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Channel state information (CSI) feedback in massive MIMO systems is too large due to large pilot overhead. It is due to the large channel matrix dimension which depends on the number of base station (BS) antennas and consumes the majority of scarce radio resources. To solve this problem, we proposed a scheme for efficient CSI acquisition and reduced pilot overhead. It is based on the separation mechanism for the channel matrix. The spatial correlation among multiuser channel matrices in the virtual angular domain is utilized to split the channel matrix. Then, the two parts of the matrix are estimated by deploying the compressed sensing (CS) techniques. This scheme is novel in the sense that the user equipment (UE) directly transmits the received symbols from the BS to the BS, so a joint CSI recovery is performed at the BS. Simulation results show that the proposed channel estimation scheme effectively estimates the channel with reduced pilot overhead and improved performance as compared with the state-of-the-art schemes.

Massive multiple-input multiple-output (MIMO) systems are equipped with a large number of antennas at the base station (BS), which can significantly improve the spectrum efficiency and energy efficiency of the system. It is regarded as the most promising technology in the fifth-generation (5G) wireless communication system [

Based on analyzing the channel estimation of existing FDD multiuser massive MIMO systems, this paper proposes a new channel estimation scheme to further reduce the pilot overhead. The main feature of the proposed algorithm is it utilizes the common sparse structure between multiple channel matrices, the channel matrix is split into two parts, and the channel estimation problem is transformed into the signal recovery problem in the CS model. The simulation results show that the channel estimation performance is improved.

This paper considers a narrowband flat block fading multiuser massive MIMO system. The system comprises a BS and

System model of joint channel sparse structure.

Consider both the BS side and the UE side antennas are uniform linear array (ULA) models. Usually, channel

It is worth noting that in massive MIMO systems, due to the limited scattering environment at the BS, the angular domain channel

The row vector of the angular domain channel

There is partial common support between different

There is a statistical sparse boundary for channel sparsity

Based on the above assumptions, this paper considers decomposing the channel matrix into two parts [

Illustration of channel matrix decomposition into two parts.

The transmitted pilot sequence is

In order to utilize the CS technique for sparse channel estimation, the following new variables are defined to match the standard CS measurement model [

Substituting equations (

Thus, the channel estimation problem is transformed into the CS recovery problem, where

In order to overcome the problem of excessive pilot overhead and feedback overhead in channel estimation of massive MIMO systems and to alleviate the resource consumption of the UE, this paper considers the distributed joint channel estimation scheme [

According to the spatial correlation of the channel matrix of multiuser massive MIMO systems, unlike the literature [

Specifically, in the first stage, this paper uses the orthogonal matching pursuit (OMP) algorithm [

Thus, after iterative calculation, the common supporting index set of the

At this point, part of the common support channel can be expressed as

After the common support index set between the channels is identified, the second stage is to identify the respective support sets of

Compared with the original channel matrix

At this point, the recovered two parts of the channel are added to obtain the channel estimation in the angular domain:

The specific steps of the proposed algorithm are shown in Algorithm

Input:

From

The BS update the common support set

Let

BS obtains partial joint support channel estimation by Equation (

Let the unique support set of the channel

From

Let

Let

The BS obtains a unique support channel estimate by Equation (

Output: According to Equation (

For a multiuser FDD massive MIMO system, this paper considers the case of one BS and

Simulation parameters.

Parameter | Value |
---|---|

Number of BS antennas, |
128 |

Number of UE antennas, |
2–12 |

Number of UE, |
20 |

Channel sparsity, |
15 |

Interchannel common sparsity, |
6 |

SNR | 28 dB |

Pilot length, |
45 |

Channel model | Narrowband flat block fading |

Figure

Performance comparison of NMSE with a pilot length under different algorithms.

Figure

Performance comparison of NMSE with SNR under different algorithms.

Figure _{c} = 6, the pilot length is

Performance comparison of NMSE with a number of users under different algorithms.

Figure

Performance comparison of BER with SNR under different algorithms.

Figure

Performance comparison of sum rate with SNR under different algorithms.

Figure

Spectral efficiency versus feedback bits: a comparison of the algorithms.

This paper studies the channel estimation problem in 5G FDD multiuser massive MIMO systems. In order to reduce the pilot overhead, this paper proposes to use the spatial correlation between multiuser channels to split the channel matrix into two more sparse channel matrices and then use compressed sensing technology to estimate the two parts of the channel separately. Different from the traditional channel estimation scheme, this paper considers that multiple UE do not perform channel estimation locally after receiving the pilot signal from the BS but directly provide feedback the received signal to the BS, and perform joint recovery of the channel at the BS end. The simulation results show that the proposed scheme can effectively reduce pilot overhead while ensuring good channel estimation performance. Compared with the algorithm in [

The data (figures) used to support the findings of this study are included within the article. Further details can be provided upon request.

The authors declare that they have no conflicts of interest.

This work was supported by the National Funding from the FCT—Fundação para a Ciência e a Tecnologia through the UID/EEA/50008/2019 Project; by the Brazilian National Council for Research and Development (CNPq) via Grant no. 309335/2017-5; and by the International Scientific Partnership Program ISPP at King Saud University through ISPP#0129. This work was also supported by the National Research Foundation (NRF), South Korea, funded by the Ministry of Science and ICT (MSIT), South Korea, under Grant NRF-2019R1C1C1007277.