A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

AMIRI MOJTABA; AKHAVAN AMIR

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Information Systems and Telecommunication Year:2022 | Volume:10 | Issue:4 (40) Page(s): 249-256

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems

Author(s)

AMIRI MOJTABA | AKHAVAN AMIR | Issue Writer Certificate

Keywords

Massive MIMO

MMSE Detector

Error Recovery

Compressive Sensing

Iteratively Reweighted Least Squares (IRLS) Method

Abstract

Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in large-scale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the Compressive Sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that Error Recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

AMIRI, MOJTABA, & AKHAVAN, AMIR. (2022). A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), 10(4 (40)), 249-256. SID. https://sid.ir/paper/1005303/en

Vancouver: Copy

AMIRI MOJTABA, AKHAVAN AMIR. A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST)[Internet]. 2022;10(4 (40)):249-256. Available from: https://sid.ir/paper/1005303/en

IEEE: Copy

MOJTABA AMIRI, and AMIR AKHAVAN, “A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems,” JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), vol. 10, no. 4 (40), pp. 249–256, 2022, [Online]. Available: https://sid.ir/paper/1005303/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops