Localization-assisted UAV multiple access for future dense cellular networks

In order to cope with the growing pressure of existing base stations (BS) in cellular networks caused by dense terminal access and data transmission, in this study we propose to use a moving unmanned aerial vehicle (UAV) to assist the BS to provide extra multiple access for ground terminals instead of the base station basing on the detected positions of the flying device, thus easing the pressure of the conventional network. In order to acquire the real-time accurate location of the moving UAV, a Bayesian statistical inference framework based UAV localization algorithm is proposed, which is implemented via sequential particle filters and hence has a low complexity. Utilizing the estimation localization information, a distance-based time duplex multiple access mechanism is proposed to establish links between the UAV and ground terminals. Numerical simulations demonstrate that the proposed Bayesian localization algorithm can acquire accurate UAV locations, and is superior to existing RSS-based localization schemes. Further relying on the location-awareness accessing scheme, the network throughput will be significantly improved.