2015 IEEE 34th Symposium on Reliable Distributed Systems Workshops
Conflict Graph based Channel Allocation in Cognitive Radio Networks Vinesh Teotia, Vipin Kumar and Sonajharia Minz School of Computer and Systems Sciences Jawaharlal Nehru University, Delhi, India Email:
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[email protected] Abstract—Cognitive radio technology provides a framework for flexible way to utilize the white spaces using the various spectrum sharing techniques. Interference plays an important role in communication when the channels are shared by the licensed and unlicensed users. Further, the signal to interference plus noise ratio also provide the bounds for the channel capacity. For this the authors introduce a conflict graph based approach for optimal channel allocation in cognitive radio networks named as Conflict Graph based Channel Allocation(CGCA) scheme. The proposed CGCA scheme was simulated and observed that the CGCA scheme outperformed Interference Aware Channel Assignment (IACA) scheme in terms of channel allocation. The channel allocation of the proposed CGCA was observed to have increased by 19 channels, when the unlicensed users shared the network as compared to the IACA technique.
available frequency band. For an efficient communication, the suspension time of licensed user must be minimum. When the spectrum mobility process is over then the above cycle adopts in dynamic manner.
Index Terms - Channel capacity, Cognitive networks, Signal to interference plus noise ratio, graph theory.
The allocation of bandwidth is a major concern in cognitive radio networks. Interference is one of the important parameters in bandwidth allocation. In this work, the authors proposed a scheme based on link interference ratio using conflict graph based model with some improvement in IACA scheme [10].
I.
In spectrum sharing, the interference to the licensed users because of unlicensed users should be minimum. Therefore, in channel allocation the interference plays a key role [4], [5], [6], [7], [8]. The channel allocation between secondary users is a major concern in cognitive radio networks. In the channel assignment process, the wireless link and network topology should be considered in a manner such that the interference is minimum. As less interference leads to better system performance [9].
I NTRODUCTION
In today’s scenario, as the number of wireless users is increasing exponentially, there is a growing need towards efficient use of the spectrum. For the efficient use of spectrum, the concept of cognitive radio was introduced by J. Mittola [1]. In [2], Mitolla et al. provide a flexible way to utilize the spectrum. The concept of cognition emerged from the human brain [3]. The signal processing aspect of cognitive radio was described by Simon Hakin [3].
The rest of the paper is organized as follows: In section II, the related work and motivation is presented, In section III, system model and concepts are presented, the simulation result and analysis is discussed, In section IV, finally the conclusion of the work is presented. II.
The concept of cognitive radio provides a framework to utilize the TV white spaces intelligently. The cognitive radio is a software defined radio with some added functions namely cognitive capability and reconfigurability. The concept of cognitive radio works on the basis of cognitive cycle. The cognitive cycle has some main functions namely, spectrum sensing, spectrum decision, spectrum sharing and spectrum mobility. In the spectrum sensing process, the information about the spectrum holes is found. In spectrum decision process, the ideal spectrum bands are chosen for communication using the parameter for channel. In the spectrum sharing process, the unlicensed users share the spectrum in such a fashion that the quality of service of licensed users must be maintained. In spectrum mobility process, when the secondary users use the frequency band of the spectrum and the licensed users come and reclaim the frequency band for communication. In such a situation, the unlicensed user must stop the communication and leave that band for licensed user, the unlicensed user must go to the next available frequency band. Therefore, in the above process, a hand-off is required and such type of hand-off is called spectrum hand-off. The unlicensed user can resume it’s transmission of data at any time whenever find the 978-1-5090-0092-0/15 1060-9857/15 $31.00 ©$31.00 2015 IEEE © 2015 IEEE DOI 10.1109/SRDSW.2015.19
R ELATED W ORK AND M OTIVATION FOR THE P ROPOSED A LGORITHM
In [11], Anand Prabhu Subramaniam et al. presented the channel assignment problem in multi channel wireless mesh networks. In this network, centralized and distributed scenario are considered. The algorithms for both the cases is presented. The main aim of the algorithms are to assign the channels to communication links in the network in such a way that the interference in the network should be minimum. To minimize the interference, the optimization problem is formulated with linear programming and semi definite programming formulation. These optimization problems are used to obtain the lower bound of the interference in the network. The lower bound is used to check the quality of the solutions which is obtained by proposed algorithms. From the simulation results, the authors found that the proposed algorithms improve the network throughput. In this paper only one channel is considered with one communication link. This is the main limitation of this work. In [12], Anh Tuan Hoang et al. presented the channel and power allocation problem to maximize the spectrum utilization with prediction to the primary users. For this, the authors 52
developed an dynamic interference graph based model. In this model, SINR requirement and feasible assignment is also considered as a parameter for channel allocation and power control. The performance of the dynamic interference graph model is found good. The proposed model consistently outperform the other models namely Fixed graph based model, Minimum transmission power based model, minimum interference power based algorithms and random algorithms. The authors observed that the dynamic graph based model outperforms others while random algorithm perform worst in each case. The fairness among channel premise equipment is not considered in this work. Therefore it can be considered as the main limitation of this work.
