Yinsheng Liu

Channel Estimation for OFDM

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.

Joint Estimation of Channel Impulse Response and Carrier Frequency Offset for OFDM Systems

In this paper, an order recursive method is proposed to solve the joint estimation of channel impulse response (CIR) and carrier frequency offset (CFO) for orthogonal frequency-division multiplexing (OFDM) transmission. As long as one can obtain the solution for Qth-order Taylor expansion, the solution for (Q + 1)th order can also be obtained via a simple recursive relation. The proposed recursive algorithm actually provides a method to handle any Qth-order Taylor expansion, instead of just the second order adopted in the technical literature. Significant improvement can be observed by adopting higher order approximation. Analytical mean-square-error (MSE) performance results are given, demonstrating the efficiency of the proposed algorithm.

Cooperative Store–Carry–Forward Scheme for Intermittently Connected Vehicular Networks

Intermittently connected vehicular networks (ICVNs) consist of stationary roadside units (RSUs) deployed along the highway and mobile vehicles. ICVNs are generally infrastructure constrained with a long inter-RSU distance, leading to large dark areas and transmission outage. In this paper, we propose a novel cooperative store–carry–forward (CSCF) scheme to reduce the transmission outage time of vehicles in the dark areas. The CSCF scheme utilizes bidirectional vehicle streams and selects two vehicles in both directions to serve as relays successively for the target vehicle via inter-RSU cooperation. Compared with the existing schemes, simulation results demonstrate that the proposed CSCF scheme has a great advantage in reducing transmission outage time.

Cooperative Precoding for Cognitive Transmission in Two-Tier Networks

In this paper, we study cooperative precoder design in two-tier networks, consisting of a macro-cell (MC) and several small-cells (SCs). By exploiting multiuser Vandermonde-subspace frequency division multiplexing (VFDM) transmission, an MC downlink can co-exist with cognitive SCs. In this paper, we first propose a cooperative cross-tier precoder (CTP) among the transmitters in the SCs to increase the transmitted dimension. The cooperative CTP allows us to use more efficient intra-tier precoder (ITP) in SCs to handle intracell interference and improve the throughput of the cognitive system. And then, three ITPs, a block-diagonal zero-forcing (BD-ZF) ITP, a capacity-achieving (CA) ITP, and a generalized MMSE channel inversion (GMI) ITP, are developed. Complexities of all CTPs and ITPs are discussed and compared. The overhead of channel state information (CSI) exchange is analyzed. Numerical results are presented to demonstrate the throughput improvement of the proposed schemes and to discover the impact of the imperfect CSI. From the complexity comparison and the numerical results, the GMI ITP offers a good tradeoff between complexity and throughput.

Channel Estimation for Macrocellular OFDM Uplinks in Time-Varying Channels

This paper deals with channel estimation in time-varying channels for orthogonal frequency division multiplexing (OFDM) uplink transmission. By modeling the uplink channel properly, the time-varying channel response can be determined by estimating the channel parameters. These unknown channel parameters are coupled with each other due to multipath propagation. By exploiting the orthogonality of the training symbol, the channel parameters can be easily separated. Therefore, we can estimate the unknown channel parameters path by path, instead of complex joint estimation. This leads to great reduction of computational complexity. Moreover, an order-recursive algorithm is proposed, which can approximate the nonlinear nature of Doppler shifts with any-order Taylor expansion rather than only the second-order one in the literature. The proposed algorithm can outperform the existing algorithms due to the employment of higher order Taylor expansion. Theoretical analysis and simulations are also given, demonstrating the efficiency of the proposed algorithm.

Quantization and Feedback of Spatial Covariance Matrix for Massive MIMO Systems With Cascaded Precoding

In this paper, we investigate the quantization and the feedback of downlink spatial covariance matrix for massive multiple-input multiple-output (MIMO) systems with cascaded precoding. Massive MIMO has gained a lot of attention recently because of its ability to significantly improve the network performance. To reduce the overhead of downlink channel estimation and uplink feedback in frequency-division duplex massive MIMO systems, cascaded precoding has been used to convert high-dimensional physical channels into low-dimensional effective channels. For the cascaded precoding, the inner precoder is determined by the downlink spatial covariance matrix, which is unknown in the base station (BS). To address this issue, we propose a spatial spectrum-based approach for the quantization and the feedback of the spatial covariance matrix. In this manner, the BS can obtain partial information on the downlink spatial covariance matrix. Our result shows that the inner precoder based on the proposed approach can be viewed as modulated discrete prolate spheroidal sequences and thus achieves much smaller spatial leakage than the traditional discrete Fourier transform submatrix-based precoding. Practical issues for the application of the proposed approach are also addressed in this paper.

Channel estimation for orthogonal frequency division multiplexing uplinks in time-varying channels

This study deals with channel parameter based channel estimation in time-varying channels for orthogonal frequency division multiplexing in an uplink transmission. By modelling the uplink channel properly, the time-varying ‘channel response’ can be determined by estimating the corresponding ‘channel parameters’. A novel algorithm is proposed in this study to estimate the channel parameters by exploiting the time-frequency-representation of the time-varying channel response. Moreover, a decision-directed receiver structure is adopted to cancel the impact of residual Doppler shifts. Theoretical analysis on the performance is given as well. The optimum power allocation ratio of training signal power to data signal power is also discussed. Simulations are performed, demonstrating the effectiveness of the proposed algorithm.

Noise Power Estimation in SC-FDMA Systems

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

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

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.