Enhanced Protection of Hidden Primary Users

through Filtering based on Suspect Channels

Classification

José Marinho

ISEC - Polytechnic Institute of Coimbra

CISUC - University of Coimbra

Coimbra, Portugal

fafe @ isec.pt

Edmundo Monteiro

DEI - University of Coimbra

CISUC - University of Coimbra

Coimbra, Portugal

edmundo @ dei.uc.pt

Abstract— Cognitive radio (CR) users (i.e., secondary users) are

wireless nodes which are allowed to select and access licensed

frequency bands, provided that they do not cause any harmful

interference to the respective incumbent users (i.e., primary

users). One of the possible approaches consists in having the

secondary users locating and accessing vacant frequencies

dynamically. However, in scenarios with hidden primary users

(i.e., a secondary user cannot sense a primary user on its

respective channel, but the two coverage areas overlap each

other), it is unfeasible to address the protection of primary users

with solutions which are exclusively based on local and non-

cooperative sensing schemes. In this work, we propose a

cooperative approach which enables an effective protection of

primary users in fully distributed CR scenarios, even when the

hidden node problem is a concern. Our proposal is based on a

key concept we designate as “filtering based on suspect channels”

and takes full advantage of any underlying learning scheme

based on observation and past experience. It was also successfully

integrated with an existing CR medium access control (MAC)

protocol. Simulation results show the proposal is effectively

capable of delivering high levels of protection concerning the

primary users, while preserving the secondary users’

communication performance.

Cognitive Radio, Medium Access Control, Multi-hop Ad-hoc

networks, Prediction, Hidden Primary Users

I. INTRODUCTION

Cognitive Radio (CR) is currently considered one of the key solutions to overcome the current scarcity of the radio spectrum and, consequently, to increase the performance of unlicensed wireless communications [1]. This goal is achieved by allowing unlicensed devices, also designated as secondary users (SU), to freely access the entire or large portions of the spectrum, as long as they do not cause any harmful interference to the incumbent users, also designated as primary users (PU). This is a dramatic shift when compared to what has been the common practice in terms of spectrum regulation and which has resulted in unlicensed users being restricted to a few and often crowded channels, while most licensed spectrum bands remain underutilized [2]. This globally observed underutilization of the radio spectrum creates numerous spectrum holes, i.e., vacant frequency bands, which vary dynamically in time and frequency. The CR approach which

has received most attentions both from industry and academia is designated as overlay CR. It consists in the SUs opportunistically accessing the spectrum holes. CR is a complex and multi-disciplinary research area which relates to a multitude of engineering and computer science disciplines not limited to the to the physical and medium access control (MAC) layers, such as software defined radios, signal processing, communication protocols, and machine learning.

In our previous work [3], we proposed an innovative CR medium access control (MAC) protocol, designated as CoSBT-MAC (Cooperative Sensing-Before-Transmit-based MAC), which targets fully distributed (i.e., ad-hoc) multi-hop CR scenarios. CoSBT-MAC aims at addressing all the following relevant issues in the context of the mentioned type of scenario: increased sensing accuracy through a cooperative scheme; balanced usage of the spectrum holes; support for high variability in terms of spectrum opportunities; handling of the hidden PU and SU problems; and increase in the performance which is delivered to the SUs. The preliminary evaluation results of CoSBT-MAC were concluding in terms of the achieved performance and promising concerning the protection of PUs [3].

CoSBT-MAC can effectively reduce the number of missed PU detections which are, for instance, due to the hidden node problem or sensors’ imperfections. However, it cannot completely avoid this problem by itself, especially in scenarios with hidden PUs. Therefore and as the next step in our research activities about CR MAC protocols, we decided to evaluate and optimize CoSBT-MAC in terms of PU protection, assuming the SUs are capable of probabilistically determining busy and idle times on the targeted channels. These traffic modeling capabilities are intended to result from learning based on past experience and observation, and are a core issue in CR [1][4]-[7]. Basically, existing proposals try to efficiently exploit deterministic PU behaviors or to evaluate statistics occupancy over time for each channel [8]. With such prediction capabilities, the SUs are able to take optimized spectrum decisions (e.g., selecting a channel which will probably be sensed idle and won’t experience any PU activity during data transmission). Modeling traffic patterns is feasible as several research works have indicated that channel occupancy can be statistically modeled [9]. It must be highlighted that our aim is

2012 IEEE 8th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

978-1-4673-1430-5/12/$31.00 ©2012 IEEE 419

not proposing any novel learning algorithm or evaluating any existing one (this can possibly be considered in future work). Currently, we aim to analyze and optimize the behavior of CoSBT-MAC concerning PU protection in multi-hop CR scenarios, without sacrificing communication performance. CoSBT-MAC is also intended to be able to take full advantage of any underlying prediction scheme [3].

