Abstract
This paper focuses on energy-efficient coordinated multi-point (CoMP) downlink in multi-antenna multi-cell wireless communications systems. We provide an overview of transmit beamforming designs for various energy efficiency (EE) metrics including maximizing the overall network EE, sum weighted EE, and fairness EE. Generally, an EE optimization problem is a nonconvex program for which finding the globally optimal solutions requires high computational effort. Consequently, several low-complexity suboptimal approaches have been proposed. Here, we sum up the main concepts of the recently proposed algorithms based on the state-of-the-art successive convex approximation (SCA) framework. Moreover, we discuss the application to the newly posted EE problems including new EE metrics and power consumption models. Furthermore, distributed implementation developed based on alternating direction method of multipliers (ADMM) for the provided solutions is also discussed. For the sake of completeness, we provide numerical comparison of the SCA based approaches and the conventional solutions developed based on parametric transformations (PTs). We also demonstrate the differences and roles of different EE objectives and power consumption models.
Highlights
Fifth generation (5G) wireless network visions foresee the challenges of the data traffic demand caused by the upcoming explosive growth of wireless devices and applications [1]
Novel algorithms have been developed based on the state-of-the-art local optimization toolbox, namely successive convex approximation (SCA) algorithm, which efficiently solves the EEmax problems; the proposed framework is a one-loop iterative procedure which finds out locally optimal solutions after a relatively small number of iterations and, significantly reduces the complexity compared to the existing parametric transformations (PTs) approach [10]; the convergence of the SCA-based methods is provably guaranteed [7, 10], and the procedure is well suited for the implementation in a distributed manner [11]
Overview: We provide a summary of the basic concepts of the SCA-based algorithms; introduce some key transformations which turn the EEmax problems into representations that successfully leverage the principle of the SCA; revisit the problems of maximizing the network EE (NEE), sum weighted EE (SWEE), and maxminEE; and discuss how to arrive at efficient solutions
Summary
Fifth generation (5G) wireless network visions foresee the challenges of the data traffic demand caused by the upcoming explosive growth of wireless devices and applications [1]. An optimal solution of the EEmax problem in multi-user multiple-input single-output (MISO) downlink system has been provided in [7] using a branch-reduce-and-bound approach Even though this approach guarantees finding the global optimum, it still requires very high computational complexity. Novel algorithms have been developed based on the state-of-the-art local optimization toolbox, namely successive convex approximation (SCA) algorithm, which efficiently solves the EEmax problems; the proposed framework is a one-loop iterative procedure which finds out locally optimal solutions after a relatively small number of iterations and, significantly reduces the complexity compared to the existing PT approach [10]; the convergence of the SCA-based methods is provably guaranteed [7, 10], and the procedure is well suited for the implementation in a distributed manner [11].
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