An on-demand routing protocol for multi-hop multi-radio multi-channel cognitive radio networks

An on-demand routing protocol for multi-hop multi-radio multi-channel cognitive radio networks Ahmed Chehata, Wessam Ajib, Halima Elbiaze Computer science department Université du Québec à Montréal, Canada chehata.ahmed@courrier.uqam.ca, ajib.wessam@uqam.ca, elbiaze.halima@uqam.ca Abstract Cognitive radio networks are composed of spectrumagile devices capable of changing their configurations and transmission parameters on the fly based on their spectral environment. This capability opens up the possibility of designing flexible and dynamic spectrum access strategies with the purpose of opportunistically reusing portions of the spectrum temporarily vacated by licensed primary users. However, this flexibility in the spectrum access brings a new complexity in the design of communication protocols at different layers. In this paper, we consider the problem of routing in multi-hop cognitive radio networks. We propose a multi-radio multi-channel on-demand solution that is able to effectively manage the transmission activities of cognitive and primary users. The routing metric should be carefully developed in order to provide a tradeoff between the channel diversity of the routing path and the endto-end delay. Through simulations, we highlight the performance of our proposed solution and compare it to multi-radio multichannel on-demand distance vector protocol. I. INTRODUCTION A recent report by the Federal Communications Commission (FCC) [] has showed that the fixed spectrum assignment policy is becoming inefficient and unsuitable for today s wireless communication systems. This inefficient spectrum usage can be overcome by allowing unlicensed users having cognitive radios (CRs) to dynamically access the bands without interfering with the licensed primary users. A CR is a spectrumagile radio able to switch between channels (i.e. frequency bands) in the environments of dynamic spectrum usage by sensing the different bands and opportunistically utilizing the bands showing a lack of activities of licensed users. Research on cognitive radio networks (CRNs) experienced a tremendous increase in volume during the last few years [2], [3], [4]. In a CRN, the transmission of Primary Users (PUs) should not be affected by the activities of Secondary Users (SUs). In addition, a SU should immediately interrupt its transmission whenever a neighboring PU activity is detected, which requires continuous spectrum monitoring. In fact, the connectivity map of a multi-hop CRN is determined by the available bands and by the activities of PUs. More specifically, the task of finding and selecting the appropriate path from a source to a destination node in an environment that evolves dynamically can be a highly challenging problem. In a CRN, the possible environments can be classified according to the primary users activities into three categories: 978--4577-228-4//$26. c 2 IEEE Static: The PUs are rarely active and hence the system doesn t differ from other wireless systems. In this case, it can be assumed that once a SU finds an available band, it can exploit that band for an unlimited period of time. Dynamic: The PUs have dynamic activities; i.e., they come and go more frequently which cause an intermittent availability of frequency bands. Opportunistic: The PUs are highly active which makes it rare for a SU to have a whole uninterrupted end-to-end transmission. Routing in multi-hop CRNs exhibits similarities with routing in multi-hop multi-channel Ad-hoc and Mesh networks but faces several new challenges. Both routings target the creation and maintenance of a routing path by selecting the proper relay nodes and the channel to be used on each link of the path. However in multi-hop CRNs, routing should deal with primary transmissions which dynamically change the spectrum availability. Due to this key challenge, the spectrum information is required and collaboration between the path selection and the spectrum decision is needed. Another challenge is how to measure the quality of different paths. Classical measures, such as throughput and delay, should be coupled with spectrum availability/stability and the dynamics of PU activity. A third challenge is the route maintenance and recovery. Link failure in multi-hop CRNs may happen after a sudden appearance of PU. Hence, effective signaling procedures are required to quickly recover the broken paths. In the past few years, much of the research on CRN have been made on PHY and MAC layers and little research have focused on the network and routing protocols for multi-hop CRNs. In [9], the authors provides a survey on the challenges and solutions given for routing in multi-hop CRNs. The authors of [] proposes a new routing protocol for CRNs that optimizes the end-to-end with service differentaiation. The authors [5] and [6] propose comprehensive frameworks to address channel assignment and routing based on the creation of a layered graph which features a number of layers equal to the number of available channels. However, the proposed routing approaches are centralized requiring network-wide signaling support to generate the layered-graph. In [7] a routing protocol that accounts for long and short terms spectral availability is proposed but maintenance and recovery have not been considered in depth. In [8] a tree-based protocol has been

