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\title{Multi-Channel Scheduling with Optimal Spectrum Channel Hole Filling (MCS-OSHF) for Cognitive Radio Wireless Networks}
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             \author[1]{N  Shribala}

             \author[2]{Dr. P.  Srihari}

             \author[3]{Dr. B. C.  Jinaga}

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\date{\small \em Received: 11 December 2015 Accepted: 1 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


In this study, a contemporary method of scheduling algorithm has been proposed for working on scheduling of varying size data-frames transmission in CR based wireless networks. The objective of the proposed model is to achieve maximum throughput, and also reduction of loss of dataframes in the transmission. Some of the key elements that are considered in the development of the model are optimal bandwidth and idle channel availability. Using the three level hierarchical approach, the scheduling strategy is constructed. The optimal idle channel allocation, allocation with considerable transmission intervals allocation and optimal multiple channels models are considered at respective levels in the hierarchy in the proposed algorithm. The proposed model while tested under simulated environment in comparison to the other two bench marking models, the outcome depicts that the process is more efficient and supports in improving the overall process of scheduling of data-frames as per the desired objectives of the model.

\end{abstract}


\keywords{secondary spectrum usage, cognitive radio network, quality of service, spectrum sensing, channel scheduling, spectrum hole filling.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
ireless communication systems are emerging much faster in terms of performance and efficiency, and the public radio spectrum bands do not have the scope of service for such advancements, as the bands were already licensed to the service providers earlier. Despite that, still there are many licensed spectrum bands that are underutilized in the spatial domain and also time domain \hyperref[b0]{[1]}.In order to utilize the unutilized spectrum band as opportunistic access for improving the efficiency of the spectrum usage, Cognitive Radio (CR) solutions are providing quality solutions. \hyperref[b1]{[2]} \hyperref[b2]{[3]}. Spectrum and Channel sensing methods are introduced to handle one of the key issues envisaged with CR is about the protection of Primary Users (PUs) from any kind of interference resulting from Secondary Users (SUs) communications.\par
In the case of opportunistic access, SU shall identify any idle channels for the service, and can utilize the channel, but the crux is that irrespective of whether it focuses on the idle channel, still it has to ensure that current channel and additional channels are sensed. Only in such conditions, when a PU channel appears, SU can recover immediately the service channel. During the process of channel sensing, SU can't communication with other channels.\par
As per IEEE 802.16e Worldwide Interoperability for Microwave Access (WiMax) \hyperref[b3]{[4]}, the system allows the mobile station to perform channel scanning, by allowing mobile station to cut the communication with the base station, the efficacy of the process for QoS can be assured. But, in the case of IEEE 802.11 WLAN \hyperref[b4]{[5]}, such process is not facilitated unlike WiMax, and hence there shall be issues of packet losses and disruptions emerging due to channel scanning. To achieve the system with minimal QoS disruption, the interface of SU equipped with WLAN models has to be designed effectively.\par
This paper proposes the model of channel sensing scheduling which ensures interests of PUs are addressed, with the emphasis on sensing the channels only during the pre-defined time schedule, whilst managing the QoS for SUs for the delay and packet loss issues. As the interests of the PUs have to be given priority, certain level of SUs QoS may not be satisfied in the model.\par
In the further sections of this report, the emphasis is on, the literature pertaining the subject is discussed in section 2 and in section 3, the inputs related to proposed model of QoS-aware multichannel scheduling that has Optimal Spectrum Hole Filling model is proposed. Section -4 depicts the experimental results, and is followed by Section 5 with conclusion of the proposed model. 
