Scalable Cell-Free Massive MIMO with Multiple CPUs
Abstract
:1. Introduction
1.1. Related Work
- Despite [18] having proved that excellent performance can be achieved when all the APs are connected to a single CPU for joint transmission, it is difficult to let a single CPU control all the APs when the number of APs is large. Moreover, joint transmission requires a single CPU to centralize the signal processing, which puts high demands on the CPU’s processing capacity.
- The traditional precoding and power control of CF-M-MIMO act on the overall APs and UEs, which are difficult to realize in practice. Although a series of studies have proved the superiority of the final results [18,24,25], the complexity of these algorithms grows polynomially with the number of APs or UEs. Moreover, each AP needs to transmit instantaneous CSIs to the CPU, which is also difficult to achieve scalability when there are a large number of APs in the CF-M-MIMO systems.
- We consider a taxonomy with four different implementations of CF-M-MIMO with multiple CPUs, which are classified by different degrees of cooperation among the CPUs. The four different levels of cooperation can be called centralized connectivity, distributed connectivity and complex processing, distributed connectivity and simple processing, and no connectivity, respectively. The difference of these levels is shown in Table 1. We derive novel SE expressions for different levels of multiple CPUs cooperation in the uplink transmission. In addition, unlike most scenarios that specify the number of CPUs participating in the service, we consider a completely user-centric way to select APs.
- We propose a novel signal processing algorithm for cooperation among multiple CPUs. Each CPU processes the local information from its APs, and then transmits these signals to a CPU for final decoding. Based on the generalized Rayleigh quotient, we use simple weighted processing to linearly combine received signals from multiple CPUs with statistical CSIs.
- We compare the performance of different cooperation levels. Monte Carlo simulation results show that our proposed distributed connectivity scheme can achieve scalability with lower backhual burden, and the performance loss is negligible compared to the centralized connectivity scheme.
1.2. Paper Structure
2. System Model
2.1. Channel Estimation
2.2. Uplink Payload Transmission
2.3. Dynamic Cooperation Clustering Network
3. Multiple CPUs Cooperative Transmission
3.1. Level 4: Centralized Connectivity
3.2. Level 3: Distributed Connectivity and Complex Processing
Algorithm 1 Optimization algorithm for Level 3 |
Input: Channel gain , MMSE combining , noise variance , DCC matrix Output: 1: Initialization: calculate -dimensional vector 2: for u = 1:U do 3: for k = 1:K do 4: if then 5: calculate diagonal matrix 6: update 7: calculate by (23). 8: end if 9: end for 10: end for |
3.3. Level 2: Distributed Connectivity and Simple Processing
3.4. Level 1: No Connectivity
4. Simulation Results
4.1. Uplink Transmission
4.2. Power Allocation
4.3. Varying Numbers of CPUs
4.4. Varying Numbers of UEs
4.5. Varying the Uplink Transmit Power
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
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Type of CSIs | Level of Computational Complexity | |
---|---|---|
Level 4 centralized connectivity | instantaneous CSIs | high |
Level 3 distributed connectivity and complex processing | statistical CSIs | medium |
Level 2 distributed connectivity and simple processing | statistical CSIs | low |
Level 1 no connectivity | − | lowest |
Each Coherence Block | Statistical Parameters | |
---|---|---|
Level 4 | ||
Level 3 | ||
Level 2 | − | |
Level 1 | − | − |
Computing Combining Vectors | Computing Weighted Vectors | |||
---|---|---|---|---|
Multiplications | Divisions | Multiplications | Divisions | |
Level 4 | − | − | ||
Level 3 | ||||
Level 2 | − | − | ||
Level 1 | − | − |
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Li, F.; Sun, Q.; Ji, X.; Chen, X. Scalable Cell-Free Massive MIMO with Multiple CPUs. Mathematics 2022, 10, 1900. https://doi.org/10.3390/math10111900
Li F, Sun Q, Ji X, Chen X. Scalable Cell-Free Massive MIMO with Multiple CPUs. Mathematics. 2022; 10(11):1900. https://doi.org/10.3390/math10111900
Chicago/Turabian StyleLi, Feiyang, Qiang Sun, Xiaodi Ji, and Xiaomin Chen. 2022. "Scalable Cell-Free Massive MIMO with Multiple CPUs" Mathematics 10, no. 11: 1900. https://doi.org/10.3390/math10111900
APA StyleLi, F., Sun, Q., Ji, X., & Chen, X. (2022). Scalable Cell-Free Massive MIMO with Multiple CPUs. Mathematics, 10(11), 1900. https://doi.org/10.3390/math10111900