in this paper proposes a scheme with some modification in IACA scheme. With the proposed modification in IACA, it was found that CGCA scheme outperform to IACA scheme. In this paper, the primary and secondary users or licensed and unlicensed users are synonyms.The detailed description of CGCA scheme is described in next section. III.
S YSTEM M ODEL AND C ONCEPTS
In this paper the cognitive wireless mesh network is considered and modeled as bidirectional graph using the concept of graph theory. An undirected graph G(V, E) is called communicating graph, if the vertex of the graph V, are the network nodes. Two nodes are connected by an edge if they are within the transmission range of each other. In [17], the authors described the two cases namely the set of communication links that interfere with each other or transmission on same channel, which can be represented as a conflict graph. In [18], Anand prabhu et al. described that a communication between two nodes U and V is successful if signal to interference plus noise ratio at the receiver end V is above the certain threshold value which depend upon the transmission characteristics.
In [13], Stefan Geirhofer et al. presented a framework for cognitive coexistence between infrastructure network and adhoc networks. Using the information of sensing and prediction the ad-hoc link activity, the power and transmission time are distributed in such a way that minimize the overlap transmission between infrastructure and ad-hoc system. For predicting the ad-hoc link activity, the continuous Markov chain is used. The authors proposed an interference aware resource allocation method. The main aim of the proposed method is to reduce the interference in an efficient way. With the help of the proposed method, an convex optimization problem is formulated. To solve the optimization problem, the closed form solution is found. The authors found that the utilization of communication links reduce the interference.
To define conflict graph, let Vc denotes the corresponding node in the network and given as Vc ={linku,v (u, v) is a communication link in the network}. Now, linku,v (u, v) is created if node u and node v have the same channel. A link can be considered as available, if the associated nodes with that the link lies in the transmission range of each other and do not fall in the transmission range of licensed users [19], [20], [10]. The link in a graph become a vertex in conflict graph. The conflict graph is shown in figure 1.
In [14], Maryan Ahmed et al. presented the channel assignment in cognitive wireless mesh networks in such a way that the connectivity of the network should be maintained. For this, the authors designed the integer linear programming problem. To design the integer linear programming problem, the concept of conflict graph is used. To reduce the interference and maintain the connectivity of the network, the conflict nodes in conflict graph is used. From the simulation results, it was found that the proposed technique perform good. In this work, only the centralized channel allocation is used. Therefore, it can be seen as the main limitation of this work.
In Figure 1(a) shows a communication graph between the nodes. In Figure 1(b) represent the conflict behavior of the nodes so it is a conflict graph. The conflict graph have four nodes which representing a communication link in the network graph. In this graph, the link AB and link BC conflicts with link CD and do not conflict with the link DE [11].
In [10], Wajahat Maqbool et al. proposed an interference aware channel assignment for cognitive mesh networks. Endto-end interference model is proposed in terms of conflict graph. The authors claim that the proposed algorithm is perform better in comparison of any other SINR based model. In this work, a graph of conflict node is created and allocation of the channels to unlicensed users based on that group. In a group of conflict node, if a channel is used for transmission and rest of the conflict users of that group must wait and can not use the channel. The activity of primary and secondary users are considered to define the weight function of the link in a conflict graph. The allocation of channels was done according to minimum weight function. The main limitation of IACA scheme is that the number of maximum channel allocation is equal to number of conflict group.