\section[{II. Related Work}]{II. Related Work}\par
Medium-Access-Control (MAC) protocols are adapted in using the DSA scheme for CRNs. In the case of MAC protocol, there are usually two phases predominantly, as contention phase and data transmission phase. In the contention phase, SUs rather than focusing on the common control channel shall focus on the idle licensed channels, through which successful SUs which shall take over the idle channels in the W In \hyperref[b5]{[6]}, distributed MAC protocol was proposed which comprise the SUs having common channels for forming groups and for multiple groups some SUs performing as gateways. The data is transmitted by SUs using the data based on their success in the contention phase.\par
In the distributed MAC protocol proposed by Chen et al \hyperref[b6]{[7]}, SUs shall form clusters that are controlled by a group leader for each cluster, which conducts the contention and data transmission process. Also in another model proposed in \hyperref[b7]{[8]}, the distributed multichannel MAC protocol was proposed in which SU pair gets the opportunity to sense and access during the contention phase, and use the available channels for the hardware constraint. In the case of distributed multichannel MAC discussed in \hyperref[b8]{[9]}, all the available access channels that are sensed using the sensing policies are accessed by the SU paid during the contention phase.\par
In all the aforesaid conditions, there is high quantum of control overheads as the SUs usually contend in random manner for channels, certainly the outcome shall be much lower with the MAC protocols.\par
[10] - \hyperref[b14]{[15]}Whereas in the case of DSA that are implemented using scheduling algorithms that can achieve higher throughput. DAS system has the process in which at the beginning of every slot, information regarding bandwidth requirement is collected from the SUs by scheduler and it is broadcasted to common control channels. From the received schedule, the SUs access the corresponding channels for the slot time that is remaining, and the model is defined as slot-based scheduling schemes.\par
[10] Proposes the scheduling algorithm which is based on integer linear programming (ILP), which is a unique channel user pair that is activated for varied time instants within the slot. Models in \hyperref[b10]{[11]} - \hyperref[b14]{[15]} presents numerous scheduling algorithms which can support in maximizing the transmission capacity for the SUs which are presented. In the scheduling algorithm discussed in \hyperref[b10]{[11]}, certain factors like the fairness, traffic demand to the SUs, link capacity, and Signal-to-interference-andnoise ratio (SINR) are considered. Whereas, in \hyperref[b11]{[12]}, the factors like fading, interference, and packet waiting times are considered, unlike \hyperref[b12]{[13]} in which the focus is upon throughput, maximum frequency and packet waiting time. In \hyperref[b13]{[14]}, that achieves proportional fairness for SUs, focus on packet waiting time and the interference caused due to SU to the PUs receiver, but in \hyperref[b14]{[15]}, the model focus on assigning the idle channels to SUs depending on if the signal-to-noise ratio (SNR\textunderscore  shall be used at the receiving SU which could be highest for any given channel.\par
The information exchange taking place by the scheduler in the slot based scheduling schemes are even comprised in the scheduling overhead for the SUs due to low bandwidth in the common control channel and because of such model, the effective transmission to the data channels are getting reduced and are constraining the throughput achievable. Also, the scheduling overhead works on increasing the number of channels that can work on SUs, and not any of the aforesaid \hyperref[b9]{[10]}- \hyperref[b14]{[15]} shall focus on issuing of scheduling overhead.\par
Review of the earlier models and the literature reflect that the scheduling overhead could majorly impact the system performance, and hence such issues have to be addressed in the scheduling scheme design. 
\section[{III. Multichannel Scheduling with}]{III. Multichannel Scheduling with}\par
Spectrum Hole Filling for Cognitive Radio Networks:\par
The proposed model of Multichannel scheduling with Optimal Spectrum Hole Filling (MCS-OSHF), has emphasis on medium access control strategy which shall function in Spectrum Access Controller. The key objective in the model is about QoS aware and also on dynamic channel allocation for different data-frame size that are to be transmitted in cognitive Radio wireless Networks which could enable the spectrum hole usage. The term spectrum hole usage can be defined as idle time amidst the schedules for sequence that is observed in a channel under Primary User levels. MCS-OSHF model presents the multichannel scheduling for hierarchy, and the following are the key processes adapted.\par
? The CR nodes shall assemble the varying size dataframes that are to be transmitted. ? For every data-frame in the transmission queue, a specific control frame shall be sent to the spectrum access controller, which shall inform to common controller, the requirement of each of the dataframe.  
