Investigation of low-density parity check codes concatenated multi-user massive multiple-input multiple-output systems with imperfect channel state information

Abstract

In this era of communication technology, it is desirable to increase the data rate while minimizing the error to improve the system’s reliability. One of these techniques is massive multiple-input multiple-output (mMIMO), which increases the spectral efficiency by providing the data to multiple users simultaneously through spatial multiplexing. The mMIMO system processes the received signal by prior estimation of the channel, which has a finite variance leading to imperfect channel state information (ICSI) at the receiver. In the fifth-generation technology, spectral efficiency using mMIMO may decrease as the number of subscribers increases, resulting in more interference and affecting system capacity. The ICSI provides another challenge, as the processed data at the receiver’s output may now be more erroneous. Thus, this article provides an insight into the impact of an increase in the number of users on the variation in bit error rate with the signal-to-noise ratio in multi-user mMIMO (MU-mMIMO) and low-density parity check (LDPC) codes concatenated MU-mMIMO systems having ICSI at the receiver for quadrature amplitude modulation (QAM) and 16-QAM as modulation techniques. It has been shown that the performance of the concatenated scheme outperforms the conventional mMIMO system.

Mitigating the Interference Caused by Pilot Contamination in Multi-cell Massive Multiple Input Multiple Output Systems Using Low Density Parity Check Codes in Uplink Scenario

“…If the i th user in l th cell is the desired user, then the estimated channel vector corresponding to this user is given in Eq. (19). (19) where, the desired channel vector for the i th user of the l th cell is wlli whereas the factor (20) where, elli is the sum of interfering channel vectors and noise vector ̃.…”

“…(19). (19) where, the desired channel vector for the i th user of the l th cell is wlli whereas the factor (20) where, elli is the sum of interfering channel vectors and noise vector ̃. The desired channel vector wlli of the i th user and ̃ have variances blli and 1 respectively.…”

“…An efficient approach for designing the pilot sequence has been proposed which is capable of achieving capacity and SINR fulfillments for users in presence of PC [18]. It has been proposed by Bhardwaj et al [19] that the performance degradation in MU-m-MIMO systems with increase in the user base can be overcome by incorporating LDPC codes. Yin et al [20] have proposed interference free CE performance through covariance aided framework using Bayesian estimator.…”

“…̃ is an M×1 noise vector corresponding to Er. In terms of error and the desired channel vector,(19) can be rewritten and is given by Eq (20)…”

Channel capacity analysis of non-orthogonal multiple access and massive multiple-input multiple-output wireless communication networks considering perfect and imperfect channel state information

“…They can help us avoid the fading of the channel and mitigate significantly, and can also help in avoiding interference. [1][2][3][4][5][6][7] Conventional MIMO technologies have been used in the previous fourth generation of wireless technology. [8][9][10] Through the massive multipleinput multiple-output (M-MIMO) we can create a high gain beam toward different users; M-MIMO is an extension of MIMO.…”