Efficient idle channel discovery mechanism through cooperative parallel sensing in cognitive radio network
© The Author(s) 2018
Received: 11 August 2017
Accepted: 18 March 2018
Published: 4 April 2018
Finding holes from the underutilized portion of spectrum at various times and locations is the most important function in cognitive radio networks (CRNs). This requires efficient sensing policy at the MAC layer that can discover more idle channels in less time. Whereas, the sensing policy depends on the channel sensing order that decides how a secondary user senses the primary user band in minimum period of time. Spectrum sensing policies for searching idle channels from the underutilized primary band can significantly affect the performance of secondary user in terms of throughput, reliability, and energy efficiency. In this paper, we have analyzed MAC protocol structure for ad hoc radio networks which used random channel sensing. This results in poor performance, either due to the channels being skipped or the time for sensing the band being significantly longer. We propose a parallel sensing scheme with sequential channel selection order as part of MAC protocol, which can discover all the free channels in the primary user band in less time. For the proposed scheme, we have performed analysis over the number of channels sensed and the number of idle channels discovered. Furthermore, energy efficiency and throughput of the system have also been evaluated. The results show considerable improvement for the proposed scheme when compared with the contemporary scheme.
Spectrum demand has increased over the years due to exponential growth in the wireless technologies and applications. Also, the existing spectrum has limited space to accommodate future services as it has already been allocated to different services, thus resulting into spectrum scarcity . However, the studies show that the existing spectrum is not being fully utilized as there are potential opportunities in the form of spectrum holes in space and time [2, 3]. Cognitive radio has emerged to gain benefits from the underutilized spectrum resources . In a cognitive radio network (CRN), depending on the activity of primary users, the number of channels available to secondary users (SUs) always varies in time and space, whereas, in traditional networks it remains fixed .
Cognitive radio exploits the spectrum opportunities in the existing underutilized licensed and unlicensed bands, which improves the overall spectral efficiency . For example, the IEEE 802.22 standard for CRN opportunistically uses the existing TV band for transmission; both networks coexist without any noticeable disturbance . Therefore, CRN is treated as a secondary network in waiting with low priority; where, secondary user (SU) opportunistically accesses the spectrum holes or vacant channels of the primary user (PU) from the underutilized primary network band. CRN is required to sense the primary user band for spectrum holes by protecting the PUs; however, the aim is to discover maximum number of spectrum holes accurately and quickly.
Sensing is an important function of cognitive radio, mostly implemented at physical layer; however, some of its features such as determining the sensing time, sensing order, and method of sharing sensing results among contending SUs are carried out at MAC layer . Designing a MAC protocol which could accurately sense and utilize the spectrum opportunities for optimal network performance is a challenging task . The ultimate goal of the MAC layer sensing is to maximize the pool of available resources to CRN’s SUs, with minimal delay yet with accuracy. This requires efficient sensing policies with optimal sensing period and order. Sensing order represents the sequence in which the channels of primary network are searched by SUs to discover the opportunities. Proper and well-defined sensing order can improve the performance of SUs in terms of throughput, delay, and energy efficiency .
In CRN, available spectrum holes in PU band are the most precious resource. When SUs sense the PU band, then missing the discovery of the vacant PU channels, such as due to inefficient sensing mechanism, would tantamount to missed opportunity. Therefore, by using optimal sensing scheme in multiple channel and single radio environment, the secondary users can maximize the pool of available resources under the constraints of detection accuracy and latency. Which, in turn, improves the overall performance of CRN MAC protocol in terms of throughput, delay, and energy efficiency. Therefore, finding better sensing scheme and channel selection order in CRN MAC protocol has been a problem of interest in the literature [11–14].
In , spectrum sensing schemes are proposed with adaptive sensing window and BER-assisted detection that improve probability of detection and spectrum utilization. However, comparison of these schemes are only given with the fixed sensing window and adaptive sensing window. In , authors propose a cooperative spectrum sensing MAC protocol that optimizes the sensing and access parameters and compares this with non-optimized design for better throughput. In both schemes, comparison is not given with the other relevant schemes.
In , parallel cooperative spectrum-sensing is proposed for infrastructure-based CRNs, wherein a number of SUs simultaneously sense multiple channels from the spectrum. Each SU is assigned to sense a particular channel, and when SU finds that channel occupied by PU, it sends sensing message request to base station (BS). Through coordination, base station selects different SUs, such that each SU senses the channels in parallel and reports back the status of that channel to BS. Similarly in , cooperative parallel spectrum sensing is proposed for a centralized CRN; all the SUs send their sensing results to a central node and the central node allocates channel to each SU based on gathered information and previous history.
