Resource Allocation for Low-Power loT with Imperfect CSI

In the dynamic Low-Power Internet of Things (IoT) scenario with vehicle-to-vehicle (V2V) communications, each vehicle with TinyML will cause interference to others under a dynamic environment, resulting in cumulative overlapping interference and imperfect channel state information (CSI). Aiming at the problem of interference avoidance and resource allocation considering cumulative overlapping interference in large-scale V2V vehicular networks with imperfect CSI, this paper proposed an interference hypergraph-based resource allocation for large-scale Low-Power IoT with imperfect CSI. To analyze the complex cumulative overlapping interference, we construct the interference hypergraph model and obtain multiple virtual sub-cells. Then, a queue-based delay model is built for delay-sensitive services and a stochastic resource allocation model considering the quality of service (QoS) and imperfect CSI are established under the multiple virtual sub-cells. Since the introduced imperfect CSI with random parameters will make the model uncertain and hard to solve, we employ Gaussian theory to transform the uncertain constraints into deterministic constraints and propose a resource allocation scheme for large-scale Low-Power IoT with imperfect CSI. Simulation results demonstrate that the proposed scheme achieves high throughput and energy efficiency