The Experts below are selected from a list of 242313 Experts worldwide ranked by ideXlab platform
Giuseppe Caire - One of the best experts on this subject based on the ideXlab platform.
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Channel State Feedback Over the MIMO-MAC
IEEE Transactions on Information Theory, 2011Co-Authors: K. R. Kumar, Giuseppe CaireAbstract:In order to exploit the full multiplexing gain of multi-antenna multi-user downlink schemes, accurate Channel State information at the transmitter (i.e., at the base station) is required. We consider the design of a closed-loop Channel State information feedback scheme, where user terminals feed back their Channel State information simultaneously to a multi-antenna base station. The underlying information theoretic problem consists of lossy source-Channel coding of multiple independent analog sources (i.e., the users' Channel coefficients) over a Gaussian multiple-input multiple-output multi-access Channel (MIMO-MAC). Unlike the classical source-Channel coding setting, this application requires low latency, otherwise the Channel State information would be outdated. Hence, source-Channel codewords can span only a single fading State of the uplink (feedback) Channel. Furthermore, the transmitters are ignorant of the realization of the uplink Channel coefficients. In this scenario, the scaling of the maximum of the estimated downlink Channel mean-square errors with the SNR dominates the performance of the multiuser downlink. This scaling is described by the distortion SNR exponent, previously introduced in a single-user MIMO setting. This paper analyzes the max-min distortion SNR exponent of the MIMO-MAC for both separated source-Channel coding, and a particular hybrid digital-analog joint source-Channel coding scheme. For the case of single-antenna users, we prove that the distortion SNR exponent of separated source-Channel coding can be achieved by the concatenation of scalar quantization and uncoded quadrature-amplitude modulation (QAM) transmission, with lattice decoding at the base-station receiver. The resulting scheme has very low encoding latency (only a few symbols of the uplink slot) and generally outperforms currently proposed Channel State feedback schemes based on analog unquantized transmission or vector quantization with fixed codebooks.
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multiuser mimo achievable rates with downlink training and Channel State feedback
IEEE Transactions on Information Theory, 2010Co-Authors: Giuseppe Caire, Nihar Jindal, Mari Kobayashi, Niranjay RavindranAbstract:In this paper, we consider a multiple-input-multiple-output (MIMO) fading broadcast Channel and compute achievable ergodic rates when Channel State information (CSI) is acquired at the receivers via downlink training and it is provided to the transmitter by Channel State feedback. Unquantized (analog) and quantized (digital) Channel State feedback schemes are analyzed and compared under various assumptions. Digital feedback is shown to be potentially superior when the feedback Channel uses per Channel State coefficient is larger than 1. Also, we show that by proper design of the digital feedback link, errors in the feedback have a minor effect even if simple uncoded modulation is used on the feedback Channel. We discuss first the case of an unfaded additive white Gaussian noise (AWGN) feedback Channel with orthogonal access and then the case of fading MIMO multiple access (MIMO-MAC). We show that by exploiting the MIMO-MAC nature of the uplink Channel, a much better scaling of the feedback Channel resource with the number of base station (BS) antennas can be achieved. Finally, for the case of delayed feedback, we show that in the realistic case where the fading process has (normalized) maximum Doppler frequency shift 0 ? F < 1/2, a fraction 1 - 2F of the optimal multiplexing gain is achievable. The general conclusion of this work is that very significant downlink throughput is achievable with simple and efficient Channel State feedback, provided that the feedback link is properly designed.
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A low-latency low-complexity scheme for efficient Channel State feedback in MIMO multiuser communications
Proceedings - IEEE Military Communications Conference MILCOM, 2010Co-Authors: Giuseppe Caire, K. R. KumarAbstract:In order to exploit the full degrees of freedom of multi-antenna multi-user systems, accurate Channel State information at the transmitter is needed. We consider a low latency and low complexity design for a closed-loop Channel State information feedback scheme, where user terminals feed back their Channel State information simultaneously to a multi-antenna base station. The underlying theoretical problem consists of the lossy transmission of multiple independent analog sources over a Gaussian MIMO Multi-Access Channel (MIMO-MAC), under a Mean-Square Error (MSE) criterion. We analyze distortion exponent, i.e., the order of decrease of the Channel State information MSE for increasing Channel SNR, and design very simple "almost uncoded" schemes that outperform conventional approaches such as finite-rate feedback and uncoded "analog" feedback.