This paper presents a solution which enables delivering high levels of protection to hidden PUs in distributed CR networks and resulted in the ECoSBT-MAC (Enhanced CoSBT-MAC) protocol. To the best of our knowledge, the CR MAC proposal in this work is definitively novel as it solely relies on our designated filtering based on suspect channels approach and on CoSBT-MAC, which is a novel proposal itself.

The remainder of the paper is organized as follows. In section II, a brief description of CoSBT-MAC is provided. Section III presents and discusses the inefficiency problem which has motivated the work in this paper. Section IV describes the proposed solution and how it was integrated with CoSBT-MAC. Section V evaluates, through simulation, ECoSBT-MAC concerning the protection of PUs and its impact on communication performance, when compared to CoSBT-MAC. Finally, conclusions are drawn in section VI.

II. COSBT-MAC BRIEF DESCRIPTION

CoSBT-MAC [3] is a CR MAC proposal which aims at providing an increase in the performance which is delivered to the SUs, as well as an effective protection of the PUs. The targeted scenarios are multi-hop ad-hoc CR networks which suffer from the hidden PU and hidden SU problems. In the remainder of this section, the main characteristics of CoSBT-MAC are briefly described (refer to our previous work [3] for additional details).

With CoSBT-MAC, every SU must be equipped with two radios, one dedicated for operating the common control channel (CCC), which is used for signaling, and the other, which is dynamically reconfigurable, for data transmission. CoSBT-MAC strictly follows a “sense-before-transmit” approach, i.e., accesses to the spectrum follow the following steps: any time data is ready to be sent, the sender and the receiver start agreeing on a candidate channel to be used for transmission; then, they synchronously sense the channel on the dynamically reconfigurable radio; finally, if no activity is sensed, the data is sent over the selected channel. In order to increase the protection of PUs, any neighbor which is not currently transmitting any data (i.e., any idle neighbor) also cooperates in sensing and notifies the sender or receiver if any activity is sensed. The participation of idle neighbors is induced by the control frames they overhear on the CCC. This is a cooperative and, therefore, more accurate sensing scheme [1].

CoSBT-MAC is based on a two-phase handshake (see Fig. 1). In the first phase (i.e., spectrum sensing), the sender starts sending an RTS frame to the receiver. This frame includes a list of non-reserved channels. Then, the receiver randomly selects a channel which is considered as being not reserved at both ends and replies with a CTSS (Clear To Sense) frame

which includes the selected channel. In order to avoid the hidden node problem in multi-hop networks, the sender replays the CTSS frame as an fCTSS (forwarded CTSS) frame. A neighboring idle SU which overhears any CTSS or fCTSS frame switches its reconfigurable radio to the targeted channel and also senses it. Neighbors which detect any activity on the channel inform the sender or receiver through a CTS (Clear To Send) frame with the selected channel set as unavailable. This must be done before the sender and the receiver enter the next negotiation phase (i.e., the decision phase). Therefore, idle neighbors perform sensing for a shorter time than the sender and the receiver do. Non-idle neighbors, i.e., which do not participate in sensing, also send a similar CTS frame if they have the intended channel set as reserved.

In the spectrum decision phase, the receiver starts sending a CTS frame with the appropriate state of the selected channel to the sender. Then, based on the content of the received CTS frame and on its own knowledge (provided by neighbors and local sensing), the sender replies with an fCTS (forwarded CTS) frame with the appropriate conclusions. If the selected channel is considered vacant, fCTS also includes the respective reservation time and the sender starts transmitting the data frame on the reconfigurable radio. Due to any potential hidden SU problem, the receiver also forwards this fCTS frame in turn. The SU neighbors which overhear the fCTS frame set the respective channel as reserved for the specified amount of time.

III. COSBT-MAC WITH PREDICTION CAPABILITIES BASED

ON LOCAL OBSERVATION

In our previous work [3], which was briefly described in the previous section, a sender-receiver pair randomly selects a channel for sensing among those which are available on both sides (i.e., not already reserved for transmission in their neighborhood). These channels are the designated candidate channels. Random selection is the approach of most existing CR MAC proposals [1]. However, the applicability and feasibility of learning based on past experience and observation is often considered a core issue in CR, as it has potential for modeling traffic patterns on the channels and, therefore, incorporating prediction in spectrum decision. According to Wellens, Riihijarvi, and Mahonen [8], the solutions which exploit PU activity statistics perform much better than those which are essentially random-based. In the specific context of

RTS

CTSS

fCTSS

CTS

fCTS

fCTSSENDER

RECEIVER

IDLE NEIGHBOURS

OF THE SENDER

IDLE NEIGHBOURS

OF THE RECEIVER

RTS - Request to Send CTSS - Clear to Sense fCTSS - forwarded CTSS

Overheard packet CTS - Clear to Send fCTS - forwarded CTS

Switch to the selected channel and sense it (on the reconfigurable radio)

Transmit/receive the data packet (on the reconfigurable radio)

The selected channel is set as being reserved for the amount of time specified in fCTS

Spectrum sensing Spectrum decision Spectrum access

Figure 1. Interaction between CoSBT-MAC secondary users

420

the “sense-before-transmit” approach which has been adopted for CoSBT-MAC, priority must be given to a channel which is likely to be vacant during sensing and transmission times. This is expected to result in a significant decrease regarding the number of harmful interferences to PUs. Several proposals have already been proposed concerning prediction and dynamic modeling of PU activity in the context of CR scenarios [1].