proposed. However, the proposed routing recovery mechanism requires that if a node is affected by a PU activity in a certain channel, then all the nodes (including those unaffected) must dismiss such a channel. Moreover, a root node is needed to gather all the dynamic system information which requires an important amount of energy and computation. In this paper, we propose an on-demand routing protocol for multi-hop multi-radio multi-channel CRNs which is able to effectively manage the set of radios and channels available in the network. A local recovery mechanism is also presented to deal with the primary users onsets. The rest of the paper is organized as follows. In Section II, we present the metric used in our solution. Sections III describes the on-demand routing protocol and the route and maintenance recovery mechanism. Then, simulation results are illustrated in Section IV and we conclude in Section V. II. THE ROUTING METRIC The routing metric used in multi-hop CRNs should reflect the bands availability, the links quality, the PU activities and QoS requirements of SUs. In this paper, we use Weighted Cumulative Expected Transmission Time (WCETT) metric that was developed to find high-throughput routing paths in multi-radio, multi-hop wireless networks. This metric assigns weights to each link based on its quality; and then, these weights are combined. In the following, we show how to calculate WCETT. A. Expected Transmission count (ETX) ETX, proposed by [], is defined as the expected number of transmissions at the link-layer needed to successfully transmit a packet over a link. To calculate ETX, each node sends small probe packets and the neighboring nodes acknowledge the probe packets correctly received. Thus, every node knows the ratio of probes received in the forward and reverse directions, denoted d f and d r respectively. Then, the likelihood that a packet arrives and is acknowledged correctly is d f d r. It is assumed that each attempt to transmit a packet is statistically independent from the precedent ones and from the packet size which implies that the sending attempt can be considered a Bernoulli trial. Then, the ETT over link l is: ET X(l) = d f d r. () The ETX of a path p, ET X(p), is the summation of the ETX of all the links l p belonging to path, p. ET X(p) = l p ET X(l). (2) ETX selects routes with knowledge of the delivery ratios which is a more pertinent information than the hop count metric, thus increasing the throughput and improving the network utilization. However, ETX has the disadvantage of only considering the link loss rate and not the link data transmission rate (related to the transmission delay). B. Expected Transmission Time (ETT) ETT metric was subsequently proposed by [2] to improve the ETX metric by integrating the link data transmission rate. ETT can be defined as the bandwidth-adjusted version of ETX since the latest is multiplied by the link bandwidth to obtain the packet transmission delay. Let S denote the packet size and B l the bandwidth of link l, then: ET T (l) = ET X(l) (S/B l ). (3) By introducing the link bandwidth into the calculation of the path cost, the ETT metric captures the impact of the link capacity on the routing performance in addition of the impact of physical interference (related to the link loss rate). C. Weighted Cumulative ETT (WCETT) The WCETT metric [2] is an extension of ETT metric suggesting to compute the path metric as something more than just the sum of the metric values of the individual links belonging to this path. Considering only the summation does not take into account the fact that concatenated links may interfere with each other, if they use the same channel. Hence, WCETT aims to specifically reduce intra-flow interference by minimizing the number of nodes using the same channel in the end-to-end routing path. Let N be the total number of channels of a system, the sum of transmission times over all hops on channel j, j N, is defined as: X j = ET T (l). (4) j is used on linkl As the total path throughput will be dominated by the bottleneck channel, which has the largest X j, [2] propose to use a weighted average between