Hongjie Hu

Bio: Hongjie Hu is an academic researcher from Huawei. The author has contributed to research in topics: Orthogonal frequency-division multiplexing & Communication channel. The author has an hindex of 4, co-authored 8 publications receiving 227 citations.

Channel Estimation for OFDM

Abstract: Orthogonal frequency division multiplexing (OFDM) has been widely adopted in modern wireless communication systems due to its robustness against the frequency selectivity of wireless channels. For coherent detection, channel estimation is essential for receiver design. Channel estimation is also necessary for diversity combining or interference suppression where there are multiple receive antennas. In this paper, we will present a survey on channel estimation for OFDM. This survey will first review traditional channel estimation approaches based on channel frequency response (CFR). Parametric model (PM)-based channel estimation, which is particularly suitable for sparse channels, will be also investigated in this survey. Following the success of turbo codes and low-density parity check (LDPC) codes, iterative processing has been widely adopted in the design of receivers, and iterative channel estimation has received a lot of attention since that time. Iterative channel estimation will be emphasized in this survey as the emerging iterative receiver improves system performance significantly. The combination of multiple-input multiple-output (MIMO) and OFDM has been widely accepted in modern communication systems, and channel estimation in MIMO-OFDM systems will also be addressed in this survey. Open issues and future work are discussed at the end of this paper.

Noise Power Estimation in SC-FDMA Systems

Abstract: This article addresses the problem of noise power estimation for single-carrier frequency-division-multiple-access (SC-FDMA), which is used in long-term evolution (LTE). If channels are known, signal-to-noise ratio (SNR) can be derived from the noise power. A maximum-likelihood (ML) estimator is first derived to obtain a temporal noise power and then a minimum-variance unbiased (MVU) estimator is adopted to obtain an average noise power over the time-frequency plane. Since there is no analytical solution for the MVU estimator, an iterative approach based on fixed-point iteration (FPI) is employed to solve MVU estimation.

MAP-Based Iterative Channel Estimation for OFDM With Multiple Transmit Antennas Over Time-Varying Channels

Abstract: This paper investigates iterative channel estimation (ICE) for orthogonal frequency-division multiplexing (OFDM) with multiple transmit antennas. To improve performance of channel estimation, we exploit the soft information of unknown data symbols on both the expected transmit antenna and the interfering transmit antenna. Maximum a posteriori (MAP)-based ICE is derived and is implemented using the fixed-point iteration (FPI). For an OFDM system with multiple transmit antennas, the proposed MAP-based ICE suggests a harmonic-average-based soft symbol on the expected transmit antenna while an arithmetic-average-based soft symbol on the interfering transmit antennas. Similar to an OFDM system with a single transmit antenna, MAP-based ICE can achieve the Cramer-Rao bound (CRB) within only one iteration, when the signal-to-noise ratio (SNR) is large enough.

MAP Based Iterative Channel Estimation for OFDM Systems: Approach, Convergence, and Performance Bound

Abstract: Iterative channel estimation (ICE) usually exploits soft information of unknown data symbols as references to improve estimation performance. This paper investigates ICE for orthogonal frequency division multiplexing (OFDM) over wireless channels. The optimum ICE is derived in terms of maximum a posteriori (MAP) criterion, which can be solved using fixed-point iteration (FPI). Furthermore, the derived MAP ICE is closely related to the well-known expectation-maximization (EM) estimation. We also demonstrate that the MAP ICE converges within only one step when the signal-to-noise ratio (SNR) is large through analysis and simulation results.

Vehicular Communications: A Physical Layer Perspective

Abstract: Vehicular communications have attracted more and more attention recently from both industry and academia due to their strong potential to enhance road safety, improve traffic efficiency, and provide rich on-board information and entertainment services. In this paper, we discuss fundamental physical layer issues that enable efficient vehicular communications and present a comprehensive overview of the state-of-the-art research. We first introduce vehicular channel characteristics and modeling, which are the key underlying features differentiating vehicular communications from other types of wireless systems. We then present schemes to estimate the time-varying vehicular channels and various modulation techniques to deal with high-mobility channels. After reviewing resource allocation for vehicular communications, we discuss the potential to enable vehicular communications over the millimeter wave bands. Finally, we identify the challenges and opportunities associated with vehicular communications.

