Journal
IEEE SYSTEMS JOURNAL
Volume 11, Issue 4, Pages 2267-2278Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2014.2364252
Keywords
Cloud radio access networks (C-RANs); fronthaul limitation; hybrid coordinated multi-point transmission; queue-aware resource allocation
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Funding
- National Natural Science Foundation of China [61222103, 61361166005]
- National High Technology Research and Development Program of China [2014AA01A701]
- State Major Science and Technology Special Projects [2013ZX03001001]
- Beijing Natural Science Foundation [4131003]
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The cloud radio access network (C-RAN) provides high spectral and energy efficiency performances, low expenditures, and intelligent centralized system structures to operators, which have attracted intense interests in both academia and industry. In this paper, a hybrid coordinated multipoint transmission (H-CoMP) scheme is designed for the downlink transmission in C-RANs and fulfills the flexible tradeoff between cooperation gain and fronthaul consumption. The queue-aware power and rate allocation with constraints of average fronthaul consumption for the delay-sensitive traffic are formulated as an infinite horizon constrained partially observed Markov decision process, which takes both the urgent queue state information and the imperfect channel state information at transmitters (CSIT) into account. To deal with the curse of dimensionality involved with the equivalent Bellman equation, the linear approximation of postdecision value functions is utilized. A stochastic gradient algorithm is presented to allocate the queue-aware power and transmission rate with H-CoMP, which is robust against unpredicted traffic arrivals and uncertainties caused by the imperfect CSIT. Furthermore, to substantially reduce the computing complexity, an online learning algorithm is proposed to estimate the per-queue postdecision value functions and update the Lagrange multipliers. The simulation results demonstrate performance gains of the proposed stochastic gradient algorithms and confirm the asymptotical convergence of the proposed online learning algorithm.
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