If CoSBT-MAC-based SUs are assumed to have prediction capabilities based on past observation (i.e., local sensing), then, a candidate channel must additionally have no PU activity predicted for the expected two-phase handshake and data transmission times (see section II). With this additional restriction, CoSBT-MAC is expected to perform better concerning the protection of PUs, without requiring any further modification. Therefore, we first modified our implementation of CoSBT-MAC [3] in order for the SUs to have emulated prediction capabilities concerning PU activity on the channels they can sense. We considered one hundred percent prediction accuracy and any initial learning/convergence period to be already over. This assumption is not really practical as there is probably no learning or traffic modeling mechanisms, based on observation and past experience, which result in fully accurate prediction [1][8]. However, throughout this work our aim is to state the limits which can be reached by CoSBT-MAC when prediction capabilities are available, i.e., an upper bound limit to the expectable benefits. In section V, non-perfect prediction capabilities are also considered for the evaluation of our proposal.

The modified version of CoSBT-MAC was evaluated through simulation concerning the protection of PUs. The scenario in Fig. 2 was used to run simulations with a 1 kbit/s and 500 kbit/s constant bit rate loads per sender-receiver pair, and a 2 Mbit/s physical bit rate. Four channels were considered available, being each one accessed by a specific primary user (the rank of the respective PU is used to designate a given channel). The other simulation settings are specified in section V. In each sender-receiver pair, the sender was chosen in order to maximize the potential number of interferences to the PUs (e.g., in pair 1-2, SU1 has an overlapped coverage area with PU2, but SU2 does not).

Without hidden PUs in the network, the number of interferences would have decreased one hundred percent with the modified version of CoSBT-MAC. However, due to the hidden PU problem, prediction capabilities have resulted in deceiving contributions concerning the observed reduction in the number of interferences to the PUs. On the average, decreases of just 17.4% and 2.4% were observed with 1 kbit/s and 500 kbit/s loads, respectively. The reason is as follows. With learning based on observation, a SU assumes a given channel is always idle if no activity has ever been observed in the past. Therefore, the channel which is used by a hidden PU is always a candidate channel, unless it is already reserved by any neighboring pair. On the contrary, a SU which succeeds in effectively sensing the PU activity on a channel is able to model the respective traffic pattern through the underlying learning algorithm. Therefore, it does not consider the channel whenever any PU activity is predicted.

For instance, with prediction capabilities enabled, pair 1-2 does not consider channel 1 and channel 4 as candidate channels when it predicts them to be busy during the expected two-phase handshake and data transmission times. On the contrary, channels which are used by PU2 and PU3, and which suffer from the hidden PU problem in the perspective of pair 1-2, are always candidates (provided that they are not already reserved by any neighboring pair). This behavior results in an unbalanced selection of the channels which can be observed in Fig. 3 (i.e., channels 2 and 3 are more often selected).

In the next section, we propose an effective approach to address the inefficiency which has just been described concerning the protection of hidden PUs when learning through observation and past experience is enabled.

IV. ENHANCED COSBT-MAC DESCRIPTION

According to the discussion in the previous section and to the results in our previous work [3], there is still room for CoSBT-MAC to be improved concerning the protection of PUs. In this section, we propose an effective solution which is

Figure 2. A scenario with hidden nodes

(a) 1 kbit/s load

(b) 500 kbit/s load

Figure 3. Channels selected by pair 1-2 with CoSBT-MAC

421

capable of providing such type of improvements. Its key idea is what we designated as a filtering based on suspect channels approach and assumes the availability of underlying prediction capabilities. In the next subsection, the suspect channel concept is firstly defined. Then, section IV.B describes the proposed filtering algorithm. Finally the last subsection describes how this algorithm was integrated with CoSBT-MAC and resulted in the ECoSBT-MAC proposal.

A. Suspect channel definition

In order to reduce the level of interference to hidden PUs, a CoSBT-MAC-based SU should consider a channel to be suspect if it believes that channel is free of any PU activity in a given period, but another SU in its neighborhood has an opposite opinion. Concretely, a SU classifies a channel as being suspect if its underlying learning algorithm currently considers the probability of PU appearance to be zero for that channel, but at least one of its neighbors does not, or no PU activity was detected during the previous sensing phase (see Fig. 1), but at least a neighbor reported the contrary. When there is effectively any PU accessing a given channel, zero PU activity probability means the SU could not observe and, therefore, appropriately model any active traffic pattern.

