  The cloud radio access network (C-RAN) provides high spectral and energy
efficiency performances, low expenditures and intelligent centralized system
structures to operators, which has attracted intense interests in both academia
and industry. In this paper, a hybrid coordinated multi-point transmission
(H-CoMP) scheme is designed for the downlink transmission in C-RANs, which
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
(POMDP), which takes both the urgent queue state information (QSI) 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 post-decision 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 post-decision 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.
