Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Flexible Base Station Sleeping and Resource Allocation for Green Uplink Fully-Decoupled RAN

Yu Sun, Haibo Zhou, Kai Yu, Yunting Xu, Bo Qian, Lin X. Cai

TL;DR

This work tackles energy efficiency in uplink FD-RAN by developing a holistic power-consumption model that captures UBSs, fronthaul, and edge cloud dynamics, and by formulating a joint optimization of UE association, UBS sleeping, and transmit power. The problem is decomposed into a continuous power-control subproblem and a nonlinear integer subproblem, solved via the SLMDB framework using successive lower-bound concave-convex fractional programming and Dinkelbach's algorithm, and a modified many-to-many swap-matching approach (TriMSM) with low-complexity power-control variants. The proposed methods demonstrate substantial energy-efficiency gains over traditional architectures, with simulations showing at least 18.9% EE improvement and favorable QoS outcomes due to flexible sleeping enabled by FD-RAN’s decoupled structure and centralized processing. The results underscore the practical impact of BS sleeping and adaptive resource allocation in green next-generation networks, and point to future work on downlink green strategies with delay and feedback considerations.

Abstract

The fully-decoupled radio access network (FD-RAN) is an innovative architecture designed for next-generation mobile communication networks, featuring decoupled control and data planes as well as separated uplink and downlink transmissions. To further enhance energy efficiency, this paper explores a green approach to FD-RAN by incorporating adaptive base station (BS) sleeping and resource allocation. First, we introduce a holistic power consumption model and formulate a energy efficiency maximization problem for FD-RAN, involving joint optimization of user equipment (UE) association, BS sleeping, and power control. Subsequently, the optimization problem is decomposed into two subproblems. The first subproblem, involving UE power control, is solved using a successive lower-bound maximization approach based on Dinkelbach's algorithm. The second subproblem, addressing UE association and BS sleeping, is tackled via a modified, low-complexity many-to-many swap matching algorithm. Extensive simulation results demonstrate the superior effectiveness of FD-RAN with our proposed algorithms, revealing the sources of energy efficiency gains.

Flexible Base Station Sleeping and Resource Allocation for Green Uplink Fully-Decoupled RAN

TL;DR

This work tackles energy efficiency in uplink FD-RAN by developing a holistic power-consumption model that captures UBSs, fronthaul, and edge cloud dynamics, and by formulating a joint optimization of UE association, UBS sleeping, and transmit power. The problem is decomposed into a continuous power-control subproblem and a nonlinear integer subproblem, solved via the SLMDB framework using successive lower-bound concave-convex fractional programming and Dinkelbach's algorithm, and a modified many-to-many swap-matching approach (TriMSM) with low-complexity power-control variants. The proposed methods demonstrate substantial energy-efficiency gains over traditional architectures, with simulations showing at least 18.9% EE improvement and favorable QoS outcomes due to flexible sleeping enabled by FD-RAN’s decoupled structure and centralized processing. The results underscore the practical impact of BS sleeping and adaptive resource allocation in green next-generation networks, and point to future work on downlink green strategies with delay and feedback considerations.

Abstract

The fully-decoupled radio access network (FD-RAN) is an innovative architecture designed for next-generation mobile communication networks, featuring decoupled control and data planes as well as separated uplink and downlink transmissions. To further enhance energy efficiency, this paper explores a green approach to FD-RAN by incorporating adaptive base station (BS) sleeping and resource allocation. First, we introduce a holistic power consumption model and formulate a energy efficiency maximization problem for FD-RAN, involving joint optimization of user equipment (UE) association, BS sleeping, and power control. Subsequently, the optimization problem is decomposed into two subproblems. The first subproblem, involving UE power control, is solved using a successive lower-bound maximization approach based on Dinkelbach's algorithm. The second subproblem, addressing UE association and BS sleeping, is tackled via a modified, low-complexity many-to-many swap matching algorithm. Extensive simulation results demonstrate the superior effectiveness of FD-RAN with our proposed algorithms, revealing the sources of energy efficiency gains.
Paper Structure (44 sections, 7 theorems, 53 equations, 12 figures, 3 tables, 2 algorithms)

This paper contains 44 sections, 7 theorems, 53 equations, 12 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Model
  4. Network Model
  5. Data Communication Model
  6. Channel Modeling and Estimation
  7. Derivation of Uplink Rate
  8. Power Consumption Model
  9. Uplink Base Stations
  10. Fronthauls
  11. Edge Cloud
  12. User Equipments
  13. Holistic Power Consumption
  14. Problem Formulation
  15. Energy Efficiency Maximization Problem
  16. ...and 29 more sections

Key Result

Lemma 1

In FD-RAN, when all UBSs have identical configurations for the number of antennas, bandwidth, quantization, spectral efficiency, and streams, except for their load, the power consumption of UBSs can be rewritten as: where the first term represents the fixed part of power consumption in all UBSs, and $P_{\mathrm{trf}}$ denotes a constant power coefficient.

Figures (12)

  • Figure 1: UE association and UBS sleeping in green uplink FD-RAN.
  • Figure 2: UBS sleeping and UE associations in FD-RAN ($M=16$, $K=10$).
  • Figure 3: Energy efficiency versus different architectures (using the TriMSM+EIPC algorithm).
  • Figure 4: QoS violation percentage versus different architectures (with different number of UEs ($M=16$) and UBSs ($K=10$)).
  • Figure 5: QoS violation percentage versus different algorithms (with different number of UEs ($M=16$) and UBSs ($K=10$)).
  • ...and 7 more figures

Theorems & Definitions (23)

  • Lemma 1
  • Proof 1
  • Lemma 2
  • Proof 2
  • Lemma 3
  • Proof 3
  • Lemma 4
  • Proof 4
  • Theorem 1
  • Proof 5
  • ...and 13 more