\section[{a) MCS-OSHF Scheduling Strategy}]{a) MCS-OSHF Scheduling Strategy}\par
In MCS-OSHF, the channel scheduling for respecttive data-frame i w is carried out as: The selection criteria for the channels are that of desired bandwidth and the ones that are idle for time slot transmission expected. If none of the channel exists in such criteria, under considering other such conditions like, the arrival of a data-frame and the channel scheduling time is not being sync, or in the case where the multiple channels meet scheduling criteria, or multiple data-frames arriving with same criteria, or if less number of channels are identified with desired criteria, in such conditions, the data-frame segmenting and channel allocation shall be carried out by MCS-OSHF.\par
However, the data-frame transmission time i w if realized to be much lesser than the available transmission time frame for a target channel, and also if the opportunity for a channel usage is found to be extremely high, in such conditions the following processes are performed by the spectrum access controller.\par
The process of scheduling an infrequent channel, with the extremely high transmission time frame shall be adapted rather than desired transmission time frame for data-frame i w . In case of failing to trace a channel with the given criteria, selection of the infrequent channel sets that has some kind of lower time frame that the desired time frame for the data-frame i w , in order to aggregate the transmission time slots for the selected channels, which shall be greater than desired transmission time frame.\par
Also segments the data-frame i w multiple dataframes as to each partition in the data-frame shall transmit by one of the channels, from the set of channels that are selected. Also, if the spectrum access controller do not achieve the schedule under above criteria, channels with idle times are selected which could meet the criteria for transmission time frame i w In the case of idle time frame is not found sufficient, then the data-frames are segmented in to minimum number of data-frames, so as the new dataframes shall be transmitted using the minimum channels that are compatible with the idle time slots.\par
Also, in the instances where the spectrum access controllers fail to schedule channels using any of the above criterions, then the data-frame is buffered and in frequent intervals the attempts are made to schedule. Despite of such process, if the scheduling fails within the lifetime of data-frame, then such data-frames are dropped and acknowledgment to CR nodes are sent about failure.\par
Mathematical notations and the process flow algorithm for MCS-OSHF model has been depicted in the following section.    Towards performing the channel scheduling, MCS-OSHF focus on tracking possible optimal channel (Sec 3.3), and in the instance of failure, attempts the further selection criteria like the minimal number of idle channels  {\ref (3.4)}, and the process as detailed in the aforesaid section  {\ref (3.5)}. Process of segmenting is carried out on the basis of demand, thus leading to minimal overhead. In the instances of MCS-OSHF failing to schedule any of the channels, the failure acknowledgment is communicated to CR nodes after dropping the data-frames.) (0 ) m m if ritf ritf rbw rbw < < ? < < begin i. m ritf ritf ? ii. m rbw rbw ? iii. oc c ? d. End // of\textbf{0} 
\section[{IV. Experimental Setup and Empirical Analysis}]{IV. Experimental Setup and Empirical Analysis}\par
Using the simulation study the performance of proposed model of MCS-OSHF is assessed in comparison to the benchmarking models like QoSaware Channel Sensing Scheduling (QCSS) \hyperref[b15]{[16]}  There is huge deviation in the varying size dataframes that are formed in the data size of 10GB to 25GB. In the range of 32kb to 512kb, there is variation in the data-frame size. In the comparison of model to QCSS \hyperref[b15]{[16]} and NSSS [17], performance of OCA-UTI is assessed using QoS metrics -data-frame loss against transmission data -frame loads (see figure -1), and also the transmission throughput that is achieved in data frame load (see figure  {\ref -2}). Also the process overhead that is observed in the transmission data-frame load (see figure \hyperref[fig_3]{-3}) is also depicted.  The quantum of data-frame loss in correlation to data-frame load is depicted in Figure  {\ref .}1 and it is imperative that the data-frame load is normalized amid the value of 0 and 1 that depicts the number of dataframes per second. The study reflects that MCS-OSHF shall certainly reduce the data-frame loss compared to the other models opted for simulation. (See Figure -1). However, in terms of multiple channel selection, and the process of data-frame segmentation too, MCS-OSHF still leads the minor process overhead rather than the other two models considered in the study. (See figure \hyperref[fig_3]{3}).For achieving the maximum throughput using the minimal data-frame loss, such mechanism is certainly tolerable. 
