arXiv:2406.01888v2 Announce Type: replace Abstract: Next-generation cellular networks must support heterogeneous service objectives, ranging from throughput-oriented eMBB to latency- and regularity-constrained URLLC and XR traffic. Enforcing these objectives at the RAN scheduler is difficult because wireless channels, queues, and application arrivals evolve at the millisecond timescale while spectrum resources are shared across users. We formulate service-aware RAN scheduling as a constrained Markov decision process with per-user throughput and service-regularity constraints. A Lagrangian relaxation gives an exact decomposition of the relaxed problem into per-user control problems with a common resource price. We prove fixed-dual indexability for an illustrative time-since-last-service (TSLS) debt arm with a throughput objective and TSLS constraint, note the analogous scalar throughput-debt result, and assume fixed-dual indexability for the full vector state used in deployment. We design Windex, an index-driven scheduler that learns dual-dependent per-user break-even resource prices using compact neural approximators over queue, channel, TSLS, and violation-deficit state. We further design CWindex, a contextual extension that infers service class from queue-history time series when explicit labels are unavailable. We implement Windex as an application compatible with a realtime 5G radio intelligent controller. Inference takes 10-20 $\mu$s per user on a general-purpose CPU, well within a 1 ms control budget. Trace-driven emulation using over-the-air channel measurements and larger simulations show that Windex and CWindex substantially reduce per-user service violations while maintaining throughput efficiency.