6 research outputs found
On Power and Load Coupling in Cellular Networks for Energy Optimization
We consider the problem of minimization of sum transmission energy in
cellular networks where coupling occurs between cells due to mutual
interference. The coupling relation is characterized by the
signal-to-interference-and-noise-ratio (SINR) coupling model. Both cell load
and transmission power, where cell load measures the average level of resource
usage in the cell, interact via the coupling model. The coupling is implicitly
characterized with load and power as the variables of interest using two
equivalent equations, namely, non-linear load coupling equation (NLCE) and
non-linear power coupling equation (NPCE), respectively. By analyzing the NLCE
and NPCE, we prove that operating at full load is optimal in minimizing sum
energy, and provide an iterative power adjustment algorithm to obtain the
corresponding optimal power solution with guaranteed convergence, where in each
iteration a standard bisection search is employed. To obtain the algorithmic
result, we use the properties of the so-called standard interference function;
the proof is non-standard because the NPCE cannot even be expressed as a
closed-form expression with power as the implicit variable of interest. We
present numerical results illustrating the theoretical findings for a real-life
and large-scale cellular network, showing the advantage of our solution
compared to the conventional solution of deploying uniform power for base
stations.Comment: Accepted for publication in IEEE Transactions on Wireless
Communication
Spectral Efficiency and Energy Efficiency of OFDM Systems: Impact of Power Amplifiers and Countermeasures
In wireless communication systems, the nonlinear effect and inefficiency of
power amplifier (PA) have posed practical challenges for system designs to
achieve high spectral efficiency (SE) and energy efficiency (EE). In this
paper, we analyze the impact of PA on the SE-EE tradeoff of orthogonal
frequency division multiplex (OFDM) systems. An ideal PA that is always linear
and incurs no additional power consumption can be shown to yield a decreasing
convex function in the SE-EE tradeoff. In contrast, we show that a practical PA
has an SE-EE tradeoff that has a turning point and decreases sharply after its
maximum EE point. In other words, the Pareto-optimal tradeoff boundary of the
SE-EE curve is very narrow. A wide range of SE-EE tradeoff, however, is desired
for future wireless communications that have dynamic demand depending on the
traffic loads, channel conditions, and system applications, e.g.,
high-SE-with-low-EE for rate-limited systems and high-EE-with-low-SE for
energy-limited systems. For the SE-EE tradeoff improvement, we propose a PA
switching (PAS) technique. In a PAS transmitter, one or more PAs are switched
on intermittently to maximize the EE and deliver an overall required SE. As a
consequence, a high EE over a wide range SE can be achieved, which is verified
by numerical evaluations: with 15% SE reduction for low SE demand, the PAS
between a low power PA and a high power PA can improve EE by 323%, while a
single high power PA transmitter improves EE by only 68%.Comment: to be published, IEEE J. Sel. Areas Commu
Energy Efficiency Maximization through Cooperative Transmit and Receive Antenna Selection for Multicell MU-MIMO System
The capacity of Multiple Input Multiple Output (MIMO) system is highly related to the number of active antennas. But as the active antenna number increases, the MIMO system will consume more energy. To maximize the energy efficiency of MIMO system, we propose an antenna selection scheme which can maximize the energy efficiency of BS cluster. In the scheme, ergodic energy efficiency is derived according to large scale channel state information (CSI). Based on this ergodic energy efficiency, we introduce a cost function varied with the number of antennas, in which the effect to the energy efficiency of both the serving BS and the neighbor BS is considered. With this function, we can transform the whole system optimization problem to a sectional optimization problem and obtain a suboptimal antenna set using a heuristic algorithm. Simulation results verify that the proposed approach performs better than the comparison schemes in terms of network energy efficiency and achieves 98% network energy efficiency of the centralized antenna selection scheme. Besides, since the proposed scheme does not need the complete CSI of the neighbor BS, it can effectively reduce the signaling overhead