6,909 research outputs found
Performance Analysis and Optimal Power Allocation for Linear Receivers Based on Superimposed Training
In this paper, we derive a performance comparison between two training-based
schemes for Multiple-Input Multiple-Output (MIMO) systems. The two schemes are
thetime-division multiplexing scheme and the recently proposed data-dependent
superimposed pilot scheme. For both schemes, a closed-form expressions for the
Bit Error Rate (BER) is provided. We also determine, for both schemes, the
optimal allocation of power between pilot and data that minimizes the BER
Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems
Radio-frequency (RF) impairments, that exist intimately in wireless
communications systems, can severely degrade the performance of traditional
multiple-input multiple-output (MIMO) systems. Although compensation schemes
can cancel out part of these RF impairments, there still remains a certain
amount of impairments. These residual impairments have fundamental impact on
the MIMO system performance. However, most of the previous works have neglected
this factor. In this paper, a training-based MIMO system with residual transmit
RF impairments (RTRI) is considered. In particular, we derive a new channel
estimator for the proposed model, and find that RTRI can create an irreducible
estimation error floor. Moreover, we show that, in the presence of RTRI, the
optimal training sequence length can be larger than the number of transmit
antennas, especially in the low and high signal-to-noise ratio (SNR) regimes.
An increase in the proposed approximated achievable rate is also observed by
adopting the optimal training sequence length. When the training and data
symbol powers are required to be equal, we demonstrate that, at high SNRs,
systems with RTRI demand more training, whereas at low SNRs, such demands are
nearly the same for all practical levels of RTRI.Comment: Accepted for publication at the IEEE International Conference on
Communications (ICC 2014), 6 pages, 5 figure
Two-Way Training for Discriminatory Channel Estimation in Wireless MIMO Systems
This work examines the use of two-way training to efficiently discriminate
the channel estimation performances at a legitimate receiver (LR) and an
unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless
system. This work improves upon the original discriminatory channel estimation
(DCE) scheme proposed by Chang et al where multiple stages of feedback and
retraining were used. While most studies on physical layer secrecy are under
the information-theoretic framework and focus directly on the data transmission
phase, studies on DCE focus on the training phase and aim to provide a
practical signal processing technique to discriminate between the channel
estimation performances at LR and UR. A key feature of DCE designs is the
insertion of artificial noise (AN) in the training signal to degrade the
channel estimation performance at UR. To do so, AN must be placed in a
carefully chosen subspace based on the transmitter's knowledge of LR's channel
in order to minimize its effect on LR. In this paper, we adopt the idea of
two-way training that allows both the transmitter and LR to send training
signals to facilitate channel estimation at both ends. Both reciprocal and
non-reciprocal channels are considered and a two-way DCE scheme is proposed for
each scenario. {For mathematical tractability, we assume that all terminals
employ the linear minimum mean square error criterion for channel estimation.
Based on the mean square error (MSE) of the channel estimates at all
terminals,} we formulate and solve an optimization problem where the optimal
power allocation between the training signal and AN is found by minimizing the
MSE of LR's channel estimate subject to a constraint on the MSE achievable at
UR. Numerical results show that the proposed DCE schemes can effectively
discriminate between the channel estimation and hence the data detection
performances at LR and UR.Comment: 1
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