the maximum value of X j and the sum of all ETTs. This results in the formula: W CET T = ( β) l p ET T (l) + β max j N X j, (5) where β is a tunable parameter ( β ). The max X j j N term explicitly captures the intra-flow interference since the paths having more channel diversity will have lower weights. Therefore, (5) can be seen as a tradeoff between the path latency (first term) and the channel diversity of the selected path (second term). In [2], the authors studied thoroughly the impact of β on routing performance in multi-channel multiradio multi-hop wireless networks and show its minor effect on the throughput. The impact of β in our system model is similar to [2] and hence we select a fixed value of β =.5 to balance the channel diversity and the latency of the path. A. System Model III. PROTOCOL DESCRIPTION We consider a wireless communication system with N channels for data traffic and one additional channel for signaling traffic. We assume stationary secondary and primary users. The locations, the number and the transmission standards of the PUs are assumed unknown to the SUs. The transmissions of

PUs are sensed by a spectrum sensing mechanism, which is out of the scope of this paper. Each SU has two radio interfaces: a fixed interface assigned for long intervals to some specific fixed channel and used for data reception; a switchable interface dynamically assigned to any data traffic channel over short time scales. The corresponding channel is called switchable channel. The switchable interface allows node X to transmit to a neighbor node Y by switching to the fixed channel used by Y. B. Interface and Channel Assignment ) Fixed Interface Assignment: The purpose of the fixed interface assignment is to choose a fixed channel for reception and to inform the neighbor nodes about it. For example, if node A uses channel as its fixed channel, then all the transmissions destined to A will be on channel. Therefore, it is beneficial if neighbor nodes use different fixed channels for balancing the usage of the available channels. Each node has two tables: MyNeighborsTable (MNT), containing the fixed channels of the node s neighbors and ChannelUsageTable (CUT) containing a count of the number of nodes in its twohop neighborhood using each channel as their fixed channel. Initially, each node chooses randomly its fixed channel. Periodically, each node broadcasts a Hello packet, or route discovery packet, including its own fixed channel and its MNT on every channel. When a node receives a Hello packet from a neighbor node, it updates its MNT with the neighbor fixed channel and its CUT using the MNT of its neighbor node. Updating CUT with each neighbor s MNT ensures that CUT will contain two-hop channel usage information. When an entry is not updated for a specified maximum lifetime, it will be removed. This ensures that stale entries of nodes that have moved away are removed from the MNT and CUT. Fig. illustrates the assignment algorithm on a simple example of an ad-hoc wireless network with 3 nodes and 3 channels. Once the flow of Hello packets has ended, each node consults its CUT. If the number of other nodes using its own fixed channel is large, then the node may change its fixed channel to a less used channel. Afterwards, the node transmits a Hello packet to inform its neighbors of its new fixed channel. 2) Switchable Interface Assignment: After selecting the fixed channels, the system needs to manage the assignment of switchable channels. In fact, to be able to use the available channels in the network, the nodes have to dynamically change their switchable channel and hence a protocol is needed to decide when to switch. The protocol must ensure that all the neighbors of a node X can communicate with it on-demand; which requires that all the neighbors of X have to be always aware of the fixed channel of X. At the sender node, each channel (the fixed channel and the switchable channels) is associated with a packet queue, as shown in Fig. 2. When multiple channels are used, a packet broadcast on a channel is received only by the nodes listening to that channel. Therefore, to broadcast a packet a copy of that packet is added to each channel s queue, and sent out when that channel is scheduled for transmission. In this way, we Fig.. Example of the assignment of fixed channel on a simple topology of an ad-hoc wireless network with 3 nodes make sure that all the nodes receive a copy of the broadcasted message. For example in a 5-channels network, each broadcast involves sending 5 copies of the same packet on 5 channels. Fig. 2. Packet queue maintained by each node for each channel Once the packets are inserted in the corresponding queues, the fixed interface transmits packets queued up for transmission on the fixed channel and switchable interface transmits