Vehicular Communications: A Physical Layer Perspective

Abstract: Vehicular communications have attracted more and more attention recently from both industry and academia due to its strong potential to enhance road safety, improve traffic efficiency, and provide rich on-board information and entertainment services. In this paper, we discuss fundamental physical layer issues that enable efficient vehicular communications and present a comprehensive overview of the state-of-the-art research. We first introduce vehicular channel characteristics and modeling, which are the key underlying features differentiating vehicular communications from other types of wireless systems. We then present schemes to estimate the time-varying vehicular channels and various modulation techniques to deal with high-mobility channels. After reviewing resource allocation for vehicular communications, we discuss the potential to enable vehicular communications over the millimeter wave bands. Finally, we identify the challenges and opportunities associated with vehicular communications.

Vehicular Position Tracking Using LTE Signals

Abstract: This paper proposes and validates, in the field, an approach for position tracking that is based on Long-Term Evolution (LTE) downlink signal measurements. A setup for real data live gathering is used to collect LTE signals while driving a car in the town of Rapperswil, Switzerland. The collected data are then processed to extract the received LTE cell-specific reference signals (CRSs), which are exploited for estimating pseudoranges. More precisely, the pseudoranges are evaluated by using the “ESPRIT and Kalman Filter for Time-of-Arrival Tracking” (EKAT) algorithm and by taking advantage of signal combining in the time, frequency, spatial, and cell ID domains. Finally, the pseudoranges are corrected for base station's clock bias and drift, which are previously estimated, and are used in a positioning filter. The obtained results demonstrate the feasibility of a position tracking system based on the reception of LTE downlink signals.

Channel Estimation for Diffusive Molecular Communications

Abstract: In molecular communication (MC) systems, the expected number of molecules observed at the receiver over time after the instantaneous release of molecules by the transmitter is referred to as the channel impulse response (CIR). Knowledge of the CIR is needed for the design of detection and equalization schemes. In this paper, we present a training-based CIR estimation framework for MC systems, which aims at estimating the CIR based on the observed number of molecules at the receiver due to emission of a sequence of known numbers of molecules by the transmitter. Thereby, we distinguish two scenarios depending on whether or not statistical channel knowledge is available. In particular, we derive maximum likelihood and least sum of square errors estimators, which do not require any knowledge of the channel statistics. For the case, when statistical channel knowledge is available, the corresponding maximum a posteriori and linear minimum mean square error estimators are provided. As performance bound, we derive the classical Cramer Rao (CR) lower bound, valid for any unbiased estimator, which does not exploit statistical channel knowledge, and the Bayesian CR lower bound, valid for any unbiased estimator, which exploits statistical channel knowledge. Finally, we propose the optimal and suboptimal training sequence designs for the considered MC system. Simulation results confirm the analysis and compare the performance of the proposed estimation techniques with the respective CR lower bounds.

Waveform Design for 5G Networks: Analysis and Comparison

Abstract: The next-generation cellular network is to provide a large variety of services for different kinds of terminals, from traditional voice and data services over mobile phones to small packet transmission over massive machine-type terminals. Although orthogonal-subcarrier-based waveforms are widely used nowadays in many practical systems, they can hardly meet the requirements in the coming 5G networks. Therefore, more flexible waveforms have been proposed to address the unprecedented challenges. In this paper, we will provide comprehensive analysis and comparison for typical waveforms. To offer an insightful analysis, we will not only introduce the basic principles of the waveforms, but also reveal the underlying characteristics. Moreover, a comprehensive comparison in terms of different performance metrics will also be presented in thispaper, which provides an overall understanding of the new waveforms.