Linear adaptive channel equalization for multiuser MIMO-OFDM systems

Atapattu, Lakmali; Arachchige, Gayan Munasinghe; Ziri-Castro, Karla; Suzuki, Hajime; Jayalath, Dhammika

doi:10.1109/atnac.2012.6398080

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…For the results presented in this study, we apply an iterative, time‐domain, minimum mean square error (MMSE) equaliser [24] via the maximum a posteriori algorithm [27] to mitigate the ISI in the i th SISO subchannel, as was used for the PEVD‐based solution in [6]. So the ISI present in the SISO channels is individually equalised to obtain an estimate of the information symbols [28].…”

Section: Proposed Mimo Communications Systemmentioning

confidence: 99%

“…The diagonal structure of

D false(z false)

in (7) converts the MIMO‐channel equalisation problem into a SISO equalisation problem:

y_{i}^{normal′} false[t false] = d_{i} false[t false] * x_{i} false[t false] + η_{i} false[t false],

where the asterisk denotes convolution,

y_{i}^{normal′} false[t false]

x_{i} false[t false]

and

η_{i} false[t false]

are the i th elements of the signal vectors

y^{'} false[t false]

x false[t false]

, and

η false[t false]

, respectively. The ISI present in the SISO channels

y_{i}^{normal′} false[t false]

is then individually equalised to obtain an estimate

{\hat{x}}_{i} false[t false]

of the information symbols [28].…”

Section: Proposed Mimo Communications Systemmentioning

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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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: Proposed Mimo Communications Systemmentioning

confidence: 99%

“…The diagonal structure of

D false(z false)

in (7) converts the MIMO‐channel equalisation problem into a SISO equalisation problem:

y_{i}^{normal′} false[t false] = d_{i} false[t false] * x_{i} false[t false] + η_{i} false[t false],

where the asterisk denotes convolution,

y_{i}^{normal′} false[t false]

x_{i} false[t false]

and

η_{i} false[t false]

are the i th elements of the signal vectors

y^{'} false[t false]

x false[t false]

, and

η false[t false]

, respectively. The ISI present in the SISO channels

y_{i}^{normal′} false[t false]

is then individually equalised to obtain an estimate

{\hat{x}}_{i} false[t false]

of the information symbols [28].…”

Section: Proposed Mimo Communications Systemmentioning

confidence: 99%

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

Hassan¹,

Redif²,

Lambotharan

2019

IET signal process.

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“…Channel equalization strategies for massive MIMO orthogonal frequency division multiplexing (OFDM) systems have been extensively researched in [5][6][7][8][9][10][11], with linear and nonlinear methods being distinguished. The former contains equalizers with zero forcing (ZF) and minimum mean square error (MMSE) ,while the latter includes the maximum likelihood (ML) and lattice reduction-aided (LRA) equalizers.…”

Section: Introductionmentioning

confidence: 99%

Classification Weighted Deep Neural Network Based Channel Equalization for Massive MIMO-OFDM Systems

Ge¹,

Qi²,

Guo³

et al. 2022

RADIOENGINEERING

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Massive multi-input multi-output (MIMO) has attracted significant interest in academia and industry, which can efficiently increase the transmission rate. However, the error rate of conventional channel equalizations in massive MIMO systems may be high owing to the dynamic channel states in practical conditions. To solve this problem, in this paper, we propose an improved channel equalization framework based on the deep neural network (DNN). Based on the analyzed relationship between the input and output of the DNN, the data can be recovered without the channel state information. Furthermore, aiming at reducing the convergence time and enhancing the learning ability of the DNN, a classification weighted algorithm is proposed to optimize the cost function of the DNN, which is named as classification weighted deep neural network (CW-DNN). Simulation results demonstrate that compared to conventional counterparts, the proposed CW-DNN based equalizer can achieve a better normalized mean square error (NMSE). Upon approximating the optimal neural network parameters with the significantly improved convergence speed and reduced training time of the network, under the condition of the fixed learning rate.

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“…The linear equalizers do not employ a feedback path to adapt the equalizer and therefore, provide simpler implementations whereas in nonlinear equalizer the output is fed back to input in order to change the subsequent outputs of the equalizer and are widely employed in wireless applications where the channel distortions are too critical for a linear equalizer to manage [8].…”

Section: Significance Of Equalizationmentioning

confidence: 99%

“…This estimator minimizes the mean square error (MSE), which is a common measure of estimator quality [8][7] [9].The MMSE tries to find a coefficient W which minimizes (5) A matrix which satisfies MMSE detector for meeting this constraint is given by…”

Section: B Minimum Mean Square Error (Mmse)mentioning

confidence: 99%

A Comprehensive Study of Channel Equalization Techniques in MIMO-OFDM Systems

Dash¹,

Potluri²

2016

International Journal of Advanced Research in Computer and Comm

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Today's communication scenario demands high data rates and large system capacity where MIMO-OFDM proves to be an ultimate combination .The never ending thirst for such high performance wireless communication results in signal vulnerability to channel impairments. One of the primary causes resulting in signal degradation due to the multipath propagation is channel ISI (Inter symbol interference). The process of channel estimation and equalization together accomplishes the job of combating the effect of ISI. Initially CIR(Channel Impulse Response) is estimated based on a known sequence of bits(Pilot sequence) followed by equalization process which takes care of altering the channel response based on the estimated behavior thereby extracting the signal of interest. It is also possible to implement non pilot aided approaches like blind channel equalizer algorithms without possessing knowledge of the channel. This paper does a comprehensive survey of different types of equalizers thereby providing an in depth understanding of equalization.

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Linear adaptive channel equalization for multiuser MIMO-OFDM systems

Cited by 15 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

Classification Weighted Deep Neural Network Based Channel Equalization for Massive MIMO-OFDM Systems

A Comprehensive Study of Channel Equalization Techniques in MIMO-OFDM Systems

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