An iterative intercell interference cancellation algorithm is introduced to improve the receiver performance of uplink transmission in multicell networks. At first, the uplink signal detection is performed independently in each cell according to minimum mean squared error (MMSE) criterion. Subsequently, the detection results are applied to reconstruct the transmit signals of different users and cancel their interference to neighboring cells. With the help of reconstruction results, the MMSE detection matrix of each cell is updated. The channel responses of both efficient and interference links are estimated with the help of pilots. The pilot allocation parameter is introduced to indicate the quality of channel estimation. The simulation results indicate that intercell interference can be greatly mitigated by the proposed algorithm with a moderate number of receiver antennas at the base station.
Multicell cooperative receiver processing [
In the existing research [
In this paper, the iterative intercell interference cancellation in multicell uplink transmission is proposed. At first, channel estimation is performed with the help of uplink pilots. In order to suppress the pilot contamination [
According to the above descriptions, our contributions mainly include the following two points. The first contribution is that the iterative detection algorithm based on minimum mean squared error (MMSE) criterion is introduced. The detection results are exploited to reconstruct the intercell interference, which is cancelled at the receiver in the uplink transmission. During iterative processing, the MMSE receiver matrix is updated based on the signal detection result in the previous iteration. According to the proposed iterative algorithm, the detection performance of the uplink transmission is improved. The second contribution is that the detection at the receiver is implemented based on the estimated channel responses. The channel estimation error is fully considered and its effect is verified in the simulations.
In the following, lower and upper bold face letter denote the vector and matrix, respectively. For the matrix
In this paper, our focus is on uplink signal transmission in multicell scenario. We assume that there is only one base station per cell and the base station has multiple antennas. Since each cell has only one base station, the cell index
A multicell network with
In the multicell scenario shown in Figure
In the following analysis, the block fading assumption is applied, in which the channel response maintains constant during one coherent interval. In addition, the pilot sequence is inserted into the beginning of each coherent interval and the duration of the pilot transmission is called the training phase.
In order to describe the pilot configuration, the pilot allocation parameter
During the training phase,
In this paper, the transmit power of each pilot sequence is proportional to its length
According to the above analysis, when the pilot allocation parameter
After pilot transmission, the data symbols of users are transmitted. In the following,
Based on the above two expressions, the following discussion is mainly focused on the iterative intercell interference cancellation algorithm with pilotaided channel estimation.
At first, the channel estimation is realized based on the pilot sequences. The estimation is realized according to minimizing mean squared error (MMSE) criterion. Afterwards, the iterative intercell interference cancellation algorithm is introduced.
With the pilot allocation parameter
In channel estimation, the channel response is obtained based on the received signal and the pilot sequence. In order to better represent the process of channel estimation, the received signal matrix
The pilot setting described in Section
According to the above property, by left multiplying
Based on MMSE criterion [
In the following, we define
In the next subsection, the iterative intercell interference cancellation algorithm is implemented based on the above channel estimation results.
For simplicity, the proposed iterative algorithm is firstly given in twocell scenario. In each cell, there is only one user. The twocell signal model is given by
The iterative intercell interference scheme for a twocell network.
For
Based on (
After
When the iterative processing converges,
Furthermore, for the general multicell networks, we define
In practice, interferences from nonneighboring cells are much smaller than those from neighboring cells. For the sake of complexity, we assume that each BS only interferes its neighboring BSs. Generally speaking, for any BS
According to the above analysis, for interference cancellation of
With
In this section, the performance of the proposed iterative interference cancellation algorithm is given. Users in each cell are randomly distributed. The intracell receiver correlation matrix is given by
In Figure
The simulation parameter in Figure
BS Antenna Number 


User Number Per Cell 



Cell Number 



Iteration Number 

Bit error rate (BER) performance of the proposed algorithm with
From Figure
In addition, the performance curve with
In order to facilitate the effect of channel estimation, the simulation results with different pilot allocation parameters in
Bit error rate (BER) performance of the proposed algorithm with
In Figure
The simulation parameter in Figure
BS Antenna Number 


User Number Per Cell 



Cell Number 



Iteration Number 

Bit error rate (BER) performance of the proposed algorithm with
In addition, it can be seen from Figure
Furthermore, the convergence property of the proposed algorithm is given in Figure
Bit error rate (BER) convergence of the proposed algorithm. In addition, the receiver antenna number
In this paper, an iterative intercell interference cancellation algorithm is proposed. The principle of this algorithm is based on minimizing mean squared error (MMSE) criterion. In addition, the concrete expressions of the iterative cancellation are derived. From the numerical results, it can be seen that intercell interference can be mitigated efficiently with a moderate number of antennas.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
This work was supported by National Natural Science Foundation of China (61601047, 61671080, and 61871050) and Huawei HIRP Project HIRPO20161103.