Scalable resource allocation for dynamic TDD with traffic and propagation awareness

This paper presents a scalable scheduler for dynamic TDD systems in ultra-dense indoor deployments applicable to diverse traffic and radio propagation conditions. To minimize signalling for the channel estimation and feedback, the proposed scheduler employs offline BS-to-BS measurements to approximate the real interference for different traffic conditions. Thus, no CSI is required once the network is online. Furthermore, we show that signalling can be further reduced by considering only meaningful interferers above a received power threshold for the traffic information exchange. To perform the scheduling, a function that maps interference into individual BS activation probabilities is also introduced. Results show that the proposed scheme performs reasonably well in high interference situations compared to comparable scalable schedulers, and optimally when interference is low, both in average and 5th percentile sense.