In [10], the authors proposed an end-to-end interference model. In this model, the activity of licensed and unlicensed users are added to physical interference model. According to shanon capacity formula, the capacity of a link is maximum, when the interference on that link is minimum. To maximize the capacity of a link, the three parameters namely link interference ratio, licensed user activity and unlicensed user activity are considered in [10]. Two nodes u and v communicate via linku,v if the signal to interference plus noise ratio at receiver node is follow the same threshold limit. The communication between nodes depend on some communication parameters namely channel, data rate, power etc. The signal straight of the linku,v between nodes u and v denoted by P (linku,v ). The packet from node u to node v is delivered successfully iff P (linku , v) ≥ SIN R (1) N + τ (u)Pu (a) + Pv (s)
From the above discussion, it is observed that Interference is an important parameter towards better channel utilization. In [12], [11], [13], [14], [15], [16], the authors provide different approaches towards better channel allocation. In [10], the number of allocated channels is efficient but not going to optimal sense. Therefore some modifications is required in IACA algorithm. To address the above problem, the authors
av
bv εB
. In equation (1), N denotes the noise, A denotes the set of unlicensed users which lies in the neighborhood m ∈ / v, av 53
of node is the set of nodes from which node v can sense a packet. τ (a) can be defined as the time the time which occupies the channel of node v. The weight of the signal strength of intersecting node u is denoted by Pu (a). Let S denotes the set which contain the neighborhood unlicensed users on same channel. Sv can be defined as a node which lies in the interference range of v. The main parameters which are used to calculate signal to interference plus noise ratio are data rates, channel characteristics and modulation scheme. In [18], A. P. Subramaniam et al. define the interference ratio using SNR and SINR. The linku,v denotes the link between node u and v. The interference ratio IRu,v (u) for the node u in linku,v = (u, v), where 0 ≤ IRu (linku,v ) ≤ 1 is defined below as in equation 2 and equation 3: SIN Ru (linku,v ) (2) IRu (linku,v ) = SN Ru (linkuv ) where P (linku,v ) SN Ru (linku,v ) = (3) N and SN Ru (linku,v ) =
N+
mεM (v)−u
Pv (u) τ (m)Pv (m) +
sv εS−u
IV.
In this section an scheme for channel allocation is presented step by step. Firstly, an weight function is defined, which is based on interference link ratio and primary/secondary user activity. The weight function is defined for each link and can change in dynamic way. The weight function is given as in equation 7: Wu,v = IRu,v + activity ofP Uac (u, v)/SUac (u, v)
(7)
Based on the weight function defined in equation (7), allocation of channel is done. The link which has minimum weight is more suitable for communication so the allocation of channels to unlicensed users is based on minimum weight function. Algorithm 1: Conflict graph based channel allocation in cognitive radio networks Input: The activity of primary and secondary users on channels, Interference ratio, the set of available nodes, source node S and destination node D Output: C.A // Channel allocation 1 for i ← 1 to n do 2 Define weight function
Pv (s)
(4) . The total interference ratio can be defined as the sum of interference ratio at each link and given as in equation 5:
W = IR + PU activity/SU activity 3
(5)
4
where IR1 , IR2 , IR3 , . . . , IRn denotes the link interference ratio of link1 , link2 , link3 , . . . , linkn respectively.
5
IR = IR1 + IR2 + . . . + IRn
T HE P ROPOSED A LGORITHM
6 7
8
a Communication graph
9 10 11 12 13
14
Create the connectivity of the graph Create a list of channels which will be used for the transmission Create conflict graph using the above information Make a list of conflict node group Create channel queue consisting of the nodes in the conflict graph according to minimum weight function if queue=0 then go to step 3. else Allocate the channels to unlicensed users having the minimum weight from the queue list Remove the allocated channel from the channel list created in step 3. go to step 6. Repeat step 6 to step 9 until allocation is done for all channels. return C.A
b Conflict graph
A. Complexity of the Proposed Algorithm
Fig. 1: Example of conflict and communication graph [11]
In the proposed algorithm,the total number of comparisons among the nodes to obtain the conflict graph is non-linear i.e n2 . To create a list of conflict node group for each node would result in n number of operations. Thus the total number of comparisons for channel allocation would lead to a total number of n2 × n. Therefore, the time complexity of CGCA algorithm would be O(n3 ).
As linkuv = (u, v) exists as conflict link for all u, vεA. To remove this confliction an algorithm is proposed in the the next section. The indicator Random variable is used for the availability of the channels with respect to licensed user activity. The indicator function is given as in equation 6: 1 if channel is busy I= (6) 0 if channel is free
V.