A SU stops classifying a channel as being suspect whenever it senses any activity on it or zero probability PU appearance is not considered anymore by the underlying learning algorithm. Besides, when scenarios with mobility and/or time-varying traffic patterns are considered, the classification of a channel as suspect must be subject to a timeout (i.e., the SU reverts the status of the channel to non-suspect if it does not listen to any information which indicates the contrary for a certain amount of time). The timeout value must be tuned according to the specificities of the scenario (i.e., smaller values are considered when changes in the scenario are frequent). This is a common practice concerning learning-based approaches for traffic modeling [5][10][11].

The channels the SUs in Fig. 2 should consider as suspect, i.e., the channels which are accessed by PUs they can interfere with, but cannot sense or learn the respective traffic patterns (i.e., hidden PUs), are identified trough shaded cells in Table I. The tracking of suspect channels by the SUs is intended to be exclusively based on the contents of the control frames they receive and overhear. If the received information only relates to the sensing and prediction outputs of the respective senders, we get what we designate as the one-hop approach (see section a) in Table 1), i.e., the fusion of sensing and learning outputs is

limited to neighboring SUs. In table 1, symbol is used to identify which channels the SUs are able to classify as suspect based on the one-hop approach, provided that enough frames/information have already been exchanged. It can be observed that 39% of the suspect channels are not appropriately detected when the one-hop approach is selected.

The protection of PUs can be further increased if the same scheme is used with a designated two-hop approach (i.e., which considers information from one-hop neighbors and their own one-hop neighbors) or higher distance approaches (see sections b) and c) in Table 1). The set of “neighbors of the neighbors” of a given SU may also include some of its own neighbors.

Therefore, we do not designate them as two-hop neighbors. When the two-hop approach is applied to the scenario in Fig. 2, all the situations with potential interference to PUs are detected. In general terms (i.e., for any scenario), the minimum distance which is required for the approach to be effective equals the maximum number of hops between any SU and any other SU which is in the coverage area of any PU it can interfere with, but cannot sense.

In Table1, what we designate as a false suspect channel problem (i.e., a SU considers a channel to be suspect despite being out of its area of interference) can be observed. It relates to cells which are not shaded, but are tagged as suspect. This problem increases as the selected distance for the approach also increases. Therefore, the number of hops to be used must be appropriately selected in order to maximize the detection of suspect channels, while minimizing the false suspect channel problem in the targeted scenario. Without loss of generality, we consider the scenario in Fig. 2 and the two-hop approach in the remainder of this document.

The SUs which are in a shadow area for all the channels, such as SU7 in Fig. 2, can have all the channels tagged as suspect (see Table I). To deal with such situation, we identify three different possible approaches: (1) the SU stops communicating until the situation reverts; (2) the SU starts using an unlicensed frequency band; or (3) the SU considers there are no preferred channels and, therefore, it does not take into account suspect classification in the spectrum decision process. All the approaches have pros and cons, both in the perspective of the SUs and of the PUs. In the remainder sections, the last option, which considers suspect channels as lower priority channels instead of forbidden channels, will be considered.

B. Filtering based on suspect channels

In the context of the “sense-before-transmit” approach which was adopted for CoSBT-MAC, the concept of suspect channel is applied through an approach we designate as the filtering based on suspect channels (FBSC) algorithm. FBSC is applied when a channel is selected by the sender-receiver pair before sensing is performed [3].

TABLE I. SUSPECT CHANNELS

SU1 SU2 SU3 SU4 SU5 SU6 SU7 SU8 SU9 SU10

a) One-hop approach

PU1

PU2

PU3

PU4

b) Two-hop approach

PU1

PU2

PU3

PU4

c) Three-hop approach

PU1

PU2

PU3

PU4

422

Fig. 4 describes the FBSC algorithm concerning the two-hop approach. For every SU, it requires making the distinction between channels which are not suspect based on the outputs of the one-hop neighbors, and those which are not suspect based on the outputs of the “one-hop neighbors of the one-hop neighbors”. This approach enables the FBSC algorithm to consider two distinct criteria as follows. If the use of the two criteria (i.e., channels which are suspect based on the two-hop approach) results in every channel being tagged as suspect, then the operation is repeated omitting criterion Nsr2 (see step 6 in Fig. 4). If the situation remains, no filtering based on suspect channels is performed (see step 8 in Fig. 4). This is one of the three possible approaches which were mentioned in the previous subsection. Globally, we consider the conclusions which result from the information provided by one-hop neighbors to be more relevant than those which also take into account two-hop neighbors (e.g., they are more likely to suffer from the false suspect channel problem). As a final remark, it can be mentioned that the algorithm in Fig. 4 can easily be adapted to higher distance approaches or to the one-hop approach. This requires changing the number of considered criteria and providing the respective inputs.

