“…Extensive research has shown that, in massive MIMO systems, signal processing techniques can be greatly simplified. Specifically, simple linear precoding methods at the BS, such as zero forcing (ZF) [6], [7] and matched filter (MF) [8], can achieve near optimal spectral efficiency when the number of BS antennas is much larger than the number of users [9]. This is an appealing advantage because excessive numbers of antennas lead to huge complexity when nonlinear precoding is employed.…”
This paper investigates the performance of linear precoders in massive multiple-input multipleoutput (MIMO) systems. Different from the existing research, in this paper, we consider a more realistic scenario, where the input signals are taken from finite-alphabet constellation sets, such as phase shift keying (PSK) or quadrature amplitude modulation (QAM), instead of Gaussian signals. The expressions are derived for the achievable mutual information with two commonly known linear precoders, i.e., zero forcing (ZF) and matched filter (MF), in the scenarios were perfect and imperfect channel state information (CSI) is known at the base station (BS). Also, the performance upper bound of mutual information with precoding techniques is analyzed. Both the theoretical analysis and simulation results show that ZF and MF precoders are near optimal when the number of antennas equipped at the BS is much larger than the number of users, which is similar to the case of Gaussian inputs. However, different from the Gaussian inputs, for the case of finite-alphabet inputs, the increase in the number of antennas does not always mean the improvement of performance; specifically, after the number of antennas at the BS, reaches a certain value, more antennas actually almost have no help for the performance improvement of mutual information, which is true whether the CSI is perfect or imperfect.
“…Extensive research has shown that, in massive MIMO systems, signal processing techniques can be greatly simplified. Specifically, simple linear precoding methods at the BS, such as zero forcing (ZF) [6], [7] and matched filter (MF) [8], can achieve near optimal spectral efficiency when the number of BS antennas is much larger than the number of users [9]. This is an appealing advantage because excessive numbers of antennas lead to huge complexity when nonlinear precoding is employed.…”
This paper investigates the performance of linear precoders in massive multiple-input multipleoutput (MIMO) systems. Different from the existing research, in this paper, we consider a more realistic scenario, where the input signals are taken from finite-alphabet constellation sets, such as phase shift keying (PSK) or quadrature amplitude modulation (QAM), instead of Gaussian signals. The expressions are derived for the achievable mutual information with two commonly known linear precoders, i.e., zero forcing (ZF) and matched filter (MF), in the scenarios were perfect and imperfect channel state information (CSI) is known at the base station (BS). Also, the performance upper bound of mutual information with precoding techniques is analyzed. Both the theoretical analysis and simulation results show that ZF and MF precoders are near optimal when the number of antennas equipped at the BS is much larger than the number of users, which is similar to the case of Gaussian inputs. However, different from the Gaussian inputs, for the case of finite-alphabet inputs, the increase in the number of antennas does not always mean the improvement of performance; specifically, after the number of antennas at the BS, reaches a certain value, more antennas actually almost have no help for the performance improvement of mutual information, which is true whether the CSI is perfect or imperfect.
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