Channel Estimation for Frequency Division Duplexing Multi-user Massive MIMO Systems via Tensor Compressive Sensing

<p class="p1">To make full use of space multiplexing gains for the multi-user massive multiple-input multiple-output, accurate channel state information at the transmitter (CSIT) is required. However, the large number of users and antennas make CSIT a higher-order data representation. Tensor-based compressive sensing (TCS) is a promising method that is suitable for high-dimensional data processing; it can reduce training pilot and feedback overhead during channel estimation. In this paper, we consider the channel estimation in frequency division duplexing (FDD) multi-user massive MIMO system. A novel estimation framework for three dimensional CSIT is presented, in which the modes include the number of transmitting antennas, receiving antennas, and users. The TCS technique is employed to complete the reconstruction of three dimensional CSIT. The simulation results are given to demonstrate that the proposed estimation approach outperforms existing algorithms.</p>

Download Full-text

Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

Wireless Communications and Mobile Computing ◽

10.1155/2019/6374764 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Athar Waseem ◽

Aqdas Naveed ◽

Sardar Ali ◽

Muhammad Arshad ◽

Haris Anis ◽

...

Keyword(s):

Channel Estimation ◽

Compressive Sensing ◽

Massive Mimo ◽

Communication Systems ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Sampling Theorem ◽

Base Station ◽

Mimo Channels ◽

Pilot Design

Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.

Download Full-text

LDPC Coded Massive MIMO Systems

Entropy ◽

10.3390/e21030231 ◽

2019 ◽

Vol 21 (3) ◽

pp. 231 ◽

Cited By ~ 1

Author(s):

Inho Hwang ◽

Han Park ◽

Jeong Lee

Keyword(s):

Massive Mimo ◽

Information Transfer ◽

Mimo Systems ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Three Dimensional ◽

Ldpc Codes ◽

Low Complexity ◽

Graph Representation ◽

Factor Graph

We design a coded massive multiple-input multiple-output (MIMO) system using low-density parity-check (LDPC) codes and iterative joint detection and decoding (JDD) algorithm employing a low complexity detection. We introduce the factor graph representation of the LDPC coded massive MIMO system, based on which the message updating rule in the JDD is defined. We devise a tool for analyzing extrinsic information transfer (EXIT) characteristics of messages flowing in the JDD and the three-dimensional (3-D) EXIT chart provides a visualization of the JDD behavior. Based on the proposed 3-D EXIT analysis, we design jointly the degree distribution of irregular LDPC codes and the JDD strategy for the coded massive MIMO system. The JDD strategy was determined to achieve a higher error correction capability with a given amount of computational complexity. It was observed that the coded massive MIMO system equipped with the proposed LDPC codes and the proposed JDD strategy has lower bit error rate than conventional LDPC coded massive MIMO systems.

Download Full-text

Compressive Sensing Based Channel Estimation for High-order MIMO Systems

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v14i07.8218 ◽

2018 ◽

Vol 14 (07) ◽

pp. 30

Author(s):

Zhiguo Lv ◽

Ying Li

Keyword(s):

Channel Estimation ◽

Compressive Sensing ◽

Narrow Band ◽

Mimo Systems ◽

Mimo System ◽

Estimation Error ◽

Multiple Input Multiple Output ◽

High Order ◽

Error Performance ◽

Input Multiple Output

The high-order multiple-input multiple-output (MIMO) system can remarkably increase the data rate or enhance the reliability. However, it is difficult to perform channel estimation because of the massive number of antennas. The Narrow Band Estimation Antenna Processing (NBEAP) scheme is used to deal with this issue. Nevertheless, the accuracy of the channel estimation needs to be improved. In this paper, a compressive sensing based scheme named Narrow Band Estimation Fixed Antenna Processing (NBEFAP) is proposed to estimate the channel state information (CSI) for high-order MIMO systems. A simple pilot structure is designed to decrease the computation complexity. In addition, the pilot length is adjusted according to the time-varying sparsity level of the CSI. Compared with NBEAP scheme, NBEFAP scheme can improve the estimation error performance. Simulation results verify the effectiveness of the NBEFAP scheme.

Download Full-text

Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing

IEEE Access ◽

10.1109/access.2021.3069335 ◽

2021 ◽

Vol 9 ◽

pp. 49738-49749

Author(s):

Ting Jiang ◽

Maozhong Song ◽

Xuejian Zhao ◽

Xu Liu

Keyword(s):

Channel Estimation ◽

Compressive Sensing ◽

Millimeter Wave ◽

Massive Mimo ◽

Mimo Systems

Download Full-text

The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels

Entropy ◽

10.3390/e23111552 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1552

Author(s):

Tongzhou Han ◽

Danfeng Zhao

Keyword(s):

Power Control ◽

Massive Mimo ◽

Mimo Systems ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Time Varying ◽

Input Multiple Output ◽

Efficiency Performance ◽

A Cell ◽

Time Varying Channel

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.