S IMULATION R ESULT AND A NALYSIS
In this section, the channel allocation based on weight function defined in last section are simulated using Matlab. The results are evaluated in terms of improvement in channel allocation based on the parameters namely interference link
According to equation (6), the licensed and unlicensed users activity on the channels is obtained and used to design the algorithm, which is described in text section. 54
ratio and primary/secondary user activity and compared with IACA scheme by varying the number of channel. It was found that the number of allocation is increased and going to an optimal value, when the allocation of channels to secondary users was done based on the proposed CGCA algorithm as compared to existing algorithm(IACA). During this simulation, the SNR and SINR generated randomly. The activity of licensed and unlicensed users on channels is calculated using the indicator function as defined in equation 6. In Figure-2, the number of channel allocation according to the threshold value. The number of channel to be allocated are 27. The minimum and maximum number of allocated channels to unlicensed users according to IACA scheme is 2 and 8 respectively, while the minimum and maximum number of channel allocation according to proposed CGCA scheme is 2 and 24 respectively. from the Figure-2, It is observed the channel allocation is better if the channel allocation to unlicensed users according to proposed CGCA scheme in comparison to IACA scheme. The mean of the channel allocation in case of IACA scheme is 6.78, while the mean number of channel allocation according to proposed CGCA scheme is found to be 14.05.
Fig. 3: Iteration wise channel allocation
20 but in case of existing IACA scheme, the minimum and maximum number of allocation to unlicensed users is found to be 4 and 12 respectively. The mean number of allocation to unlicensed users according to proposed CGCA scheme and IACA scheme is found to be 12.62 and 7.88 respectively. From the analysis of Figure-3, it can be observed that the proposed CGCA scheme performs better in comparison of existing IACA scheme. If the allocation of channels to unlicensed users are done based on the proposed CGCA scheme then the mean number of improvement in channel allocation is found to be 5.26. It is also observed that the behavior of channel allocation approaches to a constant value as the number of iterations tends to n, where n being a large value.
Fig. 2: Threshold based channel allocation
If the threshold value increases the channel allocation to unlicensed users increases. In case of IACA scheme, if the threshold value is above 0.6 then channel allocation is approaches to a constant value but in case of proposed CGCA scheme, the number of channel allocation increases as the threshold value increases. From the above analysis, it can be observed that the threshold value should be fix for rest of the simulation. In this simulation, the threshold value is taken as the mean value of the threshold i.e 5.5.
Fig. 4: Channel allocation according to number of available channels
The Figure-4 represents number of allocated channels as the number of free channels increases. For each value, the simulation is run 10 times and then taken the average of the values. From the Fig-4, it can be observed that as the number of free channels increases, the channel allocation is also increases in case of proposed CGCA scheme in comparison of IACA scheme. The maximum number of allocation of channels to unlicensed users according to proposed CGCA scheme and existing IACA scheme are found to be 50 and 31 respectively. It is a clear improvement of 19 channels in comparison of
The Figure-3 depicts the channel allocation to unlicensed users according to number of iterations with fixed threshold value. The total number of channels in this scenario is 50. For each value, the simulation runs 10 times and average is taken for each value. From Figure-3, it can be seen that the minimum and maximum number of free channels for allocation is 17 and 32 respectively. In case of proposed CGCA scheme, the minimum and maximum number of allocations are 6 and 55
R EFERENCES
IACA scheme. it can be observed that the channel allocation to unlicensed user performs better if the allocation is done based on the proposed technique.
[1]
[2]
[3]
[4]
[5]
[6]
[7]
Fig. 5: Comparison of all the scenario [8]
The Figure-5 shows the comparison of proposed CGCA scheme and IACA scheme in different scenarios namely threshold based, iteration based and availability of channels. From the Figure 5(a), it can be observed that the minimum and maximum allocation of channels to unlicensed users of the proposed CGCA scheme is better than IACA scheme. The median of the proposed CGCA scheme is also better than IACA scheme. In Figure-5b, it can be seen that the maximum, minimum and median of the proposed CGCA scheme are better than IACA scheme. From Figure-5c, it is observed that the minimum maximum and median of the proposed CGCA scheme were better than IACA scheme.
[9]
[10]
[11]
[12]
From the analysis of Figure-5, it is concluded that the proposed CGCA scheme outperforms IACA scheme in terms of optimality of channel allocation. Therefore the proposed CGCA scheme provides better channel allocation and outperform the IACA scheme.
[13]
[14]
VI.
C ONCLUSION
This paper proposes an approach for channel allocation in cognitive radio networks that consists of link interference ratio as the main parameter for channel allocation. The simulation results have shown that: (1) In case of threshold based allocation, the channel allocation improves as the value of threshold increases; (2) In case of iteration based allocation, the channel allocation was observed to have improved using the proposed CGCA scheme ; (3) It is found that there is a significant improvement in channel allocation as the number of available channels increases. This concludes that the proposed CGCA scheme outperforms the IACA scheme.
