Framework for Link-Level Energy Efficiency Optimization with Informed Transmitter

Abstract

The dramatic increase of network infrastructure comes at the cost of rapidly increasing energy consumption, which makes optimization of energy efficiency (EE) an important topic. Since EE is often modeled as the ratio of rate to power, we present a mathematical framework called fractional programming that provides insight into this class of optimization problems, as well as algorithms for computing the solution. The main idea is that the objective function is transformed to a weighted sum of rate and power. A generic problem formulation for systems dissipating transmit-independent circuit power in addition to transmit-dependent power is presented. We show that a broad class of EE maximization problems can be solved efficiently, provided the rate is a concave function of the transmit power.We elaborate examples of various system models including time-varying parallel channels. Rate functions with an arbitrary discrete modulation scheme are also treated. The examples considered lead to water-filling solutions, but these are different from the dual problems of power minimization under rate constraints and rate maximization under power constraints, respectively, because the constraints need not be active. We also demonstrate that if the solution to a rate maximization problem is known, it can be utilized to reduce the EE problem into a one-dimensional convex problem.

Resource Allocation for Energy Efficiency in 5G Wireless Networks

“…However, besides the benefits of massive MIMO, the beamforming design, which involves a large-scale size of beamformer, leads to the dramatically high computational complexity. On the other hand, EE performance has been designed for 100-fold increase as a requirement in 5G communication systems [8,25,60]. However, in massive MIMO HetNets, the use of low power largescale antennas at MBS and the higher channel gain by SBSs coverage offers better QoS; however, both MBS and SBSs consume a large amount of power including transmission and non-transmission powers, which are proportional to the number of their antennas and the number of SBSs.…”

Energy-efficient joint transmit beamforming and subarray selection with non-linear power amplifier efficiency

“…Then, we must ensure ag = 0 ⇒wg = 0, i.e., the beamforming weights associated with the gth antenna group are forced 1 The mathematical presentations can be straightforwardly extended to multi-cell MISO systems with centralized algorithms. 2 This means that the signal is equally divided into antennas inside a group.…”

“…Bearing this in mind, recent research has been focusing on the energy-efficient transmission [2][3][4][5][6][7][8][9]. Employing more antennas requires extra circuitry, having a significant impact on the complexity and the processing power consumption.…”

Energy Efficient Resource Allocation for Cognitive Radios: A Generalized Sensing Analysis

“…Proof: Following the same approach of generating the Lagrangian function in Theorem 2 20 and utilizing (19), (22) and the calculus of variation techniques in Appendix F, the new modified Lagrangian function is expressed as,…”

“…In addition to the difference in the system model assumptions, the utilization of quasi-convexity and pseudo-convexity analysis of the targeted problem is distinct from that in [21], [22].…”