thus unavailable at the CT. To deal with the issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signal, an ML estimator is first developed. To reduce the computational complexity of the ML estimator, a median-based (MB) estimator is then proposed. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Simulation results show that the estimation errors of both estimators can be as small as 0.015. Meanwhile, the ML estimator outperforms the MB estimator in terms of the estimation accuracy if the sensed primary signal at the CT is weak. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and the estimation accuracy.

  • WCNC - Proactive Cross-Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks
    2017 IEEE Wireless Communications and Networking Conference (WCNC), 2017
    Co-Authors: Lin Zhang, Ming Xiao, Guodong Zhao
    Abstract:

    In an underlay cognitive radio network, the cross- Channel Gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-Channel Gain. In particular, the CT proactively acts as a full-duplex amplify-and- forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the instantaneous cross-Channel Gain by observing the power adaption. Numerical results show that the estimation error of the proactive estimation scheme can be as small as $1.7\%$ with success estimation probability around $91\%$. By comparing with the state of art, we show the advantages of the proposed proactive estimator.

  • WCNC - Primary Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks
    IEEE Transactions on Communications, 2017
    Co-Authors: Lin Zhang, Guodong Zhao, Wenli Zhou, Yingchang Liang
    Abstract:

    In cognitive radio networks, the Channel Gain between primary transceivers, namely, primary Channel Gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary Channel Gain is estimated in the primary system, and thus unavailable at the CT. To deal with the issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signal, an ML estimator is first developed. To reduce the computational complexity of the ML estimator, a median-based (MB) estimator is then proposed. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Simulation results show that the estimation errors of both estimators can be as small as 0.015. Meanwhile, the ML estimator outperforms the MB estimator in terms of the estimation accuracy if the sensed primary signal at the CT is weak. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and the estimation accuracy.

  • proactive cross Channel Gain estimation for spectrum sharing in cognitive radio
    IEEE Journal on Selected Areas in Communications, 2016
    Co-Authors: Lin Zhang, Guodong Zhao, Ming Xiao, Yingchang Liang
    Abstract:

    In an underlay cognitive radio network, the cross-Channel Gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-Channel Gain. Specifically, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the cross-Channel Gain by observing the power adaption. To demonstrate the accuracy of the estimation, we analytically characterize both an upper bound and a lower bound of the estimation performance. Furthermore, we study the impact of CT’s relaying on the primary transmission and observe that the impact is related to the CT’s location. By introducing a factor $\phi~(0\leq \phi \leq 1)$ to denote the probability that the CT’s relaying improves the primary transmission instead of causes interference, we design the CT location as a function of $\phi $ . Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of the art, we show the advantages of the proposed estimator.

  • primary Channel Gain estimation for spectrum sharing in cognitive radio networks
    arXiv: Information Theory, 2016
    Co-Authors: Lin Zhang, Guodong Zhao, Wenli Zhou, Yingchang Liang
    Abstract:

    In cognitive radio networks, the Channel Gain between primary transceivers, namely, primary Channel Gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary Channel Gain is estimated in the primary system and thus unavailable at the CT. To deal with this issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signals, a ML estimator is first developed. After demonstrating the high computational complexity of the ML estimator, a median based (MB) estimator with proved low complexity is then proposed. Furthermore, the estimation accuracy of the MB estimation is theoretically characterized. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Numerical results show that the estimation errors of the ML estimator and the MB estimator can be as small as $0.6$ dB and $0.7$ dB, respectively.

Georgios B Giannakis - One of the best experts on this subject based on the ideXlab platform.

  • a variational bayes approach to adaptive Channel Gain cartography
    International Conference on Acoustics Speech and Signal Processing, 2019
    Co-Authors: Donghoon Lee, Georgios B Giannakis
    Abstract:

    Channel-Gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. State-of-the-art on this subject includes tomography-based approaches, where shadowing effects are modeled by the weighted integral of a spatial loss field (SLF) that captures the propagation environment. To learn SLFs exhibiting statistical heterogeneity induced by spatially diverse propagation environments, the present work develops a Bayesian approach comprising a piecewise homogeneous SLF with an underlying hidden Markov random field model. Built on a variational Bayes scheme, the novel approach yields efficient field estimators at affordable complexity. In addition, a data-adaptive sensor selection algorithm is developed to collect informative measurements for effective learning of the SLF. Numerical tests demonstrate the capabilities of the novel approach.

  • ICASSP - A Variational Bayes Approach to Adaptive Channel-Gain Cartography
    ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019
    Co-Authors: Donghoon Lee, Georgios B Giannakis
    Abstract:

    Channel-Gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. State-of-the-art on this subject includes tomography-based approaches, where shadowing effects are modeled by the weighted integral of a spatial loss field (SLF) that captures the propagation environment. To learn SLFs exhibiting statistical heterogeneity induced by spatially diverse propagation environments, the present work develops a Bayesian approach comprising a piecewise homogeneous SLF with an underlying hidden Markov random field model. Built on a variational Bayes scheme, the novel approach yields efficient field estimators at affordable complexity. In addition, a data-adaptive sensor selection algorithm is developed to collect informative measurements for effective learning of the SLF. Numerical tests demonstrate the capabilities of the novel approach.

