12 research outputs found
What is the Value of Joint Processing of Pilots and Data in Block-Fading Channels?
The spectral efficiency achievable with joint processing of pilot and data
symbol observations is compared with that achievable through the conventional
(separate) approach of first estimating the channel on the basis of the pilot
symbols alone, and subsequently detecting the data symbols. Studied on the
basis of a mutual information lower bound, joint processing is found to provide
a non-negligible advantage relative to separate processing, particularly for
fast fading. It is shown that, regardless of the fading rate, only a very small
number of pilot symbols (at most one per transmit antenna and per channel
coherence interval) should be transmitted if joint processing is allowed.Comment: To appear at IEEE Int. Symposium on Information Theory; 5 page
Secrecy Sum-Rates with Regularized Channel Inversion Precoding under Imperfect CSI at the Transmitter
In this paper, we study the performance of regularized channel inversion
precoding in MISO broadcast channels with confidential messages under imperfect
channel state information at the transmitter (CSIT). We obtain an approximation
for the achievable secrecy sum-rate which is almost surely exact as the number
of transmit antennas and the number of users grow to infinity in a fixed ratio.
Simulations prove this anaylsis accurate even for finite-size systems. For FDD
systems, we determine how the CSIT error must scale with the SNR, and we derive
the number of feedback bits required to ensure a constant high-SNR rate gap to
the case with perfect CSIT. For TDD systems, we study the optimum amount of
channel training that maximizes the high-SNR secrecy sum-rate.Comment: IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP), May 2013. arXiv admin note: text overlap with
arXiv:1304.585
Finite-Blocklength Bounds on the Maximum Coding Rate of Rician Fading Channels with Applications to Pilot-Assisted Transmission
We present nonasymptotic bounds on the maximum coding rate achievable over a
Rician block-fading channel for a fixed packet size and a fixed packet error
probability. Our bounds, which apply to the scenario where no a priori channel
state information is available at the receiver, allow one to quantify the
tradeoff between the rate gains resulting from the exploitation of
time-frequency diversity and the rate loss resulting from fast channel
variations and pilot-symbol overhead
One-Bit Massive MIMO: Channel Estimation and High-Order Modulations
We investigate the information-theoretic throughout achievable on a fading
communication link when the receiver is equipped with one-bit analog-to-digital
converters (ADCs). The analysis is conducted for the setting where neither the
transmitter nor the receiver have a priori information on the realization of
the fading channels. This means that channel-state information needs to be
acquired at the receiver on the basis of the one-bit quantized channel outputs.
We show that least-squares (LS) channel estimation combined with joint pilot
and data processing is capacity achieving in the single-user,
single-receive-antenna case.
We also investigate the achievable uplink throughput in a massive
multiple-input multiple-output system where each element of the antenna array
at the receiver base-station feeds a one-bit ADC. We show that LS channel
estimation and maximum-ratio combining are sufficient to support both multiuser
operation and the use of high-order constellations. This holds in spite of the
severe nonlinearity introduced by the one-bit ADCs
On the distribution of an effective channel estimator for multi-cell massive MIMO
Accurate channel estimation is of utmost importance for massive MIMO systems to provide significant improvements in spectral and energy efficiency. In this work, we present a study on the distribution of a simple but yet effective and practical channel estimator for multi-cell massive MIMO systems suffering from pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without previous knowledge of the inter-cell large-scale channel coefficients and noise power, asymptotically approximating the performance of the linear MMSE estimator as the number of antennas increases. We prove that the distribution of the proposed channel estimator can be accurately approximated by the circularly-symmetric complex normal distribution, when the number of antennas, M, deployed at the base station is greater than 10
A Data-Aided Channel Estimation Scheme for Decoupled Systems in Heterogeneous Networks
Uplink/downlink (UL/DL) decoupling promises more flexible cell association
and higher throughput in heterogeneous networks (HetNets), however, it hampers
the acquisition of DL channel state information (CSI) in time-division-duplex
(TDD) systems due to different base stations (BSs) connected in UL/DL. In this
paper, we propose a novel data-aided (DA) channel estimation scheme to address
this problem by utilizing decoded UL data to exploit CSI from received UL data
signal in decoupled HetNets where a massive multiple-input multiple-output BS
and dense small cell BSs are deployed. We analytically estimate BER performance
of UL decoded data, which are used to derive an approximated normalized mean
square error (NMSE) expression of the DA minimum mean square error (MMSE)
estimator. Compared with the conventional least square (LS) and MMSE, it is
shown that NMSE performances of all estimators are determined by their
signal-to-noise ratio (SNR)-like terms and there is an increment consisting of
UL data power, UL data length and BER values in the SNR-like term of DA method,
which suggests DA method outperforms the conventional ones in any scenarios.
Higher UL data power, longer UL data length and better BER performance lead to
more accurate estimated channels with DA method. Numerical results verify that
the analytical BER and NMSE results are close to the simulated ones and a
remarkable gain in both NMSE and DL rate can be achieved by DA method in
multiple scenarios with different modulations
Generalized Nearest Neighbor Decoding
It is well known that for Gaussian channels, a nearest neighbor decoding
rule, which seeks the minimum Euclidean distance between a codeword and the
received channel output vector, is the maximum likelihood solution and hence
capacity-achieving. Nearest neighbor decoding remains a convenient and yet
mismatched solution for general channels, and the key message of this paper is
that the performance of the nearest neighbor decoding can be improved by
generalizing its decoding metric to incorporate channel state dependent output
processing and codeword scaling. Using generalized mutual information, which is
a lower bound to the mismatched capacity under independent and identically
distributed codebook ensemble, as the performance measure, this paper
establishes the optimal generalized nearest neighbor decoding rule, under
Gaussian channel input. Several {restricted forms of the} generalized nearest
neighbor decoding rule are also derived and compared with existing solutions.
The results are illustrated through several case studies for fading channels
with imperfect receiver channel state information and for channels with
quantization effects.Comment: 30 pages, 8 figure
Large System Analysis of Linear Precoding in Correlated MISO Broadcast Channels under Limited Feedback
In this paper, we study the sum rate performance of zero-forcing (ZF) and
regularized ZF (RZF) precoding in large MISO broadcast systems under the
assumptions of imperfect channel state information at the transmitter and
per-user channel transmit correlation. Our analysis assumes that the number of
transmit antennas and the number of single-antenna users are large
while their ratio remains bounded. We derive deterministic approximations of
the empirical signal-to-interference plus noise ratio (SINR) at the receivers,
which are tight as . In the course of this derivation, the
per-user channel correlation model requires the development of a novel
deterministic equivalent of the empirical Stieltjes transform of large
dimensional random matrices with generalized variance profile. The
deterministic SINR approximations enable us to solve various practical
optimization problems. Under sum rate maximization, we derive (i) for RZF the
optimal regularization parameter, (ii) for ZF the optimal number of users,
(iii) for ZF and RZF the optimal power allocation scheme and (iv) the optimal
amount of feedback in large FDD/TDD multi-user systems. Numerical simulations
suggest that the deterministic approximations are accurate even for small
.Comment: submitted to IEEE Transactions on Information Theor