Channel Estimation, Carrier Recovery, and Data Detection in the Presence of Phase Noise in OFDM Relay Systems

Due to its time-varying nature, oscillator phase noise can significantly degrade the performance of channel estimation, carrier recovery, and data detection blocks in high-speed wireless communication systems. In this paper, we analyze joint channel, carrier frequency offset (CFO), and phase noise estimation plus data detection in orthogonal frequency division multiplexing (OFDM) relay systems. To achieve this goal, a detailed transmission framework involving both training and data symbols is presented. In the data transmission phase, a combtype OFDM symbol consisting of both pilots and data symbols is proposed to track phase noise over an OFDM frame. Next, a novel algorithm that applies the training symbols to jointly estimate the channel responses, CFO, and phase noise based on the maximum a posteriori criterion is proposed. Additionally, a new hybrid Cramér-Rao lower bound for evaluating the performance of channel estimation and carrier recovery algorithms in OFDM relay networks is derived. Finally, an iterative receiver for joint phase noise estimation and data detection at the destination node is derived. Extensive simulations demonstrate that the application of the proposed estimation and receiver blocks significantly improves the performance of OFDM relay networks in the presence of phase noise

Estimation of Channel Transfer Function and Carrier Frequency Offset for OFDM Systems with Phase Noise

The joint estimation of carrier frequency offset (CFO) and channel transfer function (CTF) for orthogonal frequency-division multiplexing (OFDM) systems with phase noise is discussed in this paper. A CFO estimation algorithm is developed by exploring the time-frequency structure of specially designed training symbols, and it provides a very accurate estimation of the CFO in the presence of both unknown frequency-selective fading and phase noise. Based on the estimated CFO, phase noise and frequency-selective fading are jointly estimated by employing the maximum a posteriori (MAP) criterion. Specifically, the fading channel is estimated in the form of the frequency-domain CTF. The estimation of the CTF eliminates the requirement of a priori knowledge of channel length, and it is simpler compared with the time-domain channel impulse response (CIR) estimation methods used in the literature. Theoretical analysis with the Cramer-Rao lower bound (CRLB) demonstrates that the proposed CFO and CTF estimation algorithms can achieve near-optimum performance

Channel Estimation for OFDM Systems in the Presence of Carrier Frequency Offset and Phase Noise

Channel estimation for orthogonal frequency division multiplexing (OFDM) system at the presence of carrier frequency offset (CFO) and phase noise is discussed in this paper. A CFO estimation algorithm is developed by exploiting the time-frequency structure of training symbols, and it provides a very accurate estimation of CFO at the presence of both unknown frequency selective fading and phase noise. Based on the estimated CFO, the phase noise and frequency selective fading are jointly estimated by employing the maximum a posteriori (MAP) criterion. Specifically, the fading channel is estimated in the form of frequency domain channel transfer function (CTF). The estimation of CTF eliminates the requirement of the priori knowledge of channel length, and it is simpler compared to the time domain channel impulse response (CIR) estimation method in the literature. Theoretical analysis with Cramer-Rao lower bound demonstrates that the joint phase noise and CTF estimation can achieve near optimum performance

Channel, Phase Noise, and Frequency Offset in OFDM Systems: Joint Estimation, Data Detection, and Hybrid Cramer-Rao Lower Bound

Oscillator phase noise (PHN) and carrier frequency offset (CFO) can adversely impact the performance of orthogonal frequency division multiplexing (OFDM) systems, since they can result in inter carrier interference and rotation of the signal constellation. In this paper, we propose an expectation conditional maximization (ECM) based algorithm for joint estimation of channel, PHN, and CFO in OFDM systems. We present the signal model for the estimation problem and derive the hybrid Cramer-Rao lower bound (HCRB) for the joint estimation problem. Next, we propose an iterative receiver based on an extended Kalman filter for joint data detection and PHN tracking. Numerical results show that, compared to existing algorithms, the performance of the proposed ECM-based estimator is closer to the derived HCRB and outperforms the existing estimation algorithms at moderate-to-high signal-to-noise ratio (SNR). In addition, the combined estimation algorithm and iterative receiver are more computationally efficient than existing algorithms and result in improved average uncoded and coded bit error rate (BER) performance