The meaning of the inputs in Fig. 4 can be extracted from previous discussion, except concerning Qs and Qr. Without Qs and Qr, the FBSC algorithm would be as follows regarding step

2 and step 3: Nsr1 := Ns1 Nr1; and Nsr2 := Ns2 Nr2. This seems adequate as it only considers the channels which are not suspect both on the receiver and on the sender sides, according to the two criteria. However, there is a problem with that

approach. For instance, pair 1-2 and pair 5-6 would not consider channel 1, despite SU1 and SU5 are able to sense it and, therefore, should always consider it as a candidate channel (i.e., provided that it is not reserved and no activity is predicted). Considering the union of the channel sets (i.e., Nsr1

:= Ns1 Nr1; and Nsr2 := Ns2 Nr2) is neither a solution. It avoids the kind of problem we just mentioned, but creates another one. For instance, with this approach pair 3-4 would wrongly consider channel 1, even if SU3 has already classified it as suspect based on sensing information provided by neighbor SU5, and SU4 is out of its coverage area (i.e., there is a hidden PU problem).

The solution we propose to address the problem which was mentioned in the previous paragraph is as follows: if in a given sender-receiver pair only one the SUs classifies a given channel as not being suspect for a given criterion, and that SU considers the channel to have non-zero PU appearance probability, then that channel should be considered by the sender-receiver pair as a non-suspect channel. This additional information is provided by Qs and Qr in Fig. 4. Basically, these two inputs enable making distinction between two possible reasons for a SU not to classify a channel as being suspect: (1) the SU is out of the coverage area of the respective PU and has not yet received or overheard any information which makes it classify the channel as being suspect; or (2) the SU can effectively sense and learn (i.e., assuming learning is enabled) the activity pattern on the channel.

The next subsection describes how the FBSC algorithm was effectively integrated with the CoSBT-MAC protocol, i.e., how the exchange and processing of relevant information for tracking suspect channels is achieved. This concrete implementation did not require any relevant modification or overhead (see the evaluation results in section V) to the native structure of CoSBT-MAC.

C. Integration of filtering based on suspect channels with

CoSBT-MAC: ECoSBT-MAC

With CoSBT-MAC, filtering based on suspect channels must be performed by the receiver before it selects a given channel for sensing, i.e., before it informs the sender through a CTSS frame about the selected channel (see Fig. 1). Therefore, the receiver needs to know the channels the sender considers to be suspect for all the filtering criteria. With ECoSBT-MAC, this information is provided through additional arrays of bits in RTS frames. Concretely, three additional arrays of bits are required concerning the approach in Fig. 4. They are used to represent Ns1, Ns2, and Qs, respectively. Besides, RTS frames also include an additional array of bits to represent the designated Qsn input in Fig. 5, which is required for identifying suspect channels with the two-hop approach.

Fig. 5 describes how the two arrays which relate to Qs and Qsn in RTS frames are used by ECoSBT-MAC for tracking suspect channels. Any time a SU is notified by a neighbor that a given channel has non-zero PU appearance probability (i.e., via Qs in the received/overheard RTS frames), the SU includes that channel in a local channel set designated as Qrn (see step 6 in Fig. 5). It also uses that information to track suspect channels with the one-hop approach (see step 2 in Fig. 5). Qrn

Inputs:

Cs, the channels which are not reserved and have no predicted PU

activity at the sender;

Cr, the channels which are not reserved and have no predicted PU

activity at the receiver;

Ns1, the channels which are not suspect at the sender, based on the

outputs of its one-hop neighbors;

Nr1, the channels which are not suspect at the receiver, based on the

outputs of its one-hop neighbors;

Ns2, the channels which are not suspect at the sender, based on the

outputs of the “one-hop neighbors of its one-hop neighbors”;

Nr2, the channels which are not suspect at the receiver, based on the outputs of the “one-hop neighbors of its one-hop neighbors”;

Qs, the channels the sender considers having non-zero PU activity probability;

Qr, the channels the receiver considers having non-zero PU activity probability.

Output: Cc, a set of candidate channels for sensing/access.

1. Csr := Cs Cr.

2. Nsr1 := (Ns1 Nr1) (Ns1 Qs) (Nr1 Qr). //Criterion 1

3. Nsr2 := (Ns2 Nr2) (Ns2 Qs) (Nr2 Qr). //Criterion 2

4. Cc := Csr Nsr1 Nsr2.

5. if Cc then return Cc.

6. Cc := Csr Nsr1.

7. if Cc then return Cc.

8. Cc := Csr

9. return Cc.

Figure 4. Filtering based on suspect channels (two-hop approach)

423

is included in the RTS frames the SU transmits, under the designation of Qsn (see Fig. 5), and enables tracking suspect channels with the two-hop approach (see step 3 in Fig. 5).