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mimo downlink scheduling with non perfect Channel State knowledge
Information Theory Workshop, 2009Co-Authors: Hooman Shiranimehr, Giuseppe CaireAbstract:Downlink scheduling schemes are well-known and widely investigated. In the multiuser MIMO (broadcast) case, downlink scheduling in the presence of non-perfect CSI is only scantly treated. In this paper we provide a general framework within which the problem can be addressed systematically. Then, we focus on the special case of proportional fairness and “hard fairness”, with Gaussian coding and linear beamforming. We find that the naive scheduler that ignores the quality of the Channel State information may be very suboptimal. We propose novel simple schemes that perform very well in practice. Also, we illuminate the key role played by the Channel State prediction error: our schemes treat in a fundamentally different way users with “predictable” or “non-predictable” Channels, and allocate these classes of users over time in a near-optimal fashion.
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mimo downlink scheduling with non perfect Channel State knowledge
arXiv: Information Theory, 2009Co-Authors: Hooman Shiranimehr, Giuseppe Caire, Michael J NeelyAbstract:Downlink scheduling schemes are well-known and widely investigated under the assumption that the Channel State is perfectly known to the scheduler. In the multiuser MIMO (broadcast) case, downlink scheduling in the presence of non-perfect Channel State information (CSI) is only scantly treated. In this paper we provide a general framework that addresses the problem systematically. Also, we illuminate the key role played by the Channel State prediction error: our scheme treats in a fundamentally different way users with small Channel prediction error ("predictable" users) and users with large Channel prediction error ("non-predictable" users), and can be interpreted as a near-optimal opportunistic time-sharing strategy between MIMO downlink beamforming to predictable users and space-time coding to nonpredictable users. Our results, based on a realistic MIMO Channel model used in 3GPP standardization, show that the proposed algorithms can significantly outperform a conventional "mismatched" scheduling scheme that treats the available CSI as if it was perfect.
Michael J Neely - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Transmit Covariance Design in MIMO Fading Systems With Unknown Channel Distributions and Inaccurate Channel State Information
IEEE Transactions on Wireless Communications, 2017Co-Authors: Michael J NeelyAbstract:This paper considers dynamic transmit covariance design in point-to-point multiple-input multiple-output fading systems with unknown Channel State distributions and inaccurate Channel State information subject to both long-term and short-term power constraints. First, the case of instantaneous but possibly inaccurate Channel State information at the transmitter (CSIT) is treated. By extending the drift-plus-penalty technique, a dynamic transmit covariance policy is developed and is shown to approach optimality with an $O(\delta)$ gap, where $\delta $ is the inaccuracy measure of CSIT, regardless of the Channel State distribution and without requiring knowledge of this distribution. Next, the case of delayed and inaccurate Channel State information is considered. The optimal transmit covariance solution that maximizes the ergodic capacity is fundamentally different in this case, and a different online algorithm based on convex projections is developed. The proposed algorithm for this delayed-CSIT case also has an $O(\delta)$ optimality gap, where $\delta $ is again the inaccuracy measure of CSIT.
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Dynamic Transmit Covariance Design in MIMO Fading Systems With Unknown Channel Distributions and Inaccurate Channel State Information
arXiv: Information Theory, 2015Co-Authors: Michael J NeelyAbstract:This paper considers dynamic transmit covariance design in point-to-point MIMO fading systems with unknown Channel State distributions and inaccurate Channel State information subject to both long term and short term power constraints. First, the case of instantaneous but possibly inaccurate Channel State information at the transmitter (CSIT) is treated. By extending the drift-plus-penalty technique, a dynamic transmit covariance policy is developed and is shown to approach optimality with an $O(\delta)$ gap, where $\delta$ is the inaccuracy measure of CSIT, regardless of the Channel State distribution and without requiring knowledge of this distribution. Next, the case of delayed and inaccurate Channel State information is considered. The optimal transmit covariance solution that maximizes the ergodic capacity is fundamentally different in this case, and a different online algorithm based on convex projections is developed. The proposed algorithm for this delayed-CSIT case also has an $O(\delta)$ optimality gap, where $\delta$ is again the inaccuracy measure of CSIT.
