Bo Gong

Structured Distributed Compressive Channel Estimation Over Doubly Selective Channels

For an orthogonal frequency-division multiplexing (OFDM) system over a doubly selective channel, a large number of pilot subcarriers are needed to estimate the numerous channel parameters, resulting in low-spectral efficiency. In this paper, by exploiting temporal correlation of practical wireless channels, we propose a highly efficient structured distributed compressive sensing-based joint multisymbol channel estimation scheme. Specifically, by using the complex exponential basis expansion model (CE-BEM) and exploiting the sparsity in the delay domain within multiple OFDM symbols, we turn to estimate jointly sparse CE-BEM coefficient vectors rather than numerous channel taps. Then a sparse pilot pattern within multiple OFDM symbols is designed to obtain an ICI-free structure and transform the channel estimation problem into a joint-block-sparse model. Next, a novel block-based simultaneous orthogonal matching pursuit algorithm is proposed to jointly recover coefficient vectors accurately. Finally, to reduce the CE-BEM modeling error, we carry out smoothing treatments of already estimated channel taps via piecewise linear approximation. Simulation results demonstrate that the proposed channel estimation scheme can achieve higher estimation accuracy than conventional schemes, although with a smaller number of pilot subcarriers.

Dynamic sparse channel estimation over doubly selective channels: Differential simultaneous orthogonal matching pursuit

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system over a dynamic sparse channel has become a challenging problem. Due to the special feature of dynamic sparse channels: path delays change over time, the channel sparsity changes dynamically. We propose a differential simultaneous orthogonal matching pursuit (DSOMP) algorithm based joint multi-symbol channel estimation to estimate dynamic channel parameters accurately. Taking advantage of the complex exponential basis expansion model (CE-BEM) in the time domain and exploiting the channel sparsity in the delay domain, we can transform the original goal into estimating a series of jointly sparse CE-BEM coefficient vectors. We employ DSOMP algorithm on the jointly sparse receivers on pilot subcarriers to estimate the dynamic sparsity, and then employ the actual sparsity on channel estimation. Simulation results demonstrate that the proposed DSOMP algorithm achieves better performance than the conventional SOMP algorithm.

Structured distributed sparse channel estimation for high mobility OFDM systems

In high mobility environments, the significant Doppler shift introduces severe inter-carrier interference (ICI) to an orthogonal frequency-division multiplexing (OFDM) system, which makes channel estimation very challenging. In this paper, we propose an efficient structured distributed compressive sensing (SDCS) based joint channel estimation scheme within multiple OFDM symbols. By utilizing the complex exponential basis expansion model (CE-BEM) in the time domain and exploiting the channel sparsity in the delay domain, we obtain a joint-block-sparse model. Then a novel block-based simultaneous orthogonal matching pursuit (BSOMP) is proposed to make joint estimation of channel parameters accurately. Simulation results demonstrate that the proposed SDCS scheme achieves better channel estimation performance than the conventional CS and DCS scheme over high mobility channels.

Dynamic sparse channel estimation over doubly selective channels for large-scale MIMO systems

Doubly selective channel estimation for a dynamic sparse channel in the large-scale multiple-input multiple-output (MIMO) systems has become a challenging problem. Considering the two parts: the large number of channel coefficients for MIMO systems requiring unaffordable pilot overheads, and the special feature of dynamic sparse channels: path delays change over time, we propose a differential block simultaneous orthogonal matching pursuit (DBSOMP) algorithm based on joint multi-symbol channel estimation to estimate dynamic channel parameters accurately. Taking advantage of the complex basis expansion model (CE-BEM), we can transform the original goal into estimating a series of jointly sparse CE-BEM coefficient vectors. Considering the common sparsity of the coefficients in different BEM orders among different antennas, we firstly arrange the coefficients in the way of block structure. The channel information corresponding to continuous OFDM symbols has temporal time correlation. Then based on superimposed pilot design, we apply DBSOMP algorithm to estimate the dynamic sparsity of each time slot and apply the actual sparsity on channel estimation. Simulation results demonstrate that the proposed DBSOMP algorithm achieves better performance than the conventional BSOMP algorithm.

