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Local-Partial Signal Combining Schemes for Cell-Free Large-Scale MU-MIMO Systems with Limited Fronthaul Capacity and Spatial Correlation Channels

Alammari, Amr A.
; 
Sharique, Mohd
; 
Moinuddin, Athar Ali
; 
Ansari, Mohammad Samar
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Aligarh Muslim University; University of Chester
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2022-09-01
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Electronic and Electrical Engineering
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Abstract
Cell-free large-scale multi-user MIMO is a promising technology for the 5G-and-beyond mobile communication networks. Scalable signal processing is the key challenge in achieving the benefits of cell-free systems. This study examines a distributed approach for cell-free deployment with user-centric configuration and finite fronthaul capacity. Moreover, the impact of scaling the pilot length, the number of access points (APs), and the number of antennas per AP on the achievable average spectral efficiency are investigated. Using the dynamic cooperative clustering (DCC) technique and large-scale fading decoding process, we derive an approximation of the signal-tointerference-plus-noise ratio in the criteria of two local combining schemes: Local-Partial Regularized Zero Forcing (RZF) and Local Maximum Ratio (MR). The results indicate that distributed approaches in the cell-free system have the advantage of decreasing the fronthaul signaling and the computing complexity. The results also show that the Local-Partial RZF provides the highest average spectral efficiency among all the distributed combining schemes because the computational complexity of the Local-Partial RZF is independent of the UTs. Therefore, it does not grow as the number of user terminals (UTs) increases.
Citation
Alammari, A. A., Sharique, M., Moinuddin, A. A., & Ansari, M. S. (2022). Local-partial signal combining schemes for cell-free large-scale MU-MIMO systems with limited fronthaul capacity and spatial correlation channels. Electronics, 11(17), 2757. https://doi.org/10.3390/electronics11172757
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MDPI
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Electronics
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http://hdl.handle.net/10034/627193
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10.3390/electronics11172757
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Article
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2079-9292
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https://www.mdpi.com/2079-9292/11/17/2757
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