Optimizing Achievable Throughput for Cognitive Radio Network using Swarm Intelligence

Rozeha A. Rashid, Yakubu S. Baguda, N. Fisal, M. Adib Sarijari, S. K. S. Yusof, S. H. S. Ariffin, Alias Mohd

Faculty of Electrical Engineering, UniversitiTeknologi Malaysia, Johor, Malaysia Email: { rozeha| baguda_pg| adib_sairi| sheila| sharifah| kamilah| alias}@fke.utm.my

Abstract- Cognitive radio (CR) technology allows a secondary or cognitive user (CU) to opportunistically access frequency band of a primary user (PU) when it is not in use. However, a CU needs to perform spectrum sensing periodically to avoid causing harmful interference and thereby protecting the quality of service of PUs. A conventional frame structure for CR operation consists of sensing time slot and data transmission time slot which run consecutively. Basically, a longer sensing time produces a higher probability of detection and therefore, better PU protection. However, longer sensing time will reduce the amount of time for data transmission and hence affects the achievable throughput of a CU. In this paper, we study the fundamental tradeoff between sensing time and achievable throughput of the CUs. Based on energy detection scheme, we propose and investigate the feasibility of using Particle Swarm Optimization (PSO) in the design of sensing slot duration to maximize the achievable throughput for CUs for a given frame duration. The results show an encouraging 8.89% increase in throughput with respect to the probability of false alarm, a reduced sensing time by 80% while achieving 97.8% of normalized throughput for CU system with PSO.

Keywords: Cognitive Radio, spectrum sensing, probability of detection, throughput, sensing time, Particle Swarm Optimization

I. INTRODUCTION The rapid growth of wireless services and the static

spectrum allocation policy as practiced in many countries including Malaysia has led to current day spectrum scarcity as most radio bands are already assigned to licensed users by the regulators. However, spectrum occupancy measurements as reported in [1-3] show an inefficient spectrum usage as the workload in different spectrum bands is rather diverse where some bands are overcrowded while other portions are moderately or sparsely utilized. Local measurement displays similar characteristic as illustrated in Figure 1 [4].

Cognitive radio (CR) is a promising technology to improve spectrum utilization by adopting the concept of Opportunistic Spectrum Access (OSA). OSA promotes overlay spectrum sharing approach where unlicensed or cognitive users (CUs) can temporarily utilize unoccupied bands but need to be sufficiently agile to vacate the space (time, frequency or spatial) once licensed or primary users

(PUs) are detected as not to cause harmful interference [5][6]. Hence, spectrum sensing is a crucial task for CUs that operate under OSA scheme to robustly identify a spectrum opportunity (spectrum hole).

Figure 1. Local Spectrum Occupancy Measurement [4]

Available methods for spectrum sensing include matched

filter, energy detection and cyclostationary detection [2]. The matched filter (MF) is an optimum coherent detector. However, it requires a prior knowledge on the behavior (modulation) of the received signal. Energy detection (ED) is a non-coherent detection method that uses the energy of the received signal to determine the presence of primary signals. This simple method is able to gather spectrum-occupancy information quickly. However, its sensing capability is vulnerable to noise. Cyclostationary detector exploits the inherent periodicity in the received signal to detect primary signals with a particular modulation type by implementing a two-dimensional spectral correlation function (SCF) rather than the one-dimensional power spectral density (PSD) of the energy detector. Although its spectrum-sensing performance is robust to noise-like signal, this method demands excessive signal processing capabilities, thus accompanying a large amount of power consumption [7]. In this research, energy detector is chosen due to the assumption that CU has limited information on the primary signal.

In a CR network that operates on a frame-by-frame basis, a conventional frame structure consists of spectrum sensing and data transmission subframes respectively. A CU will suspend its data transmission at the beginning of each frame and senses the status of the frequency band for a duration of time. The remaining frame time is for data transmission.

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Longer sensing time will result in higher probability of detection, Pd, and lower probability of false alarm, Pf. Hence, a better protection for PU will be achieved and on the other hand, an improved opportunistic access for the CU. However, longer sensing time will decrease the transmission time and therefore reduces the throughput of the CU.

Authors in [8] focus on optimizing the frame duration for a fixed sensing time requirement to address the throughput-collision tradeoff problem and achieves maximum throughput. In contrast, optimal sensing and transmission are conveniently accomplished in [9] as the proposed frame architecture consisting of single slot structure allows for each sensing and transmission to be carried out for the whole frame duration simultaneously via parallel processing. However, most existing cognitive users have single antenna structure which facilitates the conventional frame structure as similarly deployed in [10]. The work in [10] addresses the sensing-throughput tradeoff in terms of maximizing achievable throughput for CUs under the constraint that PUs are sufficiently protected using linear optimization technique. In this paper, the fundamental tradeoff between sensing time and achievable throughput of the CU is also studied. Based on energy detection scheme, we propose and investigate the feasibility of using Particle Swarm Optimization (PSO) in the design of sensing slot duration to maximize the achievable throughput for CUs for a given frame duration.

