Buffer-Aware Power Control for Cognitive Radio Networks Eman Naguib, Tamer ElBatt, Mohammed Nafie Wireless Intelligent Networks Center (WINC), Nile University, Cairo, Egypt

[email protected], {telbatt,mnafie}@nileuniversity.edu.eg Abstract-In this paper we study the problem of buffer-aware power control in underlay cognitive radio networks. In particular, we investigate the role of buffer state information, manif ested through the secondary users' queue lengths, along with channel state information in the cognitive radio power control problem. Towards this objective, we formulate a constrained optimization problem to find the set of secondary user transmit powers that maximizes the sum of rates weighted by the respective buffer lengths subject to signal-to-interf erence-and-noise-ratio (SINR) and maximum power constraints. Motivated by the problem's non-convexity, we cast the problem into a sequential Geometric Programming formulation which can be solved efficiently using known solvers. Our simulation results confirm the throughput­ delay trade-off v ia comparing the per formance of the buffer­ aware scheme, measured in terms of throughput and queue length, to a baseline, buffer-independent scheme that simply maximizes the sum rate of the secondary users. The gathered numerical results reveal interesting insights about the problem. It demonstrates almost two-fold reduction in the average secondary transmitter queue length for the proposed scheme over the baseline. This is attained at the expense of slight degradation (e.g. 15% in our scenario) in the secondary sum rate (throughput).

and secondary users in an attempt to maXImIze the sum throughput of the secondary network. However, the buffer state information is not incorporated into the problem formulation. In

[5]-[7] the problem

of power allocation for secondary users

under interference constraints on the primary users, along with a minimum QoS constraint on each secondary user, is studied. The prime objective of

[5], [6]

is to maximize

the number of secondary links admitted to the network. On the other hand, the objective function, to be maximized, in

[7]

is the secondary users' sum throughput. In

[8],

the

authors explore the role of buffer state information (BSI), alongside with channel state information (CSI), in order to better understand their individual roles and potential trade-offs, in a downlink orthogonal frequency division multiple access (OFDMA) multi-user setting. Our work also focuses on the BSI vs. CSI roles in the wireless resource allocation problem, yet, in a different setting giving rise to the following two major differences from

[8]:

i) focusing on the interference channel

as opposed to the downlink broadcast channel and ii) having

This, in turn, confirms the key role buffer state information plays in balancing the f undamental throughput-delay trade-off in cognitive radio networks and opens ample room for f uture

two classes of users in a CRN, namely primary and secondary

research on multiple access and optimal resource allocation in delay-constrained cognitive radio networks.

focus in

users, as opposed to the legacy OFDMA network setting under

[8]. [9]

Perhaps

is the closest to our work as it studies the

problem of maximizing a buffer weighted sum-throughput

I. INTRODUCTION

via geometric programming. Motivated by their interest in

Cognitive radio networks (CRN) have received considerable

characterizing the capacity region for fading broadcast chan­

attention from the wireless research community over the past

nels, the authors explore a number of scheduling policies,

decade. A cognitive radio is formally defined as a radio that

among which is the queue proportional scheduling (QPS)

can change its transmitter parameters based on interaction with

policy, which maximizes a weighted sum throughput objective

the environment in which it operates [1]. The term " Cognitive

function similar to our work. Unlike this work,

Radios" (CR) was first coined by J. Mitola in the late 1990s

data rate vector such that the expected rate vector over all

to exploit the highly under-utilized scarce and non-renewable

fading states is proportional to the current queue state vector

wireless spectrum

[2]

[9]

finds a

. Cognitive radio users, which are also

and is on the boundary of the ergodic capacity region of a

called secondary users (SU), can opportunistically utilize the

fading broadcast channel. Our work, on the other hand, finds

spectrum that is unutilized by the licensed users (primary users

the power vector maximizing the buffer length weighted sum

(PU» during periods of communications inactivity. In general,

rate for a fading interference channel model.

the wireless spectrum can be divided into several channels,

In this work, we leverage the buffer state information (BSI)

either non-overlapping or partially overlapping. A channel is

and explore its role, along with the channel state information

said to be available to an SU if no PU is active on that channel

(CSI). This is of paramount importance to gain key insights

(as in the overlay cognitive radio systems) or if the interference

about the sum rate maximization power control problem

introduced by this SU to the active PUs is tolerable (as in the

and the potential role of BSI in balancing the fundamental

underlay cognitive radio systems)

throughput-delay trade-off. Towards the aforementioned ob­

[3].

