Decentralized Baseband Processing for Downlink Massive MU-MIMO-OFDM: Enhancing System Scalability, Sum Rate, and User Fairness

Kaziu B, Shanin N, Spano D, Wang L, Gerstacker W, Schober R (2025)

Publication Type: Journal article

Publication year: 2025

Journal

DOI: 10.1109/OJCOMS.2025.3613963

Abstract

Massive multi-user multiple-input multiple-output (MU-MIMO) systems enable high spatial resolution, high spectral efficiency, and improved link reliability compared to traditional MIMO systems due to the large number of antenna elements deployed at the base station (BS). Nevertheless, conventional massive MU-MIMO BS transceiver designs rely on centralized signal processing algorithms for antenna selection and user scheduling, precoding, and power allocation, which entail high fronthaul load and a prohibitive complexity at the centralized baseband processing unit. In this paper, we consider a downlink orthogonal frequency-division multiplexing (OFDM) MU-MIMO system, where each user device is served with multiple independent data streams. To address the aforementioned challenges, we first propose a novel low-complexity centralized fairness-based antenna selection and user scheduling (CFASUS) algorithm, which incorporates eigen-zero-forcing (EZF) precoding and water-filling power allocation. Next, we propose a novel decentralized BS architecture and develop a novel decentralized fairness-based antenna selection and user scheduling (DFASUS) algorithm, a decentralized precoding algorithm based on EZF precoding, and decentralized water-filling-based power allocation. Our proposed decentralized scheme relies on parallelizing the baseband processing tasks across multiple antenna clusters at the BS, while minimizing the fronthaul load requirements between the clusters. Our simulation results show that our proposed centralized and decentralized baseband signal processing schemes provide a good trade-off between system sum rate and user fairness. Furthermore, the decentralized scheme is shown to closely approach the performance of its centralized counterpart.

Authors with CRIS profile

Involved external institutions

Huawei Technology Sweden AB Sweden (SE)

How to cite

APA:

Kaziu, B., Shanin, N., Spano, D., Wang, L., Gerstacker, W., & Schober, R. (2025). Decentralized Baseband Processing for Downlink Massive MU-MIMO-OFDM: Enhancing System Scalability, Sum Rate, and User Fairness. IEEE Open Journal of the Communications Society. https://doi.org/10.1109/OJCOMS.2025.3613963

MLA:

Kaziu, Brikena, et al. "Decentralized Baseband Processing for Downlink Massive MU-MIMO-OFDM: Enhancing System Scalability, Sum Rate, and User Fairness." IEEE Open Journal of the Communications Society (2025).

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