Journal of Computer Networks and Communications

Volume 2018, Article ID 9319204, 8 pages

https://doi.org/10.1155/2018/9319204

## Pilot-Based Time Domain SNR Estimation for Broadcasting OFDM Systems

^{1}Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia^{2}Graduate School of Science and Engineering, PAF-Karachi Institute of Economics and Technology, Karachi, Pakistan^{3}Department of Electrical Engineering, COMSATS Institute of Information Technology, Wah Cantt, Pakistan

Correspondence should be addressed to Abid Muhammad Khan; moc.oohay@77kmdiba

Received 18 December 2017; Accepted 18 March 2018; Published 2 May 2018

Academic Editor: Ting Wang

Copyright © 2018 Abid Muhammad Khan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The estimation of signal-to-noise ratio (SNR) is a major issue in wireless orthogonal frequency-division multiplexing (OFDM) system. In OFDM system, each frame starts with a preamble symbol that facilitates the SNR estimation. However, the performance of preamble-based SNR estimation schemes worsens in the fast-changing environment where channel changes symbol to symbol. Accordingly, in this paper, we propose a novel pilot-based SNR estimation scheme that optimally exploits the pilot subcarriers that are inserted in each data symbol of the OFDM frame. The proposed scheme computes the circular correlation between the received signal and the comb-type pilot sequence to obtain the SNR. The simulation results are compared with the conventional preamble-based Zadoff-Chu sequence SNR estimator. The results indicate that the proposed scheme generates near-ideal accuracy; especially in low SNR regimes, in terms of the normalized mean square error (NMSE). Moreover, this scheme offers a significant saving of computation over a conventional time domain SNR estimator.

#### 1. Introduction

Noise variance and signal-to-noise ratio are the two important measures of channel quality in a wireless OFDM system, and their estimation helps in adaptive power control and adaptive modulation, thus optimizing the performance of the wireless communication system. Similarly, turbo coding, as well as hands-off algorithms, depends on the variation of the SNR in the time-varying channel. Therefore, the performance and capacity of OFDM systems are directly influenced by the precision and complexity of noise and SNR estimation.

In general, SNR estimation algorithms can be divided into two classes. In the first one, data-aided (DA) class, a known training sequence or pilot is transmitted to estimate the SNR at the receiver [1], and in the second, nondata-aided (NDA) class, the SNR is estimated blindly (without knowing anything a priori about the transmitted information). Both classes have their advantages and disadvantages with respect to their estimation accuracy and computational complexity.

In DA estimators, a considerable amount of literature has been published on the utilization of preamble-based estimation for equalization, carrier offset synchronization, and channel estimation [2–4]. Similarly, there is also abundant literature on SNR estimation based on the preamble [5]. For a packed OFDM system, preamble-based strategies are useful owing to the slow nature of the time-varying channel. In broadcasting OFDM systems, one uses the pilot-based strategies where channel variations are tracked symbol-by-symbol. However, for noise power estimation, the existing OFDM system utilizes the improved preamble-based noise estimation schemes rather than pilot-based schemes for frequency-selective fading channels [6, 7].

In OFDM receivers, two distinct domains are used for SNR estimation, namely post-FFT (frequency domain) and pre-FFT (time domain). Literature reveals that several studies have been conducted on the use of pilot-based channel estimation in the frequency domain. These studies show the performance of the channel estimation is directly affected by the placement of the pilot tones in each OFDM symbol. Therefore, previous studies [8–13] utilized the optimized pilot placement by using the artificial techniques, such as Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, Grey Wolf Optimizer, and Differential Evolution.

On the other hand, the estimation in the time domain is not affected by the loss of orthogonality that can occur due to carrier offset [14]. Moreover, the number of channel taps required for estimation in the time domain is significantly fewer than the number of FFT points where the channel frequency response needs to be estimated [15]. These features provide a robust basis for focusing on the time domain estimation schemes. Thus, a considerable amount of literature has been published on time domain estimation schemes that include the work of [16–19]. In [16, 17], Manzoor and Kim presented a correlation-based time domain SNR estimation scheme using preamble for AWGN channel. Similarly, Gafer et al. [18] introduced an SNR estimation scheme for flat fading channel in the time domain. Another SNR estimation algorithm for slow flat fading channel is proposed in [19], where maximum likelihood (ML) and data statistics approaches are utilized. In the frequency selective scenario, aforementioned schemes [17–19] are prone to performance degradation caused by variation of noise on each subcarrier. Thus, an SNR estimation per subcarrier is needed [20, 21]. These techniques can be extended to average SNR estimation schemes. Another way to overcome the limitation, due to frequency-selective channel, is to improve the structure and design of the preamble symbol, as shown in [22–25]. In [22], the whole band (total number of subcarriers) is divided into subbands (set of subcarriers). Then, time and frequency domain averaging is applied to estimate the variation of noise within the transmission bandwidth. In [23], it has been shown that by exploiting comb-type preamble, a low-complexity frequency domain SNR estimation method is achieved for the frequency-selective fading channels. In this method, the loaded preambles are arranged with a certain number of null subcarriers, which are used to estimate the noise power. However, the loaded preambles are utilized to obtain the total signal plus noise power. Similarly, Ijaz et al. [24] presented the time domain SNR estimation for the frequency-selective fading channel. It uses the correlation of the received preambles to estimate signal power while noise power is estimated by subtracting the estimated signal power from the total received symbol power. Yet another time domain preamble-based approach is presented in [25], where the comb-type pilot structure is designed by using the Zadoff-Chu (ZC) sequence, which outperforms the conventional time domain SNR estimators in terms of computational complexity due to perfect autocorrelation of the ZC sequence.

By reviewing these studies, it is found that the majority of the schemes utilize the preamble-based structure for the frequency-selective fading channel [22–25]. In the fast-changing environment, the variation of noise is not same in all OFDM symbols of the frame. Thus, the performance of these schemes degrades, and the noise at each symbol needs to be tracked. Furthermore, if man-made noise is considered, these schemes can not work. Consequently, it is more desirable to develop a SNR estimation scheme in which the noise variation is tracked symbol-by-symbol instead of at the beginning of frame using a preamble symbol. According to the best knowledge of the author, none of the time domain SNR estimators utilize the pilot subcarriers inserted in data symbol for SNR estimation. Moreover, the peak-to-average power ratio (PAPR) of OFDM symbol remains same as no extra preamble is utilized for estimation.

The remainder of this paper is organized as follows: In the next section, the conventional preamble-based estimator utilizing Zadoff-Chu sequence is presented. In Section 3, the system model used for pilot-based SNR estimation is described. In Section 4, a detailed description of the proposed pilot-based time domain SNR estimator is presented. Section 5 explains the complexity analysis. Simulation parameters are explained in Section 6. Results and analysis are given in Section 7. Section 8 concludes the paper.

#### 2. Conventional Preamble-Based Time Domain Zadoff-Chu Sequence SNR Estimator

In a conventional preamble-based ZC sequence SNR estimator, comb-type pilot subcarriers are loaded with a ZC sequence. It utilizes Q identical parts in each preamble symbol, which contains number of loaded pilot subcarriers, as depicted in Figure 1. Starting from the , each , subcarrier is modulated with a ZC sequence symbol with , for . The remainder of subcarriers are not used (nulled). According to [25], the received time sample can be written aswhererepresents the received time domain signal containing the phase shifted signal and additional noise component andshows the time domain noise signal. Thus, the time domain received signal that contains the signal plus noise is given by