The mentioned four additional arrays in RTS frames are of equal size and only require as much bits as the number of potential channels, which results in poor size overhead (see the results in section V for a concrete evaluation of the impact on performance). For instance, the scenario in Fig. 2, which considers four possible channels, only requires sixteen additional bits in the RTS frames. The two arrays which relate to Qs and Qsn are also included in CTSS frames with the same purpose of identifying suspect channels (see Fig. 5). This enables a mutual exchange of information between the sender and the receiver, and additional information to overhearing neighbors. In order to enable the SUs to track suspect channels, four additional bits (i.e., s1, s2, p1, and p2) are also added to the CTS and fCTS frames. These frames are sent after the sensing phase and relate only to a specific channel (see section II). The meaning and usage of each bit are described in Fig. 6.

As mentioned in section IV, anytime an activity is sensed by a SU on a given channel or the underlying learning algorithm considers the channel to have non-zero PU appearance probability, that channel stops being tagged as suspect according to the two-hop and one-hop approaches, and gets non-zero PU appearance probability. Consequently, the SU adds that channel to its respective channel sets (i.e., Nr1, Nr2, and Qr if we consider the receiver in Fig. 5). If mobility or any other time-varying characteristics are considered for the targeted scenario, the SUs must additionally use a timeout/forgetting mechanism (see section IV). It must be noted that the mentioned timeout scheme is also a means to turn into temporary any wrong classification decision which, for instance, resulted from occasional false sensing alarms or inaccurate predictions. Concretely, the receiver in Fig. 5 returns a suspect channel to Nr1 if it does not receive any information which enables tagging the channel as one-hop-based suspect for a certain amount of time (see the use of Qs in step 2 in Fig. 5, and s1 and p1 in Fig. 6). Concerning Nr2, a similar timeout approach is followed based on the information which is provided by Qsn in RTS/CTSS frames (see step 3 in Fig. 5), and s2 and p2 in CTS/fCTS frames (see Fig. 6). In the same context,

a channel with no activity sensed locally for a certain amount of time and having zero PU appearance probability is removed from Qr. Regarding Qrn, an entry times out when the SU stops receiving any information which revalidates it for a certain amount of time (see the use of Qs in step 6 in Fig. 5).

The integration of FBSC with CoSBT-MAC, i.e., ECoSBT-MAC, takes full advantage of the existing control frames and handshaking process inherent to this MAC protocol (see Fig. 1). This results in all the interactions required by FBSC to be provided at the expanse of just a few additional bits in existing control frames. In order to use an approach different from the two-hop approach, the number of additional bits in CTS and fCTS frames, and the number of additional arrays in RTS and CTSS frames must be adjusted. Modifying the algorithm accordingly is quite straightforward.

The next section evaluates, through simulation, the ECoSBT-MAC proposal, concerning the protection of PUs and communication performance.

V. EVALUATION RESULTS

This section aims at presenting some evaluation results concerning the ability of ECoSBT-MAC and, inherently of the underlying FBSC algorithm, to effectively increase the protection of hidden PUs at the expanse of an acceptable low communication overhead, when compared to CoSBT-MAC. Scenarios with distinct prediction accuracies are considered.

As CoSBT-MAC was implemented on the OMNET++/MiXiM framework [3], the implementation of ECoSBT-MAC was also provided for the same platform. The same simulation settings which were used for the evaluation of CoSBT-MAC are considered [3]. For instance, the bit rate is 2 Mbit/s both on the CCC and on the data channels, the MTU (Maximum Transfer Unit) is 18432 bits, switching time on the reconfigurable radios is 10

-4 seconds, sensing time is 0.5 ms,

Inputs:

Qr, Qs, Nr1, and Nr2 (see Fig. 4);

Qrn, the channels at least one neighbor of the receiver considers having non-zero PU activity probability;

Qsn, the channels at least one neighbor of the sender considers having non-zero PU activity probability.

Output: Updated information about suspect channels.

1. for every channel c Qr:

2. if c Qs and c Nr1 then remove c from Nr1;

3. if c Qsn and c Nr2 then remove c from Nr2;

4. endfor.

5. for every channel c Qs:

6. add c to Qrn;

7. endfor.

8. return.

Figure 5. Tracking of suspect channels based on received/ovreheard RTS

or CTSS frames (two-hop approach)

Inputs:

Qr, Nr1, and Nr2 (see Fig. 4);

t, the specified channel in the received/overheard frame;

s1, a bit which indicates if the sender sensed any activity on t;

s2, a bit which indicates if any neighbor of the sender sensed any

activity on t;

p1, a bit which indicates if the sender considers t to have non-zero PU

activity probability;

p2, a bit which indicates if any neighbor of the sender considers t to have non-zero PU activity probability.

Output: Updated information about suspect channels.