the other packets on the other channels. To ensure fairness in the system, the channel with the oldest queued packet is always selected as the next switchable channel. The switchable interface changes channels only when packets are queued for another channel and when either: the switchable channel has an empty queue; or the switchable channel has been selected for more than MaxTime duration. 3) Route Discovery Process: The proposed routing is an on-demand protocol that is similar to Ad hoc On Demand Distance Vector (AODV) protocol. The protocol uses the WCETT metric. The routing process starts when a route is needed between two nodes. The source node broadcasts a route request (RREQ) message across the network. The RREQ packet transmitted by a node X on channel i contains the measured WCETT and the channels used along the path. In addition, each node has a table called PrimaryListTable (PLT) allowing to manage the onset of the primary users in the route maintenance and recovery phase. A node rebroadcasts the RREQ if: The sequence number of RREQ is new. In that case, the WCETT value of the path is stored in a local table. The sequence number of RREQ is not new, which means an RREQ with the same sequence number has been processed, but its WCETT value is smaller than the one of previous RREQ with the same sequence number. This condition will help raising the probability of finding the lowest cost route. When the destination node receives a RREQ, it sends back a route reply (RREP) if the received RREQ s cost is smaller than the previous received RREQ with the same sequence number. The source will finally use the path having the lowest cost for data transmission and stores locally the other best paths. 4) Local Route Maintenance and Recovery: Overcoming the sudden onset of PUs is the responsibility of the route recovery process which is critical in our protocol since PUs frequently interrupt ongoing secondary transmissions. Therefore, the route error message (RERR) in AODV is extended to recover an interrupted link by PU. We suppose that only one channel is interrupted at a time. When a SU (called the detector) detects PU activity on a specific channel, it will perform a local recovery as follows. The detector sends a RERR containing the PrimaryListTable (PLT) to the source of the path. PLT has a list of all the used channels by the detector and his direct neighbors. Once a PU activity is detected, PLT is updated by setting the occupied channel to. Then, the source transmits the data packets over the second best path available on its local table until the broken path is recovered. Also, the detector will broadcast another RERR message to all his direct neighbors to update their PLT. The route maintenance and recovery messages are sent from the switched interfaces and received by the fixed interfaces. Therefore, if a neighbor node is transmitting on a channel that has been set to - by the detector, this neighbor should switch the channel by running the route discovery process. Then, the neighbor updates its PLT and notifies the detector about its newly selected channel. The detector will then send a new RREP with the new channel information to the source that will afterwards be able to reuse the corrupted path without disturbing the PU. IV. EXPERIMENTAL RESULTS AND PERFORMANCE EVALUATION In this section, we evaluate the performances of our proposed protocol (that we call CR-AODV) by simulations via Network Simulator-2 (ns-2) [3], based on the Cognitive Radio Cognitive Network (CRCN) simulator [4]. We carry out a multitude of tests with random topology where 2 SUs (non mobile) and 5 PUs are randomly placed in 5 5 m 2. The transmission range of each user is adjusted to 25 meters. We use IEEE 82.b at basic rate 6 Mbps with saturated UDP traffic. Each simulation is run for 5 seconds. Simulation s parameters are summarized in the following table: End-to-End Throughput (Mbps) 6 5 4 3 2 TABLE I THE PARAMETERS OF SIMULATIONS Parameters Values Topology 5 5m 2 Number of nodes 2 Traffic type CBR Transmission throughput 6 Mbps Packet size 52 bytes Simulation duration 5 seconds MAC layer IEEE 82. Transport layer UDP CR-AODV with 5 available channels CR-AODV with 3 available channels MM AODV with 5 available channels MM AODV with 3 available channels 2 3 4 5 6 7 8 Number of Flows Fig. 3. End-to-end throughputs of CR-AODV and MM-AODV with varying number of channles In the first experiments, we start up the transmission of 3 out of 5 PUs at the beginning of each run and we compare the end-to-end throughput of CR-AODV with that of Multiradio Multi-channel AODV (MM-AODV) [5] while also varying the number of channels. Firstly, we fix the number of channels to 5, and later to 3, and we study the end-to-end throughput while raising the number of flows. From Fig. 3,