\section[{V. Conclusion}]{V. Conclusion}\par
MCS-OSHF (Multichannel scheduling with spectrum hole filling) model is focused on improving the channel scheduling protocol for CR based wireless networks. The emphasis in the model is about maximizing optimal channel allocation for better throughput and also minimal transmission loss of dataframes. Using the hierarchical approach which facilitates the optimal idle channel, using a specific process, in terms of following the order in the hierarchy the process of data-frames scheduling is carried out. From the detailed experimental studies that are carried out in comparison with other such models like NSSS and QCSSS, the inputs from the study depict much more \begin{figure}[htbp]
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\caption{\label{fig_0}}\end{figure}
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\noindent\textbf{12}\includegraphics[]{image-3.png}
\caption{\label{fig_2}Figure 1 :Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-4.png}
\caption{\label{fig_3}Figure 3 :}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.07441634241245136\textwidth}P{0.3365272373540856\textwidth}P{0.002480544747081712\textwidth}P{0.0008268482490272373\textwidth}P{0.002480544747081712\textwidth}P{0.0016536964980544747\textwidth}P{0.005787937743190662\textwidth}P{0.07358949416342413\textwidth}P{0.0008268482490272373\textwidth}P{0.0016536964980544747\textwidth}P{0.0016536964980544747\textwidth}P{0.002480544747081712\textwidth}P{0.006614785992217899\textwidth}P{0.010749027237354086\textwidth}P{0.24309338521400775\textwidth}P{0.004961089494163424\textwidth}P{0.019844357976653695\textwidth}P{0.033073929961089495\textwidth}P{0.02728599221789883\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{| | AP}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{?}\tabcellsep i w\tabcellsep =\tabcellsep \multicolumn{2}{l}{1 = ? j}\tabcellsep | ?\tabcellsep ( ) | j ap AP i w\\
\tabcellsep \multicolumn{14}{l}{// |AP| shall be spectrum access controllers of count}\\
\tabcellsep \multicolumn{14}{l}{that is observed as i w with the average of the tentative}\\
\tabcellsep \multicolumn{14}{l}{arrival times of a data-frame}\\
\tabcellsep \multicolumn{14}{l}{i w atspectrum access controller ap is estimated as}\\
\tabcellsep \multicolumn{4}{l}{follows:}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep a ?\tabcellsep i w\tabcellsep =\tabcellsep a ?\tabcellsep mf\tabcellsep \multicolumn{2}{l}{( ) i w}\tabcellsep +\tabcellsep ?\tabcellsep \multicolumn{2}{l}{mf}\tabcellsep \multicolumn{2}{l}{( ) i w}\tabcellsep +\tabcellsep ?\tabcellsep i w\tabcellsep // the cumulative value of\\
Year 2016\tabcellsep \multicolumn{6}{l}{arrival time time ( ) a ? mf i w ?}\tabcellsep \multicolumn{8}{l}{( ) i w and tentative transmission time mf of the message frame mf , process i w ? of the}\\
42\tabcellsep \multicolumn{14}{l}{data-frame i w . As per the message evaluated from Data-frame}\\
Volume XVI Issue VII Version I\tabcellsep \multicolumn{14}{l}{mf of data-frame i w , the spectrum access controller shall schedule channels using proposed model of MCS-OSHF.}\\
Global Journal of Computer Science and Technology\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ? The data-frame arrival time shall be calculated as the aggregate value of cumulative average time taken for a data-frame to reach the possible spectrum access controllers and the process-time ( time taken for analyzing the message frame)\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Let\tabcellsep ?\tabcellsep mf\tabcellsep ( ) i w\tabcellsep seen as process-timefor analyzing\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep acontrol frame mf for a specificdata-frame i w .\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Let\tabcellsep a\tabcellsep mf\tabcellsep ( ) i w\end{longtable} \par
  {\small\itshape [Note: ( ) © 2016 Global Journals Inc. (US) 1 E in the beginning. Considerable quantum of slot time is lost in the communication to the scheduling overhead reaching an access point ap , the outcome is estimated as: ? seen as control frame arrival time mf atspectrum access controller ap . The time taken by the data-frame tentatively for transmission time i w to]} 
\caption{\label{tab_0}?}\end{figure}
 			\footnote{© 2016 Global Journals Inc. (US) 1} 			\footnote{© 2016 Global Journals Inc. (US)} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{document}
