An Improved RB Allocation and MCS Selection Strategy
Hao Wei, Hengzhou Ye, Feng-Yi Huang · 2020
The amount of data in the internet of things (IoT) system is increasing, but the channel capacity is limited. In order to make full use of resources, it is urgent to find a suitable channel resource allocation and transmission rate selection algorithm. Most of the existing methods reduce age of information (AoI) blindly, regardless of whether the sample has coverage problems at the source node. In this paper, we establish two mathematical models for AoI and sample coverage, and an improved method based on the greedy strategy is proposed. The algorithm can reasonably allocate resource blocks (RBs) in the link and appropriately select modulation and coding scheme (MCS) to minimize AoI, avoiding sample coverage as much as possible. Simulation experiment analysis shows that applying the strategy determined by the proposed method to different scenarios can obtain better comprehensive performance.