Multiuser MIMO Achievable Rates With Downlink Training and Channel State Feedback

By Giuseppe Caire, Nihar Jindal, Mari Kobayashi, Niranjay Ravindran. IEEE Transactions on Information Theory, 2007

631 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/26002f66ba2f5f4cc085fb235b4589e0feab366c

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Citations in the corpus

Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling (2014, background): “In Frequency division duplexing (FDD) systems, where uplink and downlink transmissions occur in separate bands, CSIT is made available to the transmitter through explicit downlink training and uplink feedback [4].”
Fundamental Limits of Cooperation (2012, neutral): “Qualitatively similar observations to the ones made in this section for the uplink can bemade for the downlink, which could be analyzed by extending the pilot-assisted scheme in [43] to the multicell realm.”
Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends (2022, background): “in the high SNR regime [13], [28], [88], [102], [146].”
Joint Spatial Division and Multiplexing—The Large-Scale Array Regime (2012, methodology): “If the CSIT feedback scheme is designed to achieve exponent and the channel SNR is sufficiently large, the feedback error is negligible with respect to the downlink estimation error [3].”
Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, background): “The signal-to-noise ratio (SNR) can be defined accordingly by removing the interference term in (1.11).”
Completely Stale Transmitter Channel State Information is Still Very Useful (2010, background): “The standard approach of dealing with feedback delay is to exploit the time correlation of the channel to predict the current channel state from the delayed measurements [4].”
Federated Learning via Over-the-Air Computation (2018, methodology): “The feedback problem can be addressed using unquantized analog feedback or quantized digital feedback [39].”
Power Scaling of Uplink Massive MIMO Systems With Arbitrary-Rank Channel Means (2014, background): “Corollary 10: If p u = E u / √ M for a ﬁxed E u , when M grows without bound, the limit of (98) will exist only when (cid:0) (cid:1) If p u = E u /M for a ﬁxed E u , when M grows without bound, (98) will converge to a non-zero constant value only when K n = 0 and (98) becomes Proof: From the proof o”
The Multicell Multiuser MIMO Uplink with Very Large Antenna Arrays and a Finite-Dimensional Channel (2013, methodology): “To obtain these lower bounds we use the techniques of [23], [24], and use the channel estimate in (10).”
Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems (2011, background): “This is not optimal from an information-theoretic point of view, and higher rates can be achieved if the BS communicates with several users in the same time-frequency resource [3], [4].”
Pilot Contamination and Precoding in Multi-Cell TDD Systems (2009, methodology): “Single-cell analysis of TDD systems are also provided in [32].”
Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs (2015, background): “precoding) at the BS, where collocated inputs are available for cooperative joint encoding [191]–[196].”