Yinsheng Liu

Bio: Yinsheng Liu is an academic researcher from Beijing Jiaotong University. The author has contributed to research in topics: MIMO & Orthogonal frequency-division multiplexing. The author has an hindex of 8, co-authored 40 publications receiving 413 citations. Previous affiliations of Yinsheng Liu include Southeast University.

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.

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

Abstract: 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.

Covariance Matrix Reconstruction for DOA Estimation in Hybrid Massive MIMO Systems

Abstract: Multiple signal classification (MUSIC) has been widely applied in wireless communications for direction-of-arrival (DOA) estimation. For massive multiple-input multiple-output (MIMO) systems operating at millimeter-wave bands, hybrid analog-digital structure has been adopted in transceiver design to reduce the cost of radio frequency chains. In hybrid massive MIMO systems, the received signals at the antennas are not sent to the receiver directly, and spatial covariance matrix, which is essential in MUSIC algorithm, is thus unavailable. As a consequence, MUSIC algorithm cannot be directly used in hybrid massive MIMO systems. In this letter, we propose a beam sweeping approach for spatial covariance matrix reconstruction in hybrid massive MIMO systems. In particular, analog beamformer switches the beam direction to a group of predetermined DOA angles in turn, and then the spatial covariance matrix can be reconstructed by solving a set of linear equations. Insightful analysis on the reconstruction accuracy is also presented in this letter. Simulation results show that the proposed approach can reconstruct the spatial covariance matrix accurately so that MUSIC algorithm can be well used for DOA estimation in hybrid massive MIMO systems.

Cooperative Precoding for Cognitive Transmission in Two-Tier Networks

Abstract: 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.

Pilot Reuse for Vehicle-to-Vehicle Underlay Massive MIMO Transmission

Abstract: Pilot overhead constitutes a bottleneck in vehicle-to-vehicle (V2V) underlay massive multiple-input multiple-output (MIMO) cellular networks. To decrease the pilot overhead and further improve the spectral efficiency, we propose pilot reuse for V2V underlay massive MIMO transmission. First, based on the angle domain sparsity of massive MIMO channels, we derive the channel angle domain condition under which the channel estimation mean-square-error for cellular and V2V links can be both minimized. Motivated by this condition, we then develop an angle information assisted pilot assignment (AIAPA) algorithm. Numerical results indicate that the proposed angle information assisted pilot reuse scheme can significantly improve the performance of spectral efficiency compared with the conventional orthogonal pilot scheme for both cellular and V2V links.

Table Of Integrals Series And Products

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Resource Allocation for D2D-Enabled Vehicular Communications

Abstract: The widely deployed cellular network, assisted with device-to-device (D2D) communications, can provide a promising solution to support efficient and reliable vehicular communications. Fast channel variations caused by high mobility in a vehicular environment need to be properly accounted for when designing resource allocation schemes for the D2D-enabled vehicular networks. In this paper, we perform spectrum sharing and power allocation based only on slowly varying large-scale fading information of wireless channels. Pursuant to differing requirements for different types of links, i.e., high capacity for vehicle-to-infrastructure (V2I) links and ultrareliability for vehicle-to-vehicle (V2V) links, we attempt to maximize the ergodic capacity of the V2I connections while ensuring reliability guarantee for each V2V link. Sum ergodic capacity of all V2I links is first taken as the optimization objective to maximize the overall V2I link throughput. Minimum ergodic capacity maximization is then considered to provide a more uniform capacity performance across all V2I links. Novel algorithms that yield optimal resource allocation and are robust to channel variations are proposed. Their desirable performance is confirmed by computer simulation.

Deep Learning-Based Channel Estimation for Doubly Selective Fading Channels

Abstract: In this paper, online deep learning (DL)-based channel estimation algorithm for doubly selective fading channels is proposed by employing the deep neural network (DNN). With properly selected inputs, the DNN can not only exploit the features of channel variation from previous channel estimates but also extract additional features from pilots and received signals. Moreover, the DNN can take the advantages of the least squares estimation to further improve the performance of channel estimation. The DNN is first trained with simulated data in an off-line manner and then it could track the dynamic channel in an online manner. To reduce the performance degradation from random initialization, a pre-training approach is designed to refine the initial parameters of the DNN with several epochs of training. The proposed algorithm benefits from the excellent learning and generalization capability of DL and requires no prior knowledge about the channel statistics. Hence, it is more suitable for communication systems with modeling errors or non-stationary channels, such as high-mobility vehicular systems, underwater acoustic systems, and molecular communication systems. The numerical results show that the proposed DL-based algorithm outperforms the existing estimator in terms of both efficiency and robustness, especially when the channel statistics are time-varying.

Cooperative Vehicular Networking: A Survey

Abstract: With the remarkable progress of cooperative communication technology in recent years, its transformation to vehicular networking is gaining momentum. Such a transformation has brought a new research challenge in facing the realization of cooperative vehicular networking (CVN). This paper presents a comprehensive survey of recent advances in the field of CVN. We cover important aspects of CVN research, including physical, medium access control, and routing protocols, as well as link scheduling and security. We also classify these research efforts in a taxonomy of cooperative vehicular networks. A set of key requirements for realizing the vision of cooperative vehicular networks is then identified and discussed. We also discuss open research challenges in enabling CVN. Lastly, the paper concludes by highlighting key points of research and future directions in the domain of CVN.

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.