On Superimposed Training for MIMO Channel Estimation and Symbol Detection

He, Shuangchi; Tugnait, J.K.; Meng, Xiaohong

doi:10.1109/tsp.2007.893941

Cited by 70 publications

(73 citation statements)

References 26 publications

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“…In this section, an SiT based channel estimation technique of [26] is extended for the estimation of massive-MIMO sparse uplink channels. For n t -th transmitter, the training sequence c n t (k) is periodic with period P =PN t , whereP is a positive integer.…”

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

confidence: 99%

“…The training sequence c n t (k) can be designed by first choosing a periodic base sequencē c o (k) with a period ofP [26] such that,…”

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

confidence: 99%

“…The mean square consistent estimated n r n t = [d n r n t ,0 ,d n r n t ,1 , · · · ,d n r n t ,(P−1) ] T of d n r n t is computed as in [26] and is given byd…”

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

confidence: 99%

“…The estimate of the noise variance σ 2 n r is obtained as in [26]. The L e × (L e + L − 1) convolutional matrixĤ is given bŷ …”

Section: Minimum Mean Square Error (Mmse) Based Equalizermentioning

confidence: 99%

“…Section 3 presents first-order statistics of the received signals along with the proposed channel estimation techniques. In Section 3.1, an SiT least squares (SiT-LS) based channel estimation technique available in literature [26] is presented. Section 3.2 presents the proposed SiT-StOMP and SiT-GP channel estimation techniques for the case of sparse uplink channels in massive-MIMO.…”

Section: Introductionmentioning

confidence: 99%

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

confidence: 99%

“…The training sequence c n t (k) can be designed by first choosing a periodic base sequencē c o (k) with a period ofP [26] such that,…”

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

confidence: 99%

“…The mean square consistent estimated n r n t = [d n r n t ,0 ,d n r n t ,1 , · · · ,d n r n t ,(P−1) ] T of d n r n t is computed as in [26] and is given byd…”

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

confidence: 99%

“…The estimate of the noise variance σ 2 n r is obtained as in [26]. The L e × (L e + L − 1) convolutional matrixĤ is given bŷ …”

Section: Minimum Mean Square Error (Mmse) Based Equalizermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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