A Novel training based QR-RLS channel estimator for MIMO OFDM systems

Rawal, Divyang; Ok, Park Youn; Vijaykumar, C.

doi:10.1109/wiad.2010.5544945

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…Preamble aided channel estimation is performed in time-domain, estimated channel is then used for data detection during data transmission within that frame. This method is completely represented in [13].…”

Section: Qr-rls Channel Estimatormentioning

confidence: 99%

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Training Based Channel Estimation in MIMO-OFDM Systems

Imani¹,

Bakhshi²

2012

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OFDM combined with the MIMO technique has become a core and attractive technology in future wireless communication systems and can be used to both improve capacity and quality of mobile wireless systems. Accurate and efficient channel estimation plays a key role in MIMO-OFDM communication systems, which is typically realized by using pilot or training sequences by virtue of low complexity and considerable performance. In this paper, we discuss some methods for channel estimation based training symbols in MIMO-OFDM systems. The results confirm the superiority of the represented methods over the existing ones in terms of bandwidth efficiency and estimation error

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Section: Qr-rls Channel Estimatormentioning

confidence: 99%

“…Note that the channel is considered approx. constant for OFDM symbol duration and First Symbol of each subframe is Preamble symbol (for more details about this section, see [13]).…”

Section: R Umentioning

confidence: 99%

Training Based Channel Estimation in MIMO-OFDM Systems

Imani¹,

Bakhshi²

2012

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“…(9), which is described fully in [5]. After estimation let the estimated channel at preamble symbol is designated asHpre q (n) from eq.…”

Section: A Qr-rls Based Coarse Channel Estimatormentioning

confidence: 99%

“…To fulfill this necessity many author suggested adaptive channel estimators [3] [4]. In [5], author shown that QR-RLS based channel estimator gives better performances in terms of stability, complexity and channel tracking for flat fading channel, which fades frame to frame.…”

Section: Introductionmentioning

confidence: 99%

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A Joint QR-LS Based Coarse-Fine Channel Estimation and QR-LRL Detection for Mobile WiMAX 802.16m

Rawal¹,

Park²,

Vijaykumar³

et al. 2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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In this paper, We extended our previous work of QR-RLS based MIMO(Multiple input Multiple output) channel estimation to Mobile Wimax 802.16m system. Mobile wimax system provides high data rate, also fulfills user's requirement like VOD(Video on demand)at very high vehicle speed and also provides better cell coverage area. Channel estimation is crucial part to achieve this goals especially in fast fading environment. Generally, Mobile Wimax systems uses Preamble and Pilots for channel estimation purpose.In the proposed method both preamble and pilots are jointly used for robust channel estimation. At First, QR-RLS Estimator uses Preamble for coarse channel estimation at start of every frame. Once the coarse channel is estimated, then pilots (scattered throughout time-frequency grid) are jointly used with the coarse channel component to derive the channel fading rate. This fading rate is then used to finely estimate the channel at pilot as well as data subcarrier. Thus robust estimation results without adding any overhead. Jointly estimated channel is then used with QR-LRL based data detection, where hard decision values are calculated. Simulation results are shown under various slow-fast channel fading conditions. Results are compared with pilot based channel estimation with LS(least square) interpolation, which shows that joint coarse-Fine estimation gives better performance.

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Estimation of QPSK signal via different smart array antennas deploying gradient approach

Subhashini

Jagarlamudi

Lalitha

2011

IEEE Technology Students' Symposium

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Smart Antenna systems have recently received increasing interest as the demand for better quality and new value added services on the existing wireless communication systems. These system of antennas include different array geometry and different adaptive techniques to enhance the received modulated signal with a fixed DOA by suppressing the inferences. Although enormous study has been done on smart antennas ,special emphasis has not been provided to the antenna array structure. In this paper the performance of smart antenna in terms of the received signal for linear , circular and planararrays is observed. The adaptive array structure for all the geometry's is trained using modified LMS algorithm and RLS algorithm. Comparison is made for different array structures using both the adaptive techniques.

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A Novel training based QR-RLS channel estimator for MIMO OFDM systems

Cited by 3 publications

References 7 publications

Training Based Channel Estimation in MIMO-OFDM Systems

Training Based Channel Estimation in MIMO-OFDM Systems

A Joint QR-LS Based Coarse-Fine Channel Estimation and QR-LRL Detection for Mobile WiMAX 802.16m

Estimation of QPSK signal via different smart array antennas deploying gradient approach

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