Low‐complexity joint detection of carrier frequency offset and sector cell index for LTE cellular system

Abstract

SUMMARYIn this paper, we propose a low-complexity joint detection of sector cell index and integer carrier frequency offset for a long-term evolution downlink system. In order to implement a low-cost detection scheme, a primary synchronization signal used for joint parameter estimation is grouped into a number of subsets so that the pilots in each subset are strongly correlated. The estimation performance and the computational load of the proposed scheme are compared with those of the commonly used conventional detection scheme using a differential correlation to remove common channel effects. It is found that the proposed scheme can jointly detect the cell index and integer carrier frequency offset with higher accuracy and much lower complexity, compared with the conventional scheme.

Improved Joint Estimation of Set Information and Frequency Offset for LTE Device-to-Device Communications

“…Toward this, a synchronization source user equipment (UE) periodically transmits two specified synchronization signals, named as primary sidelink synchronization signal (PSSS) and secondary sidelink synchronization signal (SSSS). Most of the existing synchronization schemes are designed for downlink (DL) detection and are not the best solution for sidelink synchronization, but they can be applied to the D2D scenarios [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. The sidelink identity is acquired by combining the set information (SI) transmitted in the PSSS and the sidelink ID transmitted in the SSSS.…”

“…During the first step of the procedure, the D2D device exploits the redundant cyclic prefix (CP) to find the fractional frequency offset (FFO) and initial STO [12][13][14]. In the second step, the PSSS is used to retrieve the SI and detect integer frequency offset (IFO) [20][21][22][23][24][25][26][27]. After the completion of this stage, the SSSS is identified to determine the frame boundary and the sidelink ID [16][17][18].…”

“…There are many works concentrated mainly on the detection of the PSSS in the literature [19][20][21][22][23][24][25][26][27]. Some of these methods perform in the time domain [19,20], while others use the frequency-domain samples obtained from the discrete Fourier transform (DFT) unit [21][22][23][24][25][26][27]. In [19,20], PSSS detection scheme has been presented to be performed by looking for the peak of the cross-correlation in the time domain.…”

“…However, the performance of these approaches is significantly degraded in the case when the IFO is present. To solve this drawback, various differential correlation based PSSS detection schemes have been proposed in the frequency domain [21][22][23], wherein the IFO is estimated along with the PSSS. As for the IFO, it can be estimated either in a joint or decoupled way.…”

“…As for the IFO, it can be estimated either in a joint or decoupled way. The works in [21][22][23][24] estimate the IFO together during the PSSS match process, which is accomplished by evaluating a frequency-shifted version of the received PSSS and requires a large amount of computational complexity because of using correlation banks for a large number of hypotheses. Maximum likelihood (ML) detection has been efficiently adopted to detect the IFO and PSSS in [25,26], which are based upon the reduced-rank approximation of the channel and the PSSS matrix, respectively.…”