In this paper, we consider the problem of buffer-aware

jective, we formulate a constrained power control optimization

power control for secondary users in underlay cognitive radio

problem, for a set of primary and secondary interference links,

networks. In

that maximizes the weighted sum rate, where the weights are

[4],

powers are allocated for, both, primary

978-1-4673- 5051-8/12/$31.00 ©2012 IEEE

1036

Asilomar 2012

taken to be the secondary user queue lengths. Unfortunately,

both the buffer state information (BSI) and the channel state

the problem is found to be non-convex. However, it can be

information (CSI) at all secondary users in each time slot.

cast into a geometric programming framework, known to be convex

[10],

and, hence, can be solved efficiently using known

solvers, e.g., Matlab

cvx

[11].

The signal to interference plus noise ratio (SINR) of sec­ ondary user link i is given by

Our simulation results reveal key

insights about the problem at hand and the associated trade­ offs and exhibit a compelling approach to balance the fun­ damental throughput-delay trade-off via simply incorporating the queue lengths as weights in the original sum rate objective function used in the buffer-independent baseline scheme. Our main contribution in this paper is two-fold. First,

Pthfi (1) N pP p s N0+ uk=1 k hPkis + ", Uj=I,ji'i jShji where P t is secondary link i's transmit power, pe is primary link k's transmit power , hji is the channel gain between the SINRSt

=

",M

transmitter and the receiver of the jth and ith secondary links,

h�:

accounting for the BSI, along with the CSI, in a secondary

respectively and

user power control problem in a cognitive interference channel.

ter of the kth primary link and the receiver of the ith secondary

Second, casting the non-convex optimization problem at hand

link.

into a geometric programming framework, guided by earlier

(AWGN). The packet arrival process at secondary transmitter

attempts in the literature for a variety of problems in different

i is assumed to be Poisson with rate

settings, e.g.,

[12].

This, in turn, yields considerable compu­

No

is the noise power of additive white Gaussian noise

A. The Baseline Problem

II introduces the system model and assumptions underlying control problem in fading interference channels, with primary and secondary links, assess its complexity and present the solution approach, via casting it into a geometric programming framework, in Section III. In Section IV, we present our performance evaluation study and associated numerical results in an attempt to quantify the benefits, and potential trade-offs, associated with the concept of buffer-aware power control. Finally, conclusions are drawn and potential directions for future work are pointed out in Section V. II.

In this section, we formulate the baseline power control problem for cognitive interference channels which would be later extended to the buffer-aware power control problem in the next subsection. The baseline problem is similar to the problem addressed in

N

[14] except for the cognitive radio network setting

that imposes guaranteed QoS constraints for primary users. The prime objective of this problem is to find the set of transmit powers that maximizes the sum rate (i.e. throughput) of the

N secondary links subject to primary QoS (SINR) con­

straints and secondary maximum power constraints. Formally, the problem can be written as follows

SYSTEM MODEL

N

We consider a cognitive radio network with M primary links and

Y i.

RADIO NETWORKS

expensive brute-force exhaustive search.

this work. Afterwards, we formulate the buffer-aware power

Ai,

III. BUFFER-AWARE POWER CONTROL FOR COGNITIVE

tational savings compared to, the otherwise computationally The rest of this paper is organized as follows. Section

is the channel gain between the transmit­

max

secondary links, sharing the same frequency channel

2:: 10g(1+SINRf)

(2)

i=1

with different transmitter-receiver pairs, i.e. comprising an s.t.

interference channel. The cognitive radio network is assumed to operate in the underlay mode whereby the secondary users can coexist and share the same frequency channel with the

SINR� 21'� 0::; Pt ::; Pmax

Yk Yi

primary user(s) with the aid of power control and interference

I'f

management schemes. Time is divided into fixed duration slots

where

where multiple packets may fit in a single slot depending on

successful decoding at the receiver of primary link k. The first

the data rate attained. This, in turn, depends on the transmit

constraint in

power decided by the optimization problem, buffer state infor­

at the primary receiver. This problem is known to be non­

mation, channel state information and interference conditions.