  • adaptive bayesian Channel Gain cartography
    International Conference on Acoustics Speech and Signal Processing, 2018
    Co-Authors: Donghoon Lee, Dimitris Berberidis, Georgios B Giannakis
    Abstract:

    Channel Gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) depending on the propagation environment. Currently, the SLF is learned via regularization methods tailored to the propagation environment. However, the effectiveness of existing approaches remains unclear especially when the propagation environment involves heterogeneous characteristics. To cope with this, the present work considers a piecewise homogeneous SLF with a hidden Markov random field (MRF) model under the Bayesian framework. Efficient field estimators are obtained by using samples from Markov chain Monte Carlo (MCMC). Furthermore, an uncertainty sampling algorithm is developed to adaptively collect measurements. Real data tests demonstrate the capabilities of the novel approach.

  • ICASSP - Adaptive Bayesian Channel Gain Cartography
    2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018
    Co-Authors: Donghoon Lee, Dimitris Berberidis, Georgios B Giannakis
    Abstract:

    Channel Gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) depending on the propagation environment. Currently, the SLF is learned via regularization methods tailored to the propagation environment. However, the effectiveness of existing approaches remains unclear especially when the propagation environment involves heterogeneous characteristics. To cope with this, the present work considers a piecewise homogeneous SLF with a hidden Markov random field (MRF) model under the Bayesian framework. Efficient field estimators are obtained by using samples from Markov chain Monte Carlo (MCMC). Furthermore, an uncertainty sampling algorithm is developed to adaptively collect measurements. Real data tests demonstrate the capabilities of the novel approach.

  • Channel Gain Cartography for Cognitive Radios Leveraging Low Rank and Sparsity
    IEEE Transactions on Wireless Communications, 2017
    Co-Authors: Donghoon Lee, Seung-jun Kim, Georgios B Giannakis
    Abstract:

    Channel Gain cartography aims at inferring the Channel Gains between two arbitrary points in space based on the measurements (samples) of the Gains collected by a set of radios deployed in the area. Channel Gain maps are useful for various sensing and resource allocation tasks essential for the operation of cognitive radio networks. In this paper, the Channel Gains are modeled as the tomographic accumulations of an underlying spatial loss field (SLF), which captures the attenuation in the signal strength due to the obstacles in the propagation path. In order to estimate the map accurately with a relatively small number of measurements, the SLF is postulated to have a low-rank structure possibly with sparse deviations. Efficient batch and online algorithms are derived for the resulting map reconstruction problem. Comprehensive tests with both synthetic and real data sets corroborate that the algorithms can accurately reveal the structure of the propagation medium, and produce the desired Channel Gain maps.

Yingchang Liang - One of the best experts on this subject based on the ideXlab platform.

  • primary Channel Gain estimation for spectrum sharing in cognitive radio networks
    Wireless Communications and Networking Conference, 2017
    Co-Authors: Lin Zhang, Guodong Zhao, Wenli Zhou, Yingchang Liang
    Abstract:

    In cognitive radio networks, the Channel Gain between primary transceivers, namely, primary Channel Gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary Channel Gain is estimated in the primary system, and thus unavailable at the CT. To deal with the issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signal, an ML estimator is first developed. To reduce the computational complexity of the ML estimator, a median-based (MB) estimator is then proposed. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Simulation results show that the estimation errors of both estimators can be as small as 0.015. Meanwhile, the ML estimator outperforms the MB estimator in terms of the estimation accuracy if the sensed primary signal at the CT is weak. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and the estimation accuracy.

  • WCNC - Primary Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks
    IEEE Transactions on Communications, 2017
    Co-Authors: Lin Zhang, Guodong Zhao, Wenli Zhou, Yingchang Liang
    Abstract:

    In cognitive radio networks, the Channel Gain between primary transceivers, namely, primary Channel Gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary Channel Gain is estimated in the primary system, and thus unavailable at the CT. To deal with the issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signal, an ML estimator is first developed. To reduce the computational complexity of the ML estimator, a median-based (MB) estimator is then proposed. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Simulation results show that the estimation errors of both estimators can be as small as 0.015. Meanwhile, the ML estimator outperforms the MB estimator in terms of the estimation accuracy if the sensed primary signal at the CT is weak. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and the estimation accuracy.

  • proactive cross Channel Gain estimation for spectrum sharing in cognitive radio
    IEEE Journal on Selected Areas in Communications, 2016
    Co-Authors: Lin Zhang, Guodong Zhao, Ming Xiao, Yingchang Liang
    Abstract:

    In an underlay cognitive radio network, the cross-Channel Gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-Channel Gain. Specifically, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the cross-Channel Gain by observing the power adaption. To demonstrate the accuracy of the estimation, we analytically characterize both an upper bound and a lower bound of the estimation performance. Furthermore, we study the impact of CT’s relaying on the primary transmission and observe that the impact is related to the CT’s location. By introducing a factor $\phi~(0\leq \phi \leq 1)$ to denote the probability that the CT’s relaying improves the primary transmission instead of causes interference, we design the CT location as a function of $\phi $ . Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of the art, we show the advantages of the proposed estimator.

