Iterative channel estimation using soft decision feedback

Sandell, Magnus; Luschi, C.; Strauch, P.; Yan, Ran

doi:10.1109/glocom.1998.776006

Cited by 66 publications

(29 citation statements)

References 3 publications

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“…For hard-decision feedback, the nonlinearity operates according to [17] if if (8) where is the LLR output of the turbo decoder after iteration corresponding to code symbol . If, on the other hand, softdecision feedback is desired, then [22] (9)…”

Section: A Channel Gain Estimationmentioning

confidence: 99%

Iterative channel estimation and decoding of pilot symbol assisted turbo codes over flat-fading channels

Valenti

Woerner

2001

IEEE J. Select. Areas Commun.

259

166

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Abstract-A method for coherently detecting and decoding turbo-coded binary phase shift keying (BPSK) signals transmitted over frequency-flat fading channels is discussed. Estimates of the complex channel gain and variance of the additive noise are derived first from known pilot symbols and an estimation filter. After each iteration of turbo decoding, the channel estimates are refined using information fed back from the decoder. Both hard-decision and soft-decision feedback are considered and compared with three baseline turbo-coded systems: 1) a BPSK system that has perfect channel estimates; 2) a system that uses differential phase shift keying and hence needs no estimates; and 3) a system that performs channel estimation using pilot symbols but has no feedback path from decoder to estimator. Performance can be further improved by borrowing channel estimates from the previously decoded frame. Simulation results show the influence of pilot symbol spacing, estimation filter size and type, and fade rate. Performance within 0.49 and 1.16 dB of turbo-coded BPSK with perfect coherent detection is observed at a bit-error rate of 10 4 for normalized fade rates of = 0 005 and = 0 02, respectively.

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Section: A Channel Gain Estimationmentioning

confidence: 99%

Iterative channel estimation and decoding of pilot symbol assisted turbo codes over flat-fading channels

Valenti

Woerner

2001

IEEE J. Select. Areas Commun.

259

166

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“…Also note that and are the quadrature components of the Gaussian noise . At the receiver, the CIR is estimated using an iterative CIR estimation technique [10], [11]. Specifically, during the first turbo equalization iteration the CIR is estimated using the least mean square (LMS) algorithm [12] and the training sequence mentioned previously.…”

Section: System Modelmentioning

confidence: 99%

Reduced complexity in-phase/quadrature-phase M-QAM turbo equalization using iterative channel estimation

Yeap

Wong

Hanzo

2003

IEEE Trans. Wireless Commun.

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Abstract-A reduced complexity trellis-based turbo equalizer known as the in-phase (I)/quadrature-phase (Q) turbo equalizer (TEQ-IQ) invoking iterative channel impulse response (CIR) estimation is proposed. The underlying principle of TEQ-IQ is based on equalizing the I and Q component of the transmitted signal independently. This requires the equalization of a reduced set of separate I and Q signal components in comparison to all of the possible I/Q phasor combinations considered by the conventional trellis-based equalizer. It was observed that the TEQ-IQ operating in conjunction with iterative CIR estimation was capable of achieving the same performance as the full-complexity conventional turbo equalizer (TEQ-CT) benefiting from perfect CIR information for both 4-and 16-quadrature amplitude modulation (QAM) transmissions, while attaining a complexity reduction factor of 1.1 and 12.2, respectively. For 64-QAM, the TEQ-CT receiver was too complex to be investigated by simulation. However, by assuming that only two turbo equalization iterations were required, which is the lowest possible number of iterations, the complexity of the TEQ-IQ was estimated to be a factor of 51.5 lower than that of the TEQ-CT. Furthermore, at BER = 10 3 the performance of the TEQ-IQ 64-QAM receiver using iterative CIR estimation was only 1.5 dB away from the associated decoding performance curve of the nondispersive Gaussian channel.

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“…The existing stateof-the-art solutions [22]- [31] combine the decision-directed (DD) CE (DDCE) solutions with powerful iterative detection and decoding schemes in order to form semi-blind joint CE and turbo detection-decoding, where only a modest training overhead is required for generating an initial MMSE CE or least squares CE. The most effective schemes [26]- [31] employ soft-decision aided CEs in the semi-blind joint CE and turbo detection-decoding process, which are more robust against error propagation than the hard-decision aided CE schemes. Consequently, these joint soft-decision based CE and turbo detection-decoding schemes are capable of achieving a better overall system performance than their hard-decision based counterparts.…”

Section: B Review Of Near-capacity Mimo Systemsmentioning

confidence: 99%

Two-Tier Channel Estimation Aided Near-Capacity MIMO Transceivers Relying on Norm-Based Joint Transmit and Receive Antenna Selection

Zhang

Chen

Hanzo

2015

IEEE Trans. Wireless Commun.

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Abstract-We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multipleinput-multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a lowcomplexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains while dispensing with the idealized simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximum-likelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process.Index Terms-Multiple-input-multiple-output (MIMO), nearcapacity system, joint transmit and receive antenna selection, joint channel estimation and three-stage turbo detection-decoding.

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Iterative channel estimation using soft decision feedback

Cited by 66 publications

References 3 publications

Iterative channel estimation and decoding of pilot symbol assisted turbo codes over flat-fading channels

Iterative channel estimation and decoding of pilot symbol assisted turbo codes over flat-fading channels

Reduced complexity in-phase/quadrature-phase M-QAM turbo equalization using iterative channel estimation

Two-Tier Channel Estimation Aided Near-Capacity MIMO Transceivers Relying on Norm-Based Joint Transmit and Receive Antenna Selection

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