The Experts below are selected from a list of 376110 Experts worldwide ranked by ideXlab platform
Gerhard Kramer - One of the best experts on this subject based on the ideXlab platform.
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sum Rate Capacity for symmetric gaussian multiple access channels with feedback
IEEE Transactions on Information Theory, 2020Co-Authors: Erixhen Sula, Michael Gastpar, Gerhard KramerAbstract:The feedback sum-Rate Capacity is established for the symmetric $J$ -user Gaussian multiple-access channel (GMAC). The main contribution is a converse bound that combines the dependence-balance argument of Hekstra and Willems (1989) with a variant of the factorization of a convex envelope of Geng and Nair (2014). The converse bound matches the achievable sum-Rate of the Fourier-Modulated Estimate Correction stRategy of Kramer (2002).
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sum Rate Capacity for symmetric gaussian multiple access channels with feedback
International Symposium on Information Theory, 2018Co-Authors: Erixhen Sula, Michael Gastpar, Gerhard KramerAbstract:The feedback sum-Rate Capacity is established for the symmetric three-user Gaussian multiple-access channel (GMAC). The main contribution is a converse bound that combines the dependence-balance argument of Hekstra and Willems (1989) with a variant of the “doubling trick” of Geng and Nair (2014). The converse bound matches the achievable sum-Rate of the Fourier-Modulated Estimate Correction stRategy of Kramer (2002). The proof arguments extend to GMACs with more than three users.
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ISIT - Sum-Rate Capacity for Symmetric Gaussian Multiple Access Channels with Feedback
2018 IEEE International Symposium on Information Theory (ISIT), 2018Co-Authors: Erixhen Sula, Michael Gastpar, Gerhard KramerAbstract:The feedback sum-Rate Capacity is established for the symmetric three-user Gaussian multiple-access channel (GMAC). The main contribution is a converse bound that combines the dependence-balance argument of Hekstra and Willems (1989) with a variant of the “doubling trick” of Geng and Nair (2014). The converse bound matches the achievable sum-Rate of the Fourier-Modulated Estimate Correction stRategy of Kramer (2002). The proof arguments extend to GMACs with more than three users.
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noisy interference sum Rate Capacity of parallel gaussian interference channels
IEEE Transactions on Information Theory, 2011Co-Authors: Xiaohu Shang, Biao Chen, Gerhard Kramer, H V PoorAbstract:The sum-Rate Capacity of the parallel Gaussian interference channel is shown to be achieved by independent transmission across subchannels and treating interference as noise if the channel coefficients and power constraints satisfy a certain condition. The condition requires the interference to be weak, a situation commonly encountered, e.g., in digital subscriber line transmission. The optimal power allocation is characterized by using the concavity of the sum-Rate Capacity as a function of the power constraints.
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noisy interference sum Rate Capacity of parallel gaussian interference channels
International Symposium on Information Theory, 2009Co-Authors: Xiaohu Shang, Biao Chen, Gerhard Kramer, Vincent H PoorAbstract:The sum-Rate Capacity of the parallel Gaussian interference channel is studied. Sufficient conditions are derived in terms of channel coefficients and power constraints such that the sum-Rate Capacity can be achieved by: 1) independent transmission across sub-channels, and 2) treating interference as noise in each sub-channel. The optimal power allocation is characterized for such parallel channels.
Xiaohu Shang - One of the best experts on this subject based on the ideXlab platform.
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noisy interference sum Rate Capacity for vector gaussian interference channels
IEEE Transactions on Information Theory, 2013Co-Authors: Xiaohu Shang, H V PoorAbstract:New sufficient conditions for a vector Gaussian interference channel to achieve the sum-Rate Capacity by treating interference as noise are derived, which generalize and extend the existing results. More concise conditions for multiple-input single-output, and single-input multiple-output scenarios are obtained.
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noisy interference sum Rate Capacity of parallel gaussian interference channels
IEEE Transactions on Information Theory, 2011Co-Authors: Xiaohu Shang, Biao Chen, Gerhard Kramer, H V PoorAbstract:The sum-Rate Capacity of the parallel Gaussian interference channel is shown to be achieved by independent transmission across subchannels and treating interference as noise if the channel coefficients and power constraints satisfy a certain condition. The condition requires the interference to be weak, a situation commonly encountered, e.g., in digital subscriber line transmission. The optimal power allocation is characterized by using the concavity of the sum-Rate Capacity as a function of the power constraints.