  • primary Channel Gain estimation for spectrum sharing in cognitive radio networks
    arXiv: Information Theory, 2016
    Co-Authors: Lin Zhang, Guodong Zhao, Wenli Zhou, Yingchang Liang
    Abstract:

    In cognitive radio networks, the Channel Gain between primary transceivers, namely, primary Channel Gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary Channel Gain is estimated in the primary system and thus unavailable at the CT. To deal with this issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signals, a ML estimator is first developed. After demonstrating the high computational complexity of the ML estimator, a median based (MB) estimator with proved low complexity is then proposed. Furthermore, the estimation accuracy of the MB estimation is theoretically characterized. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Numerical results show that the estimation errors of the ML estimator and the MB estimator can be as small as $0.6$ dB and $0.7$ dB, respectively.

Zhi Chen - One of the best experts on this subject based on the ideXlab platform.

  • ICCC - Cooperative cross-Channel Gain estimation for underlay spectrum sharing
    2017 IEEE CIC International Conference on Communications in China (ICCC), 2017
    Co-Authors: Bo Chang, Zhi Chen, Chuanxue Jin, Wanbin Tang, Jiangxun Dan
    Abstract:

    In this paper, we propose a cooperative method to estimate the cross-Channel Gain (CCG) between the cognitive transmitter (CT) and primary receiver (PR) for underlay spectrum sharing. This method belongs to spectrum sensing technique. In particular, we introduce multiple cognitive receivers to assist the CT to estimate the CCG. Here, we design and compare three estimators for the CT based on the CT's collected measurements. Compared with conventional non-cooperative CCG estimation method, the proposed method can improve the root mean square error by 3 dB and the successful estimation probability by 37% under a typical scenario.

  • ICNC - Estimating cross-Channel Gain without using backhaul link in two-tier heterogeneous networks
    2016 International Conference on Computing Networking and Communications (ICNC), 2016
    Co-Authors: Huang Xiaoning, Guodong Zhao, Zhi Chen
    Abstract:

    Estimating the cross-Channel Gain from a small cell base station to an active macro user is critical in two-tier heterogeneous networks (HetNets). Conventional methods require the backhaul link, which is too complicated to be used in HetNets. In this paper, we propose a new method to simplify the system. We find that by measuring the signal-to-noise ratio (SNR) and recognizing the modulation level of macro cell's signal, the small cell base station can autonomously obtain the cross-Channel Gain without using the backhaul link. Simulation results indicate that the proposed method has about 2% estimation error, where the cross-Channel Gain is usually in the range of −70 dB to −110 dB.

  • ICC - Interference-free probing for relay-assisted cross-Channel Gain estimation in two-tier networks
    2016 IEEE International Conference on Communications (ICC), 2016
    Co-Authors: Bijia Huang, Guodong Zhao, Zhi Chen
    Abstract:

    In frequency division duplex (FDD) two-tier networks, the probing technique has recently been introduced into the cross-Channel Gain estimation, which requires the tier-two user to act as a relay for the tier-one user. Then the tier-two user can autonomously estimate the cross-Channel Gain. However, the improper location of the tier-two user, i.e., the relay, may cause severe interference to the tier-one user. In this paper, we analyze the impacts of the probing on the tier-one user and find two location regions, in which the probing does not cause interference. Based on that, we develop a detection method to let the tier-two user autonomously identify its located region. Then the interference caused by the probing can be avoided. Simulation results demonstrate that the proposed method has about 90% correct detection probability.

  • cross Channel Gain estimation with amplify and forward relaying in cognitive radio
    Global Communications Conference, 2013
    Co-Authors: Lin Zhang, Guodong Zhao, Zhi Chen
    Abstract:

    In this paper, we develop a new proactive estimation method to obtain the cross-Channel Gain from cognitive transmitter to primary receiver without any backhaul between cognitive and primary users. In conventional proactive methods, the jamming signal is used for probing, which introduces the extra interference to primary receivers. In our method, the relayed primary signal is used for probing, which instead assists the primary transmission. Simulation results demonstrate that the proposed method with 2% estimation errors can obtain up to about 72% throughput improvement introduced by the cross-Channel Gain.

  • GLOBECOM - Cross-Channel Gain estimation with amplify-and-forward relaying in cognitive radio
    2013 IEEE Global Communications Conference (GLOBECOM), 2013
    Co-Authors: Lin Zhang, Guodong Zhao, Zhi Chen
    Abstract:

    In this paper, we develop a new proactive estimation method to obtain the cross-Channel Gain from cognitive transmitter to primary receiver without any backhaul between cognitive and primary users. In conventional proactive methods, the jamming signal is used for probing, which introduces the extra interference to primary receivers. In our method, the relayed primary signal is used for probing, which instead assists the primary transmission. Simulation results demonstrate that the proposed method with 2% estimation errors can obtain up to about 72% throughput improvement introduced by the cross-Channel Gain.