MAXIMIZING SPECTRUM UTILIZATION OF COGNITIVE RADIO NETWORKS USING CHANNEL ALLOCATION AND POWER CONTROL

Anh Tuan Hoang and Ying-Chang LiangVehicular Technology Conference, 2006. VTC-2006 Fall. 2006

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Outline

Introduction Problem Definition Channel Allocation / Power Control

Algorithms Numerical Results and Discussion Conclusion and Comments

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Introduction

Consider a cognitive radio (CR) network in which a set of base stations make opportunistic unlicensed spectrum access to transmit data to their subscribers

The objective of this paper Maximize the spectrum utilization of the

cognitive network while appropriately protecting primary users

Develop spectrum-allocation/power-control schemes

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Introduction (cont’d)4

Pros and Cons for CR networks By allowing opportunistic spectrum access, the

overall spectrum utilization can be improved. Transmission from cognitive networks can

cause harmful interference to primary users of the spectrum.

Important design criteria for cognitive radio network Maximizing the spectrum utilization and

minimizing the interference caused to primary users

Introduction (cont’d)5

The operational constraints The total amount of interference caused by all

opportunistic transmissions to each PU must not exceed a predefined threshold

For each CPE, the received signal to interference plus noise ratio (SINR) must exceed a predefined threshold

The system utilization The total number of CPEs that can be supported

while meeting the above two constraints The utilization maximizing problem can be

structured as a linear mixed (0-1) integer programming.

Introduction (cont’d)6

However, solving for an optimal solution of the linear programming is NP-hard. Propose a heuristic scheme for channel

allocation and power control This heuristic scheme’s concept is based on

Using a dynamic interference graph that captures not only the pair-wise but also aggregate interference effects when multiple transmissions happen simultaneously on one channel.

Introduction (cont’d)7

Works on channel allocation and power control problem Model interference effects based on the SINR

include [6] and [7] The objective of [6] is to maximize spectrum utilization, [7] is to minimize total transmit power to satisfy the rate

requirements of all links. Power control problems for concurrently

interfering transmissions with the objective of guaranteeing SINR constrains In this paper, they use Perron-Fronbeniuos

theorem to check the feasibility of a particular channel allocation[6] A. Behzad and I. Rubin, “Multiple access protocol for power-controlled wireless access nets,”

IEEE Transactions on Mobile Computing, vol. 3, no. 4, pp. 307–316, Oct.-Dec. 2004.[7] G. Kulkarni, S. Adlakha, and M. Srivastava, “Subcarrier allocation and bit loading algorithms for OFDMA-based wireless networks,” IEEE Transactions on Mobile Computing, vol. 4, no. 6, pp. 652–662, Nov./Dec. 2005.

Problem Definition8

System model Number of channels: K Number of primary users: M CR Network consisting of B

cells Within each cell, there is a base

station (BS) serving a number of fixed customer premise equipments (CPEs)

Number of CPEs: N Considering the downlink

situation in which data are transmitted from BSs to CPEs

Problem Definition (cont’d)9

Operational requirements SINR requirement for CPEs:

is the SINR at CPE i. is the channel gain from the BS serving CPE j to

CPE i on channel c is denoted as the transmit power for the

transmission toward CPE i on channel c.

Aggregate interferencecicijG

ciP

The inequality can be regarded as the minimum SINR to achieve a certain bit error rate (BER) performance at each CPE.

Problem Definition (cont’d)10

Protecting primary users

(zeta-bar) is the predefined tolerable threshold of primary user

is the channel gain from the BS serving CPE i to PU p on channel c

is denoted as the set of all Pus that user channel c

For each PU, the total interference from all opportunistic transmissions does not exceed a predefined tolerable threshold

cpiG

c

Problem Definition (cont’d)11

Maximizing spectrum utilization The objective function is find out the maximum

total number of CPE served

Let aci be a binary variable denoting

whether or not channel c is assigned to the transmission toward CPE i.One CPE only can occupy a channel at a time.

