Coarse Carrier Frequency Offset Estimation Research Articles

PRONTO: Preamble Overhead Reduction With Neural Networks for Coarse Synchronization

In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and frequency synchronization using the first field of the preamble known as the legacy short training field (L-STF). The L-STF occupies upto 40% of the preamble length and takes upto 32 us of airtime. With the goal of reducing communication overhead, we propose a modified waveform, where the preamble length is reduced by eliminating the L-STF. To decode this modified waveform, we propose a neural network (NN)-based scheme called PRONTO that performs coarse time and frequency estimations using other preamble fields, specifically the legacy long training field (L-LTF). Our contributions are threefold: (i) We present PRONTO featuring customized convolutional neural networks (CNNs) for packet detection and coarse carrier frequency offset (CFO) estimation, along with data augmentation steps for robust training. (ii) We propose a generalized decision flow that makes PRONTO compatible with legacy waveforms that include the standard L-STF. (iii) We validate the outcomes on an over-the-air WiFi dataset from a testbed of software defined radios (SDRs). Our evaluations show that PRONTO can perform packet detection with 100% accuracy, and coarse CFO estimation with errors as small as 3%. We demonstrate that PRONTO provides upto 40% preamble length reduction with no bit error rate (BER) degradation. We further show that PRONTO is able to achieve the same performance in new environments without the need to re-train the CNNs. Finally, we experimentally show the speedup achieved by PRONTO through GPU parallelization over the corresponding CPU-only implementations.

A novel orthogonal frequency division multiplexing with index modulation waveform with carrier frequency offset resistance and low peak‐to‐average power ratio

SummaryIn this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) scheme with high carrier frequency offset (CFO) resistance and low peak‐to‐average power ratio (PAPR). In this scheme, we consider a hybrid model with two subblock types, namely, pilot subblocks and standard subblocks. In pilot subblocks, active subcarriers are utilized for PAPR reduction while inactive carriers generated by the index modulation (IM) are utilized for the coarse CFO estimation. For standard subblocks, we consider unique subcarrier activation patterns (SAPs) with high‐diversity IM to enhance the bit error performance of the overall system. Additionally, the inactive data tones in standard subblocks are utilized for fine CFO estimation, which enhances the CFO estimation quite significantly. Furthermore, in this paper, we show that proposed hybrid OFDM‐IM (H‐OFDM‐IM) scheme can outperform conventional OFDM‐IM and classical OFDM both in CFO estimation and PAPR reduction without requiring transmission of any side information. Finally, we show both mathematically and through computer simulations that proposed H‐OFDM‐IM can achieve a satisfactory bit error rate (BER) performance under high CFO scenarios.

An Approach to Wideband and High Accuracy Microwave Photonic Signal Carrier Recovery Based on Carrier Period Measurement

We propose and experimentally demonstrate an approach to carrier frequency and phase recovery of a microwave photonic signal based on carrier period measurement (CPM). In the approach, a coarse carrier frequency offset (CFO) estimation is first performed, which is realized by measuring the crossing period of the microwave signal with its mean level. Then, an envelope detection method is used to simultaneously estimate the residual CFO and the carrier phase. Through the two steps, wideband and high-accuracy carrier recovery can be realized. The proposed approach is validated by an experiment. Effective carrier recovery of a microwave photonic signal is achieved with a carrier frequency varying from 7 to 37 GHz carrying a 2-Gbaud on–off -keying or quadrature phase shift keying baseband signal. The CFO estimation range is from −21 to +9 GHz, and the CFO estimation errors are less than 0.1 MHz.

Coarse Frequency Offset Estimation in MIMO Systems Using Neural Networks: A Solution With Higher Compatibility

Carrier frequency offset (CFO), which often occurs due to the mismatch between the local oscillators in transmitter and receiver, limits the performance of multiple-input multiple-output (MIMO) wireless communication systems. To recover the CFO, the first step is coarse CFO estimation. This paper presents a neural network (NN) based coarse CFO estimator which has higher compatibility with a variety of MIMO systems, comparing with traditional CFO estimators. Instead of performing closed form calculation as some traditional estimators do, the proposed estimator transforms the estimation problem to a classification problem: classify the optimal coarse CFO estimate from a pool of coarse CFO candidates. Taking the advantage of neural networks, the proposed NN estimator can perform coarse CFO estimations for MIMO systems with different numbers of antennas and a variety of channel models. Meanwhile, the testing results show that the proposed NN estimator has promising performance and wide CFO acquisition range.

CFO and Channel Estimation for Frequency Selective MIMO-FBMC/OQAM Systems

This letter estimates carrier frequency offset (CFO) and channel in multiple-input multiple-output (MIMO) filter bank multi-carrier systems based on offset quadrature amplitude modulation. A two-step preamble-based approach is proposed using a novel frame structure. For the first step, we develop a coarse CFO estimator, which does not require channel knowledge, and ensures that fine CFO can be approximated as a constant carrier phase. For the second step, by modeling the carrier phase and channel together as an effective channel, we develop minimum mean square error estimator for effective channel estimation in time domain. Unlike some of the existing estimators, the proposed one does not assume a frequency-flat MIMO channel. We derive the Cramer-Rao lower bound for the proposed estimator, and use it to evaluate the effect of the data interference on the estimation in the second step. We demonstrate the improved performance of the proposed estimator over existing ones.