convex in the transmit powers in the presence of interference

is the mInimum SINR requirement necessary for

(2)

represents the SINR threshold requirement

10g(1 + SINR)

We adopt a quasi-static Rayleigh block fading channel model

which renders the rate function

whereby the channel is fixed over a number of slots, denoted t,

Nevertheless, it has been shown in

depending on the channel coherence time defined as the period

this problem nicely lends itself to geometric programming

over which the channel impulse response is assumed invariant

[13]

. The primary user is assumed to always have packets to

transmit and, hence, attempts transmission in each slot with probability one using a pre-specified fixed power known to

[14]

non-concave.

that the structure of

formulations known to be convex and, hence, can be solved efficiently using known solvers.

B. Buffer-aware Power Control Problem Formulation

the secondary users. At this first look at the problem, we

In this section, we formulate the buffer-aware power control

adopt a centralized solution while defer the more challenging

problem for cognitive radio interference channels via incor­

distributed solutions to future research. The central authority

porating the queue lengths of the secondary users in the

solving the problem is assumed to have perfect knowledge of

optimization objective function. In particular, we introduce a

1037

We solve the geometric program formulated in

generalized objective function in the form of weighted sum

[4]

throughput where the weights are chosen to be the queue

the lines of

lengths at the secondary transmitters. The proposed structure

discussed next.

of the objective function and weights are inspired by the

(as an indicator of delays). Accordingly, nodes with longer

along

The original objective function is given by

intuition that the buffer-aware scheme constitutes an attempt to trade-off between the sum rate throughput and buffer lengths

(4)

where we adopt the iterative solution approach

N

queues should get higher rates in order to empty their queues

Ii

[i.

II (1 + SINRD'Lf=ll; i=l

g(SINRS) = max

i = 1,2,." N

is the buffer state information for user

i.

faster and, hence, maintain shorter queues than the baseline on

where

the average, as confirmed by the numerical results. It should

function is equivalent to maximizing the weighted sum-rate

This

be noted that the above policy is sub-optimal in the sense of

function known to be non-convex in the powers and, hence,

sum rate throughput optimality, as will be shown in Section

is also non-convex in the powers. Therefore, we propose an

IV. However, the loss in throughput performance is tolerable

approximate function which is cast as a GP function, solved

compared to almost two-fold reduction in the average queue

in iterations to achieve a required accuracy that is

length. The buffer-aware power control problem solved in each

N

SINRf?'Yf

s.t.

0::;

where

Ii

In order to guarantee convergence, the approximate function

I'

L N log(1+S1NRD i=l Lj=llj

max

c II(SINRDi

g(SINRS) = max

time slot can be written formally as,

(3)

Vk Vi

Pt ::; Pmax

g(SINRS)

needs to satisfy

[12]:

1) g(SINRS) ? g(SINRS) VSINRs? ° 2) g(SINR�) = g(SINR�) 3) 'Vg(SINR�) = 'Vg(SINR�) where the symbol

'V is the gradient.

SINR�

Also,

denotes the

solution to the approximate problem in the previous iteration.

is the queue length at secondary transmitter

i.

The above three conditions are sufficient to guarantee that the Once

solution of each approximate problem increases the objective

is non-convex in

function. Furthermore, they guarantee that upon convergence

the transmit powers due to the role of interference in the rate

to the solution, the Karush-Kuhn-Tucker of the original prob­

function.

lem are satisfied. From the above three conditions (similar to

more, it is straightforward to show that

(3)

Fortunately, we show in the next section that this complexity

[4]),

hurdle can be circumvented via exploiting the structure of the


objective function and constraints which can be approximately cast into a geometric programming formulation. C. Reformulation as a Geometric Program (GP)

c=

This reformulation involves casting the objective function in a monomial form and the constraints in a posynomial form [12]. For ease of exposition, we first define a monomial as f : ++ --+ and f() = d where the mUltiplication constant d ? 0, and the exponential constants

Rn

x xla(l) X2a(2) ... ,Xna(n) R and a posynomial is the

R

a(i), i

=1,2,3 .. ,n E sum of monomials. Thus, the resulting geometric program can written down as N

max

S.t.

