THE JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOMMUNICATIONS Volume 14, Supplement, October 2007

HU Hao, ZHOU Wen-an

A novel spectrum sharing scheme for cognitive radios CLC number TN929.5 Document A

Abstract A novel approach, which combines spectrum adaptation and orthogonal frequency division multiplexing (OFDM), is proposed to share the licensed spectrum dynamically for cognitive radio systems. Given spectrum sensing and channel estimation information by the receiver, an improved model due to signal power thresholds is adopted to achieve spectrum adaptation for unlicensed users. In order to efficiently allocate the unlicensed signal power, a dynamic power allocation algorithm is also proposed. Simulation results indicate that the propositional scheme solves the partial interference problem of interference temperature model (ITM) and improves the spectrum utilization.

Keywords cognitive radio, dynamic spectrum sharing, spectrum adaptation, dynamic power allocation

1 lntroductlon

As access to available spectrum is becoming increasingly difficult, many techniques about improving the spectrum utilization have been presented. Cognitive radio [ 1, 21, defined as an intelligent wireless communication system that is aware of its environment and adapts its statistical variations according to learning from it, is viewed as an efficient approach for improving the utilization of the precious spectrum resource.

There are two basic dynamic spectrum sharing approaches in cognitive radio. One, based on hard constraints, prohibits unlicensed users from accessing the licensed frequency band, and the other based on soft, constrains unlicensed signal power below a predefined limit, such as the ITM proposed by the FCC [3]. However, the partial interference problem may introduce severely interference to licensed users.

Meanwhile, OFDM is a promising candidate for cognitive radio system to provide high data rates while avoiding interference with licensed user transmission, a variant of OFDM called spectrum-adaptation OFDM (SA-OFDM) is proposed in this paper, where the implementation achieves high data rates via a novel dynamic power allocation algorithm

Received date: 2007-05-10 HU Hao (H), ZHOU Wen-an PCN and CAD Center, Beijing University of Posts and Telecommunications, Beijing 100876, China E-mail: antony 1022@gmail.com

Article ID 1005-8885 (2007) S 1-0069-05

based on power threshold model (PTM), solving the partial interference problem of ITM [4].

The rest of the paper is organized as follows: Section 2 introduces the SA-OFDM framework. Section 3 provides two mathematical models of spectrum sharing scheme. Section 4 formulates the data rate maximization problem and presents the proposed algorithm. Simulation results are presented in Section 5, and several concluding remarks are made in Sect. 6.

2 System framework

A general schematic of an SA-OFDM transceiver is shown in Fig. 1. A high speed data stream X ( n ) is modulated using M-ary quadrature amplitude modulation (MQAM). Then, the modulated data stream is split into N slower data streams using a serial-to-parallel (SP) converter. Note that each subcarrier in the SA-OFDM system needs to be assigned independent power constraints, which are determined by dynamic spectrum sensing and channel estimation techniques. The inverse fast Fourier transform (FFT) is then applied to these modulated subcarrier signals. Prior to transmission, a guard interval with a length greater than the channel delay spread is added to each SA-OFDM symbol using the cyclic prefix (CP) block in order to mitigate the effects of intersymbol interference (13). Following the parallel-to-serial (P/S) conversion, the baseband SA-OFDM signal, s(n) , is then passed through the transmitter radio frequency (RF) chain, which amplifies the signal and upconverts it to the desired center frequency.

Radio Spectrum Power threshold

Fig. 1 System framework of SA-OFDM

The receiver performs the reverse operation of the transmitter, mixing the RF signal to baseband for processing, yielding the signal r (n ) . Then, the signal is converted into parallel streams using S/P converter, the cyclic prefix (CP) is discarded and the FIT is applied to transform the time domain data into the frequency domain. After multiplexed by a PIS converter, the

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data in the subcarriers with different power thresholds is demodulated into a reconstructed version of the original input,

From this system overview, the power threshold allocation block is obvious to be critical component of the transceiver. In the next section, we will describe how to set power thresholds in order to maximize achievable capacity without the partial interference problem in the FCC-proposed interference temperature model.

i ( t 2 ) .

8 Analysls of spectrum sharlng models

3.1 Interference temperature model

In 2003, the FCC Spectrum Policy Task Force originally proposed to use interference temperature model for dynamic spectrum management [4]. The interference temperature is a measure of the power and bandwidth occupied by interference, which is defined as

where P,( fc, B ) is the average interference power in Watts centered at fc, covering bandwidth B measured in Hertz. Boltzmann's constant k is 1.38 x lo-" J/K.

For a given geographic area, the FCC would establish an interference temperature limit, TL, which would be a maximum amount of tolerable interference for a given frequency band in a particular location. Any unlicensed transmitter utilizing this band must guarantee that their transmissions added to the existing interference must not exceed the interference temperature limit at a licensed receiver. Thus, our goal is to guarantee that

where P, ( f,), P,( f,), T L ( f c ) is the unlicensed signal transmit power, interference power and interference temperature limit, respectively. 6 is the propagation loss factor between unlicensed transmitter and licensed receiver, 6 E (0,l).

