Resource Allocation in Hospital Networks Based on Green Cognitive Radios 王冉茵

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 non- linear 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.4, conclusion is drawn and future work is highlighted. Sect.3 presents the numerical results and analytical observations related to the simulation results. 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.

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. A min-max formulation. transform 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 CO ₂ 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 non- decreasing 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: We can also write (3) as Where (3) (4)

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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