Channel Estimation for Two-Way Relay OFDM Networks

Yang, Weiwei; Cai, Yueming; Hu, Junquan; Yang, Wendong

doi:10.1155/2010/186182

Cited by 12 publications

(21 citation statements)

References 15 publications

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“…[59], [60], and [61] proposed several schemes for channel estimation when OFDM is used. The first two references considered both the estimation of the composite source-relay-source channels (i.e., end-to-end channels from node 1 to node 2 and vice versa) as well as individual channels between sources and relay; the third considered only the composite source-relay-source channels.…”

Section: Channel Estimationmentioning

confidence: 99%

Physical-layer network coding: Tutorial, survey, and beyond

Liew

Zhang

2013

Physical Communication

298

261

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The concept of physical-layer network coding (PNC) was proposed in 2006 for application in wireless networks. Since then it has developed into a subfield of network coding with wide followings. The basic idea of PNC is to exploit the network coding operation that occurs naturally when electromagnetic (EM) waves are superimposed on one another. This simple idea turns out to have profound and fundamental ramifications. Subsequent works by various researchers have led to many new results in the domains of 1) wireless communication; 2) wireless information theory; and 3) wireless networking. The purpose of this paper is fourfold. First, we give a brief tutorial on the basic concept of PNC. Second, we survey and discuss recent key results in the three aforementioned areas. Third, we examine a critical issue in PNC: synchronization. It has been a common belief that PNC requires tight synchronization. Our recent results suggest, however, that PNC may actually benefit from asynchrony. Fourth, we propose that PNC is not just for wireless networks; it can also be useful in optical networks. We provide an example showing that the throughput of a passive optical network (PON) could potentially be raised by 100% with PNC.

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Section: Channel Estimationmentioning

confidence: 99%

Physical-layer network coding: Tutorial, survey, and beyond

Liew

Zhang

2013

Physical Communication

298

261

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“…Specifically, in [4] and [5], the cascaded source-relay-source channels were estimated using block-based training under the assumption of timeinvariant frequency-selective fading channels. Different from [4] and [5], where the relay only amplifies and forwards the received signal, [6] allowed the relay to first estimate the channel parameters and then allocated the powers for these parameters. The channel estimation problem was extended to the TWRN with multiple antennas at all the three nodes in [7].…”

Section: Introductionmentioning

confidence: 99%

Superimposed training and channel estimation for two-way relay networks

Ren

et al. 2014

16th International Conference on Advanced Communication Technology

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Abstract-In this paper, the superimposed training strategy is introduced into the OFDM modulated amplify-and-forward (AF) two-way relay network (TWRN) to simplify the channel estimation at the destination, and the closed-form Bayesian Cramér-Rao lower bound (CRLB) is derived for the estimation of block-fading frequency-selective channels, which is used to guide the optimal training design. Through the superposition of an additional training vector at the relay under certain power allocation scheme, the separated channel information can be obtained directly at the destination. The Bayesian CRLB is derived for the random channel parameters, and orthogonal training vectors from the two source nodes are required to keep the Bayesian CRLB practical, due to the self-interference in the TWRN. A set of training vectors obtained from the minimization of the Bayesian CRLB are applied in a specific suboptimal channel estimation algorithm, and the mean-square error (MSE) performance is provided to verify the Bayesian CRLB results. Keywords-Two-way relay, channel estimation, BayesianCramér-Rao lower bound(CRLB), training design, mean-square error. I. INTRODUCTIONWith the combination of cooperative communication, twoway relay network (TWRN) emerged a few years ago, and has attracted a great deal of interest recently [1], [2], due to its improved spectral efficiency over one-way relay network (OWRN). In a TWRN, a major difficulty lies in how to effectively recover the data transmitted over an unknown fading channel from the other source terminal. Channel estimation in TWRN has been studied in [3]-[8]. Specifically, in [4] and [5], the cascaded source-relay-source channels were estimated using block-based training under the assumption of timeinvariant frequency-selective fading channels. Different from [4] and [5], where the relay only amplifies and forwards the received signal, [6] allowed the relay to first estimate the channel parameters and then allocated the powers for these parameters. The channel estimation problem was extended to the TWRN with multiple antennas at all the three nodes in [7]. A blind channel estimation algorithm based on the second order statistics of the received signal was proposed in [8] for AF TWRN. Inspired by the superimposed training in point-topoint communications, [9] and [10] designed a superimposed training strategy in AF OWRN.In this work, we introduce the superimposed training strategy into OFDM modulated AF TWRN to simplify the channel estimation at the destination, and derive the closedform Bayesian Cramér-Rao lower bound (CRLB) for the esti-

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“…Recently, the problem of channel estimation has been addressed in the ANC-based TWR networks when the channel reciprocity holds between the MAC and BC phases [9][10][11][12][13]. These studies deal with the estimation of the composite channels of the MAC and BC phases at the communicating nodes.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the maximum likelihood and linear maximum signal-to-noise ratio (SNR) estimators have been developed in flat fading channels in [9]. For orthogonal frequency division multiplexing (OFDM) systems in frequency-selective fading channels, the least square (LS) estimator and optimal training sequences to minimize the mean square error (MSE) have been derived in [10,11], which have subsequently been extended for the joint estimation of carrier frequency offset and channel responses in [13]. The LS estimators, derived for block-based (or equivalently, preamble-based) signals in the time domain [10] and in the frequency domain [11], are shown to require the same optimal training condition and to provide the same MSE.…”

Section: Introductionmentioning

confidence: 99%

“…For orthogonal frequency division multiplexing (OFDM) systems in frequency-selective fading channels, the least square (LS) estimator and optimal training sequences to minimize the mean square error (MSE) have been derived in [10,11], which have subsequently been extended for the joint estimation of carrier frequency offset and channel responses in [13]. The LS estimators, derived for block-based (or equivalently, preamble-based) signals in the time domain [10] and in the frequency domain [11], are shown to require the same optimal training condition and to provide the same MSE. Some training sequences satisfying the optimal training condition with low peak-to-average power ratio (PAPR), preferred for the low-cost and low-power design of OFDM systems http://jwcn.eurasipjournals.com/content/2013/1/140 [14], have also been suggested in [11].…”

Section: Introductionmentioning

confidence: 99%

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Channel estimation and optimal training with the LMMSE criterion for OFDM-based two-way relay networks

Tran

Wang

Song

et al. 2013

J Wireless Com Network

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In this paper, we consider the linear minimum mean square error (LMMSE) estimation of channel for the two-way relay network employing analog network coding and orthogonal frequency division multiplexing. The channel responses required for self-interference cancelation and coherent detection are estimated in the time domain with the preamble. We derive the optimal training condition for minimizing the mean square error (MSE) of the LMMSE estimator and obtain the corresponding MSE. It is observed that the LMMSE estimator has the optimal training condition equivalent to that of the least square estimator and that the former is less sensitive to the training sequences than the latter. We also propose new training sequences not only satisfying the optimal training condition but also providing the minimum peak-to-average power ratio.

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Channel Estimation for Two-Way Relay OFDM Networks

Cited by 12 publications

References 15 publications

Physical-layer network coding: Tutorial, survey, and beyond

Physical-layer network coding: Tutorial, survey, and beyond

Superimposed training and channel estimation for two-way relay networks

Channel estimation and optimal training with the LMMSE criterion for OFDM-based two-way relay networks

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