c

II(SINRf)i i=l

SINRf(No

+

SINR�?:.I� o

::; P/ ::; Pmax

where c and


(4)

'£�1 P;:h�: (P/hfi )

+

,£f=l,Ni PfhJi)

it can be shown that

Ii

L;:'=llm

)(

(SINR�i) ) 1 + SINR�i

(5)

,.

n(1 + S1NRS )� nS1N�:i at

(6)

We chose the weights to be the BSI of a secondary transmitter divided by the sum of all secondary transmitters queue lengths, in order to prevent the solver from going to infinity during the maximization of the objective function. The addition here to the manipulations done in

[4]

is that

we multiply a function and its approximate function by the same constant and so evaluating c and lines of what is done in

[4].


goes along the same

The problem becomes a sequential geometric programming problem, which can be solved iteratively by updating c and

::; 1


Vk

SINRS

values reached in

specified accuracy level is attained. It can be shown that c and

Vi

are parameters that depend on the queue


lengths, among other things, and will be discussed next. The first constraint arises from the definition of SINR in

in each iteration based on the

the previous iteration. Convergence is claimed when a pre­

(1)

to define the SINR of the secondary user in an acceptable posynomial for the geometric program.

are given in the following form for the baseline system:


SINR�i ,- 1 + SINR�i _

n(1 + SINR�i) n(SINR�i)i

- -:-':'-'''c= ==:'-:-,----

1038

(7) (8)

100 ,---�---,----.--,---,

IV. PERFORMANCE EVALUATION A. Simulation Setup

50

The system studied assumes fixed data rate of packet size of

1000

bits and the time period,

t,

kbps,

70 60 50 40 30 20 10

over which the

channel is assumed to be constant according to the quasi-static block fading channel assumption is

200

time slot

(The channel is constant

over

(ts)

3 time

in the block is 67

msec

msec

meanwhile a

slots). We assume a Rayleigh fading channel and

10-9

the noise power is

W. Both the secondary and primary

nodes' locations are randomly decided, yet, fixed throughout a

°0�--�--�4=-�� 6 �=8� --�1�0 --�12�--�14--�'6 Average Secondary Arrival rates (Packets/sec)

single simulation run. We consider the variance of the channel

Fig. 2. The average buffer length for secondary user 2

gain between direct links to be

1

W, between secondary users'

0.64 W 0.0625 W.

interfering links to be primary links to be

and between secondary and

Despite the fact that the mathematical framework in

(4)

I�800

is general and works for arbitrary number of primary and secondary links, we limit our attention here to a small a system of M

=

1 primary

links and

N

=

2 secondary



� •

links for ease

600 '" .� 400

of exposition and to shed light on the fundamental trade-off at hand. Moreover, this small system renders the simulation run



time reasonable for the numerous scenarios examined using

200

300 different random channel realizations and 10 packet arrival

0

rates representing different network loading conditions. The packet arrival rate for secondary user from

0.1

to

2.9

denoted

A2

Average Secondary Arrival rates (Packets/sec)

ranges

Fig. 3. The average buffer length for the secondary system

packets/sec. On the other hand, the packet

arrival rate for secondary user primary power is fixed to powers are both

2,

20 W.

10

1 is

given by

Af

=

lOA2'

1---::-� � � =:::, =-,: 12 ----:-:--4 6 8 10 14 � 16

o

The

W and the secondary links peak

The following results are those of the

Geometric Program given in

(4).

B. Simulation Results In this section, we present the simulation results that not only unveal the merits of the proposed buffer-aware power

- Baseline ...... Buffer-Aware

control scheme but also exhibit its salient features compared to the legacy buffer-independent baseline scheme studied earlier in the literature. Simulations are averaged over

300 different

is averaged over the channel quasi-static block of three time slots, and then calculated at

10 different

16

4 6 8 10 12 Average Secondary Arrival rates (Packets/sec)

randomly generated rayleigh fading channels in which each

Fig. 4. The secondary system average sum-throughput

arrival rates of the

poisson process. 200 ,-----,-----�---.--,_--_, 180 160 140 120 100 80 60 40 20

I'

I -, -, Baseline - Buffer-Awarel ,.,

':-

" , . .,. -

.i

,

' , .. "

,

:.----------,., ..-�--,-,=50-----O= 2O�--� °0��--� 50���, 00 25�--�= 0 Time (Slots)

L.--�----4==:;;6 ::���� =-12�=-�--J'6 O 1�:: 8

oO

Average Secondary Arrival rates (Packets/sec)

Fig. 5. Average Buffer Size for user 1 vs. Time for

Af

=

10.333 packets/second.