[15]
[16]
[17]
[18]
[19]
ACKNOWLEDGMENT [20]
The authors would like to thank Council of Scientific and Industrial Research (CSIR), Government of India, India for providing the financial assistance for this work. 56
J. Mitola, “Cognitive radio for flexible mobile multimedia communications,” in Mobile Multimedia Communications, 1999.(MoMuC’99) 1999 IEEE International Workshop on, pp. 3–10, IEEE, 1999. J. Mitola, Cognitive Radio—An Integrated Agent Architecture for Software Defined Radio. PhD thesis, Royal Institute of Technology (KTH), 2000. S. Haykin, “Cognitive radio: brain-empowered wireless communications,” Selected Areas in Communications, IEEE Journal on, vol. 23, no. 2, pp. 201–220, 2005. I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey,” Computer Networks, vol. 50, no. 13, pp. 2127–2159, 2006. B. Wang and K. R. Liu, “Advances in cognitive radio networks: A survey,” Selected Topics in Signal Processing, IEEE Journal of, vol. 5, no. 1, pp. 5–23, 2011. E. Z. Tragos, S. Zeadally, A. G. Fragkiadakis, and V. A. Siris, “Spectrum assignment in cognitive radio networks: A comprehensive survey.,” Communications Surveys and Tutorials, IEEE, vol. 15, no. 3, pp. 1108– 1135, 2013. E. Ahmed, A. Gani, S. Abolfazli, L. Yao, and S. Khan, “Channel assignment algorithms in cognitive radio networks: Taxonomy, open issues, and challenges,” Communications Surveys and Tutorials, IEEE, vol. pp, no. 99, p. 1, 2014. P. Kolodzy and I. Avoidance, “Spectrum policy task force,” Federal Commun. Comm., Washington, DC, Rep. ET Docket, no. 02-135, 2002. C. E. Shannon, “A mathematical theory of communication,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 5, no. 1, pp. 3–55, 2001. W. Maqbool, S. K. S. Yusof, N. Latiff, S. Hafizah, S. Nejatian, A. Farzamnia, and S. Zubair, “Interference aware channel assignment (iaca) for cognitive wireless mesh networks,” in Communications (MICC), 2013 IEEE Malaysia International Conference on, pp. 330– 334, IEEE, 2013. A. P. Subramanian, H. Gupta, S. R. Das, and J. Cao, “Minimum interference channel assignment in multiradio wireless mesh networks,” Mobile Computing, IEEE Transactions on, vol. 7, no. 12, pp. 1459– 1473, 2008. A. T. Hoang and Y.-C. Liang, “Maximizing spectrum utilization of cognitive radio networks using channel allocation and power control,” in Vehicular Technology Conference, 2006. VTC-2006 Fall. 2006 IEEE 64th, pp. 1–5, IEEE, 2006. S. Geirhofer, L. Tong, and B. M. Sadler, “Interference-aware ofdma resource allocation: A predictive approach,” in Military Communications Conference, 2008. MILCOM 2008. IEEE, pp. 1–7, IEEE, 2008. M. Ahmadi and J. Pan, “Cognitive wireless mesh networks: A connectivity preserving and interference minimizing channel assignment scheme,” in Communications, Computers and Signal Processing (PacRim), 2011 IEEE Pacific Rim Conference on, pp. 458–463, IEEE, 2011. V. Kumar and S. Minz, “Ccra: channel criticality based resource allocation in cognitive radio networks,” International Journal of Communication Systems, pp. n/a–n/a, 2015. K. Bian, J.-M. Park, and B. Gao, “Channel assignment for multi-hop cognitive radio networks,” in Cognitive Radio Networks, pp. 101–116, Springer, 2014. K. Jain, J. Padhye, V. N. Padmanabhan, and L. Qiu, “Impact of interference on multi-hop wireless network performance,” Wireless networks, vol. 11, no. 4, pp. 471–487, 2005. A. P. Subramanian, M. M. Buddhikot, and S. Miller, “Interference aware routing in multi-radio wireless mesh networks,” in Wireless Mesh Networks, 2006. WiMesh 2006. 2nd IEEE Workshop on, pp. 55–63, IEEE, 2006. S. Nejatian, S. Syed-Yusof, N. Abdul Latiff, and V. Asadpour, “Proactive integrated handoff management in cr-manets: a conceptual model,” vol. pp, pp. 2993–3006, Praise Worthy Prize, 2012. S. Nejatian, S. Syed-Yusof, N. Abdul Latiff, and V. Asadpour, “Handoff management in cognitive radio networks: concepts, protocols, metrices and challenges,” in Wireless Technology and Applications (ISWTA), IEEE Symposium on, pp. 32–37, IEEE, 2012.
