17,106 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
A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks
Cell Switch-Off (CSO) is recognized as a promising approach to reduce the
energy consumption in next-generation cellular networks. However, CSO poses
serious challenges not only from the resource allocation perspective but also
from the implementation point of view. Indeed, CSO represents a difficult
optimization problem due to its NP-complete nature. Moreover, there are a
number of important practical limitations in the implementation of CSO schemes,
such as the need for minimizing the real-time complexity and the number of
on-off/off-on transitions and CSO-induced handovers. This article introduces a
novel approach to CSO based on multiobjective optimization that makes use of
the statistical description of the service demand (known by operators). In
addition, downlink and uplink coverage criteria are included and a comparative
analysis between different models to characterize intercell interference is
also presented to shed light on their impact on CSO. The framework
distinguishes itself from other proposals in two ways: 1) The number of
on-off/off-on transitions as well as handovers are minimized, and 2) the
computationally-heavy part of the algorithm is executed offline, which makes
its implementation feasible. The results show that the proposed scheme achieves
substantial energy savings in small cell deployments where service demand is
not uniformly distributed, without compromising the Quality-of-Service (QoS) or
requiring heavy real-time processing
Optimal Cell Clustering and Activation for Energy Saving in Load-Coupled Wireless Networks
Optimizing activation and deactivation of base station transmissions provides
an instrument for improving energy efficiency in cellular networks. In this
paper, we study optimal cell clustering and scheduling of activation duration
for each cluster, with the objective of minimizing the sum energy, subject to a
time constraint of delivering the users' traffic demand. The cells within a
cluster are simultaneously in transmission and napping modes, with cluster
activation and deactivation, respectively. Our optimization framework accounts
for the coupling relation among cells due to the mutual interference. Thus, the
users' achievable rates in a cell depend on the cluster composition. On the
theoretical side, we provide mathematical formulation and structural
characterization for the energy-efficient cell clustering and scheduling
optimization problem, and prove its NP hardness. On the algorithmic side, we
first show how column generation facilitates problem solving, and then present
our notion of local enumeration as a flexible and effective means for dealing
with the trade-off between optimality and the combinatorial nature of cluster
formation, as well as for the purpose of gauging the deviation from optimality.
Numerical results demonstrate that our solutions achieve more than 60% energy
saving over existing schemes, and that the solutions we obtain are within a few
percent of deviation from global optimum.Comment: Revision, IEEE Transactions on Wireless Communication
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