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on the weighted sum Rate Capacity of broadcast channels that geneRate interference
International Symposium on Information Theory, 2010Co-Authors: Xiaohu Shang, Vincent H PoorAbstract:The Capacity of a network in which a broadcast channel geneRates interference to a single-user channel is studied. The Capacity region is achieved by fully decoding the interference when it is very strong or slightly strong. When the interference is modeRate or weak, the weighted sum-Rate capacities are achieved by partially decoding the interference or by treating the interference as noise, respectively.
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ISIT - On the weighted sum-Rate Capacity of broadcast channels that geneRate interference
2010 IEEE International Symposium on Information Theory, 2010Co-Authors: Xiaohu Shang, H V PoorAbstract:The Capacity of a network in which a broadcast channel geneRates interference to a single-user channel is studied. The Capacity region is achieved by fully decoding the interference when it is very strong or slightly strong. When the interference is modeRate or weak, the weighted sum-Rate capacities are achieved by partially decoding the interference or by treating the interference as noise, respectively.
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noisy interference sum Rate Capacity of parallel gaussian interference channels
International Symposium on Information Theory, 2009Co-Authors: Xiaohu Shang, Biao Chen, Gerhard Kramer, Vincent H PoorAbstract:The sum-Rate Capacity of the parallel Gaussian interference channel is studied. Sufficient conditions are derived in terms of channel coefficients and power constraints such that the sum-Rate Capacity can be achieved by: 1) independent transmission across sub-channels, and 2) treating interference as noise in each sub-channel. The optimal power allocation is characterized for such parallel channels.
Biao Chen - One of the best experts on this subject based on the ideXlab platform.
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Capacity Bounds and Sum Rate Capacities of a Class of Discrete Memoryless Interference Channels
IEEE Transactions on Information Theory, 2014Co-Authors: Fangfang Zhu, Biao ChenAbstract:This paper studies the Capacity of a class of discrete memoryless interference channels (DMICs), where interference is defined analogous to that of a Gaussian interference channel with one-sided weak interference. The sum-Rate Capacity of this class of channels is determined. As with the Gaussian case, the sum-Rate Capacity is achieved by letting the transceiver pair subject to interference communicate at a Rate such that its message can be decoded at the unintended receiver using single user detection. It is also established that this class of DMICs is equivalent in Capacity region to certain degraded interference channels. This allows the construction of Capacity outer-bounds using the Capacity regions of associated degraded broadcast channels. The same technique is then used to determine the sum-Rate Capacity of DMICs with mixed interference as defined in this paper. The obtained Capacity bounds and sum-Rate capacities are used to resolve the capacities of several new DMICs.
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Capacity Bounds and Sum Rate Capacities of a CLass of Discrete Memoryless Interference Channels
arXiv: Information Theory, 2013Co-Authors: Fangfang Zhu, Biao ChenAbstract:This paper studies the Capacity of a class of discrete memoryless interference channels where interference is defined analogous to that of Gaussian interference channel with one-sided weak interference. The sum-Rate Capacity of this class of channels is determined. As with the Gaussian case, the sum-Rate Capacity is achieved by letting the transceiver pair subject to interference communicate at a Rate such that its message can be decoded at the unintended receiver using single user detection. It is also established that this class of discrete memoryless interference channels is equivalent in Capacity region to certain degraded interference channels. This allows the construction of Capacity outer-bounds using the Capacity regions of associated degraded broadcast channels. The same technique is then used to determine the sum-Rate Capacity of discrete memoryless interference channels with mixed interference as defined in the paper. The obtained Capacity bounds and sum-Rate capacities are used to resolve the capacities of several new discrete memoryless interference channels.
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noisy interference sum Rate Capacity of parallel gaussian interference channels
IEEE Transactions on Information Theory, 2011Co-Authors: Xiaohu Shang, Biao Chen, Gerhard Kramer, H V PoorAbstract:The sum-Rate Capacity of the parallel Gaussian interference channel is shown to be achieved by independent transmission across subchannels and treating interference as noise if the channel coefficients and power constraints satisfy a certain condition. The condition requires the interference to be weak, a situation commonly encountered, e.g., in digital subscriber line transmission. The optimal power allocation is characterized by using the concavity of the sum-Rate Capacity as a function of the power constraints.