Fig. 1. The average buffer length for secondary user 1

Figures

1

and

secondary user

2

of both users (in packets/sec). The following key observations plot the average buffer size (in packets) for

1 and 2, respectively, vs. the average arrival rate

can be distilled. First, the average buffer sizes for both users, under the baseline as well as the proposed buffer aware power

1039

15 rr==::===;--�--�--�---C---:=:7l Baseline -Buffer-Aware

1 ,·,-,

.'.'.'.'.'.'.'.'.'.'"

.1

investigate the role of the buffer state information, manifested through the secondary users' queue lengths, along with the

I

i

channel state information in the cognitive radio power con­ trol problem. First, we formulated a constrained optimization

10 I

problem to find the set of secondary user transmit powers

, . ..:-,.,.,1

that maximizes the buffer length weighted sum rate subject



!'- - - - . -

J.--..:!

to signal-to-interference-and-noise-ratio (SINR) and maximum

_�-----J

..� - . -'

power constraints. Second, motivated by the sheer complexity

_

of the original problem attributed to its non-convexity, we

OO O -�2��-�= � -���-��0, 0�-�I� � ---2�O�

cast the problem into a sequential Geometric Programming

Time (Slots)

formulation which can be solved efficiently using known

,\� =1.0333 packets/second.

solvers. The simulation studies reveal interesting insights

control scheme, monotonically increase with the packet arrival

scheme shortens the secondary users queue lengths, on the

Fig. 6. Average Buffer Size for user 2 vs, Time for

about the problem. We note that the proposed buffer aware

rate which agrees with intuition. It is worth noting that the

average, while satisfying the primary's QoS constraints. The

slight dip observed in the baseline curves in figures 1,

overall system average buffer size is improved, at the expense

and

3

2

are attributed to simulation artifacts. Furthermore, it

of a slight decrease in the secondary network throughput.

can be observed that, in contrast to the baseline algorithm,

These results constitute a motivation for potential directions

our proposed scheme reduces the average buffer size for both

of future research, e.g., distributed buffer-aware power control

users. We also notice that both buffers grow with increasing

algorithms as well as generalize the problem to incorporate

arrival rates as shown in Figures

link scheduling and secondary user QoS constraints.

1

and

2,

but our scheme

allows an evident abate in the growth rate especially for user

1.

REFERENCES

Although both schemes become unstable and the queue lengths

[I] I. Akyildiz, W. Lee, M. Vuran, and S. Mohanty, "Next genera­

grow rapidly at high loads, it is evident that for the shown

tion/dynamic spectrum access/cognitive radio wireless networks: A survey," ELSevier Computer Networks, vol. 50, pp. 2127-2159, Sep.

range of moderate loads, the baseline scheme breaks down at a lower rate compared to the buffer-aware scheme. Figure

3

2006. [2] j, Mitola III, "Cognitive radio: An integrated agent architecture for

confirms the aforementioned trends and relative performance

software defined radio," PhD, dissertation, KTH Royal Institute of Technology, May 2000, [3] j, Xiang, Y. Zhang, and T. Skeie, "Medium access control protocols in cognitive radio networks," Wiley Wireless Communications and Mobile Computing Journal, vol. 10, no, l, pp. 31-49, Sep. 2010, [4] j, Tadrous, A, Sultan, M, Nafie, and A. El-Keyi, "Power control for constrained throughput maximization in spectrum shared networks;' in

observations for the average system buffer length. Hence, it confirms the queue length benefits of the buffer-aware scheme compared to the buffer-independent baseline. However, this constitutes only one side of the story. On the other hand, Fig.