A Fuzzy MOE Receiver for Uplink MC-CDMA Systems with Carrier Frequency Offset over Multipath Fading Channels

A novel fuzzy minimum output energy (MOE) detector is proposed for uplink multicarrier CDMA (MC-CDMA) systems with carrier frequency offset (CFO) over multipath fading channels. The proposed receiver involves the following stages. First, the fuzzy CFO constrained MOE detector after coarse CFO estimation is proposed to suppress multiple access interference and combat the degradation problem of the conventional MOE detector caused by the CFO effect. Next, using the signal subspace projection technique, the proposed detector can further reduce the enhanced noise due to the fuzzy CFO constrained detector. Finally, the output data obtained from these detectors are coherently combined to offer multipath diversity gain in accordance with the maximum ratio combining criterion. Furthermore, the proposed single input single output (SISO) robust detector can be easily extended for a multiple input multiple output (MIMO) MC-CDMA system with a high rate of performance. Simulation results show that the proposed SISO detector, which offers a similar performance as the optimal detector, can provide robustness against CFO and outperform the conventional detectors. The proposed MIMO detector with spatial multiplexing gain also exhibits excellent performance.

Iterative Frequency Domain Equalization and Carrier Synchronization for Multi-Resolution Constellations

Broadband broadcast and multicast wireless systems usually employ OFDM modulations (Orthogonal Frequency Division Multiplexing) combined with non-uniform hierarchical constellations. However, these schemes are very prone to nonlinear distortion effects and have high carrier synchronization requirements. SC-FDE (Single-Carrier with Frequency-Domain Equalization) is an attractive alternative for OFDM, especially when an efficient power amplification is intended. In this paper we consider the use of SC-FDE schemes combined with non-uniform hierarchical constellations in broadband broadcast and multicast wireless systems. We study the impact of residual CFO (Carrier Frequency Offset) on the performance of multi-resolution schemes and we propose iterative frequency domain receivers with joint detection and carrier synchronization to cope with residual CFO estimation errors (a coarse CFO estimation and compensation is assumed before the equalization procedure). Our results show that while a very high carrier synchronization accuracy is required for the least protected bits, the most protected bits are relatively robust to the CFO. By employing the proposed receiver we increase significantly the robustness to residual CFO estimation errors.

On the performance of a maximum likelihood method with delayed correlation for the coarse carrier frequency offset estimation of OFDM signals with multiple preamble symbols

Estimation of carrier frequency offset (CFO) is an important issue in the design of a wireless receiver that employs orthogonal frequency division multiplexing (OFDM) techniques. In this paper, using the ten short training symbols specified in the signal format of the IEEE 802.11a WLAN, we investigate the performance of a coarse CFO estimation scheme for OFDM signals with multiple preamble symbols. This scheme, which we call DC-ML, employs the maximum likelihood (ML) method with delayed correlation (DC). For AWGN channels under moderate signal to noise ratio (SNR) conditions, we develop an analysis to evaluate the variance of estimation error (VEER). The analysis is corroborated in light of simulations, and compared with the formulated Cramer-Rao lower bound (CRLB). VEER of the DC-ML in a multipath environment is studied via simulations. Numerical results show that a certain parameter combination can result in minimum VEER. Simulation results justify that the probability of estimation error (PEER) approximates Gaussian distribution in both AWGN and multipath scenarios. We also present a Two-Branch DC-ML (TBDC-ML) scheme, which comprises two correlation branches of DC-ML, and an associative ambiguity resolution algorithm. Numerical examples reveal that TBDC-ML outperforms DC-ML in both VEER and PEER. Assuming that the estimation error resulting from the two branches is jointly Gaussian, we derive the joint probability density function (pdf) and validate it via simulations.

Cell search scheme for long-term evolution of TD-SCDMA system

Cell search is an important aspect for 3G long-term evolution (LTE). This article deals with cell search in the time-division-synchronou code-division multiple access (TD-SCDMA) LTE system. On the basis of the synchronization channel (SCH) and cell specific reference symbols (CSRSs), the proposed cell search procedure includes five stages: frame detection and coarse timing, coarse carrier frequency offset (CFO) estimation, fine timing, fine CFO estimation, and cell identification. The key features of the proposed method are as follows: first, the neighboring three cells' CSRSs are frequency division multiplexed (FD) to mitigate inter-cell interference. Second, the frequency domain differential cross-correlation, computed from CSRSs are maximally ratio combined for cell identification. Finally, the large set Kasami sequences are quadrature phase shift key (QPSK) modulated to be cell specific sequences (CSSs), to support a large number of target cells. Simulations show that the FD method is better than the code division multiplexed (CD) method.