However, the interference temperature model does not regulate absolute interference but average interference. Thus, the absolute interference would exceed the interference temperature limit in some frequency bands, while the average interference over the licensed signal may be under the threshold. This partial interference will introduce severely interference to licensed users.

3.2 Power threshold model

In this subsection, an improved model based on power threshold is adopted for SA-OFDM system, which has a stronger requirement

Note that this requirement implies that the total interference at the licensed receiver is under the interference temperature limit.

According to Eq. (4), we can solve the partial interference

In the SA-OFDM system, mathematically, the complex- problem via changing the unlicensed transmit power.

baseband signal s(t) can be expressed as N-l , 2 n L

s ( t ) = C d , e T ; o < ~ < T ( 5 ) i=o

where d, denotes the data symbol of the ith subchannel, and T is the duration of each symbol.

Assume that AB is the bandwidth of each subchannel, and Jni = f, - B l 2 , the average transmit power threshold of

unlicensed signal for ith subchannel is defined as

where Blic c B is the frequency band where licensed signals are detected, TL$) is a function of frequency measured on the ith subchannel, which is defined by

(7)

In order to achieve spectrum adaptation, TL is chosen to be

maximum or minimum, according to the existence of the licensed signal, which can increase the data rate of the unlicensed signal without interference.

Note that the subcarrier number is large enough that the transmit power of the unlicensed signal can satisfy Eq. (3) to use the licensed spectrum. In order to maximize both capacity and spectral efficiency while minimizing absolute interference, the unlicensed signal transmit power on each subchannel should be equal to the power threshold

According to Eq. (8), the complex baseband OFDM signal can be reformulated as

(9)

where p is the average symbol power for the modulation scheme used by SA-OFDM system.

Supplement HU Hao, et al.: A novel spectrum sharing scheme for cognitive radios 71

Now we can shape the unlicensed signal power spectrum via proper power thresholds to achieve dynamic spectrum sharing, taking advantage of the power threshold model.

4 Dynamle power allocatlon algorlthm

In this section, we propose a simple and efficient dynamic power allocation algorithm that maximizes data rate of the SA-OFDM system under the constraints of total transmit power and average power threshold in each subcarrier.

4.1 Problem formulation

Assume the total bandwidth is divided into N subchannels in SA-OFDM system, and denote the target average total power Pbudget and maximum tolerable BER as R,,,,,, the power gain

and the variance of the additive white Gaussian noise o f the ith subchannel as H , and ct2, respectively.

If the transmit power allocated to the ith subchannel is P(b,), then the corresponding number of loaded bits can be obtained by

where is SNR gap that is calculated according to the gap approximation analysis [5] based on the modulation scheme used (M-QAM in this case), the desired BER, and the system performance margin, which can be written as

Then the problem considered in this paper can be formulated as to maximum the transmit bit numbers under the constraints 0fPLget 3 PTH and 'ma,.

N

maximize B = C ~ , , = I

N

subject to c P ( 6 , ) d Pbudge1 (12) , = I

P(b,) d PTH(i);

RBER, d Rmax;

Vis {I, 2 ,..., N }

Vis (1, 2 ,..., N }

where PTH(i) is the power threshold of the ith subchannel.

The problem above is still a convex problem since the additional power constraint is convex, thus the solution using a Lagrange multiplier technique will be optimal.

N

(13)

F ( P , 4, 4) = cb, + 4 + 4 ( p ( b j ) - PTH (i)) , = I

where A,, A2 are Lagrange multiplier factors. Then the problem

can be solved by the greedy algorithm with gap approximation [6]. However, the greedy algorithm heavily burdens the computational load because of its extensive sorting operations in all subchannels. In addition, how to allocate transmit power under constraints efficiently is still a problem.

4.2 Proposed algorithm

In this section, we present a novel dynamic power allocation algorithm with gap approximation under the constraints of transmit power.

In order to improve the computational efficiency, the proposed algorithm allocates integral bits for each subchannel initially, which is different from the conventional greedy algorithm. The solution is to assign the same transmit power to all the subchannels at first, that is

According to Eq. (lo), the bit allocation vector bini is calcu- lated and the upper limit 4 of bits allocation for each subchannel is given by

where y is the largest modulation level of the SA-OFDM system. Then we round off to make b, be an integer number, which can be expressed as

where d is the step length of bit allocation according to the modulation scheme. After the initial transmit power allocation vector P = [P(b, ) , P(b,), ... , P(6,)IT is calculated according to

Eqs. (10) and (16). In order to reduce the total power under the power

constrains, the bit allocation must be adjusted through following three steps.

Step 1 Calculate the power reduction and increase when change d bits allocation for each subchannel.

A F ( b , ) = P ( b , ) - P ( b , - d ) (174

AP+(b,)=P(b, +d) -P(b , ) ( 17b)

Step 2 Exclude the subchannel with the maximal power reduction, and find out the subchannel with the minimal power augment in the remainders.

n- =argmaxM-(b,), n+ =argminhP+(bt) (18) I

IE N ic N\{n-}

72 The Journal of CHUPT 2007

-20 E m P -30-

2 8 -40- R L.