Fig. 4. Route Disconnectivity Ratio (%) End-to-End Throughput (Mbps) 6 5 4 3 2 2 3 4 5 6 7 8 Number of Flows End-to-end throughput of CR-AODV as the number of PUs increases 9 8 7 6 5 4 3 2 CR-AODV PU MM AODV 3 PUs 5 PUs 2 3 4 5 Number of Primary Users Fig. 5. The ratio of disconnected flows increases as the number of PUs increases CR-AODV outperforms MM-AODV. since it assigns better the available channels to the fixed and switched interfaces. Moreover, the WCETT metric increases the performance of the routing protocol because it takes into account the interference among links that use the same channel, while MM-AODV simply use the number of hops as metric. Furthermore, we can notice that with only three available channels, CR-AODV gives higher throughput than MM-AODV with five channels. In the second experiments, we evaluate the impact of PU s onsets on the throughput of CR-AODV s. We can notice from Fig. 4 that a higher number of PUs degrades the end-toend secondary throughput. This is because when the number of PUs increases, the available channels become scarce and difficult to find by SUs. The third simulation is carried out to quantify the effect of PU s onset on the connectivity of paths among SUs. This test shows how many flows are disconnected due to the sudden activity of PUs and should be recovered. At the beginning of each run, no PUs are active, and we start 5 arbitrary flows among SUs. While these flows are ongoing, we suddenly turn on varying number of PUs at the same time. The results in Fig. 5 shows that CR-AODV has the lowest disconnectivity ratio since the local route maintenance and recovery mechanism guarantees a much better connectivity. V. CONCLUSION In this work, we have proposed an on-demand routing solution for multi-hop multi-channel multi-radio cognitive radio networks. Unlike most of the previous work, the protocol doesn t use a dedicated control channel or a central entity to manage the system resources. The key concept in this protocol is to efficiently use and exploit the multiple available channels and interfaces through a competent assignment strategy and an adequate route metric. Moreover, a light and local recovery maintenance mechanism has been proposed. We evaluated the proposed CR-AODV by simulation on a random topology for general performance tests. In the future, we will focus on how to introduce the switching delay of the switchable interface in our protocol and on minimizing the broadcasts of RERR to reduce overheads. REFERENCES [] R. Engelman and K.. A. Abrokwah, Report of the spectrum efficiency working group, Nov. 22. [2] S. Haykin, Cognitive radio: brain-empowered wireless communications, IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 2 22, Feb. 25. [3] I. Mitola, J. and J. Maguire, G.Q., Cognitive radio: making software radios more personal, IEEE Personal Commun. Mag., vol. 6, no. 4, pp. 3 8, Aug. 999. [4] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey, Comput. Netw., vol. 5, no. 3, pp. 227 259, 26. [5] C. Xin, B. Xie, and C.-C. Shen, A novel layered graph model for topology formation and routing in dynamic spectrum access networks, in IEEE Int. Symp. Dynamic Spectrum Access Networks, DySPAN 25, Nov. 25, pp. 38 37. [6] X. Zhou, L. Lin, J. Wang, and X. Zhang, Cross-layer routing design in cognitive radio networks by colored multigraph model, Wireless Personal Commun., vol. 49, pp. 23 3, 29. [7] P. Ioannis, S. Wong, and S. Lu, Samer: Spectrum aware mesh routing in cognitive radio networks, in IEEE Int. Symp. Dynamic Spectrum Access Networks, DySPAN 28, Oct. 28. [8] G.-M. Zhu, I. Akyildiz, and G.-S. Kuo, Stod-rp: A spectrum-tree based on-demand routing protocol for multi-hop cognitive radio networks, in IEEE Global Telecommunications Conf., Globecom 8., Dec. 28. [9] K. R. Chowdhury and I. F. Akyildiz, Crp: A routing protocol for cognitive radio ad hoc networks, IEEE J. Sel. Areas Commun., vol. 29, no. 4, pp. 794 84, April 2. [] M. Cesana, F. Cuomo, and E. Ekici, Routing in cognitive radio networks: Challenges and solutions, Elsevier Ad Hoc Networks, vol. 9, no. 3, pp. 228 248, 2. [] D. S. J. De Couto, D. Aguayo, J. Bicket, and R. Morris, A highthroughput path metric for multi-hop wireless routing, in Int. Conf. on Mobile Computing and Networking (ACM Mobicom 3), 23, pp. 34 46. [2] R. Draves, J. Padhye, and B. Zill, Routing in multi-radio, multihop wireless mesh networks, in Int. Conf. on Mobile Computing and Networking (ACM Mobicom 4), 24, pp. 4 28. [3] The Network Simulator NS-2, http://www.isi.edu/nsnam/ns/, 996. [4] Cognitive radio cognitive network simulator, http://stuweb.ee.mtu.edu/ ljialian/, 29. [5] multi-channel multi-interface simulation in NS-2, http://www.cse.msu. edu/ wangbo/ns2/nshowto8.html, 26.