Download Full-text

Statistical Beamforming for Massive MIMO Systems with Distinct Spatial Correlations

Sensors ◽

10.3390/s20216255 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6255

Author(s):

Taehyoung Kim ◽

Sangjoon Park

Keyword(s):

Massive Mimo ◽

Degrees Of Freedom ◽

Mimo Systems ◽

Mimo System ◽

Multiple Input Multiple Output ◽

High Signal ◽

Spatial Correlations ◽

Channel Correlation ◽

Low Correlation ◽

Noise Ratio

In this paper, we propose a novel statistical beamforming (SBF) method called the partial-nulling-based SBF (PN-SBF) to serve a number of users that are undergoing distinct degrees of spatial channel correlations in massive multiple-input multiple-output (MIMO) systems. We consider a massive MIMO system with two user groups. The first group experiences a low spatial channel correlation, whereas the second group has a high spatial channel correlation, which can happen in massive MIMO systems that are based on fifth-generation networks. By analyzing the statistical signal-to-interference-plus-noise ratio, it can be observed that the statistical beamforming vector for the low-correlation group should be designed as the orthogonal complement for the space spanned by the aggregated channel covariance matrices of the high-correlation group. Meanwhile, the spatial degrees of freedom for the high-correlation group should be preserved without cancelling the interference to the low-correlation group. Accordingly, a group-common pre-beamforming matrix is applied to the low-correlation group to cancel the interference to the high-correlation group. In addition, to deal with the intra-group interference in each group, the post-beamforming vector for each group is designed in the manner of maximizing the signal-to-leakage-and-noise ratio, which yields additional performance improvements for the PN-SBF. The simulation results verify that the proposed PN-SBF outperforms the conventional SBF schemes in terms of the ergodic sum rate for the massive MIMO systems with distinct spatial correlations, without the rate ceiling effect in the high signal-to-noise ratio region unlike conventional SBF schemes.

Download Full-text

Pilot Decontamination Using Asynchronous Fractional Pilot Scheduling in Massive MIMO Systems

Sensors ◽

10.3390/s20216213 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6213

Author(s):

Muhammad Irshad Zahoor ◽

Zheng Dou ◽

Syed Bilal Hussain Shah ◽

Imran Ullah Khan ◽

Sikander Ayub ◽

...

Keyword(s):

Massive Mimo ◽

Mimo Systems ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Multiple Input ◽

Input Multiple Output ◽

Ideal Number ◽

Sum Rate ◽

Inter Cell Interference ◽

Time Division

Due to large spectral efficiency and low power consumption, the Massive Multiple-Input-Multiple-Output (MIMO) became a promising technology for the 5G system. However, pilot contamination (PC) limits the performance of massive MIMO systems. Therefore, two pilot scheduling schemes (i.e., Fractional Pilot Reuse (FPR) and asynchronous fractional pilot scheduling scheme (AFPS)) are proposed, which significantly mitigated the PC in the uplink time division duplex (TDD) massive MIMO system. In the FPR scheme, all the users are distributed into the central cell and edge cell users depending upon their signal to interference plus noise ratio (SINR). Further, the capacity of central and edge users is derived in terms of sum-rate, and the ideal number of the pilot is calculated which significantly maximized the sum rate. In the proposed AFPS scheme, the users are grouped into central users and edge users depending upon the interference they receive. The central users are assigned the same set of pilots because these users are less affected by interference, while the edge users are assigned the orthogonal pilots because these users are severely affected by interference. Consequently, the pilot overhead is reduced and inter-cell interference (ICI) is minimized. Further, results verify that the proposed schemes outperform the previous proposed traditional schemes, in terms of improved sum rates.

Download Full-text

Diversity-Multiplexing-Nulling Trade-Off Analysis of Multiuser MIMO System for Intercell Interference Coordination

Wireless Communications and Mobile Computing ◽

10.1155/2017/5709367 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7

Author(s):

Jinwoo Kim ◽

Chung G. Kang

Keyword(s):

Mimo Systems ◽

Mimo System ◽

Signal To Noise Ratio ◽

Multiple Input Multiple Output ◽

Three Dimensional ◽

Mobile Station ◽

Optimal Number ◽

Error Performance ◽

Trade Off ◽

Intercell Interference

A fundamental performance trade-off of multicell multiuser multiple-input multiple-output (MU-MIMO) systems is explored for achieving intercell and intracell interference-free conditions. In particular, we analyze the three-dimensional diversity-multiplexing-nulling trade-off (DMNT) among the diversity order (i.e., the slope of the error performance curve), multiplexing order (i.e., the number of users that are simultaneously served by MU-MIMO), and nulling order (i.e., the number of users with zero interference in a victim cell). This trade-off quantifies the performance of MU-MIMO in terms of its diversity and multiplexing order, while nulling the intercell interference toward the victim cell in the neighbor. First, we design a precoding matrix to mitigate both intercell and intracell interference for a linear precoding-based MU-MIMO system. Then, the trade-off relationship is obtained by analyzing the distribution of the signal-to-noise ratio (SNR) at the user terminals. Furthermore, we demonstrate how DMNT can be applied to estimate the long-term throughput for each mobile station, which allows for determining the optimal number of multiplexing order and throughput loss due to the interference nulling.

Download Full-text