Primary Synchronization Signal Detection Method for Device-to-Device in LTE-Rel 12 and Beyond

Bhamri, Ankit; Li, Zexian; Lindh, Lars; Ribeiro, C.B.

doi:10.1109/vtcfall.2015.7391094

“…This section is dedicated to the description of the NSSS model [2]. It is assumed that the time-frequency synchronization has been properly performed thanks to NPSS [6][7][8][9], in such way that the residual synchronization errors are negligible. Furthermore, since the signal is narrowband, it can be reasonably supposed that the frequency response of the channel is constant over the NSSS resource block of 12 subcarriers.…”

Section: Nsss Modelmentioning

confidence: 99%

“…The total number of complex multiplications involved in the suggested method is reduced to 8 × 65 + 132 × 128 = 17416, where 8 × 65 corresponds to (15), and 132 × 128 corresponds to (16) or (17). Thus, it is of order 15 times less complex than the exhaustive cell ID search through (8) or (9). Moreover, we can still reduce the complexity by using the symmetry of En(u) allowing us to factorize elements in the sum in (16) and (17).…”

Section: Description Of the Algorithmmentioning

confidence: 99%

“…The attachment procedure between a UE and an evolved Node B (eNB) starts with the synchronization process. In NB-IoT, it is split into two successive steps: the UE physically synchronizes with the eNB in time and frequency thanks to the narrowband primary synchronization signal (NPSS) [6][7][8][9], and then estimates the eNB's cell ID carried by the narrowband secondary synchronization signal (NSSS) [10][11][12]. Only few paper deal with the cell ID estimation in NB-IoT.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low-Complexity Sub-Optimal Cell ID Estimation in NB-IoT System

Savaux¹,

Kanj²

2020

Preprint

0

View full text Add to dashboard Cite

This paper deals with cell ID estimation in narrowband-internet of things (NB-IoT) system. The cell ID value is carried by the narrowband secondary synchronization signal (NSSS). We suggest a low-complexity sub-optimal estimator, based on the auto- correlation of the received observations. It is up to thirty times less complex than the optimal maximum likelihood (ML) estimator based on cross-correlation. In addition, we present three methods allowing the receiver to take advantage of the different repetitions of the NSSS. They are based on a hard decision after every estimation, a soft combination of the different observations of the NSSS, and an hybrid mix between the two firsts, respectively. The advantages and drawbacks of the presented techniques are stated, and a performance analysis is proposed, which is further discussed through simulations results. It is shown the that different methods reach the performance of ML after several repetitions for a lower overall complexity.

show abstract

“…Fig. 2, we compare the suggested method with the exhaustive ML cell ID estimation for N R = 1, where the channel is supposed to be known as in (8) and (16), and unknown as in (9) and (17 largely outperforms the suggested method. However, it must be reminded that the ML is 30 times computationally more complex than the sub-optimal technique.…”

Section: Hybrid Hard Decision-ml Combinationmentioning

confidence: 99%

Low-Complexity Sub-Optimal Cell ID Estimation in NB-IoT System

Savaux¹,

Kanj²

2020

Preprint

0

View full text Add to dashboard Cite

This paper deals with cell ID estimation in narrowband-internet of things (NB-IoT) system. The cell ID value is carried by the narrowband secondary synchronization signal (NSSS). We suggest a low-complexity sub-optimal estimator, based on the auto- correlation of the received observations. It is up to thirty times less complex than the optimal maximum likelihood (ML) estimator based on cross-correlation. In addition, we present three methods allowing the receiver to take advantage of the different repetitions of the NSSS. They are based on a hard decision after every estimation, a soft combination of the different observations of the NSSS, and an hybrid mix between the two firsts, respectively. The advantages and drawbacks of the presented techniques are stated, and a performance analysis is proposed, which is further discussed through simulations results. It is shown the that different methods reach the performance of ML after several repetitions for a lower overall complexity.

show abstract

“…Moreover, every device must be synchronized with an evolved node B (eNB) to connect the network. The synchronization process is carried out according to two main steps: the devices physically synchronize with the eNBs using the narrowband primary synchronization signal (NPSS) [8][9][10][11] and then seek the cell identifier (ID) of the neighboring eNBs through the narrowband secondary synchronization signal (NSSS). In this paper, we focus on the cell ID estimation process.…”

Section: Introductionmentioning

confidence: 99%

DFT-based low-complexity optimal cell ID estimation in NB-IoT

Savaux

¹

2020

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

This paper deals with cell identifier (ID) estimation for narrowband-Internet of things (NB-IoT) system. It is suggested to transform the usual maximum likelihood (ML) estimator expression in order to highlight a discrete Fourier transform (DFT), which can be computed with fast algorithms. Therefore, the proposed method is a DFT-based low-complexity cell ID estimator that can be qualified as optimal in the ML sense. The principle is extended to the practical case where the channel is unknown and must be estimated. In this scenario, the concentrated likelihood function needs to be maximized, in which the ML channel estimate is a function of the unknown cell ID parameter. This operation only involves a few additional multiplications. Simulation results reveal that the performance of the proposed method actually matches the optimal one of the ML cell ID estimator. Furthermore, the technique is robust to residual frequency offset up to several hundreds of Hertz. We also show that the mean square error of channel estimation reaches its Cramér-Rao bound (CRB).

show abstract

Primary Synchronization Signal Detection Method for Device-to-Device in LTE-Rel 12 and Beyond

Cited by 8 publications

References 6 publications

Low-Complexity Sub-Optimal Cell ID Estimation in NB-IoT System

Low-Complexity Sub-Optimal Cell ID Estimation in NB-IoT System

Low-Complexity Sub-Optimal Cell ID Estimation in NB-IoT System

DFT-based low-complexity optimal cell ID estimation in NB-IoT

Contact Info

Product

Resources

About