Communication on the Grassmann manifold: A geometric approach to the noncoherent multiple-antenna channel

By Lizhong Zheng, David Tse. IEEE Transactions on Information Theory, 2002

1,037 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cb87af63886a91f95c6eda9eb3a18088c38c04ec

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In Aerospace Engineering

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Citations in the corpus

On the capacity of MIMO broadcast channels with partial side information (2005, background): “Indeed, it is shown in [ 3 ], [4] that when the receiver does not know CSI, the capacity scales like”
An overview of limited feedback in wireless communication systems (2008, neutral): “manifold [286].”
Designing structured tight frames via an alternating projection method (2005, background): “Grassmannian frames have applications in coding theory and communications engineering [9]–[12].”
Fundamentals of Wireless Communication (2005, result): “A reading exercise is to study [153] which shows that the capacity of the noncoherent block fading channel at high SNR also has the same number of spatial degrees freedom as in (8.”
Coding for Errors and Erasures in Random Network Coding (2007, background): “Just as codes defined on the complex Grassmann manifold play an important role in noncoherent multiple-antenna channels [4], we find that codes defined in an appropriate Grassmannian associated with a vector space over a finite field play an important role here, but with a different metric associate”
Capacity and power allocation for fading MIMO channels with channel estimation error (2006, background): “Pilot or training based channel estimation schemes are studied in [7], [8], [9] for block fading channels and channels with a band-limited Doppler spectrum, respectively, and their achievable rates are derived.”
A Survey on High Mobility Wireless Communications: Challenges, Opportunities and Solutions (2016, background): “In [90], the time-varying frequency flat fading channel is represented by using a BEM model with order Q = 2dNfDTse.”
Cooperative diversity in wireless networks: algorithms and architectures (2002, background): “In this case, the ergodic capacity has been shown to increase as T'(1 - T'/K) b/s/Hz for each additional 3 dB of SNR in the high SNR regime, where T' = min{T, R, [K/2j } [94].”
How much training is needed in multiple-antenna wireless links? (2003, background): “(A similar conclusion is also drawn in [ 19 ].) Thus, it is possible to achieve capacity at high SNR by designing a transmitter/receiver pair that dedicates part of the transmission interval to training antennas.”
An overview of MIMO communications - a key to gigabit wireless (2004, background): “We refer the interested reader to [24], [26], [27] for analysis of the channel capacity when neither the transmitter nor the receiver knows the channel matrix H.”
Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems (2019, background): “The UL channels are first estimated, and then exploiting channel reciprocity [6]–[8], they are mapped to DL channels.”
On the Fundamental Tradeoff of Integrated Sensing and Communications Under Gaussian Channels (2022, positive): “• Communication DoF: In the non-coherent communication scenario, it has been shown [42] that an alternative optimal strategy is to estimate the channel using pilot symbols before transmitting data symbols.”