Step 3 If Al - (bn - ) < AP'(bn,) , then stop. If the power threshold condition of n+ th subchannel and the total transmit power constraint are satisfied, the transmit power adjustment is accomplished as follows:

P' (bn+ 1 = P' (bn+ 1 + hp' (b"+ 1 ( 1 9 4

Jv. & -

Otherwise, exclude the n'th subchannel from the remnant subchannels and return to Step 1.

The rest of transmit power can be allocated by the Greedy algorithm under the total power constraint and transmit power thresholds.

The flow chart is depicted in Fig. 2.

Initialize 6, to be the nearest integer under the power

constraints t

for i=1,2,- ,N p(b,)=P(b,)-P(b,-df p(b,)=P(b,+d)-P(b,) f-

n-=argmaxAP( b,) n+=argminAP+(b,)

subchannel from the remnant

subchi

i Allocate the remnant power

& Fig. 2 Flow chart of the proposed algorithm

I Slmulatlon results

In this section, simulation results are presented to illustrate the shaping power spectrum of unlicensed signal and compare the performance of the various spectrum sharing approaches developed in this paper. For the simulations, N=256 MQAM-modulated (M=4, 16, 64, 256) subcarriers are employed and the channel is assumed to have a multi-path (10-path) delay profile with I50 ns delay spread. The system sampling interval is 50 ns and the length of cyclic prefix is 32.

To compare those results properly, we set the period per OFDM symbol to be 14.4 ps and the interval of subcarriers to be 78.125 kHz. In this paper, it is assumed that channel estimation and spectrum sensing is perfect and the delay of the feedback of channel state information to the transmitter is negligible.

Figure 3 depicts the shaped unlicensed signal power spectrum waveform. We assume the licensed frequency band B =[0,20 MHz] , and the licensed signal exits on B, =[5,15MHz]. The power spectrum would be well suited for a dynamic spectral environment where we shape unlicensed signal to take advantage of noncontiguous free spectrum and licensed spectrum.

- l o r

I I -70; 1 2

Normalized frequency(xa rad/sample)

Fig. 3 Power spectrum waveform of SA-OFDM

Figure 4 shows the power allocation of two spectrum sharing schemes with different algorithms. From these two curves, we can find that the peak power of the unlicensed signal exceeds the interference temperature limit on some subcarriers, using Greedy algorithm [6] based on interference temperature model (ITM). However, it can be observed that our algorithm can generate shaped power spectrum via proper power thresholds, which results the same allocation to the optimal Greedy algorithm in the unoccupied licensed band with maximum capacity.

E m 2 !! a

I I , I I I I

0 5 10 15 20 -80r

I I I

0 5 10 15 20 Frequency/MHz

Fig. 4 Power allocation of two spectrum sharing models

In Figs. 5 and 6, we evaluate bit error rate and spectral

Supplement HU Hao, et al.: A novel spectrum sharing scheme for cognitive radios 73

efficiency in three scenarios, which are none spectrum sharing scheme (None), spectrum sharing schemes based on ITM and References PTM. It is clear that although the spectral efficiency of PTM approach is lower than ITM scheme due to more strict power constraint, the BER performance increases by about 7 dB as a result of partial interference in ITM scheme. It can be seen from both figures that PTM approach can achieve higher spectrum efficiency with acceptable interference to licensed user.

1.

2.

5 10 15 20 25 SNWdB

Fig. 5 BER versus SNR

-+ None -A- PTM -S ITM

Mitola J, Maguire G Q. Cognitive radio: making software radios more personal. IEEE Personal Communication, 1999,6(4): 13-1 8 Haykin S. Cognitive radio: brain-empowered wireless communications. IEEE Journal on Selected Area in Communications, 2005,23(2): 201-220 Clancy T, Walker B. Spectrum shaping for interference management in cognitive radio networks. SDR Forum Technical Conference 2006 FCC. Establishment of interference temperature metric to quantify and manage interference and to expand available unlicensed operation in certain fixed mobile and satellite frequency bands. ET Docket 03-289, Notice of Inquiry and Proposed Rulemaking, 2003 Cioffi J M et al. MMSE decision-feedback equalizers and coding part I: equalization results. IEEE Transactions on Communications, 1995,43( 10): 2582-2594 Papandreou N, Antonakopoulos T. A new computationally efficient discrete bit-loading algorithm for DMT applications. IEEE Transactions on Communications, 2005, 53(5): 785-789

Biographies: HU Hao, Ph. D Candidate in the School of Electronic Engineering, Beijing University of Posts and Telecommunications, interested in the research on cognitive radio technology and heterogeneous wireless broadband network.

SNWdB

Fig. 6 Spectrum efficiency (BER= lo-' )

6 Concludons

In this paper, we present a dynamic spectrum sharing scheme based on SA-OFDM for cognitive radio systems. In order to use the unlicensed signal power efficiently, a dynamic power allocation algorithm is also proposed. Simulation results show that the propositional scheme solves the partial interference problem of ITM and improves the spectrum utilization.

ZHOU Wen-an, associate professor in Beijing University of Posts and Telecommunications, interested in the research on wireless broadband network and service management in next generation telecommunication network.