Performance analysis of relay channel estimation

Lioliou, Panagiota; Viberg, Mats; Coldrey, Mikael

doi:10.1109/acssc.2009.5470126

Cited by 19 publications

(13 citation statements)

References 6 publications

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“…5 establishes the importance of considering the effect of imperfect channel estimation on the performance of AF MIMO cooperative networks. [9], K=1, P=3 (Perf. CSI) AF [9], K=2, P=2 (Perf.…”

Section: B Weighted Least Squaresmentioning

confidence: 99%

“…However, the estimators in [8] and [9] are only applicable to the case of single antenna relays and require a total of K + 1 time slots, where K denotes the number of relays in the network, to estimate the network's channels. Moreover, the full-duplex assumption in [8], [9] may not be realistic from a practical point of view.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the effect of the estimator in [7] on the performance of MIMO cooperative networks is not investigated. Finally, in [8] and [9] the authors devise new channel estimators for full-duplex multirelay MIMO cooperative networks, i.e., where all nodes within the network can transmit and receive at the same time. However, the estimators in [8] and [9] are only applicable to the case of single antenna relays and require a total of K + 1 time slots, where K denotes the number of relays in the network, to estimate the network's channels.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, in [8] and [9] the authors devise new channel estimators for full-duplex multirelay MIMO cooperative networks, i.e., where all nodes within the network can transmit and receive at the same time. However, the estimators in [8] and [9] are only applicable to the case of single antenna relays and require a total of K + 1 time slots, where K denotes the number of relays in the network, to estimate the network's channels. Moreover, the full-duplex assumption in [8], [9] may not be realistic from a practical point of view.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous estimation of multi-relay MIMO channels

Mehrpouyan

Blostein

Ottersten

2013

2013 IEEE Global Communications Conference (GLOBECOM)

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Abstract-This paper addresses training-based channel estimation in distributed amplify-and-forward (AF) multi-input multi-output (MIMO) multi-relay networks. To reduce channel estimation overhead and delay, a training algorithm that allows for simultaneous estimation of the entire MIMO cooperative network's channel parameters at the destination node is proposed. The exact Cramér-Rao lower bound (CRLB) for the problem is presented in closedform. Channel estimators that are capable of estimating the overall source-relay-destination channel parameters at the destination are also derived. Numerical results show that while reducing delay, the proposed channel estimators are close to the derived CRLB over a wide range of signal-to-noise ratio values and outperform existing channel estimation methods. Finally, extensive simulations demonstrate that the proposed training method and channel estimators can be effectively deployed in combination with cooperative optimization algorithms to significantly enhance the performance of AF relaying MIMO systems in terms of average-bit-error-rate.

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Section: B Weighted Least Squaresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simultaneous estimation of multi-relay MIMO channels

Mehrpouyan

Blostein

Ottersten

2013

2013 IEEE Global Communications Conference (GLOBECOM)

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“…The vectors h 1,i and h 2,i refer to the ith row of H 1 and the ith column of H 2 , respectively. As shown in [11], in order to obtain channel knowledge of the individual links from source to relays, H 1 , and from relays to destination, H 2 , the total channel training interval should be divided into R frames. For each such frame, we create different compound channels by varying the amplifying factors at the relays.…”

Section: Extension To Multiple Relaysmentioning

confidence: 99%

Bayesian Channel Estimation Techniques for AF MIMO Relaying Systems

Lioliou

Viberg

Matthaiou

2011

2011 IEEE Vehicular Technology Conference (VTC Fall)

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Abstract-In this paper, we consider the fundamental problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over random channels. Using the Bayesian framework, linear minimum mean square error (LMMSE) and expectation-maximization (EM) based maximum a posteriori (MAP) channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. The performance of the proposed algorithms is evaluated in terms of the mean square error (MSE) as a function of the signal-tonoise ratio (SNR) during the training interval. Our simulation results show that the incorporation of prior knowledge into the channel estimation algorithm offers improved performance, especially in the low SNR regime. I. INTRODUCTIONRecently, the introduction of relaying nodes in wireless channels has triggered a significant research interest [1]. The deployment of relays has been identified as a suitable technique for providing broader coverage, higher transmission rates, and increased reliability. Moreover, when the source and destination are equipped with multiple antennas, the concept of relaying is combined with that of multiple-input multipleoutput (MIMO) systems. Then, the system's performance can be further enhanced by exploiting the spatial dimension [2].For different relaying strategies, the problem of power allocation and network beamforming has been well studied in the existing literature [3]. Several designs for the optimal relay amplifying factor have been proposed for maximizing the received signal-to-noise ratio (SNR) or some other performance measure, such as the channel capacity [3], [4]. Most existing relaying schemes, however, assume that perfect instantaneous channel state information (CSI) is available at the receiver. Therefore, it becomes clear that to exploit the advantages of MIMO relaying networks, an accurate CSI of all involved links is required. Despite the importance of this prerequisite, channel estimation is often ignored by either assuming perfect CSI or by considering only the estimation of the compound (from source to destination) channel [5]. While CSI of the compound channel guarantees feasible data detection at the destination, the knowledge of the individual channel responses can be utilized to further improve the overall system performance.In this paper, we study stochastic channel estimation techniques for MIMO relaying systems with random channels. In particular, we develop linear minimum mean square error (LMMSE) and expectation-maximization (EM) based maximum a posteriori (MAP) channel estimation schemes for

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