The rest of the paper is organized as follows. Section II presents the frame structure model for CU system. The details of sensing parameters and mechanisms used are explained in Section III. The problem formulation regarding the sensing-throughput tradeoff is also included. The PSO algorithm is introduced in Section IV while Section V gives the results and discussion. The conclusion of this paper is outlined in Section VI.

II. SYSTEM MODEL In this work, we consider CU to operate in a frame-by

frame basis. The frame structure with duration Tf is shown in Figure 2. The structure consists of sensing slot with duration Ts and transmission slot with time Tt. The sensing task is executed at the beginning of the frame to assess the status of a channel whether it is active or idle. If the channel is idle, CU will transmit to its intended receiver in the remaining duration of a frame. At the end of the frame, if PU is detected, CU’s data transmission will be ceased to protect the PU from harmful interference. Otherwise, CU will access the frequency band again in the next frame. The process is repeated.

Figure 2. Frame Structure for CU System

The utilization of licensed channel by PU follows a Markov chain process of exponential ON/OFF states. During the ON period, generated packets of PU is transmitted immediately on the channel.

In this paper, only CU operation in a frame is considered.

Since CU will not transmit if PU is detected, only conditional achievable throughput of CU is studied where PU is not active at the time of sensing. PSO is then applied to minimize the sensing time while maximizing the achievable throughput for CU within a given frame period.

III. CHANNEL SENSING In the following, a brief review of sensing hypotheses, the

energy detection scheme and its relation to the sensing performance metrics of probabilities of detection and false alarm are provided. The formulation of sensing-throughput tradeoff is also presented for the purpose of addressing the optimization problem. A. Sensing Hypotheses

The sensed signal, X[n] of CU will have two hypotheses as follows:

H0: X[n] =W[n] if PU is absent . H1: X[n] =hW[n] + S[n] if PU is present (1)

where n = 1, …, N; N is the number of samples and h is the channel gain that is assumed to be 0 under hypothesis H0 and 1 under hypothesis H1. The noise W[n] is assumed to be additive white Gaussian (AWGN) with zero mean and variance σw

2. S[n] is the PU’s signal and is assumed to be a random Gaussian process with zero mean and variance σx

2. A block diagram of an energy detector is given in Figure 3.

The output of the energy detector, Y, which serves as decision statistic, is described by [11]:

∑ (2) Comparing with a threshold, γ, and based on optimal

decision yielded by the likelihood ratio Neyman-Pearson hypothesis testing [11], Pd and Pf can now be defined as the probabilities that the CU’s sensing algorithm detects a PU under H0 and H1, respectively.

Pf = P(Y > γ | H0) Pd = P(Y > γ | H1) (3)

Since we are interested in low SNR regime, where signal-to-noise ratio (SNR) is taken as | | , large number of samples should be used. Thus, we can use central limit theorem to approximate the decision statistic as Gaussian. Then

(4)

(5)

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where Q(.) is the complementary distribution function of the standard Gaussian. Combining eq. (4) and (5), Pd is derived to be; 1 2 (6)

Thus the number of samples needed for PU detection is 2 (7)

Using the sampling time, of the system used, sensing time Ts is derived as shown in (8); (8)

It can be seen that as the number of samples needed for PU detection increases, the sensing time becomes longer. In addition, there will be an increase in Pd and a decrease in Pf, respectively. It is desirable to have a high Pd for better PU protection. Meanwhile, a low Pf is favorable for a better opportunistic access and higher achievable throughput for CU. However, a longer sensing time will reduce the amount of time for data transmission in a frame and hence, results in a lower achievable throughput for CU. Since these two magnitudes pose a trade-off, an optimal sensing time needs to be determined such that throughput for CU can be maximized and a certain Quality of Service (QoS) is attained by PU.

Figure 3. Block diagram of an energy detector

B. Sensing-Throughput Tradeoff As shown in Figure 2, the frame structure designed for the

CU system consists of one sensing slot and one data transmission slot. Obviously, the relationship is as given in (9);

(9)

Two scenarios where the CU can operate in the licensed channel is reported in [10]. First scenario is when the PU is absent and no false alarm is generated by CU. The achievable throughput is then given by; 1 (10)

In the second scenario, CU doest not detect the PU although it is active. Therefore, the achievable throughput is represented as; 1 (11)

where and denote the throughput of CU operating in the absence of PU and the throughput of CU operating the presence of PU, respectively. If we define SNRcu as the received signal-to-noise ratio of the CU’s transmission at the CU receiver and SNRpu as the signal-to-noise ratio of PU

received at the same receiver, then 1 and 1 . In the case of only one

transmission in the CR network, we will have > . The average achievable throughput of a CU system with

the frame structure of Figure 2 can be expressed as [10]; (12)

where is the probability of PU being active in the sensed channel and 1 .

