Channel Estimation and Carrier Offset Control for Cooperative MIMO Sensor Networks

Iltis, R.A.; Cagley, R.E.

doi:10.1109/acssc.2005.1599734

Cited by 8 publications

(22 citation statements)

References 6 publications

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“…where is the equivalent compound noise at with covariance (10) In (10), is the covariance of and with defined in (4). Now that the estimation of separate channels and in each hop becomes the estimation of the composite channel and the second hop channel .…”

Section: Joint Timing and Channel Estimation In The Training Periodmentioning

confidence: 99%

“…For higher accuracy, we here derive a ML estimator. From (9), the likelihood function of timing and channel is obtained as (15) where was defined in (10) and is related to and . Therefore, the exact ML estimator is highly complex.…”

Section: B Iterative Maximum-likelihood Estimationmentioning

confidence: 99%

“…It has been analytically and numerically shown in [6]- [8] that timing asynchronism brings considerable performance degradation to such systems. Furthermore, diversity gain [9] and system capacity [10] may also be lost as a result of the intersymbol interference (ISI) caused by imperfect synchronization. Therefore, appropriate mechanisms that deal with the asynchronous reception in distributed systems become especially essential.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Timing Estimation and Resynchronization for Amplify-and-Forward Communication Systems

Xiao

Xing

et al. 2010

IEEE Trans. Signal Process.

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Section: Joint Timing and Channel Estimation In The Training Periodmentioning

confidence: 99%

Section: B Iterative Maximum-likelihood Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Timing Estimation and Resynchronization for Amplify-and-Forward Communication Systems

Xiao

Xing

et al. 2010

IEEE Trans. Signal Process.

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“…Analytical and numerical results on the performance degradation brought by timing errors in a cooperative system have been reported in [11], [12] and [13]. Furthermore, it is also found that if the timing errors are large, the benefit of cooperation would even vanish in terms of diversity gain [14] and system capacity [15]. All these results demonstrate that appropriate countermeasures to the asynchronous reception in distributed MIMO systems are absolutely necessary.…”

Section: Ultiple-input-multiple-outputmentioning

confidence: 70%

Timing Synchronization in Decode-and-Forward Cooperative Communication Systems

Xiao

Serpedin

2009

IEEE Trans. Signal Process.

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Abstract-Cooperative communication systems have attracted much attention recently due to their desirable performance gain while using single antenna terminals. This paper addresses the joint timing and channel estimation problem, and furthermore the resynchronization of multiple timing offsets in a cooperative relay system. The estimations of timing and channel are conducted in two phases and the associated Cramér-Rao bounds (CRB) are derived for both phases. It is demonstrated that the conventional CRB is not valid for timing parameters under fading conditions, and a new bound called Weighted Bayesian CRB is proposed. With the timing and channel estimates, a general framework of the resynchronization filter design is developed in order to compensate the multiple timing offsets at the destination. The proposed methods are applied to different scenarios with varying degrees of timing misalignment and are numerically shown to provide excellent performances that approach the perfectly synchronized case.

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“…We do not cover the literatures from an information theory point of view, for example [16]. We also assume that all the channel parameters, including the fading coefficients, the time and frequency offsets are perfectly obtained at the destination node by some estimation algorithms, such as those proposed in [17][18][19][20][21].…”

mentioning

confidence: 99%

Asynchronous cooperative communication systems: A survey on signal designs

Wang

Xia

2011

Sci. China Inf. Sci.

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Cooperative communications is a promising technique for future high speed wireless communications. These systems may be formulated as virtual multi-input multi-output (MIMO) systems where spatial/cooperetive diversity is a key advantage. However, different from MIMO systems, one of the major challenges for cooperative communications systems is that the cooperative transmissions in cooperative systems may be neither time nor frequency synchronized, since the transmissions are from multiple cooperative nodes at different locations. The existing signal designs for co-located MIMO systems may not be able to collect the cooperative diversity in cooperative communications systems. This paper gives an overview of recent research efforts on combating the time and frequency asynchronism of the cooperative communication network. We focus on the signal designs (or space-time codings/modulations) to achieve full cooperative diversity, and summarize some of the resent distributed space-timing coding and space-frequency coding techniques to combat timing errors and frequency offsets, and in the meantime to achieve full cooperative diversity, in both one-way and two-way cooperative networks.

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Channel Estimation and Carrier Offset Control for Cooperative MIMO Sensor Networks

Cited by 8 publications

References 6 publications

Timing Estimation and Resynchronization for Amplify-and-Forward Communication Systems

Timing Estimation and Resynchronization for Amplify-and-Forward Communication Systems

Timing Synchronization in Decode-and-Forward Cooperative Communication Systems

Asynchronous cooperative communication systems: A survey on signal designs

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