HAP-enabled Communications in Rural Areas: When Diverse Services Meet Inadequate Communication Infrastructures

The high altitude platform (HAP) network has been regarded as a cost-efficient solution for providing network access to rural or remote areas. Apart from network connectivity, rural areas are predicted to have demands for diverse real-time intelligent communication services, such as smart agriculture and digital forestry. The effectiveness of real-time decision-making applications depends on the timely updating of sensing data measurements used in generating decisions. As a performance metric capable of quantifying the freshness of transmitted information, the age of information (AoI) can evaluate the freshness-aware performance of the process of updating sensory data. However, unlike urban areas, the available communication resources in rural areas may not allow for maintaining dedicated infrastructures for different types of services, e.g., conventional non-freshness-aware services and freshness-aware real-time services, thereby requiring the proper resource allocation among different services. In this article, we first introduce the anticipated services and discuss the advances of rural networks. Next, a case study on the efficient resource allocation across heterogeneous services characterized by AoI and data rate in HAP networks is presented. We also explore the potential of employing the free-space optical (FSO) backhaul framework to enhance the performance of multi-layer HAP networks. To strike a balance between the AoI and data rate, we develop both static and deep reinforcement learning (DRL)-based dynamic resource allocation schemes to allocate the communication resources provided by HAP networks. The simulation results show that the proposed dynamic DRL-based method outperforms the heuristic algorithm and can surpass the performance ceiling achieved by the proposed static allocation scheme. In particular, our presented method can improve performance by nearly 2.5 times more than the ant colony optimization (ACO) method in terms of weighted sum performance improvements. Some insights on system design and promising future research directions are also given.