SINR Requirement for Active CPEs(δ is a relatively large constant)

The Protecting Primary Users’ Constraint

Maximum Power Constraint.

Problem Definition (cont’d)12

Feasible assignment Let us deal with the question of whether it is

feasible to assign a particular channel c simultaneously to a set of transmissions toward m CPEs: (i1, i2, . . . im).

Feasibility means there exists a set of positive transmit power levels Pc = (Pc

i1, Pci2, .

. ., Pcim)T

all the SINR constraints of the m CPEs are met while the interferences caused to PUs do not exceed the acceptable threshold.

Problem Definition (cont’d)13

The Pareto-optimal transmit power vector is

Problem Definition (cont’d)14

Two-step Feasibility Check: Step 1:

Check if the maximum eigen-value of matrix Fc defined in (10) is less than one. (From the Perron-Frobenious Theorem)

If not, conclude that the assignment is not feasible, otherwise, continue at Step 2.

Step 2: Using (12) to calculate the Pareto-optimal transmit power

vector Pc∗. Then, check if Pc∗ satisfies the constraints for protecting PUs in

(7) and the maximum power constraints in (8). If yes, conclude that the assignment is feasible and Pc∗ is the

power vector that should be used. Otherwise, the assignment is not feasible.

Channel-Allocation/Power-Control Algorithms

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Constructing an interference graph To represent the interference between pairs of

unserved CPEs. Moreover, this interference graph must also take

into account the aggregate interference caused by transmissions that have been allocated

channels in previous steps.

To implement the Dynamic Graph Based approach At each step, for each unserved CPE i,

Calculate its node degree corresponding to a channel c and prior channel-allocation matrix Asgn.

Channel-Allocation/Power-Control Algorithms (cont’d)

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Node degree representation Deg(i, c, Asgn) Deg(i, c, Asgn) = ∞ if it is not feasible to assign

channel c to user i while keeping all prior assignments.

If it is feasible, Deg(i, c, Asgn) is the total number of unserved CPEs

that can not be assigned channel c anymore when this channel is assigned to CPE i.

The algorithm then picks a CPE-channel pair [i∗, c∗] that minimizes Deg(i, c, Asgn) and assigns channel c∗ to CPE i∗.

Channel-Allocation/Power-Control Algorithms (cont’d)

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UnSrv is the set of unserved CPEs.

[4-6] No more feasible CPE condition.

[8-10] All CPEs are served.

[3] Pick up the best CPE from UnSrv

Numerical Results and Discussion

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Simulation Model A square service area of size 1000×1000m in

which a cognitive radio network is deployed. Model an orthogonal frequency division

multiple access (OFDMA) system No = −100dBm. The required SINR at each CPE is 15dB. The maximum tolerable interference for each PU

is 90dBm. For each BS, the maximum transmit power on each channel is Pmax = 50mW.

Numerical Results and Discussion (cont’d)

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Performance in terms of no. of CPEs served versus no. of PUs. No. of BSs = 4, no. of CPEs = 40, no. of channels = 16.

Performance in terms of no. of CPEs served versus no. of PUs. No. of BSs = 9, no. of CPEs = 40, no. of channels = 16.

Numerical Results and Discussion (cont’d)

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Performance in terms of no. of CPEs served versus no. of PUs.No. of BSs = 9, no. of CPEs = 40, no. of channels = 8.

Performance in terms of no. of CPEs served versus no. of PUs.No. of BSs = 16, no. of CPEs = 40, no. of channels = 4.

Conclusion21

Propose a heuristic channel-allocation/power-control algorithm A realistic control framework is formulated to

guarantee protection to primary users and reliable communications for cognitive nodes.

Future works Consider fairness among CPEs A joint network-admission/resource-allocation

framework

Comments22

Feasibility Test Minimum degree greedy scheme to solve

the problem The lack of simulation