Training-Based Channel Estimation Algorithms for Dual Hop MIMO OFDM Relay Systems

In this paper, we consider minimum-mean-square error (MMSE) training-based channel estimation for two-hop multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) relaying systems. The channel estimation process is divided into two main phases. The relay-destination ch...

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Bibliographic Details
Published inIEEE transactions on communications Vol. 63; no. 12; pp. 4711 - 4726
Main Authors Millar, Andrew P., Weiss, Stephan, Stewart, Robert W.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Channel estimation
Channels
Estimation
Iterative algorithms
Mathematical models
MIMO
MIMO OFDM
MMSE channel estimation
OFDM
Optimization
Orthogonal Frequency Division Multiplexing
Phases
Relay
relay networks
Relays
Training
training sequences design
relay networks
training sequences design
MIMO OFDM
MMSE channel estimation
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Summary:In this paper, we consider minimum-mean-square error (MMSE) training-based channel estimation for two-hop multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) relaying systems. The channel estimation process is divided into two main phases. The relay-destination channel is estimated in the first phase and can be obtained using well-known point-to-point MIMO OFDM estimation methods. In the second phase, the source-relay channel is estimated at the destination with the use of a known training sequence that is transmitted from the source and forwarded to the destination by a nonregenerative relay. To obtain an estimate of the source-relay channel, the source training sequence, relay precoder, and destination processor, require to be optimized. To solve this problem, we first derive an iterative algorithm that involves sequentially solving a number of convex optimization problems to update the source, relay, and destination design variables. Since the iterative algorithm may be too computationally expensive for practical implementation, we then derive simplified solutions that have reduced computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2015.2481897