Reducing computation time of a wireless resource scheduler by exploiting temporal channel characteristics

The long term evolution downlink scheduler must attain a low computation time as it performs scheduling decisions every 1 ms. 5G New Radio introduced mini-slots for the purpose of ultra-reliable and low-latency communications further shrinking the time to make scheduling decisions to 0.125 ms. Many optimal scheduling schemes have such high computation times that they are not suitable for implementation. Previous works generally attack this problem from a computational complexity theory perspective and devise alternative non-optimal problem formulations. Here, we tackle the problem from a practical point of view and propose to reduce the quantity of users and resources in the scheduling problem over a given time. We achieve this by scheduling relatively slow varying signal-to-noise ratio (SNR) users not as frequently but for relatively longer time durations. To evaluate the performance of our idea, we derive a novel correlated bivariate received SNR distribution. The derived distribution can also be applied to a signal-to-interference ratio limited system. We show that the number of operations it takes to make scheduling decisions can be reduced by 33% with confidence probability of 0.7 and by 58% with confidence probability of 0.4. We also evaluate the potential drawbacks of the proposed scheme in terms of efficiency and error rate.