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E-grāmata: Scalable Signal Processing in Cloud Radio Access Networks

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Scalable Signal Processing in Cloud Radio Access NetworksPalielināt
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This Springerbreif  introduces a threshold-based channel sparsification approach, and then, the sparsity is exploited for scalable channel training. Last but not least, this brief introduces two scalable cooperative signal detection algorithms in C-RANs.  The authors wish to spur new research activities in the following important question: how to leverage the revolutionary architecture of C-RAN to attain unprecedented system capacity at an affordable cost and complexity.





Cloud radio access network (C-RAN) is a novel mobile network architecture that has a lot of significance in future wireless networks like 5G. the high density of remote radio heads in C-RANs leads to severe scalability issues in terms of computational and implementation complexities. This Springerbrief undertakes a comprehensive study on scalable signal processing for C-RANs, where scalable means that the computational and implementation complexities do not grow rapidly with the network size.





This Springerbrief will be target researchers and professionals working in the Cloud Radio Access Network (C-Ran) field, as well as advanced-level students studying electrical engineering.
1 Introduction
1(8)
1.1 Backgrounds
1(1)
1.2 Motivations
2(3)
1.3 Contributions
5(1)
1.4 Organization
6(1)
References
7(2)
2 System Model and Channel Sparsification
9(14)
2.1 System Model
9(2)
2.1.1 System Setup
9(1)
2.1.2 Channel Sparsification
10(1)
2.2 Distance Threshold Analysis
11(7)
2.3 Numerical Results
18(2)
2.3.1 Verification
18(1)
2.3.2 Discussion on the Distance Threshold
19(1)
2.4 Conclusions
20(1)
References
21(2)
3 Scalable Channel Estimation
23(26)
3.1 System Model
24(2)
3.1.1 Training Phase
25(1)
3.1.2 Data Transmission Phase
26(1)
3.2 Problem Formulation
26(6)
3.2.1 Throughput Optimization
26(1)
3.2.2 Local Orthogonality
27(3)
3.2.3 Problem Statement
30(1)
3.2.4 Related Work
31(1)
3.3 Training Sequence Design
32(1)
3.4 Optimal Training Length
33(5)
3.4.1 Graph with Infinite RRHs
34(1)
3.4.2 Asymptotic Behavior of the Training Length
34(2)
3.4.3 Further Discussions
36(2)
3.5 Practical Design
38(5)
3.5.1 Refined Channel Sparsification
38(1)
3.5.2 Numerical Results
39(4)
3.6 Conclusions
43(1)
Appendix
43(3)
References
46(3)
4 Scalable Signal Detection: Dynamic Nested Clustering
49(18)
4.1 System Model and Problem Formulation
49(2)
4.2 Single-Layer Dynamic Nested Clustering
51(7)
4.2.1 RRH Labelling Algorithm
52(1)
4.2.2 Single-Layer DNC
53(2)
4.2.3 Optimizing the Computational Complexity
55(2)
4.2.4 Parallel Computing
57(1)
4.3 Multi-Layer DNC Algorithm
58(5)
4.3.1 Two-Layer DNC Algorithm
60(1)
4.3.2 Optimizing the Computational Complexity
61(1)
4.3.3 Parallel Computing
62(1)
4.4 Numerical Results
63(2)
4.5 Conclusions
65(1)
References
65(2)
5 Scalable Signal Detection: Randomized Gaussian Message Passing
67(26)
5.1 Gaussian Message Passing with Channel Sparsification
67(6)
5.1.1 Bipartite Random Geometric Graph
67(2)
5.1.2 Gaussian Message Passing
69(2)
5.1.3 Related Work
71(2)
5.2 Randomized Gaussian Message Passing with Channel Sparsification
73(4)
5.2.1 Randomized Gaussian Message Passing
73(1)
5.2.2 Numerical Examples
74(3)
5.3 Convergence Analysis
77(5)
5.3.1 Convergence of GMP
77(2)
5.3.2 Convergence of RGMP
79(3)
5.4 Blockwise RGMP and Its Convergence Analysis
82(3)
5.4.1 Blockwise RGMP
82(1)
5.4.2 Convergence Analysis of B-RGMP
83(2)
5.5 Numerical Comparisons
85(4)
5.5.1 Comparison of Convergence
85(1)
5.5.2 Comparison of Convergence Speed
86(2)
5.5.3 Comparison of Performance
88(1)
5.6 Conclusions
89(1)
References
90(3)
6 Conclusions and Future Work
93(3)
6.1 Conclusions
93(1)
6.2 Future Work
94(2)
Reference 96(1)
Index 97
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