5 research outputs found
Implementation of LS, MMSE and SAGE Channel Estimators for Mobile MIMO-OFDM
The use of decision directed (DD) channel estimation in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) downlink receiver is studied in this paper. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark. The space-alternating
generalized expectation-maximization (SAGE) algorithm is used to improve the performance from that of the pilot symbol based least-squares (LS) channel estimator. The DD channel estimation improves the performance with high user velocities, where the pilot symbol density is not sufficient. Minimum mean square error (MMSE) filtering can also be used in estimating the channel
in between pilot symbols. The DD channel estimation can be used to reduce the pilot overhead without any performance degradation by transmitting data instead of pilot symbols. The pilot overhead is reduced to a third of the LTE pilot overhead, obtaining a ten percent increase in throughput. The pilot based
LS, MMSE and the SAGE channel estimators are implemented and the performance-complexity trade-offs are studied
Decision-Directed Channel Estimation Implementation for Spectral Efficiency Improvement in Mobile MIMO-OFDM
Channel estimation algorithms and their implementations
for mobile receivers are considered in this paper.
The 3GPP long term evolution (LTE) based pilot structure
is used as a benchmark in a multiple-input multiple-output
(MIMO) orthogonal frequency division multiplexing
(OFDM) receiver. The decision directed (DD) space alternating
generalized expectation-maximization (SAGE)
algorithm is used to improve the performance from that of
the pilot symbol based least-squares (LS) channel estimator.
The performance is improved with high user velocities,
where the pilot symbol density is not sufficient. Minimum
mean square error (MMSE) filtering is also used
in estimating the channel in between pilot symbols. The
pilot overhead can be reduced to a third of the LTE pilot overhead with DD channel estimation, obtaining a ten percent
increase in data throughput. Complexity reduction and
latency issues are considered in the architecture design.
The pilot based LS, MMSE and the SAGE channel estimators
are implemented with a high level synthesis tool,
synthesized with the UMC 0.18 μm CMOS technology
and the performance-complexity trade-offs are studied. The
MMSE estimator improves the performance from the simple
LS estimator with LTE pilot structure and has low power
consumption. The SAGE estimator has high power consumption
but can be used with reduced pilot density to
increase the data rate.National Science FoundationTekesElektrobitRenesas Mobile EuropeAcademy of FinlandNokia Siemens NetworksXilin
Iterative detection, decoding, and channel estimation in MIMO-OFDM
Abstract
Iterative receiver techniques, multiple-input – multiple-output (MIMO) processing, and orthogonal frequency division multiplexing (OFDM) are amongst the key physical layer technologies when aiming at higher spectral efficiency for a wireless communication system. Special focus is put on iterative detection, decoding, and channel estimation for a MIMO-OFDM system. After designing separately efficient algorithms for the detection, channel decoding, and channel estimation, the objective is to optimize them to work together through optimizing the activation schedules for soft-in soft-out (SfISfO) components.
A list parallel interference cancellation (PIC) detector is derived to approximate an a posteriori probability (APP) algorithm with reduced complexity and minimal loss of performance. It is shown that the list PIC detector with good initialization outperforms the K-best list sphere detector (LSD) in the case of small list sizes, whereas the complexities of the algorithms are of the same order. The convergence of the iterative detection and decoding is improved by using a priori information to also recalculate the candidate list, aside from the log-likelihood ratios (LLRs) of the coded bits.
Unlike in pilot based channel estimation, the least-squares (LS) channel estimator based on symbol decisions requires a matrix inversion in MIMO-OFDM. The frequency domain (FD) space-alternating generalized expectation-maximization (SAGE) channel estimator calculates the LS estimate iteratively, avoiding the matrix inversion with constant envelope modulation. The performance and computational complexity of the FD-SAGE channel estimator are compared to those of pilot based LS channel estimation with minimum mean square error (MMSE) post-processing exploiting the time correlation of the channel. A time domain (TD) SAGE channel estimator is derived to avoid the matrix inversion in channel estimation based on symbol decisions for MIMO-OFDM systems also with non-constant envelope modulation.
An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. It is proposed to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and to find out the optimal activation schedule for them. A semi-analytical expression of the EXIT function is derived for the LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of the channel estimate’s mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the channel decoder. Trellis search algorithms are shown to find the convergence with the lowest possible complexity using the EXIT functions. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided