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Energy-efficient methods for cloud radio access networks

Energy-efficient methods for cloud radio access networks

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Cloud radio access network (C-RAN) is an evolutionary radio network architecture in which a cloud-computing-based baseband (BB) signal-processing unit is shared among distributed low-cost wireless access points. This architecture offers a number of significant improvements over the traditional RANs, including better network scalability, spectral, and energy efficiency. As such C -RAN has been identified as one of the enabling technologies for the next-generation mobile networks. This chapter focuses on examining the energy-efficient transmission strategies of the C-RAN for cellular systems. In particular, we present optimization algorithms for the problem of transmit beamforming designs maximizing the network energy efficiency. In general, the energy efficiency maximization in C-RANs inherits the difficulty of resource allocation optimizations in interference-limited networks, i.e., it is an intractable non convex optimization problem. We first introduce a globally optimal method based on monotonic optimization (MO) to illustrate the optimal energy efficiency performance of the considered system. While the global optimization method requires extremely high computational effort and, thus, is not suitable for practical implementation, efficient optimization techniques achieving near -optimal performance are desirable in practice. To fulfill this gap, we present three low -complexity approaches based on the state-of-the-art local optimization framework, namely, successive convex approximation (SCA).

Chapter Contents:

  • 11.1 Introduction
  • 11.2 Energy efficiency optimization: mathematical preliminaries
  • 11.2.1 Global optimization method: monotonic optimization
  • 11.2.2 Local optimization method: successive convex approximation
  • 11.3 Cloud radio access networks: system model and energy efficiency optimization formulation
  • 11.3.1 System model
  • 11.3.2 Power constraints
  • 11.3.3 Fronthaul constraint
  • 11.3.4 Power consumption
  • 11.3.4.1 Circuit power consumption
  • 11.3.4.2 Signal processing and fronthauling power
  • 11.3.4.3 Dissipated power on PA
  • 11.3.4.4 Total power consumption
  • 11.3.5 Problem formulation
  • 11.4 Energy-efficient methods for cloud radio access networks
  • 11.4.1 Globally optimal solution via BRnB algorithm
  • 11.4.2 Suboptimal solutions via successive convex approximation
  • 11.4.2.1 SCA-based mixed integer programming
  • 11.4.2.2 SCA-based regularization method
  • 11.4.2.3 SCA-based -approximation method
  • 11.4.3 Complexity analysis of the presented optimization algorithms
  • 11.5 Numerical examples
  • 11.5.1 Convergence results
  • 11.5.2 Energy efficiency performance
  • 11.6 Conclusion
  • References

Inspec keywords: concave programming; telecommunication computing; cloud computing; mobile radio; evolutionary computation; next generation networks; radio access networks; energy conservation; cellular radio; telecommunication power management; array signal processing; resource allocation; radiofrequency interference; convex programming

Other keywords: cellular systems; evolutionary radio network architecture; successive convex approximation; energy-efficient method; network scalability; cloud-computing-based baseband signal-processing unit; nonconvex optimization problem; spectral efficiency; cloud radio access networks; global optimization method; interference-limited networks; monotonic optimization; resource allocation optimization; next-generation mobile networks; C-RAN; SCA; energy efficiency maximization; transmit beamforming design; distributed low-cost wireless access points

Subjects: Electromagnetic compatibility and interference; Digital signal processing; Radio access systems; Mobile radio systems; Communications computing; Optimisation techniques; Optimisation techniques; Signal processing and detection; Internet software

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