1. if participated in sensing phase related to channel t then

2. if no activity was sensed on t then

3. if s1 = true and t Nr1 then remove t from Nr1.

4. if s2 = true and t Nr2 then remove t from Nr2.

5. endif.

6. endif.

7. if p1 = true and t Qr and t Nr1 then remove t from Nr1.

8. if p2 = true and t Qr and t Nr2 then remove t from Nr2.

9. return.

Figure 6. Tracking of suspect channels based on received/ovreheard CTS

or fCTS frames (two-hop approach)

424

and the activity of the PUs is characterized by alternating idle and busy periods which are 0.1 and 0.01 seconds exponentially distributed, respectively. The simulation scenario is the one in Fig. 2. and 1 kbit/s and 500 kbit/s loads are considered.

Fig. 7 illustrates the observed variation in the number of interferences to the PUs and global missed PU detections when ECoSBT-MAC is used instead of native CoSBT-MAC. A new interference occurs whenever an ongoing data frame coincides with a given PU active period on the same channel. A global missed PU detection occurs whenever the selected channel is accessed for data transmission, despite any PU activity the sender and/or the receiver can interfere with occurred during the sensing phase (see Fig. 1). The one-hop and two-hop approaches are considered regarding the FBSC algorithm, as well as 100%, 80%, and 60% prediction accuracies.

The reasons for the poor (and even inverted) results which can be observed when the original CoSBT-MAC protocol is enabled with fully accurate prediction capabilities were already discussed in section III. The previous discussion about the limitations of the one-hop approach concerning the identification of suspect channels in the targeted scenario (see section IV.A and Table 1) and the discussion in section III explain the deceiving results which are observed when the one-hop approach is used with ECoSBT-MAC.

With the two-hop approach and fully accurate prediction capabilities, all the missed PU detections and interferences to PUs are prevented. As the suspect channels are appropriately identified, the SUs only access non-suspect channels, i.e., channels the sender and/or the receiver can effectively sense and accurately model the respective traffic patterns, or which are not subject to the hidden PU problem. Consequently, the accessed channels are effectively vacant throughout the handshake and data transmission phases (see Fig. 1). In our implementation of ECoSBT-MAC, transmission time is calculated based on the data bit rate and data frame size. Concerning handshake time (i.e., the delay between RTS transmission and fCTS reception), each SU considers an estimated value on startup and, then, continuously adjust it based on the observed values. Naturally, other approaches may be considered.

If we consider prediction capabilities which are not fully accurate (i.e., 80% and 60% in Fig. 7), the protection of PUs degrades, but the benefits remain relevant when compared to native CoSBT-MAC. In Fig. 7, the relevance of including Qs and Qr in the FBSC algorithm (see subsection IV.B and Fig. 4) is also illustrated. When these inputs are omitted, results get considerably worse. This enables concluding that their usage is mandatory for the proposed approach to be effective.

Fig. 8 illustrates the selection pattern for every sender-receiver pair in Fig. 2 when ECoSBT-MAC is enabled with full prediction accuracy. There is a match between the observed pattern and the expected pattern (i.e., when the two-hop suspect channel classification in Table I and the FBSC algorithm in Fig. 4 are applied to the scenario in Fig. 2). Therefore, ECoSBT-MAC appropriately implements the proposed solution for tracking suspect channels. This results in an unbalanced selection of the channels, which is illustrated in Fig. 9. Channel 2 is more often selected than the others as it is

not classified as suspect by pairs 3-4 and 7-8 (see Fig. 8), it is the only channel pair 7-8 selects, and it is considered as having zero PU appearance probability by pair 3-4. Channel 4 is the only channel which is considered non-suspect by a single sender-receiver pair (i.e., pair 1-2). This explains why it is less often selected than the others. Finally, channel 1 and channel 3 are considered as not being suspect by two and three sender-receiver pairs, respectively.

Fig. 10 presents the average degradation in the SUs’ communication performance which is observed when ECoSBT-MAC is used instead of CoSBT-MAC. The scenario in Fig. 2 is considered, as well as two additional scenarios with five and four sender-receiver pairs, respectively, and four PUs. All the SUs and PUs are in range of each other in the last two scenarios (see our previous work [3] for communication performance results concerning CoSBT-MAC in similar simulation scenarios). Relevant degradation can only be observed for loads over 500 kbit/s and always stay below 15%. This is definitively a small and acceptable cost when compared

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ECoSBT-MACPA: 60%

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T: two-hop(Qr and Qs omitted)

Interferences to PU activities (1 kbit/s load)Global missed PU detections (1 kbit/s load)Interferences to PU activities (500 kbit/s load)Global missed PU detections (500 kbit/s load)

PA - Prediction accuracy T - Tracking of suspect channels

Figure 7. Variation in the number of interferences to PUs and global

missed PU detections when compared to CoSBT-MAC

(a) 1 kbit/s load

(b) 500 kbit/s load

Figure 8. Channel selection pattern per sender-receiver pair

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to the achievable benefits regarding PU protection.

