On channel estimation using superimposed training and first-order statistics

Tugnait, J.K.; Luo, Weilin

doi:10.1109/icassp.2003.1202720

Cited by 106 publications

(88 citation statements)

References 9 publications

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“…Consequently, since (22) holds ∀y a i , the points that maximize the summation of M exponential on the LHS of (22) are the same as the points that maximize the summation of M exponentials on the RHS of (22). Hence, for a bijective function g :…”

Section: Appendixmentioning

confidence: 99%

“…A bandwidth efficient channel estimation technique in HD systems is superimposed training, where no explicit time slots are allocated for channel estimation. Instead, a periodic low power training sequence is superimposed with the data symbols at the transmitter before modulation and transmission [21,22]. The downside of this approach is that some power is consumed in superimposed training which could have otherwise been allocated to the data transmission.…”

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confidence: 99%

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Superimposed signaling inspired channel estimation in full-duplex systems

Koohian

Mehrpouyan

Nasir

et al. 2018

EURASIP J. Adv. Signal Process.

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Residual self-interference (SI) cancellation in the digital baseband is an important problem in full-duplex (FD) communication systems. In this paper, we propose a new technique for estimating the SI and communication channels in a FD communication system, which is inspired from superimposed signaling. In our proposed technique, we add a constant real number to each constellation point of a conventional modulation constellation to yield asymmetric shifted modulation constellations with respect to the origin. We show mathematically that such constellations can be used for bandwidth efficient channel estimation without ambiguity. We propose an expectation maximization (EM) estimator for use with the asymmetric shifted modulation constellations. We derive a closed-form lower bound for the mean square error (MSE) of the channel estimation error, which allows us to find the minimum shift energy needed for accurate channel estimation in a given FD communication system. The simulation results show that the proposed technique outperforms the data-aided channel estimation method, under the condition that the pilots use the same extra energy as the shift, both in terms of MSE of channel estimation error and bit error rate. The proposed technique is also robust to an increasing power of the SI signal.

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Section: Appendixmentioning

confidence: 99%

mentioning

confidence: 99%

Superimposed signaling inspired channel estimation in full-duplex systems

Koohian

Mehrpouyan

Nasir

et al. 2018

EURASIP J. Adv. Signal Process.

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“…Farhang-Bouroujeny in 1995 [7] applied super-imposed pilots for digital communication systems. There is a considerable body of work on super-imposed pilots for communication systems [8]- [11]. Most of the proposed algorithms are derived based on an underlying channel model and are hence limited in the range of channel conditions over which they can be used.…”

Section: Introductionmentioning

confidence: 99%

Bandwidth Efficient Channel Estimation Using Super-Imposed Pilots in OFDM Systems

Josiam

Rajan

2007

IEEE Trans. Wireless Commun.

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Abstract-This paper explores channel estimation using superimposed pilots for OFDM systems. The pilots are added linearly to the modulated symbol at a fraction of the total transmit power, making the method spectrally efficient. We present a series of receivers in which complexity is traded for performance; in particular, a receiver that achieves the theoretically lowest BER but has high complexity and a low complexity receiver that shows near optimal performance. We show by simulations that the performance of the receiver is independent of the channel's Doppler frequency and delay spread. We also analytically compute a bound for the bit error rate performance of the proposed receiver.

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“…For the uplink scenario of a massive-MIMO communication system, if each mobile user is assigned with a specific pilot sequence that is superimposed on the information sequence, a first order statistics of the received signal can be used to estimate the CIR as outlined in [50]. In this section, an SiT based channel estimation technique of [26] is extended for the estimation of massive-MIMO sparse uplink channels.…”

Section: Massive-mimo Sparse Uplink Channel Estimation Using a First-mentioning

confidence: 99%

“…. The channel estimator (CE) block, shown in Figure 1, is implemented by using the SiT and first-order statistics based least squares (SiT-LS) technique presented in [50] and the proposed SiT-StOMP and SiT-GP techniques. Once the CIR is estimated, the effect of superimposed training sequence is removed from the information sequence at the receiver side by the training effect remover (TER) block, as shown in Figure 1.…”

Section: Massive-mimo System Model For Uplink Communicationsmentioning

confidence: 99%

Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing

Mansoor¹,

Nawaz²,

Gulfam³

2017

Applied Sciences

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Massive multiple-input multiple-output (massive-MIMO) is foreseen as a potential technology for future 5G cellular communication networks due to its substantial benefits in terms of increased spectral and energy efficiency. These advantages of massive-MIMO are a consequence of equipping the base station (BS) with quite a large number of antenna elements, thus resulting in an aggressive spatial multiplexing. In order to effectively reap the benefits of massive-MIMO, an adequate estimate of the channel impulse response (CIR) between each transmit-receive link is of utmost importance. It has been established in the literature that certain specific multipath propagation environments lead to a sparse structured CIR in spatial and/or delay domains. In this paper, implicit training and compressed sensing based CIR estimation techniques are proposed for the case of massive-MIMO sparse uplink channels. In the proposed superimposed training (SiT) based techniques, a periodic and low power training sequence is superimposed (arithmetically added) over the information sequence, thus avoiding any dedicated time/frequency slots for the training sequence. For the estimation of such massive-MIMO sparse uplink channels, two greedy pursuits based compressed sensing approaches are proposed, viz: SiT based stage-wise orthogonal matching pursuit (SiT-StOMP) and gradient pursuit (SiT-GP). In order to demonstrate the validity of proposed techniques, a performance comparison in terms of normalized mean square error (NCMSE) and bit error rate (BER) is performed with a notable SiT based least squares (SiT-LS) channel estimation technique. The effect of channels' sparsity, training-to-information power ratio (TIR) and signal-to-noise ratio (SNR) on BER and NCMSE performance of proposed schemes is thoroughly studied. For a simulation scenario of: 4 × 64 massive-MIMO with a channel sparsity level of 80% and signal-to-noise ratio (SNR) of 10 dB, a performance gain of 18 dB and 13 dB in terms of NCMSE over SiT-LS is observed for the proposed SiT-StOMP and SiT-GP techniques, respectively. Moreover, a performance gain of about 3 dB and 2.5 dB in SNR is achieved by the proposed SiT-StOMP and SiT-GP, respectively, for a BER of 10 −2 , as compared to SiT-LS. This performance gain NCME and BER is observed to further increase with an increase in channels' sparsity.

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On channel estimation using superimposed training and first-order statistics

Cited by 106 publications

References 9 publications

Superimposed signaling inspired channel estimation in full-duplex systems

Superimposed signaling inspired channel estimation in full-duplex systems

Bandwidth Efficient Channel Estimation Using Super-Imposed Pilots in OFDM Systems

Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing

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