Traffic-Aware Band-Level Cells On Off for Energy Saving in LTE-Advanced Networks with Inter-Band Carrier Aggregation
| dc.contributor.advisor | Dereje Hailemariam (PhD) | |
| dc.contributor.author | Yilma Melaku | |
| dc.date.accessioned | 2023-12-14T14:47:58Z | |
| dc.date.available | 2023-12-14T14:47:58Z | |
| dc.date.issued | 2023-09 | |
| dc.description.abstract | Mobile network operators employ Carrier Aggregation (CA) in Long Term Evolution (LTE)-Advanced (LTE-A) networks to meet demand for high-rate mobile data from smartphones. CA allows users to use multiple LTE carriers, including fragmented component carriers (CCs) in different bands, called inter-band CA, which can increase bandwidth. However, inter-band CA requires more radio frequency (RF) chains that rely on inefficient power amplifiers. Our survey of real LTE-A network operated by ethio telecom, Ethiopia, found that although mains power from renewable sources, outages lead to a significant reliance on non-green diesel use. Additionally, the high daily traffic load variance observed in the survey indicates the need for adaptive solutions to achieve potential power savings. Previous works have focused on switching on/off cells separately, transferring users to active cells (which is a non-CA scenario), or deactivating/activating CCs at the user level showing limitations to consider both network and end-user devices. This thesis proposes a novel traffic load adaptive band-level cells on/off (BLCOO) approach for the CA scenario. BLCOO optimizes the number of serving CCs to save power for RF units and user devices during off-peak hours. The energy-saving problem is formulated as a Markov decision process (MDP) with uncertain network conditions. Deep reinforcement learning algorithms, specifically Deep Q-Networks and proximal policy optimization, are trained to solve the MDP problem for discrete and continuous evolved node B (eNB) load states. Operator data, including resource blocks usage, energy consumption, and CA configuration are used to build RF power consumption models and a custom simulation environment. The proposed algorithms are evaluated for a one-day hourly performance. The results show that, on average, 72.0% of CCs are sufficient to meet the actual traffic demand, resulting in a maximum of 18.71% and average of 14.62% reduction in RF power consumption. | |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/943 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Inter-Band CA, DQN, Energy Saving, BLCOO, MDP, Reinforcement Learning, PPO | |
| dc.title | Traffic-Aware Band-Level Cells On Off for Energy Saving in LTE-Advanced Networks with Inter-Band Carrier Aggregation | |
| dc.type | Thesis |