Differential Downlink Transmission in Massive MU-MIMO Systems

Alsifiany, Fahad; Ikhlef, Aissa; Alageli, Mahmoud; Chambers, Jonathon

Abstract

In this paper, a differential downlink transmission scheme is proposed for a massive multiple-input multiple-output (MIMO) system without explicit channel estimation. In particular, we use a downlink precoding technique combined with a different encoding scheme to simplify the overall system complexity. A novel precoder is proposed, which, with a large number of transmit antennas, can effectively precancel the multiple access interference (MAI) for each user, thus enhancing the system performance. Maximizing the worst case signal-to-interference-plus-noise ratio (SINR) is used to optimize the precoder for the users in which full power space profile (PSP) knowledge is available to the base station (BS). In addition, we provide two suboptimal solutions based on the matched and the orthogonality approach of the PSP to separate the data streams of multiple users. The decision feedback differential detection (DFDD) technique is employed to further improve the performance. The proposed schemes eliminate the MAI, enhance system performance, and achieve a simple low complexity transmission scheme. Moreover, transmission overheads are significantly reduced using the proposed scheme, since it avoids explicit channel estimation at both ends. The Monte Carlo simulation results demonstrate the effectiveness of the proposed schemes.

Citation

Alsifiany, F., Ikhlef, A., Alageli, M., & Chambers, J. (2019). Differential Downlink Transmission in Massive MU-MIMO Systems. IEEE Access, 7, 86906-86919. https://doi.org/10.1109/access.2019.2925321

• Journal Article Type: Article
• Acceptance Date: Jun 22, 2019
• Online Publication Date: Jun 26, 2019
• Publication Date: Jun 26, 2019
• Deposit Date: Jun 25, 2019
• Publicly Available Date: Aug 2, 2019
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 86906-86919
• DOI: https://doi.org/10.1109/access.2019.2925321
• Public URL: https://durham-repository.worktribe.com/output/1328039