Blind OFDM Channel Estimation Through Simple Linear Precoding

Petropulu, Athina P.; Zhang, Ruifeng; Lin, Rui

doi:10.1109/twc.2003.821140

Cited by 127 publications

(75 citation statements)

References 24 publications

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“…In general, channel estimation can be either training-based [11][12] or blind [13] [14][15] [16], with both cases exhibiting pros and cons in terms of bandwidth efficiency, convergence speed and estimation accuracy. When it comes to cognitive radio, the majority of channel state estimation techniques proposed in the literature, regardless of being training-based or blind, assume Orthogonal Frequency Division Multiplexing (OFDM)-based systems [17][18] [19].…”

Section: Related Work and Motivationmentioning

confidence: 99%

Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks

Demestichas

Katidiotis

Tsagkaris

et al. 2008

Wireless Pers Commun

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-This paper proposes enhancements to the channel-state estimation phase of a cognitive radio system. Cognitive radio devices have the ability to dynamically select their operating configurations, based on environment aspects, goals, profiles, preferences etc. The proposed method aims at evaluating the various candidate configurations that a cognitive transmitter may operate in, by associating a capability e.g., achievable bit-rate, with each of these configurations. It takes into account calculations of channel capacity provided by channel-state estimation information (CSI) and the sensed environment, and at the same time increases the certainty about the configuration evaluations by considering past experience and knowledge through the use of Bayesian networks. Results from comprehensive scenarios show the impact of our method on the behaviour of cognitive radio systems, whereas potential application and future work are identified.

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Section: Related Work and Motivationmentioning

confidence: 99%

Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks

Demestichas

Katidiotis

Tsagkaris

et al. 2008

Wireless Pers Commun

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“…Blind channel estimation technologies using the specific statistical property of the signal and the channel, and the correlation based channel recovery were introduced in [6,7]. This kind of technology can have a very high impact, but it usually requires long observation time and high complexity.…”

Section: Introductionmentioning

confidence: 99%

Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems

Lee

Kim

2017

Applied Sciences

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Abstract:It is well known that large scale multiple-input multiple-output (LS-MIMO) systems are very attractive technology to increase both spectral efficiency (SE) and energy efficiency (EE). However, one of big obstacles to the realization of the LS-MIMO system is the overhead of reference signals (RSs), since the number of RS increases as the number of transmitter (TX) antennas increases. In this paper, the RS overhead problem is analyzed, and we propose an RS overhead reduction scheme based on beam grouping, which is called beam grouping based resource reuse (BGRR). The proposed scheme divides one cell into several sectors and reuses the RS resources for the different sectors. The resource conflict is reduced using beam separability. According to the analysis and the simulation results, the proposed scheme can remarkably reduce the RS overhead and improve the SE performance significantly.

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“…The CSI can be obtained through blind, semi blind and training based channel estimation methods [5]. The blind channel estimation is carried out by evaluating the statistical information of the channel and certain properties of the transmitted signals [6]. Semi blind channel technique is hybrid of blind and training technique, utilizing pilots and other natural constraints to perform channel estimation.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid of BFO and MCS Algorithm for Channel Estimation of MIMO-OFDM System

R¹

2015

International Journal of Innovative Research in Electrical, Ele

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Abstract:In this paper, challenges regarding the provision of channel state information (CSI) in multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM)) systems are addressed. A novel scheme that utilizes a Hybrid of Bacterial Foraging and Modified Cuckoo Search (HBFOMCS) optimization together to design pilot symbols that minimizes the channel estimate mean squared error (MSE) is proposed. The optimal selection of pilot subcarriers is a combinatorial problem that requires heavy computations. To reduce the computational complexity, the HBFOMCS optimization is utilized to determine the position of pilot subcarriers. Then, for a given pilot placement obtained by the HBFOMCS algorithm, the MSE of least squares (LS) and Minimum Mean Square Error(MMSE) based channel estimation are computed. Simulation results indicate that, the proposed pilot symbol design provides better channel estimate MSE as well as the bit error rate (BER) performance with reduced computational complexity for MMSE method when compared with the LS method and other conventional optimization based pilot design.

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Blind OFDM Channel Estimation Through Simple Linear Precoding

Cited by 127 publications

References 24 publications

Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks

Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks

Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems

A Hybrid of BFO and MCS Algorithm for Channel Estimation of MIMO-OFDM System

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