The challenge of sensing-throughput tradeoff is to find an optimal sensing time that leads to maximized transmission time, and therefore, higher throughput for the CU. The optimization problem can be described mathematically as; max (13)

s.t (14) where is the target probability of detection for PU to be sufficiently protected. In this work, of 90% is selected as of many other previous works [2], [10].

By assuming is small (<0.2) [10] and taking note of the condition of > , the second term of the optimization problem will dominate and simplifies the equation to become max 1 (15)

under similar contraint for the target probability of detection. The relationship of sensing time and achievable

throughput for CU can further be defined for energy detection scheme. Choosing , the achievable throughput for CU system is given by 1 1 (16)

where √2 1 . It can be seen obviously in (16) that the achievable thoughput of the CU system is a function of sensing time and based on (10), it can be represented mathematically by 100 (17)

The objective is to reduce sensing time and it consequently

increase the throughput as result of using the required optimal sensing time needed at any particular time depending on the probability of false alarm. Given the frame duration is set at 100 ms and represents the sensing time as the objective function, an optimal normalized achievable throughput,

, can thus be derived using (18);

Minimize 100

Minimize 100 (18)

arg min 0 0 1

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0 1

IV. PARTICLE SWARM OPTIMIZATION (PSO) Particle swarm optimization (PSO) is a population based

and stochastic optimization approach designed primarily to mimic the social behaviour of school of fish or flock of birds [12][13][14]. This social behaviour has been used in solving more complex optimization problems in a more sophisticated and efficient manner. The particles are grouped into swarm and each particle is a potential solution to the optimization problem. Each particle within a neighbourhood moves toward the best optimal solution in the neighbourhood depending on its past experience and neighbours as well. This clearly shows the unique behaviour PSO in which it cooperatively takes decision to achieved optimal solution. In a nutshell, it has been referred as symbiotic cooperative algorithm [15]. It is very obvious that the performance of each particle is determined by the fitness function. PSO has been applicable to other fields but not much work has been done related to spectrum sensing in cognitive radio networks. The key success to the deployment of PSO in many optimization problems is due to the fact that it is very simple, high convergence and searching capability [16][17][18].

PSO is primarily governed by two fundamental equations representing the velocity and position of the particle at any particular time. After each iteration, the particle position and velocity is updated until the termination condition has been reached. The termination condition can be based on the number of iteration and achievable output required. Once the required number of iterations or predetermined output has been achieved, the searching process is terminated automatically. For a particle with n dimension can be represented by vector , … … … . The position of the particcles at time t can be mathematically expressed as P , … … … while the corresponding velocity of the particles is represented as , … … … . In general, the velocity and position of the particles at t+1 can be mathematically represented using equation (19) and (20) respectively 1 (19) 1 1 (20)

Equation (19) describes the velocity of the particles at time t+1. ω is the inertia weight which keeps track of the previous velocity history on the current velocity of each particles. It balances the trade-off between the local and global exploration of the swarm. v(t) ensures that the particles are on the right flight direction and it prevents the particle from sudden change in direction. (Pl−x(t)) computes the performance of the particle relative to the past performance. In a nutshell, it draws the particles to their best known position.

(Pg−x(t)) measures the performance of particle relative to its neighbours. Both the cognitive and social components depend greatly on and respectively. It is very important to note that the global best (Pg) determines the best possible

solution for the entire swarm. It uses star structure which converges faster [14], but can be trapped in local minima. The position can be computed using (20) whenever the velocity is determined. Interestingly, we consider the probability of false alarm and throughput as the parameters which the sensing time depends on as shown in (17). The initial position of the particles is set to reflect these two parameters. The fitness is computed and if it is less than the initial best value, we replace the initial best value with the current value. The corresponding best position for the probability of false alarm and throughput are updated as well. The global and local best positions are used to compute the velocity of the particles at time t+1.

Check if the loop counter is > than number

of particles

Check if termination conditionhas been reached

Update particles velocity and

position

Update local and global best

Evaluate the objective function

F(x)

Check constraints subject to Ts, Tf

and Pf

Initialize position and velocity based

on Pf and T

Initialize PSO configuration parameters (C1, C2, W)

START

END

Set loop counter to initial value

Allocate Tt (Transmission time) based on

optimal Ts

NO

YES

NO

YES

Figure 4: Flow chart for the PSO-based optimal sensing algorithm

It is very important to note that the value 1 is used in computing the next position of the particles. This process is repeated until the minimal sensing time required is achieved. This will eventually yield more transmission time and enhances the throughput. The flow chart for the PSO based optimal sensing algorithm is given in Figure 4. It has been assumed that processing time is very negligible due to PSO

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high convergence and searching capability. But in real life scenario, it is considered to be part of the sensing time Ts since a CU will firstly sense and then determine the optimal sensing time required before transmitting the data.