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mimo downlink scheduling with non perfect Channel State knowledge
arXiv: Information Theory, 2009Co-Authors: Hooman Shiranimehr, Giuseppe Caire, Michael J NeelyAbstract:Downlink scheduling schemes are well-known and widely investigated under the assumption that the Channel State is perfectly known to the scheduler. In the multiuser MIMO (broadcast) case, downlink scheduling in the presence of non-perfect Channel State information (CSI) is only scantly treated. In this paper we provide a general framework that addresses the problem systematically. Also, we illuminate the key role played by the Channel State prediction error: our scheme treats in a fundamentally different way users with small Channel prediction error ("predictable" users) and users with large Channel prediction error ("non-predictable" users), and can be interpreted as a near-optimal opportunistic time-sharing strategy between MIMO downlink beamforming to predictable users and space-time coding to nonpredictable users. Our results, based on a realistic MIMO Channel model used in 3GPP standardization, show that the proposed algorithms can significantly outperform a conventional "mismatched" scheduling scheme that treats the available CSI as if it was perfect.
David Tse - One of the best experts on this subject based on the ideXlab platform.
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completely stale transmitter Channel State information is still very useful
IEEE Transactions on Information Theory, 2012Co-Authors: Mohammad Ali Maddahali, David TseAbstract:Transmitter Channel State information (CSIT) is crucial for the multiplexing gains offered by advanced interference management techniques such as multiuser multiple-input multiple-output (MIMO) and interference alignment. Such CSIT is usually obtained by feedback from the receivers, but the feedback is subject to delays. The usual approach is to use the fed back information to predict the current Channel State and then apply a scheme designed assuming perfect CSIT. When the feedback delay is large compared to the Channel coherence time, such a prediction approach completely fails to achieve any multiplexing gain. In this paper, we show that even in this case, the completely stale CSI is still very useful. More concretely, we show that in an MIMO broadcast Channel with transmit antennas and receivers each with 1 receive antenna, K/1+1/2+···+1/K (>;1) degrees of freedom is achievable even when the fed back Channel State is completely independent of the current Channel State. Moreover, we establish that if all receivers have independent and identically distributed Channels, then this is the optimal number of degrees of freedom achievable. In the optimal scheme, the transmitter uses the fed back CSI to learn the side information that the receivers receive from previous transmissions rather than to predict the current Channel State. Our result can be viewed as the first example of feedback providing a degree-of-freedom gain in memoryless Channels.
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completely stale transmitter Channel State information is still very useful
arXiv: Information Theory, 2010Co-Authors: Mohammad Ali Maddahali, David TseAbstract:Transmitter Channel State information (CSIT) is crucial for the multiplexing gains offered by advanced interference management techniques such as multiuser MIMO and interference alignment. Such CSIT is usually obtained by feedback from the receivers, but the feedback is subject to delays. The usual approach is to use the fed back information to predict the current Channel State and then apply a scheme designed assuming perfect CSIT. When the feedback delay is large compared to the Channel coherence time, such a prediction approach completely fails to achieve any multiplexing gain. In this paper, we show that even in this case, the completely stale CSI is still very useful. More concretely, we show that in a MIMO broadcast Channel with $K$ transmit antennas and $K$ receivers each with 1 receive antenna, $\frac{K}{1+1/2+ ...+ \frac{1}{K}} (> 1) $ degrees of freedom is achievable even when the fed back Channel State is completely independent of the current Channel State. Moreover, we establish that if all receivers have independent and identically distributed Channels, then this is the optimal number of degrees of freedom achievable. In the optimal scheme, the transmitter uses the fed back CSI to learn the side information that the receivers receive from previous transmissions rather than to predict the current Channel State. Our result can be viewed as the first example of feedback providing a degree-of-freedom gain in memoryless Channels.
