MIMO broadcast channels with finite rate feedback

By Nihar Jindal. GLOBECOM '05. IEEE Global Telecommunications Conference, 2005., 2006

1,610 citations recorded by the source corpus.

Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e. multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this paper, a system where the receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. Simple expressions for the capacity degradation due to finite rate feedback as well as the required increases in feedback load per mobile as a function of the number of access point antennas and the system SNR are provided.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7b2d109aff1242f0f57355a4bdc06f8089dbd495

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In Electrical and Electronic Engineering

Citations in the corpus

Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave Systems (2014, methodology): “independent from h̃ u, and h̃ Q u respectively, and the expectations of their projections are equal, yielding [34]”
Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends (2022, background): “in the high SNR regime [13], [28], [88], [102], [146].”
Beam Division Multiple Access Transmission for Massive MIMO Communications (2015, background): “training with orthogonal pilots, as well as, the number of feedback bits increase linearly with the number of BS antennas [11]; thus, the sheer large number of antennas makes it impractical to obtain instantaneous CSI at BS through painstaking downlink training and feedback for CSIT.”
Rate-splitting multiple access for downlink communication systems: bridging, generalizing, and outperforming SDMA and NOMA (2017, background): “This leads to the wellknown severe performance loss ofMU–LP in the presence of imperfect CSIT [13].”
An overview of limited feedback in wireless communication systems (2008, background): “When the channel is quantized, the user signals can not be perfectly orthogonalized due to inherent quantization error [53], [101].”
Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization (2011, background): “This implicitly assumes that the system is able to perform co ding across a large number of differently faded frames [6]–[9].”
Pilot Contamination and Precoding in Multi-Cell TDD Systems (2009, background): “In [3], [5]–[8], [27] such a setting is considered.”
Multi-Antenna Downlink Channels with Limited Feedback and User Selection (2007, background): “In this section we consider the case of K > M and analyze the performance of a naive CQI feedback scheme where each user feeds back its quantized CDI ĥk as well as its channel magnitude g(hk) = ‖hk‖2.”
Shifting the MIMO Paradigm (2007, background): “The main observation in [31] is that the feedback requirements scale linearly both as the number of transmit antennas grows and as a function of the SNR (in dB), unlike the single-user case.”
Completely Stale Transmitter Channel State Information is Still Very Useful (2010, neutral): “For example, for the MIMO broadcast channel, it was shown in [3] that the rate of feedback should scale linearly with .”
Networked MIMO with clustered linear precoding (2008, methodology): “For a frequency-division duplexing (FDD) system, the downlink CSI can be obtained by feedback from mobile users, and limited feedback for MU-MIMO is an ongoing topic [36]–[38], which we will not explore in this paper and perfect CSI is assumed.”
Robust Decentralized Joint Precoding using Team Deep Neural Network (2018, background): “For example, if σ((1)) = [0, 0, 0, 0], TX 1 has received perfectly the complete CSI while σ((1)) = [1, 1, 1, 1] means that TX 1 was able to receive only noise.”
Sum-Rate Maximization for Linearly Precoded Downlink Multiuser MISO Systems With Partial CSIT: A Rate-Splitting Approach (2016, methodology): “in addition to the aforementioned channel quantizatio n and feedback interpretation [5], [12], it can also correspo nd to the Doppler process in delayed/outdated CSIT [6], [7].”
Large System Analysis of Linear Precoding in Correlated MISO Broadcast Channels Under Limited Feedback (2009, methodology): “We cannot directly compare the deterministic equivalents to the upper bound in (68) for two reasons, (i) under ZF precoding andM = K, a deterministic equivalent for the peruser rate gap does not exist and (ii) [45] considers unit norm precoding vectors, whereas in this paper we only impose a total p”