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noisy interference sum Rate Capacity of parallel gaussian interference channels
International Symposium on Information Theory, 2009Co-Authors: Xiaohu Shang, Biao Chen, Gerhard Kramer, Vincent H PoorAbstract:The sum-Rate Capacity of the parallel Gaussian interference channel is studied. Sufficient conditions are derived in terms of channel coefficients and power constraints such that the sum-Rate Capacity can be achieved by: 1) independent transmission across sub-channels, and 2) treating interference as noise in each sub-channel. The optimal power allocation is characterized for such parallel channels.
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noisy interference sum Rate Capacity of parallel gaussian interference channels
arXiv: Information Theory, 2009Co-Authors: Xiaohu Shang, Biao Chen, Gerhard Kramer, Vincent H PoorAbstract:The sum-Rate Capacity of the parallel Gaussian interference channel is shown to be achieved by independent transmission across sub-channels and treating interference as noise in each sub-channel if the channel coefficients and power constraints satisfy a certain condition. The condition requires the interference to be weak, a situation commonly encountered in, e.g., digital subscriber line transmission. The optimal power allocation is characterized by using the concavity of sum-Rate Capacity as a function of the power constraints.
Yingbin Liang - One of the best experts on this subject based on the ideXlab platform.
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on the sum Rate Capacity of poisson miso multiple access channels
IEEE Transactions on Information Theory, 2017Co-Authors: Yingbin Liang, Shlomo Shamai ShitzAbstract:In this paper, we analyze the sum-Rate Capacity of two-user Poisson multiple access channels (MAC), when the receiver is equipped with single antenna. We first characterize the sum-Rate Capacity of the non-symmetric Poisson MAC when each transmitter has a single antenna. While the sum-Rate Capacity of the symmetric Poisson MAC with single antenna at each transmitter has been characterized in the literature, the special property exploited in the existing method for the symmetric case does not hold for the non-symmetric channel anymore. We obtain the optimal input that achieves the sum-Rate Capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-Rate Capacity. This is in sharp contrast to the Gaussian MAC, in which both users must transmit, either simultaneously or at different times, in order to achieve the sum-Rate Capacity. We then characterize the sum-Rate Capacity of the Poisson multiple-input single-output (MISO) MAC with multiple antennas at each transmitter and single antenna at the receiver. By converting a non-convex optimization problem with a large number of variables into a non-convex optimization problem with two variables, we show that the sum-Rate Capacity of the Poisson MISO MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with a single antenna at each transmitter.
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Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels
IEEE Transactions on Communications, 2017Co-Authors: Ain-ul Aisha, Lifeng Lai, Yingbin Liang, Shlomo Shamai ShitzAbstract:In this paper, we analyze the sum-Rate Capacity of two-user Poisson multiple input multiple output multiple-access channels (MACs), when both the transmitters and the receiver are equipped with multiple antennas. Although the sum-Rate Capacity of Poisson MISO MAC when the receiver is equipped with a single antenna has been characterized by us, the inclusion of multiple antennas at the receiver makes the problem more challenging and requires the development of new analytical tools. We first characterize the sum-Rate Capacity of the Poisson MAC when each transmitter has a single antenna and the receiver has multiple antennas. We obtain the optimal input that achieves the sum-Rate Capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-Rate Capacity, and for certain channel parameters, it is optimal for both users to transmit. We then characterize the sum-Rate Capacity of the channel where both the transmitters and the receiver are equipped with multiple antennas. We show that the sum-Rate Capacity of the Poisson MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with a single antenna at each transmitter, and has thus been characterized by the former case. We show this by developing a novel channel transformation argument.
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on the sum Rate Capacity of non symmetric poisson multiple access channel
International Symposium on Information Theory, 2016Co-Authors: Ain-ul Aisha, Yingbin Liang, Shlomo ShamaiAbstract:In this paper, we characterize the sum-Rate Capacity of the non-symmetric Poisson multiple access channel (MAC). While the sum-Rate Capacity of the symmetric Poisson MAC has been characterized in the literature, the special property exploited in the existing method for the symmetric case does not hold for the non-symmetric channel anymore. We obtain the optimal input that achieves the sum-Rate Capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-Rate Capacity. This is in sharp contrast to the Gaussian MAC, in which all users must transmit, either simultaneously or at different times, in order to achieve the sum-Rate Capacity.
