Pilot Decontamination using Time-shifted Pilot and Data-aided Channel Estimation for Massive MIMO

Nguyen, Dang-An; Nguyen, Anh-Tuan; Han, Huy-Dung; Nguyen, Vinh; Muhammad, Zia

doi:10.1109/cce.2018.8465738

Cited by 3 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After estimating the channel of the selected UE, the BS encodes the data of all the UEs for downlink data transmission where the downlink channel is defined in (28). Note from (28) that channel reciprocity is assumed for the downlink channel.…”

Section: Downlink Achievable Sum-ratementioning

confidence: 99%

“…Here, we evaluate the expectation and variance in ( 33) and (34a)-(34d). Starting from (28) and exploiting the following properties of Gaussian random variables [50]…”

Section: Appendix C Derivation Of the Interference Terms In Evaluatin...mentioning

confidence: 99%

“…In subspace-based approaches, the second-order statistics of the desired user and the interfering UE's channel is exploited [26], [27]. Such methods utilizes covariance-aided channel estimation [28]- [32]. Based on the spatial correlation of the covariance matrices, UEs that are spatially compatible can be identified with the use of nondiagonal covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Location-aided user selection and sum-rate analysis for mmWave NOMA

2023

View full text Add to dashboard Cite

show abstract

Section: Downlink Achievable Sum-ratementioning

confidence: 99%

“…Here, we evaluate the expectation and variance in ( 33) and (34a)-(34d). Starting from (28) and exploiting the following properties of Gaussian random variables [50]…”

Section: Appendix C Derivation Of the Interference Terms In Evaluatin...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Location-aided user selection and sum-rate analysis for mmWave NOMA

2023

View full text Add to dashboard Cite

show abstract

“…Many works in the literature have studied this problem (see [1,5] and references therein). In [6,7], semi-blind algorithms have been proposed to perform pilot decontamination by using the Constant Modulus Algorithm (CMA) [8,9]. Indeed, the CMA is one of the most famous blind equalization algorithms thanks to its simplicity and flexibility.…”

Section: Introductionmentioning

confidence: 99%

Semi-blind channel estimation based on Modified CMA and unitary scrambling for massive MIMO systems

Sellami

Siala²

2021

Preprint

View full text Add to dashboard Cite

Pilot contamination is one of the main impairments in multi-cell massive Multiple-Input Multiple-Output (MIMO) systems. In order to improve the channel estimation in this context, we propose to use a semi-blind channel estimator based on the constant modulus algorithm (CMA). We consider an enhanced version of the CMA namely the Modified CMA (MCMA) which modifies the cost function of the CMA algorithm to the sum of cost functions for real and imaginary parts. Due to pilot contamination, the channel estimator may estimate the channel of a contaminating user instead of that of the user of interest (the user for which the Base Station wants to estimate the channel and then the data). To avoid this, we propose to scramble the users sequences before transmission. We consider different methods to perform unitary scrambling based on rotating the transmitted symbols (one Dimensional (1-D) scrambling) and using unitary matrices (two-Dimensional (2-D) scrambling). At the base station, the received sequence of the user of interest is descrambled leading to a better convergence of the channel estimator. We also consider the case where the Automatic Repeat reQuest (ARQ) protocol is used. In this case, using scrambling leads to a significant gain in terms of BLock Error Rate (BLER) due to the change of the contaminating users data from one transmission to another induced by scrambling.

show abstract

Semi-blind channel estimation based on modified CMA and unitary scrambling for massive MIMO systems

Sellami

Siala

2021

Telecommun Syst

View full text Add to dashboard Cite

Pilot contamination is one of the main impairments in multi-cell massive Multiple-Input Multiple-Output (MIMO) systems. In order to improve the channel estimation in this context, we propose to use a semiblind channel estimator based on the constant modulus algorithm (CMA). We consider an enhanced version of the CMA namely the Modi ed CMA (MCMA) which modi es the cost function of the CMA algorithm to the sum of cost functions for real and imaginary parts. Due to pilot contamination, the channel estimator may estimate the channel of a contaminating user instead of that of the user of interest (the user for which the Base Station wants to estimate the channel and then the data). To avoid this, we propose to scramble the users sequences before transmission. We consider different methods to perform unitary scrambling based on rotating the transmitted symbols (one Dimensional (1-D) scrambling) and using unitary matrices (two-Dimensional (2-D) scrambling). At the base station, the received sequence of the user of interest is descrambled leading to a better convergence of the channel estimator. We also consider the case where the Automatic Repeat reQuest (ARQ) protocol is used. In this case, using scrambling leads to a signi cant gain in terms of BLock Error Rate (BLER) due to the change of the contaminating users data from one transmission to another induced by scrambling.

show abstract

Pilot Decontamination using Time-shifted Pilot and Data-aided Channel Estimation for Massive MIMO

Cited by 3 publications

References 16 publications

Location-aided user selection and sum-rate analysis for mmWave NOMA

Location-aided user selection and sum-rate analysis for mmWave NOMA

Semi-blind channel estimation based on Modified CMA and unitary scrambling for massive MIMO systems

Semi-blind channel estimation based on modified CMA and unitary scrambling for massive MIMO systems

Contact Info

Product

Resources

About