The presented evaluation results enable us to conclude that ECoSBT-MAC and the underlying FBSC approach has potential to effectively contribute to the protection of PUs in multi-hop scenarios with hidden PUs and SUs, while delivering high levels of communication performance.

VI. CONCLUSIONS

The motivation behind this work was to increase the effectiveness of CoSBT-MAC [3] concerning the protection of PUs in distributed multi-hop CR scenarios with hidden PUs, without sacrificing communication performance. This resulted in an effective and practical approach, designated as filtering based on suspect channels (FBSC), which is capable of efficiently taking advantage of any underlying prediction capability. Additionally, the FBSC algorithm was successfully integrated with CoSBT-MAC [3] and resulted in the fully decentralized and cooperative ECoSBT-MAC (Enhanced

CoSBT-MAC) proposal. Evaluation results prove that FBSC is an effective approach and that ECoSBT-MAC is able to deliver relevant benefits concerning the protection of PUs while preserving the communication performance of CoSBT-MAC. Beyond the future directions which have already been proposed in the work about CoSBT-MAC [3], we can mention that ECoSBT-MAC should be evaluated concerning its ability to support applications with specific requisites (e.g., real-time, quality of service, and quality of experience requisites), possibly on scenarios with mobility and other time-varying characteristics. Additional mechanisms must also be considered to account for the possibility of erroneous sensing results and misbehaved SUs which report falsified data to their neighbors. Finally, it must be noted that ECoSBT-MAC can inherently be considered an interesting framework to support the evaluation and development of proposals related to learning based on past experience and observation for CR scenarios. According to our previous work [1], most existing proposals in this area were not integrated with any CR MAC protocol and, therefore, were not properly evaluated concerning their practicality.

REFERENCES

[1] J. Marinho and E. Monteiro, "Cognitive Radio: Survey on Communication Protocols, Spectrum Decision Issues, and Future Research Directions", Wireless Networks (Springer), Volume 18, Issue 2, 2012, pp. 147-164.

[2] FCC Spectrum Policy Task Force, “Report of the spectrum efficiency working group,” Nov. 2002.

[3] J. Marinho and E. Monteiro, "Cooperative Sensing-Before-Transmit in Ad-hoc Multi-hop Cognitive Radio Scenarios", 10th International Conference on Wired/Wireless Internet Communications, Santorini, Greece, 6-8 June, 2012, pp. 186-197 (accepted).

[4] I. Akyildiz, W. Lee, and K. Chowdhury, “CRAHNs:Cognitive radio ad hoc networks”, Ad Hoc Networks (Elsevier), Volume 7, Issue 5, 2009, pp. 810-836.

[5] M. Hoyhtya, S. Pollin, and A. Mammela, “Performance improvement with predictive channel selection for cognitive radios”, First International Workshop on Cognitive Radio and Advanced Spectrum Management, CogART 2008, 2008, pp. 1-5.

[6] C. Clancy, J. Hecker, E. Stuntebeck, and T. O’Shea, “Applications of Machine Learning to Cognitive Radio Networks”, Wireless Communications ( IEEE), Volume 14, Issue 4, 2007, pp 47-52.

[7] L. Xiukui, and S. Zekavat, “Traffic pattern prediction and performance investigation for cognitive radio systems”, IEEE Conference on wireless communications and networking, WCNC 2008, 2008, pp. 894-899.

[8] M. Wellens, J. Riihijarvi, and P. Mahonen, “Evaluation of adaptive MAC-layer sensing in realistic spectrum occupancy scenarios”, IEEE Symposium on New Frontiers in Dynamic Spectrum, DySPAN 2010, 2010, pp. 1–12.

[9] X. Wang, A. Wong, and P. Ho, ”Dynamically optimized spatiotemporal prioritization for spectrum sensing in cooperative cognitive radio”, Wireless Networks (Springer), Volume 16, Issue 4, 2010, pp. 889-901.

[10] S. Salah-Eldeen, A. Elnahas and S. Ghoniemy, “A hybrid distributed channel allocation algorithm using traffic prediction,” Information and Communications Technology, 2007. ICICT 2007. ITI 5th International Conference on, 2007, pp. 115-121.

[11] K. Tsagkaris, A. Katidiotis and P. Demestichas, “Neural network-based learning schemes for cognitive radio systems,” Computer Communications, Elsevier, Vol. 31, Num. 14, Sep.2008, pp. 3394-3404.

(b) 1 kbit/s load

(b) 500 kbit/s load

Figure 9. Global channel usage pattern

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5 SUs and 4 PUs all in range of each other

4 SUs and 4 PUs all in

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Figure 10. Degradation in the SUs’ communication performance when

ECoSBT-MAC is used instead of CoSBT-MAC

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