Compressive sensing based time-varying channel estimation for millimeter wave systems

Channel estimation for millimeter wave (mmWave) systems over time-varying channels is a challenging problem, since a large number of channel coefficients need to be estimated. In this paper, by exploiting the sparsity of mmWave channel in the angular domain, we propose an efficient sparse channel estimation scheme based on compressive sensing (CS) theory. Specifically, considering that the angles of arrival/departure (AoAs/AoDs) vary more slowly than the path gains, we formulate the channel estimation into a block-sparse signal recovery problem, and then propose a novel greedy algorithm consistent with the block structure to estimate AoAs/AoDs. Based on the estimated angles, we design optimal training hybrid precoders and combiners to maximize array gains, followed by estimating the path gains utilizing the least square (LS) method. The simulation results demonstrate that our proposed scheme performs better than the existing mmWave channel estimators in both estimation accuracy and spectral efficiency over time-invariant channels, and further verify that our proposed scheme is suitable for time-varying channels.

Block Distributed Compressive Sensing-Based Doubly Selective Channel Estimation and Pilot Design for Large-Scale MIMO Systems

The doubly selective (DS) channel estimation in the large-scale multiple-input multiple-output (MIMO) systems is a challenging problem due to the large number of the channel coefficients to be estimated, which requires unaffordable and prohibitive pilot overhead. In this paper, first we conduct the analysis about the common sparsity of the basis expansion model (BEM) coefficients among all the BEM orders and all the transmit–receive antenna pairs. Then, a novel pilot pattern is proposed, which inserts the guard pilots to deal with the intercarrier interference under the superimposed pilot pattern. Moreover, by exploiting the common sparsity of the BEM coefficients among different BEM orders and different antennas, we propose a block distributed compressive sensing-based DS channel estimator for the large-scale MIMO systems. Its structured sparsity leads to the reduction of the pilot overhead under the premise of guaranteeing the accuracy of the estimation. Furthermore, taking consideration of the block structure, a pilot design algorithm referred to as block discrete stochastic optimization is proposed. It optimizes the pilot positions by reducing the coherence among different blocks of the measurement matrix. Besides, a linear smoothing method is extended to large-scale MIMO systems to improve the accuracy of the estimation. Simulation results verify the performance gains of our proposed estimator and the pilot design algorithm compared with the existing schemes.

Position-Based Interference Elimination for High-Mobility OFDM Channel Estimation in Multicell Systems

Orthogonal frequency-division multiplexing (OFDM) and multicell architecture are widely adopted in current high-speed train (HST) systems for providing high-data-rate wireless communications. In this paper, a typical multiantenna OFDM HST communication system with multicell architecture is considered, where the intercarrier interference (ICI) caused by high mobility and the multicell interference (MCI) are both taken into consideration. By exploiting the train position information, a new position-based interference elimination method is proposed to eliminate both the MCI and the ICI for a general basis expansion model. We show that the MCI and the ICI can be completely eliminated by the proposed method to get the ICI-free pilots at each receive antenna. In addition, for the considered multicell HST system, we develop a low-complexity compressed channel estimation method and consider the optimal pilot pattern design. Both the proposed interference elimination method and the optimal pilot pattern are robust to the train speed and position, as well as the multicell multiantenna system. Simulation results demonstrate the benefits and robustness of the proposed method in the multicell HST system.

Position-based ICI elimination with compressed channel estimation for SIMO-OFDM high speed train systems

In this paper, we consider a typical high speed train (HST) communication system with single-input multiple-output (SIMO) orthogonal frequency-division multiplexing (OFDM). We show that the inter-carrier interference (ICI) caused by large Doppler shifts can be mitigated by exploiting the train position information as well as the sparsity of the basis expansion model (BEM) based channel model. For the complex-exponential BEM (CE-BEM) based channel model, we show that the ICI can be completely eliminated to get the ICI-free pilots at each receive antenna. In addition, we design the pilot pattern to reduce the system coherence so as to improve the compressed sensing (CS) based channel estimation accuracy. In specific, the optimal pilot pattern is independent of the number of receive antennas, the Doppler shifts, the train position, or the train speed. Simulation results confirm the effectiveness of the proposed scheme in high-mobility environments. The results also show that the proposed scheme is robust to the train moving speed.