Dense indoor mmWave wearable networks: Managing interference and scalable MAC

MmWave based wearable networks will need to function in various environments including possibly high density settings, e.g., train cars. At such densities one might expect challenges in interference management and/or excessive overheads tracking and jointly scheduling interferers. In this paper we use simple stochastic geometric models to examine the characteristics (number and sensitivity to motion) of "strong interferers" and show that due to blocking they are not monotonic in user density. Indeed, perhaps surprisingly, the most challenging setting appears to arise at "intermediate" user densities. We then propose a simple model to evaluate the performance of current MAC designs based on clustering and hierarchical scheduling. The results exhibit a performance trade-off leading to an optimal cluster size which depends on the directionality of transmissions. More importantly, we show that at high densities the per user throughput is roughly constant, suggesting wearable networks will scale well in dense scenarios.