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completely stale transmitter Channel State information is still very useful
Allerton Conference on Communication Control and Computing, 2010Co-Authors: Mohammad Ali Maddahali, David TseAbstract:Transmitter Channel State information (CSIT) is crucial for the multiplexing gains offered by advanced interference management techniques such as multiuser MIMO and interference alignment. Such CSIT is usually obtained by feedback from the receivers, but the feedback is subject to delays. The usual approach is to use the fed back information to predict the current Channel State and then apply a scheme designed assuming perfect CSIT. When the feedback delay is large compared to the Channel coherence time, such a prediction approach completely fails to achieve any multiplexing gain. In this paper, we show that even in this case, the completely stale CSI is still very useful. More concretely, we showed that in a MIMO broadcast Channel with K transmit antennas and K receivers each with 1 receive antenna, equation (> 1) degrees of freedom is achievable even when the fed back Channel State is completely independent of the current Channel State. Moreover, we establish that if all receivers have identically distributed Channels, then this is the optimal number of degrees of freedom achievable. In the optimal scheme, the transmitter uses the fed back CSI to learn the side information that the receivers receive from previous transmissions rather than to predict the current Channel State. Our result can be viewed as the first example of feedback providing a degree-of-freedom gain in memoryless Channels.
Mohammad Ali Maddahali - One of the best experts on this subject based on the ideXlab platform.
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completely stale transmitter Channel State information is still very useful
IEEE Transactions on Information Theory, 2012Co-Authors: Mohammad Ali Maddahali, David TseAbstract:Transmitter Channel State information (CSIT) is crucial for the multiplexing gains offered by advanced interference management techniques such as multiuser multiple-input multiple-output (MIMO) and interference alignment. Such CSIT is usually obtained by feedback from the receivers, but the feedback is subject to delays. The usual approach is to use the fed back information to predict the current Channel State and then apply a scheme designed assuming perfect CSIT. When the feedback delay is large compared to the Channel coherence time, such a prediction approach completely fails to achieve any multiplexing gain. In this paper, we show that even in this case, the completely stale CSI is still very useful. More concretely, we show that in an MIMO broadcast Channel with transmit antennas and receivers each with 1 receive antenna, K/1+1/2+···+1/K (>;1) degrees of freedom is achievable even when the fed back Channel State is completely independent of the current Channel State. Moreover, we establish that if all receivers have independent and identically distributed Channels, then this is the optimal number of degrees of freedom achievable. In the optimal scheme, the transmitter uses the fed back CSI to learn the side information that the receivers receive from previous transmissions rather than to predict the current Channel State. Our result can be viewed as the first example of feedback providing a degree-of-freedom gain in memoryless Channels.
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completely stale transmitter Channel State information is still very useful
arXiv: Information Theory, 2010Co-Authors: Mohammad Ali Maddahali, David TseAbstract:Transmitter Channel State information (CSIT) is crucial for the multiplexing gains offered by advanced interference management techniques such as multiuser MIMO and interference alignment. Such CSIT is usually obtained by feedback from the receivers, but the feedback is subject to delays. The usual approach is to use the fed back information to predict the current Channel State and then apply a scheme designed assuming perfect CSIT. When the feedback delay is large compared to the Channel coherence time, such a prediction approach completely fails to achieve any multiplexing gain. In this paper, we show that even in this case, the completely stale CSI is still very useful. More concretely, we show that in a MIMO broadcast Channel with $K$ transmit antennas and $K$ receivers each with 1 receive antenna, $\frac{K}{1+1/2+ ...+ \frac{1}{K}} (> 1) $ degrees of freedom is achievable even when the fed back Channel State is completely independent of the current Channel State. Moreover, we establish that if all receivers have independent and identically distributed Channels, then this is the optimal number of degrees of freedom achievable. In the optimal scheme, the transmitter uses the fed back CSI to learn the side information that the receivers receive from previous transmissions rather than to predict the current Channel State. Our result can be viewed as the first example of feedback providing a degree-of-freedom gain in memoryless Channels.