4

covers the other side of the

IEEE GLOBECOM'JO, 2010. [5] L. Le and E, Hossain, "Resource allocation for spectrum underlay in

trade-off, namely the throughput measured in the sum rate of the secondary users. Two important observations are in order.

cognitive radio networks," IEEE Transactions on Wireless Communica­ tions, vol. 7, no, 12, pp, 5306-5315, December 2008. [6] Y. Xing, C, Mathur, M. Haleem, R. Chandramouli, and K. Subbalak­ shmi, "Dynamic spectrum access with qos and interference temperature constraints;' IEEE Trans, on Mobile Computing, vol. 6, no, 4, pp, 423433, April 2007, [7] D. Kim, L. Le, and E. Hossain, "joint rate and power allocation for cognitive radios in dynamic spectrum access environment," IEEE Transactions on Wireless Communications, vol. 7, no, l2, pp, 55175527, December 2008, [8] R. Agarwal and j, Cioffi, "Optimal resource allocation in the ofdma downlink with feedback of buffer state information," in IEEE GLOBE­

First, the throughput of both systems saturates after certain network load beyond which the nodes tend to use powers close to Pmax which gives rise to the witnessed saturation behaviour. Second, the buffer-ware system throughput, although degrades, the degradation is fortunately tolerable. This, in turn, makes a strong case for the proposed buffer-aware scheme and its potential role in future cognitive radio MAC and resource allocation schemes. This is especially true given its simplicity. Finally, we take a closer look at the dynamic behavior of

COM'09, 2009, [9] K. Seong, R, Narasimhan, and j, Cioffi, "Queue proportional scheduling

the queue lengths, under the two systems, over the course of the simulation. This is done for a given packet arrival rate at both users. It is straightforward to notice that the buffer-aware scheme consistently outperforms the baseline buffer-independent scheme, with respect to the average buffer length, throughout system operation. This profound observa­ tion confirms the great promise the proposed scheme holds in balancing the fundamental throughput-delay trade-offs in future multi-user cognitive radio networks.

[10] [II] [l2] [13] [14]

V. CONCLUSION We studied the problem of buffer-aware power control in underlay cognitive radio networks. More specifically, we

1040

via geometric programming in fading broadcast channels," IEEE JSAC, vol. 24, no. 8, pp, 1593-1602, Aug. 2006, S, Boyd and L. Vandenberghe, Convex Optimization, United Kingdom: Cambridge University Press, 2004, M, Grant and S, Boyd, "Cvx: Matlab software for disciplined convex programming," hllp:llstanford.edul boydlcvx, 2009, M, Chiang, C. Tan, D, Palomar, D. O'Neill, and D, julian, "Power control by geometric programming;' IEEE Transactions on Wireless Communications, vol. 6, no. 7, pp, 2640-265l, july 2007. T. Rappaport, Wireless Communications: Principles and Practice. Pren­ tice Hall, 1996. M, Charafeddine and A, Paulraj, "Sequential geometric programming for 2 x 2 interference channel power control;' in C1SS, 2007.

Buffer-Aware Power Control for Cognitive Radio ...

CSI roles in the wireless resource allocation problem, yet, in a different setting ... (CSI). This is of paramount importance to gain key insights about the sum rate maximization power control problem and the potential role of BSI in balancing the fundamental throughput-delay ... solvers, e.g., Matlab cvx [11]. Our simulation ...

919KB Sizes 3 Downloads 253 Views

Recommend Documents

Joint Scheduling and Flow Control for Multi-hop Cognitive Radio ...
Cognitive Radio Network with Spectrum Underlay ... multi-hop CRN overlay with a primary network in [2]. .... network can support in sense that there exists a.

Joint Scheduling and Flow Control for Multi-hop Cognitive Radio ...
Cognitive Radio Network with Spectrum Underlay ... multi-hop CRN overlay with a primary network in [2]. .... network can support in sense that there exists a.

Power Allocation for OFDM-based Cognitive Radio ... - Semantic Scholar
Cognitive radio (CR) is a highly promising technology to solve the spectrum insufficiency ... Small Cell Based Autonomic Wireless Network]. is assumed to have ...

Topology Control in Multi-channel Cognitive Radio Networks with Non ...
achieving efficient power control. Index Terms—Multi-channel Cognitive Radio networks, Dis- tributed Topology Control, Non-uniform node arrangements,.