V. RESULTS AND DISCUSSION The scenario for optimal sensing is simulated in MATLAB

and the following settings of Table 1 have been used for the experimentation:

TABLE 1. PARAMETER SETTING

Parameters

Value

Number of particles 30 Number of iteration 30 Learning factors φ1 & φ2 1 Inertia weight ω 1 Sensing time 10 mS Data transmission time 90 mS Frame time 100mS

From Table 1, it can be seen that the sensing, data

transmission and frame time are for non PSO based sensing. This clearly indicated that at any particular time, the required sensing time is always 10ms without considering the fact that it can be significantly reduced to achieve high throughput using optimal decision strategy (PSO).

As mentioned in previous section, the primary goal is to minimize sensing time and at the same time achieve high throughput. Hence, the trade-off between sensing time and throughput is achieved through PSO scheme. Our approach has been able to counter the problem of achieving high throughput for CUs and at the the same time protecting the PU as well. In order to verify the efficiency of the developed scheme, we used exponential function to generate the traffic which varies exponentially with time. As can be seen from (17), the probability of false alarm is related to data transmission and frame duration time. This will optimally minimized the required time needed to sense the presence of PU within certain constraints of probability of false alarm, frame duration and data transmission time.

As observed from Figure 5, CU system without PSO performs as per setting; sensing time of 10 ms or 10% of the frame duration and 90% normalized throughput due to the remaining time allocated for transmission. On the other hand, although system with PSO achieves similar normalized throughput, sensing time required to decide on the presence of PU is only 2 ms, which is a reduction of 80%. The decrease in sensing time contributes significantly to lower energy spent on sensing for CU system with PSO. The special feature of updating in PSO promotes system learning. Hence, the performance of CU system with PSO should further be improved. Figure 6 shows that under normal sensing, the throughput will decrease as the probability of false alarm increases. Under the same constraint of probability of false alarm, system with PSO displays similar characteristic. However, there is an increase of 8.89% of throughput

achieved.

Figure 5. Impact of sensing time with and without PSO on achievable

throughput

Figure 6. Optimal normalized achievable throughput using PSO under the

constraint of probability of false alarm Once the minimized sensing time is achieved, it will be used to allocate the transmission time within the given frame time. As shown in Figure 7, in addition to recording the optimal sensing time of 2 ms, the performance of the CU system is further enhanced by registering a maximum normalized throughput of 97.8%, an increase of 8% compared to the initial achieved throughput.

Figure 7. System performance in terms of sensing time and throughput

VI. CONCLUSION

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

2

4

6

8

10

12

14

Achievable throughput

Sen

sing

tim

e (m

S)

Sensing time - Without optimization

Optimal sensing time - PSO

80%

10-4

10-3

10-2

10-1

100

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Probability of false alarm

Ach

ieva

ble

thro

ughp

ut

Normal sensingPSO-Based sensing

8.89% Increase in overall throughput

2 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Sensing Time (ms)

Achi

evab

le th

roug

hput

PSO-Based sensing

PSO-Based sensing w ith Tt Allocation

Normal sensing

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The issue of sensing-throughput tradeoff in a frame structure consisting of sensing subframe and transmission subframe is studied for an opportunistic access CR network. From classical detection theory, it is shown that a longer sensing time leads to a higher probability of detection and on the other hand, a lower probability of false alarm. However, longer sensing time will be at the expense of transmission time and hence affects the achievable throughput of the CUs since no data is allowed to be transmitted during the sensing time slot in each frame. Using energy detection scheme, the feasibility of PSO is investigated to achieve based on which the optimal sensing time that maximizes the throughput for CU system. In order to maximize the thhroughput, PSO scheme has been used to select the best optimal sensing time for a given frame duration. The results is very encouraging as there is an 8.89% increase in throughput with respect to the probability of false alarm, a reduced sensing time by 80% while achieving 97.8% of normalized throughput for CU system with PSO and update.

Future works will include implementing PSO approach into an SDR platform and extend the strategy to cooperative sensing.

ACKNOWLEDGEMENT The authors wish to express their gratitude to Ministry of

Higher Education (MOHE), Malaysia and Research Management Center (RMC), Universiti Teknologi Malaysia for the financial support of this project under GUP research grant no: 01H35.

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