10,014 research outputs found
System-Level Modeling and Optimization of the Energy Efficiency in Cellular Networks -- A Stochastic Geometry Framework
In this paper, we analyze and optimize the energy efficiency of downlink
cellular networks. With the aid of tools from stochastic geometry, we introduce
a new closed-form analytical expression of the potential spectral efficiency
(bit/sec/m). In the interference-limited regime for data transmission,
unlike currently available mathematical frameworks, the proposed analytical
formulation depends on the transmit power and deployment density of the base
stations. This is obtained by generalizing the definition of coverage
probability and by accounting for the sensitivity of the receiver not only
during the decoding of information data, but during the cell association phase
as well. Based on the new formulation of the potential spectral efficiency, the
energy efficiency (bit/Joule) is given in a tractable closed-form formula. An
optimization problem is formulated and is comprehensively studied. It is
mathematically proved, in particular, that the energy efficiency is a unimodal
and strictly pseudo-concave function in the transmit power, given the density
of the base stations, and in the density of the base stations, given the
transmit power. Under these assumptions, therefore, a unique transmit power and
density of the base stations exist, which maximize the energy efficiency.
Numerical results are illustrated in order to confirm the obtained findings and
to prove the usefulness of the proposed framework for optimizing the network
planning and deployment of cellular networks from the energy efficiency
standpoint.Comment: To appear in IEEE Transactions on Wireless Communication
Cognitive and Energy Harvesting-Based D2D Communication in Cellular Networks: Stochastic Geometry Modeling and Analysis
While cognitive radio enables spectrum-efficient wireless communication,
radio frequency (RF) energy harvesting from ambient interference is an enabler
for energy-efficient wireless communication. In this paper, we model and
analyze cognitive and energy harvesting-based D2D communication in cellular
networks. The cognitive D2D transmitters harvest energy from ambient
interference and use one of the channels allocated to cellular users (in uplink
or downlink), which is referred to as the D2D channel, to communicate with the
corresponding receivers. We investigate two spectrum access policies for
cellular communication in the uplink or downlink, namely, random spectrum
access (RSA) policy and prioritized spectrum access (PSA) policy. In RSA, any
of the available channels including the channel used by the D2D transmitters
can be selected randomly for cellular communication, while in PSA the D2D
channel is used only when all of the other channels are occupied. A D2D
transmitter can communicate successfully with its receiver only when it
harvests enough energy to perform channel inversion toward the receiver, the
D2D channel is free, and the at the receiver is above the
required threshold; otherwise, an outage occurs for the D2D communication. We
use tools from stochastic geometry to evaluate the performance of the proposed
communication system model with general path-loss exponent in terms of outage
probability for D2D and cellular users. We show that energy harvesting can be a
reliable alternative to power cognitive D2D transmitters while achieving
acceptable performance. Under the same outage requirements as
for the non-cognitive case, cognitive channel access improves the outage
probability for D2D users for both the spectrum access policies.Comment: IEEE Transactions on Communications, to appea
Analysis and Optimization of Cellular Network with Burst Traffic
In this paper, we analyze the performance of cellular networks and study the
optimal base station (BS) density to reduce the network power consumption. In
contrast to previous works with similar purpose, we consider Poisson traffic
for users' traffic model. In such situation, each BS can be viewed as M/G/1
queuing model. Based on theory of stochastic geometry, we analyze users'
signal-to-interference-plus-noise-ratio (SINR) and obtain the average
transmission time of each packet. While most of the previous works on SINR
analysis in academia considered full buffer traffic, our analysis provides a
basic framework to estimate the performance of cellular networks with burst
traffic. We find that the users' SINR depends on the average transmission
probability of BSs, which is defined by a nonlinear equation. As it is
difficult to obtain the closed-form solution, we solve this nonlinear equation
by bisection method. Besides, we formulate the optimization problem to minimize
the area power consumption. An iteration algorithm is proposed to derive the
local optimal BS density, and the numerical result shows that the proposed
algorithm can converge to the global optimal BS density. At the end, the impact
of BS density on users' SINR and average packet delay will be discussed.Comment: This paper has been withdrawn by the author due to missuse of queue
model in Section Fou
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
A Stochastic Geometry-based Demand Response Management Framework for Cellular Networks Powered by Smart Grid
In this paper, the production decisions across multiple energy suppliers in
smart grid, powering cellular networks are investigated. The suppliers are
characterized by different offered prices and pollutant emissions levels. The
challenge is to decide the amount of energy provided by each supplier to each
of the operators such that their profitability is maximized while respecting
the maximum tolerated level of CO2 emissions. The cellular operators are
characterized by their offered quality of service (QoS) to the subscribers and
the number of users that determines their energy requirements. Stochastic
geometry is used to determine the average power needed to achieve the target
probability of coverage for each operator. The total average power requirements
of all networks are fed to an optimization framework to find the optimal amount
of energy to be provided from each supplier to the operators. The generalized
-fair utility function is used to avoid production bias among the
suppliers based on profitability of generation. Results illustrate the
production behavior of the energy suppliers versus QoS level, cost of energy,
capacity of generation, and level of fairness.Comment: 6 pages, 4 figure
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