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completely stale transmitter Channel State information is still very useful
Allerton Conference on Communication Control and Computing, 2010Co-Authors: Mohammad Ali Maddahali, David TseAbstract:Transmitter Channel State information (CSIT) is crucial for the multiplexing gains offered by advanced interference management techniques such as multiuser MIMO and interference alignment. Such CSIT is usually obtained by feedback from the receivers, but the feedback is subject to delays. The usual approach is to use the fed back information to predict the current Channel State and then apply a scheme designed assuming perfect CSIT. When the feedback delay is large compared to the Channel coherence time, such a prediction approach completely fails to achieve any multiplexing gain. In this paper, we show that even in this case, the completely stale CSI is still very useful. More concretely, we showed that in a MIMO broadcast Channel with K transmit antennas and K receivers each with 1 receive antenna, equation (> 1) degrees of freedom is achievable even when the fed back Channel State is completely independent of the current Channel State. Moreover, we establish that if all receivers have identically distributed Channels, then this is the optimal number of degrees of freedom achievable. In the optimal scheme, the transmitter uses the fed back CSI to learn the side information that the receivers receive from previous transmissions rather than to predict the current Channel State. Our result can be viewed as the first example of feedback providing a degree-of-freedom gain in memoryless Channels.
Michael Gastpar - One of the best experts on this subject based on the ideXlab platform.
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functional forwarding of Channel State information
IEEE Transactions on Information Theory, 2014Co-Authors: Jiening Zhan, Michael GastparAbstract:Based on the recent compute-and-forward technique , a novel communication strategy is proposed under which functions of the Channel State information are forwarded along the network. Those functions are chosen such that on the one hand, they can be efficiently forwarded, and on the other hand, they are maximally useful to the final decoder of the message. It is illustrated that there is generally a tension between these two requirements. The strategy is shown to perform well for certain classes of multilayer networks where Channel State information is acquired locally at each receiver. For example, for a two-stage Gaussian relay network with local Channel State information, it is shown that the proposed strategy performs optimally in a scaling-law sense, as the number of relays increases.
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CISS - On decoding equations with partial Channel State information
2010 44th Annual Conference on Information Sciences and Systems (CISS), 2010Co-Authors: Bobak Nazer, Michael GastparAbstract:We have shown that over interfering links, it is possible to decode a function of the involved messages much more efficiently than the full messages themselves. This is useful for example as a physical layer for network coding. Typically, some degree of Channel State information at the receiver is required. In this note, we study the effect of only partial Channel State information. For several models of interference, novel achievable rates are derived. These may prove to be useful as a physical layer for non-coherent network codes.
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functional forwarding of Channel State information
International Symposium on Information Theory, 2009Co-Authors: Jiening Zhan, Michael GastparAbstract:In large relay networks, the assumption of full and perfect Channel knowledge at the destination is optimistic in practice. The fading coefficients are typically measured at the relays but not directly known at the destination. Traditionally, each fading coefficient is individually forwarded to the destination. However, it is often sufficient for the decoder to know only a function of the various Channel States rather than the full information. We develop a general framework for forwarding Channel State information in relay systems with local Channel knowledge. We apply our framework to several networks and find that functional forwarding of Channel State information can be attained much more efficiently than full forwarding.
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ISIT - Functional forwarding of Channel State information
2009 IEEE International Symposium on Information Theory, 2009Co-Authors: Jiening Zhan, Michael GastparAbstract:In large relay networks, the assumption of full and perfect Channel knowledge at the destination is optimistic in practice. The fading coefficients are typically measured at the relays but not directly known at the destination. Traditionally, each fading coefficient is individually forwarded to the destination. However, it is often sufficient for the decoder to know only a function of the various Channel States rather than the full information. We develop a general framework for forwarding Channel State information in relay systems with local Channel knowledge. We apply our framework to several networks and find that functional forwarding of Channel State information can be attained much more efficiently than full forwarding.
