Research Letters in Communications

Volume 2008, Article ID 420478, 4 pages

http://dx.doi.org/10.1155/2008/420478

## Performance of Equal Power Allocation in Multiuser OFDM-Based Cognitive Radio Systems

Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada V6T 1Z4

Received 22 July 2008; Accepted 15 September 2008

Academic Editor: Adam Panagos

Copyright © 2008 Yonghong Zhang and Cyril Leung. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The performance difference between a simple equal subchannel power allocation and the optimal water-filling subchannel power allocation schemes is studied for a multiuser OFDM-based cognitive radio (CR) system. It is shown that this difference depends on the average subchannel gain variations among the CR users as well as the activity levels of the primary users.

#### 1. Introduction

Cognitive radio
(CR) [1–3] is a concept which can
potentially alleviate the pending spectrum shortage crisis. As discussed in
[2], orthogonal frequency
division multiplexing (OFDM) is an attractive modulation candidate for CR
systems. It is well known that the optimal solution to the problem of
determining the capacity of a set of parallel additive white Gaussian noise (AWGN)
subchannels, each of which may have a different noise power level, subject to a
total input signal power constraint, has a nice water-filling interpretation
[4]. We will refer to
this as optimal water-filling (OWF). In OWF, the signal powers allocated to
different subchannels are in general different and no power is allocated to
“silent” subchannels on which the noise power exceeds a certain threshold
(water level). The scheme in which the total available signal power is shared
equally among *all* (silent and nonsilent) subchannels is referred to as
plain equal power allocation (PEPA).

Simulation results in [5] indicate that the difference between OWF and PEPA is quite small in a multiple user system with Rayleigh fading when each subchannel is assigned to the user with the best channel quality for that subchannel. Analytical results in [6] show that the performance difference between OWF and PEPA decreases with the number of users and average signal-to-noise ratio (SNR). In this paper, we study the performance difference between PEPA and OWF in a multiuser OFDM-based CR system. It is found that PEPA performs almost as well as OWF when there is little variation in CR user (CRU) average subchannel gains or PU activity level is high.

#### 2. System Model

We consider a
CR system with a total bandwidth of Hz and PUs; PU has a bandwidth allocation of Hz. Frequency bands carrying PU signals are
referred to as *active*; nonactive bands are also termed *spectrum holes*.
In order to reduce the mutual interference between secondary CRUs and PUs to
acceptable levels, some subchannels adjacent to active PU bands are not used by
the CRUs.

We are interested in downlink transmissions from one CR base station (CRBS) to CRUs. It is assumed that the CRBS and the CRUs are able to accurately locate the spectrum holes. The system bandwidth of Hz can accommodate OFDM subbands (or subchannels), each with noise power . Interference among the subchannels is assumed to be negligible.

The system is time-slotted with a slot duration equal to an OFDM symbol duration . The subchannels are modelled in discrete time, with the gain for subchannel and time slot from the CRBS to CRU denoted by . For simplicity, it is assumed that for any given value of , are identically distributed random variables (RVs) with a common probability function (pdf) and cumulated distribution function (cdf) denoted as and , respectively.

At each time slot , each subchannel within , the set of available subchannels at time slot , can be used by the CRBS to transmit to at most one CRU. We use and to denote the pdf and the cdf of the selected CRUs, respectively. The number () of bits per OFDM symbol which can be supported by subchannel in time slot is given by [7] where is the subchannel gain of the selected CRU, is the power allocated to subchannel at time slot , and is an SNR gap parameter which indicates how far the system is operating from capacity. The available power constraint implies that where is the total power per time slot.

The availability of a PU band is modelled by a
two-state Markov chain. During a time slot ,
a PU band can be in one of two modes: *active* or *inactive* [8]. A PU band can
change mode once every slots. At a transition time, the probability
of a PU band changing from active to inactive mode is ,
and the probability of changing from inactive to active mode is .
The number () of available PU bands at time slots then forms a Markov chain, with a transition
probability matrix ,
where state corresponds to the event that the number of
available PU bands is equal to and the probability of moving from state to state is given by

The steady-state probability column vector is given by [9] where and . If each PU band can accommodate an equal number () of subchannels, the probability of having available subchannels is

#### 3. Achievable Bit Rates for OWF and PEPA

Although OWF provides the optimal solution for subchannel power allocation, it is more complex-to-implement than PEPA. We now derive expressions to compare the bit rates achievable by the two schemes.

##### 3.1. Upper Bounds on Achievable Bit Rate for OWF

Assuming that
OWF is applied to the subchannel gains of the selected CRUs at time
slot ,
the average bit rate (ABR) () is given by [4] In (7), can be viewed as the *equivalent noise power* on subchannel and is the water level at time .

Let be the set of time slots with , and be the number of elements in set . Grouping the time slots with , we can rewrite (7) as where is the water level at time slot .

When ,
the ABR if OWF is applied at each time slot is smaller than that if OWF is applied *in one shot* to all the subchannel gains of the selected CRUs over the time slots, that is, where is the global water level for set .

Letting , and taking the limit as , we have

In (9), , is lower than the level calculated by including all subchannels at time slots because there may exist some subchannels with above water level noises. Therefore, Taking the limit as in (11) yields

Substituting for the two occurrences of in (10) by the RHS of (12), we obtain where and .

