AI-Enabled Energy-Aware Carrier Aggregation in 5G New Radio With Dual Connectivity

Abstract

Aggregating multiple component carriers (CCs) from different frequency bands, also known as Carrier Aggregation (CA), and Dual Connectivity (DC), i.e., concurrently transmitting and receiving from two nodes or cell groups, are employed in 5G and 6G wireless networks to enhance coverage and capacity. In wireless networks with DC and CA, the performance can be boosted by dynamically adjusting the uplink (UL) transmit power level for the user equipments (UEs) and properly activating/deactivating the CCs for the UEs. In this paper, we study the problem of joint dynamic UL power-sharing and CC management. The objective is to simultaneously minimize the delay and power consumption for the UEs. The pertinent problem is a multi-objective optimization problem with both discrete and continuous variables and therefore is hard to solve. We first model it as a multi-agent reinforcement learning (RL) system with compound action to handle the problem. Then, we employ a compound-action actor-critic algorithm to find the optimal policy and propose the Joint Power-Sharing and Carrier Aggregation (JPSCA) algorithm. The performance of the JPSCA algorithm is compared with two baseline algorithms. Our results show that the performance of the JPSCA algorithm in terms of the average rate, delay, and UL transmit power level outperforms the baselines where UL power control and CC management are performed disjointly. For 25 UEs, our proposed JPSCA algorithm decreases the UE power consumption and UE delay by about 28% and 16%, respectively, concerning the all-CC and equal power-sharing schemes.

Keywords

• 5G
• carrier aggregation
• dual connectivity
• dynamic uplink power-sharing
• reinforcement learning