Resource Allocation in Hospital Networks
Based on Green Cognitive Radios
王冉茵
2015.12.09
Abstract
This paper presents an approach to solve the
joint call admission control and power allocation
problem in a hospital environment based on green
cognitive radio.
e-Healthcare
e-Healthcare is the integration of digital data processing,
computing and communication technology into the traditional
healthcare services.
Introduction
1.Pervasive health monitoring.
2.Traditional health systems.
3.e-Health systems design and
deployment.
4.Related work in the area
earlier.
Contribution
The earlier work is focused on the single objective
function namely, capacity maximization, guaranteed data rate,
guaranteed access minimum delay, minimum energy
consumption and minimum deployment and maintenance cost.
 Improvement
Formulating the resource allocation problem in e-Health
networks as a multi-objective non-convex mixed integer nonlinear programming (MINLP) problem.
Main Work
In this paper, they invoke outer approximation approach(OAA)
based linearization technique to solve the formulated joint
admission control, mode selection and power allocation
problem.
The proposed method gives guaranteed ε convergence to the
optimal solution results with reasonable computational
complexity.
Organization of the Paper
Sect.1, the paper first depicts an introduction, motivation and evolving
scenarios of the e-Health environment and the earlier related work.
Sect.2, problem formulation has been discussed by considering a system model
showing a peculiar uplink scenario where different users try to communicate
with the CPP.
Sect.3 presents the numerical results and analytical observations
related to the simulation results.
Sect.4, conclusion is drawn and future work is highlighted.
System Model and Formulation : Communication Network
in an e-Health Center
Figure 1 shows an e-Health
center consisting of three
areas and two hallways
making a department within
a hospital. There are patients,
doctors, nurses, specialists
and supporting staff working
in the center.
Three types of users in the e-Health center:
1. Protected users are passive medical devices, they don’t
transmit any data, but they are very sensitive with EMI.
2. Primary users are active devices, which can transmit
wireless signals, intended for therapeutic use.
3. Secondary users are another kind of active medical
devices ,that can transmit data opportunistically.
Weighted Resource Maximization
Problem
A multi-objective optimization
problem.
transform
A min-max formulation.
Weighted
sum
method.
The first objective
 The first objective is to maximize the number of selected
users, that is
 Minimization of the defined objective function can be
expressed as under:
 Define a binary variable
 Also define another vector.
 Then ,the total number of selected users can be written as:
 Thus, the formulation for the first objective is:
The second objective
 The second objective is to minimization the CO₂ emissions.
The third objective
 The third objective is to maximize the data rate of each user
while ensuring the minimum data rate requirement of each
secondary user.
 The above maximization objective can be expressed as:
 Use weighted sum method for this to combine the multiple
objectives in the optimization problem with w1; w2; w3
weights. Overall resource allocation problem is expressed as
follows:
(1)
The above formulation is a multi-objective
convex mixed integer non-linear
programming (MINLP) problem which is
generally NP-Hard.
Proposed Approach to a Solution
The optimization problem in (1) has a very special
structure.
With known discrete variables, the objective function
of (1) is a concave function in power, and all the constraints
are either linear or convex.
By exploiting this special structure, in this section we
will present a OAA to solve (1).
Algorithm Description
It is easily to prove that (1) satisfies the following propositions:
The OAA uses sequence of non-increasing upper and nondecreasing lower bounds for mixed integer problems that
satisfy the propositions 1–4.
The OAA converges in a finite number of iterations with
ε-convergence capability.
The primal problem is obtained by fixing θ variables. At
the jth iteration of OAA, let the values of integer variable be .
We can write the primal problem as:
(2)
 The algorithm will terminate when the difference between the
two bounds is less than ε. The master problem is derived in two
steps: In the first step, we need projection of (1) onto the integer
space-θ. We can rewrite the problem (1) as:
(3)
We can also write (3) as
(4)
Where
 The problem (4) is the projection of (1) on θ space. Since a
constraint qualification holds at the solution of every primal
problem (2) for every , the projection problem has
the same solution as the problem below:
 By introducing a new variable η, we can rewrite an
equivalent minimization problem as:
 A pseudo code for OAA is given in Algorithm 1.
Discussion on Algorithm Optimality and
Convergence
 If the problem holds all four prepositions and the discrete
variables (θ) are finite, then the Algorithm 1 terminates in a
finite number of steps at an ε -optimal solution.
 The algorithm is finitely converging.
Numerical Results
 Appropriate values are assigned to all notations as shown in Table 2.
 The simulation is performed with equal weights in (1) for
three different parts of the objective function with
 The simulation was repeated for different values and
combination of K, M and L as shown in Table 3.
 The similar response is observed when the maximum transmit
power
is varied with fixed
as shown in Fig. 4.
 Requirement of transmit power versus number of secondary
users has been shown in Fig. 6 for different values of
.
 Figures 7 and 8 show throughput of all secondary users.
Conclusions
This paper presented an approach to solve the
joint admission control and power allocation
problem in a hospital environment based on
cognitive radio.
Conclusions
Specifically, a MINLP problem for wireless access in a
hospital environment has been formulated to maximize the
number of admitted secondary users and minimize
transmit power and carbon dioxide emission.
Conclusions
The approach also satisfies the throughput of all
secondary users and the interference constraints for the
protected and primary users.
Conclusions
To solve this MINLP problem, they proposed an
enhanced standard branch and bound algorithm OAA to
find the optimal solution.
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