7 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
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
Energy saving in a 5G separation architecture under different power model assumptions
In this paper, a framework is developed to study the impact of different power model assumptions on energy saving in a 5G separation architecture comprising high power Base Stations (BSs) responsible for coverage, and low power, small cell BSs handling data transmission. Starting with a linear power model function, the achievable energy saving are derived over short timescales by operating small cell BSs in low power states rather than higher power states (termed Low Power State Saving (LPSS) gains) for single and multiple BS scenarios. It is shown how energy saving varies with different power model assumptions over long timescales in accordance with short timescale LPSS. Simulation results show that energy saving in the separation architecture varies across the six power models examined as a function of model-specific significant LPSS state changes. Furthermore, it is shown that if the architecture is based on existing small cell BSs modelled by state-of-the-art (SotA) power models, energy saving will be mainly dependent on sleep state operation. Whereas, if it is based on future BSs modelled by visionary power models, both sleep and idle state operations provide energy saving gains. Moreover, with future BSs, energy saving of up to 42% is achievable when idle state overhead is considered, while a higher saving is possible otherwise
Energy Efficient Resource and Topology Management for Heterogeneous Cellular Networks
This thesis investigates how resource and topology management techniques can be applied to achieve energy efficiency while maintaining acceptable quality of service (QoS) in heterogeneous cellular networks comprising high power macrocells and dense deployment of low power small cells. Partially centralised resource and topology management algorithms involving the sharing of decision making responsibilities regarding resource utilization and activation or deactivation of small cells among macrocells, small cells and a central node are developed. Resource management techniques are proposed to enable mobile users to be served by resources of a few small cells. A topology management scheme is applied to switch off idle small cells and switch on sleeping cells in accordance with traffic load and QoS. Resource management techniques, when combined with the topology management technique, achieve significant energy efficiency.
A choice restriction technique that restricts users to resources from only a subset of suitable small cells is proposed to mitigate interference and improve QoS. A good balance between energy efficiency and QoS is achieved through this approach. Furthermore, energy saving under different generations of small cell base stations is investigated to provide insights to guide the design of energy saving strategies and the enhancement of existing ones. Also, an online, adaptive energy efficient joint resource and topology management technique is developed to correct deteriorating QoS conditions automatically by using a novel confidence level strategy to estimate QoS and regulate decision making epochs at the central node. Finally, a novel linear search scheme is applied together with database records of performance metrics to select appropriate resource and topology management policies for different traffic loads. This approach achieves better balance between QoS and energy efficiency than previous schemes proposed in the literature