Demonstration of Real-time Spectrum Sensing for Cognitive Radio
form factor (SFF) software defined radio (SDR) development platform (DP) [7] is ..... [5] Y. Tachwali, M. Chmeiseh, F. Basma, and H. Refai, “A frequency agile.

reconfigurable antennas for sdr and cognitive radio
and WiMAX (again, several bands are proposed). Many of these systems will be required to operate simultaneously. Multi-mode, multi-band operation presents a formidable challenge to mobile phone designers, particularly for the RF parts. Of these, the

pdf-175\cognitive-radio-and-networking-for-heterogeneous-wireless ...
... apps below to open or edit this item. pdf-175\cognitive-radio-and-networking-for-heterogeneo ... visions-for-the-future-signals-and-communication-t.pdf.

Prediction of Channel State for Cognitive Radio ... - Semantic Scholar
Department of Electrical and Computer Engineering ... in [10]. HMM has been used to predict the usage behavior of a frequency band based on channel usage patterns in [11] for ..... range of 800MHz to 2500MHz is placed near the laptop and.

A Two-Tiered Cognitive Radio System for Interference ...
scheme; the number of device types to be detected; and ... The wireless communication industry has grown rapidly .... Bluetooth devices, cordless phones, and.

CycloStationary Detection for Cognitive Radio with Multiple Receivers
of cyclostationary signatures in fading channels. In [9], air interface ..... [11] M. Simon and M. Alouini, Digital Communication Over Fading Chan- nels. Wiley-IEEE ...

Prediction of Channel State for Cognitive Radio Using ...
ity, an algorithm named AA-HMM is proposed in this paper as follows. It derives from the Viterbi algorithm for first-order. HMM [20]. 1) Initialization. âiRiR+1 ...

Cognitive Radio Infrastructure using Spectrum ...
Abstract: Cognitive radio is an amazing technology that allows low cost voice and data services by identifying opportunities in spectrum, space, code and time.

pdf-175\cognitive-radio-and-networking-for-heterogeneous-wireless ...
pdf-175\cognitive-radio-and-networking-for-heterogeneo ... visions-for-the-future-signals-and-communication-t.pdf. pdf-175\cognitive-radio-and-networking-for-heterogeneou ... -visions-for-the-future-signals-and-communication-t.pdf. Open. Extract. Ope

Soft Sensing-Based Access Scheme for Cognitive Radio Networks
Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks. (WiOpt), 2012 [1]. This paper was supported by a grant from the Egyptian National ...

Dynamic Pricing Coalitional Game for Cognitive Radio ...
hierarchical network framework, taking advantage of cognitive radios (CR), in ... ous work in coalitional game-based wireless networks which typically simplifies.

Robust Beamforming in Cognitive Radio
commission (FCC) [1], spectrum utilization depends very much upon place and time and yet most ... exploited by CR [5], but higher spectrum utilization is antici- pated if coexistence between the primary (PU) and ... achieve various objectives, such a

Low Power Radio
locations, such as the office or home base, by telephone. Digital technology also ... A number of LPR manufacturers exist in the United States (see Appendix B).

radio control circuits pdf
File: Radio control circuits pdf. Download now. Click here if your download doesn't start automatically. Page 1 of 1. radio control circuits pdf. radio control circuits ...

Preschool Program Improves Cognitive Control
Nov 30, 2007 - removed from class for poor self-control at .... E-mail: [email protected] .... See supporting online material for more information. 3. C. Blair ...

Neurocognitive mechanisms of cognitive control - Semantic Scholar
Convergent evidence highlights the differential contributions of various regions of the prefrontal cortex in the service of cognitive control, but ..... Presentation of a stop-signal yields a rapid increase in activity in FEF gaze-holding neurons and

On Outage and Interference in 802.22 Cognitive Radio ...
interference free transmission. This is .... Or an alternative definition can be given as,. PP N ... tually generated by MATLAB simulation of expression derived in.

Building A Cognitive Radio Network Testbed
There have been some wireless network testbeds, such as the open access research testbed for next-generation wireless networks (ORBIT) [13] and the ...

Building A Cognitive Radio Network Testbed
We are building a CRN testbed at Tennessee Technological. University. ... with 48 nodes [15], which is an exciting advance in this area. ..... Education, 2007, pp.