Minimizing Age-of-Information in HARQ-CC Aided NOMA Systems

In this paper, we investigate the timeliness performance of a downlink wireless communication system with non-orthogonal multiple access (NOMA). The timeliness of the system is characterized by Age of Information (AoI). To efficiently utilize the time-frequency resource and achieve a tradeoff between timeliness and reliability, we propose an adaptive transmission policy under hybrid automatic repeat request with chase combining (HARQ-CC) aided NOMA systems. In particular, the BS can adaptively adjust the power allocation and decide whether to transmit old or new packets to users in the NOMA system, based on the current AoI status and the positive/negative acknowledgement (ACK/NACK) feedback signal. We first analyze the BLER under such adaptive systems, and then formulate an AoI minimization problem based on the derived BLER. By transforming the objective function to a Markov Decision Process (MDP) problem, an optimal policy is obtained to minimize the average AoI of the system. Considering the high complexity of the MDP, we further divise an alternative near-optimal policy based on Lyapunov Drift function. Furthermore, we consider the fairness of users and propose a greedy policy to minimize the maximal expected AoI of users. Based on extensive simulations, it has been found that NOMA can outperform OMA on both an overall and a user-level basis when operating with adaptive retransmission and power allocation strategies.

Automated summary

In this paper, the timeliness performance of a downlink wireless communication system with non-orthogonal multiple access (NOMA) is investigated. An adaptive transmission policy is proposed to achieve a tradeoff between timeliness and reliability in NOMA systems. The system adapts power allocation and determines whether to transmit old or new packets based on the Age of Information (AoI) status and ACK/NACK feedback signal. An optimal policy is obtained to minimize the average AoI by formulating the problem as a Markov Decision Process (MDP). Additionally, a near-optimal policy based on Lyapunov Drift function and a greedy policy to minimize the maximal expected AoI are proposed.