Base Station Sleeping Strategy Based on Load Sharing in Ultra-Dense Networks

TL;DR

The paper tackles energy inefficiency in ultra-dense networks by formulating a constrained multi-objective optimization to maximize energy efficiency $EE=\frac{R_{total}}{P_{total}}$ while minimizing the number of active BSs. It proposes an integrated two-phase solution: a dual-constraint UE-BS initial connection algorithm and a load-sharing based BS sleeping method that introduces a BS sleeping index $\beta_i$ and a closed-loop load migration with two takeover BSs. The approach jointly optimizes user association and dynamic BS states, supported by a mathematical model with constraints $C1$–$C5$ and validated through simulations showing faster convergence and superior EE compared to a baseline. The work demonstrates practical potential for greener deployment of ultra-dense networks by balancing QoS with energy use in real-time.

Abstract

To address the issues of high operational costs and low energy efficiency (EE) caused by the dense deployment of small base stations (s-BSs) in 5G ultra-dense networks (UDNs), this paper first constructs a multi-objective mathematical optimization model targeting maximizing EE and minimizing the number of active BSs. The model incorporates key constraints including BS operational state, user equipment (UE)-BS connection relationship, and load threshold, laying a theoretical foundation for the coordinated optimization of energy conservation and quality of service. Based on this model, an integrated solution combining UE-BS initial connection optimization and load-sharing based BS sleeping is proposed. In the initial connection phase, with communication quality and BS load as dual constraints, efficient matching between UEs and optimal BSs is achieved through three sequential steps: communication feasibility screening, redundant connection removal, and overload load redistribution. This resolves the problems of load imbalance and difficult identification of redundant BSs in UDNs arising from unordered initial connections. In the BS sleeping phase, a BS sleeping index, comprehensively considering UE transferability and backup BS resources, is innovatively introduced to quantify BS dormancy priority. Through a closed-loop process involving low-load BS screening, adjacent BS load evaluation, and load sharing by two takeover BSs based on their capacity, accurate dormancy of redundant BSs and collaborative load migration are realized. Simulation results in a typical UDNs scenario demonstrate that, compared with the traditional baseline scheme, the proposed solution exhibits significant advantages in convergence speed, optimization of the number of active BSs, and EE improvement.

Figures (6)

• Figure 1: Ultra-Dense Cellular Network System Model.
• Figure 2: Initial distribution of users and base stations.
• Figure 3: Initial connection of the users to the base stations.
• Figure 4: The connectivity of the users to the base stations after performing the base station sleeping decision.
• Figure 5: Variation of the number of active base stations during the iterations.