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A "Breathing" Mobile Communication Network

Chao Ge, Ge Chen, Zhipeng Jiang

TL;DR

A dynamic optimization framework for mobile communication networks inspired by the average consensus in multi-agent systems is proposed, by which all antennas cooperatively optimize their CPICH transmit power in real-time to balance their busy-degrees.

Abstract

The frequent migration of large-scale users leads to the load imbalance of mobile communication networks, which causes resource waste and decreases user experience. To address the load balancing problem, this paper proposes a dynamic optimization framework for mobile communication networks inspired by the average consensus in multi-agent systems. In this framework, all antennas cooperatively optimize their CPICH (Common Pilot Channel) transmit power in real-time to balance their busy-degrees. Then, the coverage area of each antenna would change accordingly, and we call this framework a ``breathing'' mobile communication network. To solve this optimization problem, two algorithms named BDBA (Busy-degree Dynamic Balancing Algorithm) and BFDBA (Busy-degree Fast Dynamic Balancing Algorithm) are proposed. Moreover, a fast network coverage calculation method is introduced, by which each antenna's minimum CPICH transmit power is determined under the premise of meeting the network coverage requirements. Besides, we present the theoretical analysis of the two proposed algorithms' performance, which prove that all antennas' busy-degrees will reach consensus under certain assumptions. Furthermore, simulations carried out on three large datasets demonstrate that our cooperative optimization can significantly reduce the unbalance among antennas as well as the proportion of over-busy antennas.

A "Breathing" Mobile Communication Network

TL;DR

A dynamic optimization framework for mobile communication networks inspired by the average consensus in multi-agent systems is proposed, by which all antennas cooperatively optimize their CPICH transmit power in real-time to balance their busy-degrees.

Abstract

The frequent migration of large-scale users leads to the load imbalance of mobile communication networks, which causes resource waste and decreases user experience. To address the load balancing problem, this paper proposes a dynamic optimization framework for mobile communication networks inspired by the average consensus in multi-agent systems. In this framework, all antennas cooperatively optimize their CPICH (Common Pilot Channel) transmit power in real-time to balance their busy-degrees. Then, the coverage area of each antenna would change accordingly, and we call this framework a ``breathing'' mobile communication network. To solve this optimization problem, two algorithms named BDBA (Busy-degree Dynamic Balancing Algorithm) and BFDBA (Busy-degree Fast Dynamic Balancing Algorithm) are proposed. Moreover, a fast network coverage calculation method is introduced, by which each antenna's minimum CPICH transmit power is determined under the premise of meeting the network coverage requirements. Besides, we present the theoretical analysis of the two proposed algorithms' performance, which prove that all antennas' busy-degrees will reach consensus under certain assumptions. Furthermore, simulations carried out on three large datasets demonstrate that our cooperative optimization can significantly reduce the unbalance among antennas as well as the proportion of over-busy antennas.

Paper Structure

This paper contains 32 sections, 7 theorems, 111 equations, 10 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Review of Related Literature
  3. Statement of Our Main Contributions
  4. Paper Organization
  5. Preliminaries
  6. General Mobile Communication Network Model
  7. Busy-degree
  8. Network Coverage Rate
  9. Problem Formulation
  10. Original Optimization Problem
  11. Approximation of Optimization Objective (\ref{['dvec']})
  12. One Approximate Solution of Optimization Objective (\ref{['dvec']})
  13. Algorithms for Solving Optimization Problem
  14. Busy-degree Dynamic Balancing Algorithm
  15. Busy-degree Fast Dynamic Balancing Algorithm
  16. ...and 17 more sections

Key Result

Lemma 5.1

Suppose that antenna $i$'s busy-degree takes the value by (f_def2), and the average traffic density function $g_k(\vec{a})$ is continuous on $\mathbb{R}^n, ~k\in \mathbb{Z}^+$, if the graph $\mathcal{G}$ is strongly connected and the elements of its out-degree matrix are positive, then $A(k)$ define

Figures (10)

  • Figure 1: (a) The satellite map (left) and GPS map (right) of a selected area in Beijing, where the red icons denote the antennas; (b) the comparison of network traffic hotspots at different times on the satellite map (left) and the GPS map (right), where the red circles represent the hotspots during 10:00-11:00 period and the blue circles correspond to 22:00-23:00 period. The map data is provided by National Geographic Information Public Service Platform of China, and the antenna data is provided by Beijing Mobile Company.
  • Figure 2: (a) The standard deviations of busy-degrees of all antennas in Fig.\ref{['Fig:map']} from 9:00 to 20:00. (b) The daily changes of randomly selected four antennas' busy-degrees. The MR and antenna data is provided by the Beijing Mobile Company, and the detailed calculation process is introduced in Section \ref{['simulation']}.
  • Figure 3: The flow chart of BDBA and BFDBA.
  • Figure 4: The structure of the multi-layer perceptrons.
  • Figure 5: The performances of the MLP corresponding to dataset-A (a); dataset-B (b); and dataset-C (c).
  • ...and 5 more figures

Theorems & Definitions (12)

  • Remark 4.1
  • Remark 4.2
  • Lemma 5.1
  • Lemma 5.2
  • Theorem 5.1
  • Proposition 5.1
  • Remark 5.1
  • Lemma 5.3
  • Theorem 5.2
  • Remark 5.2
  • ...and 2 more