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Scenarios 1 and 2 serve as a baseline for relative consumption-savings estimations. Scenarios 3 through 8 apply the ESFs.
1. Baseline: Maximum O-RU consumption when operating at 100% traffic load. A typical O‑RU approaches this level of operation in less than 8% of operating conditions. [6]
2. Baseline: Typical O-RU consumption when operating at approximately 30% traffic load. This consumption scenario assumes a typical aggregate of all traffic in a 24‑hour period with no ESFs enabled.
4. ESF: Symbol TRx Shutdown and User Packing, a minimum duration, low-power mode of Advanced Sleep Modes (ASM). This ESF employs an innovative cross-layer approach to proactively shut down the PA when no downlink symbols are scheduled in the corresponding slots.
5, 6, 7. ESF: RF Channel Reconfiguration, which reduces MIMO-order levels when traffic patterns allow. For a 4T4R radio, for example, shutting down O-RU MIMO antenna paths from 4 paths to 3, to 2, to SISO.
8. ESF: Deep Sleep or RU Hibernation, in which network traffic is steered away from an underused O-RU and the O-RU is effectively put to sleep with no connected user equipment.
Figure 1 illustrates the tiered energy savings potential of the ESFs from Table 1.
Of course, all energy savings estimations depend on the equipment, deployment, and traffic load as well as the ESF invoked. Each of the ESFs lead to a net reduced transmission capacity for the base station or more generally the network itself. Invoking ESFs at times of lower aggregate traffic demand can be a beneficial adaptation of the base station capacity to the predicted traffic demand. While the average throughput may continue to meet the traffic demand when ESFs are in effect, latency may fluctuate. Whether such latency impact is transparent to a specific user depends on the application. For example, a non-real-time transfer of files may be completely unaffected in terms of the total time taken for the transfer. In cases where the operational policy is more aggressive in terms of network energy savings, a tradeoff between energy savings and network latency can be achieved such that key network KPIs (as determined by the network operator) are always satisfied by formulating the NES strategy as a multi-objective optimization.