Blind channel estimation in GSM receivers: a comparison of HOS and SOCS based approaches

Petermann, T.; Kammeyer, Karl‐Dirk

doi:10.1109/host.1999.778698

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…In contrast, for this channel, the BER reduces markedly when the proposed semi‐BCE method is used. This type of system would be suitable for alleviating the effects of a quasi‐stationary multipath channel,

C false[t, τ false]

, as expected in mobile communications [20]; and the observed performance gains outweigh the increased complexity of using such a semi‐BCE system. (Note that, although these results pertain to PEVD‐based MIMO‐channel equalisation, the same conclusions can be arrived at for PQRD‐based equalisation.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The BCE methods exploit the statistical properties of the received data, where channel estimates are formed over periods of the transmission. These techniques adopt higher‐order statistics (HOS) in determining the SISO channel without prior information on the channel state [20, 21]. In [21], the authors propose a HOS‐based BCE algorithm for pattern recognition in the design of automatic modulation classification techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Channel estimation has been addressed for SISO and MIMO communication channels, using blind and semi‐blind techniques in [18, 20–23], respectively. The BCE methods exploit the statistical properties of the received data, where channel estimates are formed over periods of the transmission.…”

Section: Introductionmentioning

confidence: 99%

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Polynomial matrix decompositions and semi‐blind channel estimation for MIMO frequency‐selective channels

Hassan¹,

Redif²,

Lambotharan

2019

IET signal process.

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The authors propose a semi‐blind channel estimation (semi‐BCE) and precoding/decoding technique for frequency selective (FS) multiple‐input multiple‐output (MIMO) channels. A FS MIMO channel can be represented using a matrix whose elements are polynomials; hence their method is based on polynomial matrix decomposition. Polynomial eigenvalue decomposition (PEVD) and polynomial QR decomposition (PQRD) are the generalisation of eigenvalue decomposition and QR decomposition; they are suitable for decoupling and precoding of FS MIMO channels. As the coding of communication channels requires reliable estimation of the channel, a semi‐BCE scheme, coupled with PQRD/PEVD‐based MIMO‐channel decomposition, is attractive since this reduces training overhead (pilot transmission) considerably, resulting in higher spectral efficiency. The proposed semi‐BCE algorithm is a generalisation of a recently developed single‐input single output BCE method to MIMO systems. A new class of PQRD algorithms is introduced, which is based on the recently‐developed sequential matrix diagonalisation (SMD). The decoders produced by the proposed SMD‐based PQRD algorithm are shown to be more suitable (efficient) for MIMO‐channel equalisation than those generated by the prior art. Computer simulations show that the proposed MIMO‐channel coding strategy compares favourably to state‐of‐the‐art MIMO systems, in terms of bit error rate performance, while reducing the overhead.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Polynomial matrix decompositions and semi‐blind channel estimation for MIMO frequency‐selective channels

Hassan¹,

Redif²,

Lambotharan

2019

IET signal process.

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show abstract

“…In Equation 2 y, h, n represent the matrix form of parameters y(t), h, n(t) from Equation 1, but M represents a toeplitz like matrix of the training sequence given in Equation 4. Equation 3 shows the array which is formed with the received training sequence.…”

Section: Characteristics Of Gsmmentioning

confidence: 99%

Implementation of GSM channel estimation using open-source SDR environment

Bhatta

Mishra

2015

2015 International Conference on Microwave, Optical and Communication Engineering (ICMOCE)

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Channel estimation and equalization is a major area of research and development in communications engineering.However in the open literature there is no elaborate report on how to implement it on real signals from base-stations. In this paper we give the detailed steps to implement channel estimation block on real GSM signals using open-source software defined radio (SDR) environment. This work is expected to help all the researchers trying to implement and test channel equalization algorithms on real signal.

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“…Besides the non-blind LS scheme, we have selected the HOS-based EIGENVECTOR APPROACH TO BLIND IDEN-TIFICATION (EVI) by Boss et al [1] which is a non-linear method maximizing a fourth order cross-cumulant on a second order boundary condition. An investigation of further algorithms based on HOS and SOS can be found in [2,6,7]. A common drawback of all existing blind approaches whether they are based on HOS or SOS is the interference of the estimated channel impulse response by an unknown complex factor.…”

Section: Blind and Non-blind Channel Estimationmentioning

confidence: 99%

Iterative blind and non-blind channel estimation in GSM receivers

Petermann

Kühn²,

Kammeyer

11th IEEE International Symposium on Personal Indoor and Mobile Radio Communications. PIMRC 2000. Proceedings (Cat. No.00TH8525

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Channel estimation plays a leading role in wireless communication systems such as GSM (Global System for Mobile communications). Putting the main emphasis on investigating the application of non-blind and blind channel estimation approaches to the identification of a full I INTRODUCTIONOptimum receivers in digital communication systems require the knowledge of the transmission channel. In general, this knowledge is not available so that the channel's equivalent baseband impulse response has to be estimated. Hence, state-of-the-art mobile communication systems transmit training sequences to assist the receiver in estimating the channel impulse response. However, their repeated transmission leaves the communication system with a considerable overhead which could be used for the transmission of additional data sequences, if the channel estimation was solved blindly. In recent publications [2, 6], we have shown that some blind approaches yield promising results in estimating GSM channels although there is still a significant AE ¼ loss compared to non-blind channel estimation schemes. In order to reduce this loss, we have utilized an iterative channel estimation which employs the bits after channel decoding as a new "training" sequence. Furthermore, the separation of this sequence into different sections enables the use of GSM 1 £ This research is supported by the German NSF (DFG contr. #Ka 841/5-1) 1 By GSM, we understand GSM-900 as well as its derivatives DCS-1800or PCS-1900 for data or speech transmission at very high mobile unit speeds.Since high rate data transmission over mobile radio channels is playing a growing role, we have carried out our simulations for the TCH/F9.6 transmission mode for full rate data at ½¾ kbit/s.The paper is organized as follows: Section II gives an overview of the GSM communication system. Based on the so-called burst structure, we outline in section III the approaches of non-blind and blind channel estimation. In section IV, we describe the concept of iterative channel estimation in a GSM receiver. Finally, simulation results are presented in section V and the paper is concluded in section VI.

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Blind channel estimation in GSM receivers: a comparison of HOS and SOCS based approaches

Cited by 6 publications

References 9 publications

Polynomial matrix decompositions and semi‐blind channel estimation for MIMO frequency‐selective channels

Polynomial matrix decompositions and semi‐blind channel estimation for MIMO frequency‐selective channels

Implementation of GSM channel estimation using open-source SDR environment

Iterative blind and non-blind channel estimation in GSM receivers

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