Scalable Uplink Signal Detection in C-RANs via Randomized Gaussian Message Passing
Fan, Congmin1; Yuan, Xiaojun2,3; Zhang, Ying Jun1,4
AbstractCloud radio access network (C-RAN) is a promising architecture for unprecedented capacity enhancement in next-generation wireless networks thanks to the centralization and virtualization of base station processing. However, centralized signal processing in C-RANs involves high computational complexity that quickly becomes unaffordable when the network grows to a huge size. First, this paper endeavors to design a scalable uplink signal detection algorithm, in the sense that both the complexity per unit network area and the total computation time remain constant when the network size grows. To this end, we formulate the signal detection in C-RAN as an inference problem over a bipartite random geometric graph. By passing messages among neighboring nodes, message passing (a.k.a. belief propagation) provides an efficient way to solve the inference problem over a sparse graph. However, the traditional message-passing algorithm is not guaranteed to converge, because the corresponding bipartite random geometric graph is locally dense and contains many short loops. As a major contribution of this paper, we propose a randomized Gaussian message passing (RGMP) algorithm to improve the convergence. Instead of exchanging messages simultaneously or in a fixed order, we propose to exchange messages asynchronously in a random order. The proposed RGMP algorithm demonstrates significantly better convergence performance than conventional message passing. The randomness of the message updating schedule also simplifies the analysis, and allows the derivation of the convergence conditions for the RGMP algorithm. In addition, we generalize the RGMP algorithm to a blockwise RGMP (B-RGMP) algorithm, which allows parallel implementation. The average computation time of B-RGMP remains constant when the network size increases.
KeywordC-RAN scalable signal processing message passing belief propagation
Indexed BySCI ; EI
Funding ProjectNational Basic Research Program (973 Program)[2013CB336701]
WOS Research AreaEngineering ; Telecommunications
WOS SubjectEngineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000407726200024
EI Accession Number20173804192653
EI KeywordsComplex networks ; Signal detection ; Signal processing
EI Classification NumberInformation Theory and Signal Processing:716.1 ; Computer Systems and Equipment:722 ; Computer Programming:723.1
Original Document TypeJournals
Corresponding AuthorYuan, Xiaojun
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorYuan, Xiaojun
Affiliation1.Chinese Univ Hong Kong, Dept Informat Engn, Hong Kong, Hong Kong, Peoples R China
2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
3.Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Sichuan, Peoples R China
4.Chinese Univ Hong Kong, Inst Network Coding Shenzhen, Hong Kong, Hong Kong, Peoples R China
Corresponding Author AffilicationSchool of Information Science and Technology
Recommended Citation
GB/T 7714
Fan, Congmin,Yuan, Xiaojun,Zhang, Ying Jun. Scalable Uplink Signal Detection in C-RANs via Randomized Gaussian Message Passing[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2017,16(8):5187-5200.
APA Fan, Congmin,Yuan, Xiaojun,&Zhang, Ying Jun.(2017).Scalable Uplink Signal Detection in C-RANs via Randomized Gaussian Message Passing.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,16(8),5187-5200.
MLA Fan, Congmin,et al."Scalable Uplink Signal Detection in C-RANs via Randomized Gaussian Message Passing".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 16.8(2017):5187-5200.
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