The upper bound for in (12) can be quite loose since it includes every subchannel, regardless of its equivalent noise power . As , increases without bound. Consequently, the bound in (13) is also loose. We obtain a tighter bound by turning off any subchannel for which is greater than the RHS of (12), that is, we consider only the subchannels for which in calculating the water levels. The resulting water level is still higher than so that

Substituting in (10) by the RHS of (14), we obtain a tighter bound, namely, (13) with

##### 3.2. Achievable Bit Rate for PEPA

The ABR for PEPA is , where Grouping the time slots with , we have Letting , we obtain

#### 4. Rayleigh Fading Channel

In Section 3,
we studied the ABR for OWF and PEPA for arbitrary pdf's and cdf's of the
subchannel gains of the selected CRUs. In this section, we obtain the pdf and
cdf of the subchannel gains of the *selected* CRUs for two different
subchannel allocation strategies. The subchannel gains of the CRUs are assumed
to be Rayleigh-distributed, that is, the power gains are exponentially
distributed.

##### 4.1. Opportunistic Subchannel Assignment

Suppose that at each time , each of the subchannels is assigned to the CRU with the highest gain for that subchannel. If the average subchannel power gains for all CRUs are equal, the pdf of the power gain for the CRU assigned to any subchannel is readily obtained using a standard result in order statistics [10], that is, with corresponding cdf

##### 4.2. A Fairer Subchannel Assignment Scheme

If the average subchannel gains for CRUs are quite different, assigning a subchannel to the CRU with the highest gain may be too unfair to CRUs with poor average subchannel gains. A fairer scheme [11] is to select, for each subchannel, the CRU with the best channel gain relative to its own mean gain,that is, The distribution of a CRU's subchannel gain relative to its own mean is exponential with a mean of 1. Thus, the probability of selecting CRU is , that is, . The cdf of the power gain of the selected CRU for a subchannel is The corresponding pdf is

#### 5. Numerical Results

To compare the ABR for OWF and PEPA in a multiuser OFDM-based CR system, the expressions in (7), (18), and (13) with equal to the RHS of (15) are evaluated. The two subchannel allocation strategies in Sections 4.1 and 4.2, hereafter referred to as Case A and Case B, respectively, are considered. In Case A, the average subchannel power gain for each CRU is chosen as . In Case B, we increase the number of CRUs by six at a time. The average subchannel power gains of the six CRUs are chosen as follows: one with value , two with value and three with value . The resulting overall average subchannel gain for the six CRUs is . In our calculations, we also use the following parameter values: , , W, MHz, kHz, , , , , and .

Figure 1 shows the ABR for OWF and PEPA as a function of for CRUs. For both cases, the ABR for OWF and PEPA decreases with due to the reduced number of available subchannels. In Case A, the improvement of OWF over PEPA is at and at . In Case B, the improvement of OWF over PEPA is at and at . The difference between OWF and PEPA decreases with because with a fixed total power, the average SNR for the available subchannels increases. The difference between OWF and PEPA is known to decrease with average SNR [6]. For both cases, the proposed upper bound for OWF is very close to the actual OWF curve and the difference decreases with . The results show that the relative performance of PEPA depends on the activity level of the PUs and the variations in average subchannel gains among the CRUs.

The ABRs for OWF and PEPA were also determined as a function of the number () of CRUs. For both cases, the ABRs of OWF and PEPA increase with as a result of multiuser diversity. The ABR difference between OWF and PEPA in Case A is negligible; in Case B, the improvement of OWF over PEPA is for and for .

#### 6. Conclusions

The performance difference between the PEPA and OWF subcarrier power allocation schemes in a multiuser OFDM-based CR system was studied. A proposed upper bound for OWF was shown to be tight. When the PU activity is high or the CRU average gains are similar, the simpler PEPA scheme suffers little loss relative to OWF.

#### References

- J. Mitola III and G. Q. Maguire Jr., “Cognitive radio: making software radios more personal,”
*IEEE Personal Communications*, vol. 6, no. 4, pp. 13–18, 1999. View at Publisher · View at Google Scholar - T. A. Weiss and F. K. Jondral, “Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency,”
*IEEE Communications Magazine*, vol. 42, no. 3, pp. S8–S14, 2004. View at Publisher · View at Google Scholar - S. Haykin, “Cognitive radio: brain-empowered wireless communications,”
*IEEE Journal on Selected Areas in Communications*, vol. 23, no. 2, pp. 201–220, 2005. View at Publisher · View at Google Scholar - R. Gallager,
*Information Theory and Reliable Communication*, John Wiley & Sons, New York, NY, USA, 1968. - J. Jang and K. B. Lee, “Transmit power adaptation for multiuser OFDM systems,”
*IEEE Journal on Selected Areas in Communications*, vol. 21, no. 2, pp. 171–178, 2003. View at Publisher · View at Google Scholar - Y. Zhang and C. Leung, “Performance of equal power subchannel loading in multiuser OFDM systems,” in
*Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM '07)*, pp. 526–529, Victoria, Canada, August 2007. View at Publisher · View at Google Scholar - A. J. Goldsmith and S.-G. Chua, “Variable-rate variable-power MQAM for fading channels,”
*IEEE Transactions on Communications*, vol. 45, no. 10, pp. 1218–1230, 1997. View at Publisher · View at Google Scholar - H. Su and X. Zhang, “Cross-layer based opportunistic MAC protocols for QoS provisionings over cognitive radio wireless networks,”
*IEEE Journal on Selected Areas in Communications*, vol. 26, no. 1, pp. 118–129, 2008. View at Publisher · View at Google Scholar - P. G. Hoel, S. C. Port, and C. J. Stone,
*Introduction to Stochastic Processes*, Houghton Mifflin, Boston, Mass, USA, 1972. - H. A. David and H. N. Nagaraja,
*Order Statistics*, John Wiley & Sons, New York, NY, USA, 3rd edition, 2003. - S. Ryu, B.-H. Ryu, H. Seo, M. Shin, and S. Park, “Wireless packet scheduling algorithm for OFDMA system based on time-utility and channel state,”
*ETRI Journal*, vol. 27, no. 6, pp. 777–787, 2005. View at Google Scholar