Minimum Length Scheduling for Wireless Powered Communication Networks with Discrete Rates

Abstract

Radio frequency energy harvesting is an alternative solution to power the next generation wireless networks. The vast majority of the existing works focus on continuous rate transmission model, although discrete rate model is more realistic for practical communication networks. We study the joint optimization of energy harvesting and information transmission times with the objective of minimizing the total schedule length of a multi-user, harvest-then-transmit, wireless powered communication network while following discrete Signal-to-Noise Ratio and rate transmission model. The users are required to transmit a minimum amount of data to the access point under a maximum transmit power limit. The formulated problem is mixed integer, non-linear and non-convex. First, we solve the case where the rate allocations are given. Then, we exploit given rate allocation problem’s optimality characteristics to achieve the global optimal solution for the original problem. We propose an exponential time optimal algorithm which exhibits practical superiority to the brute force algorithm, and two polynomial time heuristics, one of which prioritizes minimizing information transmission times, while the other focuses on improving energy harvesting time. Performances of the proposed algorithms are compared both to an algorithm which assigns continuous rates to the user, i.e., best lower bound, and to an algorithm which discretize the former continuous rate solution. Simulation results show that the proposed heuristic algorithms perform close to the optimal solution, and the proposed algorithms outperform the algorithm that discretize the continuous rate solution up to 56.9% for smaller access point power and 46.7% for higher number of users. This proves the importance of optimizing the total schedule length for discrete rate model as the users will be forced to transmit at discrete rates practically.