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ICCS - On the sum-Rate Capacity of poisson MISO multiple access channels
2016 IEEE International Conference on Communication Systems (ICCS), 2016Co-Authors: Ain-ul-aisha, Yingbin Liang, Lifeng Lai, Shlomo Shamai ShitzAbstract:In this paper, we analyze the sum-Rate Capacity of Poisson multiple access channels (MAC) when each transmitter has multiple antennas. By converting a non-convex optimization problem with a large number of variables into a non-convex optimization problem with 2 variables, we show that the sum-Rate Capacity of the Poisson MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with single antenna at each transmitter. Therefore, characterizing the sum-Rate Capacity of the Poisson MAC with multiple transmit antennas is equivalent to characterizing the sum-Rate Capacity of a Poisson MAC with single antenna.
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ISIT - On the sum-Rate Capacity of non-symmetric Poisson multiple access channel
2016 IEEE International Symposium on Information Theory (ISIT), 2016Co-Authors: Ain-ul Aisha, Lifeng Lai, Yingbin Liang, Shlomo ShamaiAbstract:In this paper, we characterize the sum-Rate Capacity of the non-symmetric Poisson multiple access channel (MAC). While the sum-Rate Capacity of the symmetric Poisson MAC has been characterized in the literature, the special property exploited in the existing method for the symmetric case does not hold for the non-symmetric channel anymore. We obtain the optimal input that achieves the sum-Rate Capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-Rate Capacity. This is in sharp contrast to the Gaussian MAC, in which all users must transmit, either simultaneously or at different times, in order to achieve the sum-Rate Capacity.
Ain-ul Aisha - One of the best experts on this subject based on the ideXlab platform.
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Sum-Rate Capacity of Poisson MIMO Multiple-Access Channels
IEEE Transactions on Communications, 2017Co-Authors: Ain-ul Aisha, Lifeng Lai, Yingbin Liang, Shlomo Shamai ShitzAbstract:In this paper, we analyze the sum-Rate Capacity of two-user Poisson multiple input multiple output multiple-access channels (MACs), when both the transmitters and the receiver are equipped with multiple antennas. Although the sum-Rate Capacity of Poisson MISO MAC when the receiver is equipped with a single antenna has been characterized by us, the inclusion of multiple antennas at the receiver makes the problem more challenging and requires the development of new analytical tools. We first characterize the sum-Rate Capacity of the Poisson MAC when each transmitter has a single antenna and the receiver has multiple antennas. We obtain the optimal input that achieves the sum-Rate Capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-Rate Capacity, and for certain channel parameters, it is optimal for both users to transmit. We then characterize the sum-Rate Capacity of the channel where both the transmitters and the receiver are equipped with multiple antennas. We show that the sum-Rate Capacity of the Poisson MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with a single antenna at each transmitter, and has thus been characterized by the former case. We show this by developing a novel channel transformation argument.
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on the sum Rate Capacity of non symmetric poisson multiple access channel
International Symposium on Information Theory, 2016Co-Authors: Ain-ul Aisha, Yingbin Liang, Shlomo ShamaiAbstract:In this paper, we characterize the sum-Rate Capacity of the non-symmetric Poisson multiple access channel (MAC). While the sum-Rate Capacity of the symmetric Poisson MAC has been characterized in the literature, the special property exploited in the existing method for the symmetric case does not hold for the non-symmetric channel anymore. We obtain the optimal input that achieves the sum-Rate Capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-Rate Capacity. This is in sharp contrast to the Gaussian MAC, in which all users must transmit, either simultaneously or at different times, in order to achieve the sum-Rate Capacity.
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ISIT - On the sum-Rate Capacity of non-symmetric Poisson multiple access channel
2016 IEEE International Symposium on Information Theory (ISIT), 2016Co-Authors: Ain-ul Aisha, Lifeng Lai, Yingbin Liang, Shlomo ShamaiAbstract:In this paper, we characterize the sum-Rate Capacity of the non-symmetric Poisson multiple access channel (MAC). While the sum-Rate Capacity of the symmetric Poisson MAC has been characterized in the literature, the special property exploited in the existing method for the symmetric case does not hold for the non-symmetric channel anymore. We obtain the optimal input that achieves the sum-Rate Capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-Rate Capacity. This is in sharp contrast to the Gaussian MAC, in which all users must transmit, either simultaneously or at different times, in order to achieve the sum-Rate Capacity.
