Precoding Design for Multiuser MIMO Downlink Communication Systems Using an LQ-Based Cascade GSIC Algorithm

Deng, Juinn-Horng; Lin, Kuang-Min; Ku, Meng‐Lin

doi:10.1109/access.2017.2756910

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, here an enhanced version of ZF algorithms was planned. In 2017, Deng et al (2017) improved the performance of the MU-MIMO downlink systems using a proposed novel LQ-based GSIC pre-coder. The research also suggested that the LQ-based pre-coder scheme that was combined with the unitary transformation and cancellation matrices was also applied to the multiple user environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Review on various detectors in massive MIMO technology: a performance analysis

Manju

Ganesh

2021

IJPCC

View full text Add to dashboard Cite

Purpose Multiple-input multiple-output (MIMO) combined with multi-user massive MIMO has been a well-known approach for high spectral efficiency in wideband systems, and it was targeted to detect the MIMO signals. The increasing data rates with multiple antennas and multiple users that share the communication channel simultaneously lead to higher capacity requirements and increased complexity. Thus, different detection algorithms were developed for the Massive MIMO. Design/methodology/approach This paper focuses on the various literature analyzes on various detection algorithms and techniques for MIMO detectors. Here, it reviews several research papers and exhibits the significance of each detection method. Findings This paper provides the details of the performance analysis of the MIMO detectors and reveals the best value in the case of each performance measure. Finally, it widens the research issues that can be useful for future researchers to be accomplished in MIMO massive detectors Originality/value This paper has presented a detailed review of the detection of massive MIMO on different algorithms and techniques. The survey mainly focuses on different types of channels used in MIMO detections, the number of antennas used in transmitting signals from the source to destination, and vice-versa. The performance measures and the best performance of each of the detectors are described.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Review on various detectors in massive MIMO technology: a performance analysis

Manju

Ganesh

2021

IJPCC

View full text Add to dashboard Cite

show abstract

“…In [27], a new hybrid precoding method based on the inverter and switch was presented to maximize the sum-rate in multi-user MIMO networks, which reduced the performance loss. According to the generalized side-information cancellation, a new precoder based on LQ was presented to enhance the performance of the sum-rate in multiuser MIMO downlink networks [28]. For improving the sum-rate, a new antenna selection method based on Gram-Schmidt was proposed to overcome the impacts of the partial antenna connections in massive MIMO FIGURE 1 Time-varying multi-user MIMO distributed antenna system systems, which designed a hybrid precoding [29].…”

Section: Related Workmentioning

confidence: 99%

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Xie¹,

Ai²

2021

IET Communications

View full text Add to dashboard Cite

Sum‐rate optimization problem is a key issue in a time‐varying multi‐user multi‐input multi‐output (MU‐MIMO) distributed antenna system. Channel precoding matrix is the key technology in the achievable sum‐rate optimization problem. In this paper, two algorithms are proposed to solve the achievable sum‐rate optimization problem for a time‐varying MU‐MIMO distributed antenna system, namely one‐dimensional search algorithm (OSA) and cyclic MMSE (Minimum Mean Square Error) search algorithm (CMSA). In order to design the channel precoding matrix for the achievable sum‐rate optimization, a one‐dimensional search algorithm is proposed in the time‐varying MU‐MIMO distributed antenna system. For the problem with a large amount of computation in OSA, a cyclic MMSE search algorithm is proposed in the time‐varying MU‐MIMO distributed antenna system. Simulation results show that the proposed algorithms can effectively improve the sum‐rate compared to other algorithms. Simulation results also show that OSA is superior to CMSA in the sum‐rate, and the channel state information (CSI) coefficient has little effect on OSA and CMSA.

show abstract

Precoding Design for Multiuser MIMO Downlink Communication Systems Using an LQ-Based Cascade GSIC Algorithm

Cited by 2 publications

References 18 publications

Review on various detectors in massive MIMO technology: a performance analysis

Review on various detectors in massive MIMO technology: a performance analysis

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Contact Info

Product

Resources

About