In , efficient spectrum hole discovery with MAC layer sensing is proposed wherein the delay in finding the idle channels is minimized by optimizing the sensing period. Also, more idle channels in the PU band are discovered by channel sequencing algorithm taking into consideration the ON/OFF channel usage patterns. Comparative analysis with and without optimized sensing period is performed for different performance scenarios. Performance analysis with and without optimal channel sequencing is also performed for different scenarios. However, the comparative analysis with any other scheme is not given.
In , a channel sensing order for a multichannel CRN has been proposed in which the channels are sensed in descending order according to their achievable data rates, but with unknown free probabilities of the primary user channels. This results in efficient use of available channels during SU spectrum access. In , authors have devised an optimal sensing order for the CRN with cooperative centralized sensing, where sensing and access decisions are sequentially made by the central coordinator. The optimal sensing order with and without SU rate adaptation is calculated. Cognitive throughput is compared for the two cases when coordinator uses random sensing and intuitive sensing order; however, this requires knowledge of primary channel free user probabilities.
Authors in  propose a collision-free adaptive channel sensing order for a distributed CRN. Here, without any coordination, every SU goes through a phase and finally selects different channel sensing order. The unique sensing order for each SU in the network ensures that different channels are sensed, thus increasing the overall number of channels searched by the CRN and consequently increasing the probability of finding more idle channels. Discovery of higher number of holes improves the performance of CRN, particularly, the throughput and data delivery time. Although, the authors have explained how to achieve collision-free sensing order, but have not provided any information on how different CR nodes may communicate with each other and have also assumed zero probability of misdetection. In [20–22], sensing order is based on PU probabilities, SU’s achievable data rates and random permutations of available channels. However, the scheme lacks in energy efficiency and has higher computational complexity.
A contention-based, hardware-constrained cognitive MAC protocol (HC-MAC) is presented in  that incorporates sensing/stopping process and considers transmission constraints before sensing, thereby reducing the sensing time when the sensed spectrum fulfills the requirements of SUs in CRN. The drawback is that due to a single radio and absence of synchronization, multiple channel hidden terminals can cause collisions during the transmissions. In , fairness-oriented media access control protocol (FMAC) is proposed to bring fairness in access among the contending SUs of different CRNs by introducing three state spectrum sensing model. Here, channel busy state is further classified into channel busy by PU or channel busy by SU of other CRN. The proposed sensing algorithm can distinguish between the two busy states. Contention-based multichannel MAC (CBM-M) protocol for a distributed CRN separates the sensing mechanism from SUs by placing separate sensor nodes . Accuracy in the sensing mechanism during PU reappearance for non-slotted CRN has been emphasized in .
In this paper, we propose an efficient spectrum sensing scheme for a distributed CRN MAC. It considerably reduces the overall sensing and sharing phase time to extend the data transmission time for SUs in a given transmission cycle. Additionally, it minimizes the channel skipping problem during sensing, which maximizes the pool of available resources. Building upon the works in [27–29], we can enhance the efficiency in sensing. We separate the sensing and sharing phases in such a way that sharing phase starts after the sensing phase is complete. We present the idea of forming SU groups according to their IDs, and the SU groups are assigned equal portion of spectrum for parallel sensing. In our proposed sensing scheme, SU groups simultaneously sense the assigned portion of the PU band in parallel and share the sensing results among the SUs in a separate sharing phase. Thus, the total time of the sensing phase is reduced as every SU group has to sense an assigned portion of the spectrum in parallel and share the sensing result sequentially on the common control channel. This results in maximum number of vacant PU channels being discovered in shorter period of time, consequently, increasing the overall throughput by leaving extended time for data transmission and improving energy efficiency.
The remaining paper is organized as under. Section 2.1 describes the system model. Section 2.2 presents the detailed description of proposed sensing scheme. Performance is analyzed in Section 3. Numerical and simulation results are discussed in Section 4. Finally, the work is concluded in Section 5.
2 Parallel sensing scheme with sequential order in CRN MAC
In this section, we present our proposed parallel sensing scheme for an ad hoc CRN MAC protocol and discuss its salient features. Performance analysis of the proposed scheme has also been discussed. First, we describe the system model and working of the CRN MAC protocol.
2.1 System model
We consider a primary network with time slotted access, where a PU occupies these slots synchronously during fixed time τ or remains idle. The primary traffic pattern is assumed to be Poisson distributed as in . We consider a distributed secondary network, which by a sensing mechanism, discovers ‘M’ available channels out of ‘N’ primary channels, such that M≤N. One common control channel is assumed to be available for Nsu secondary users at all times, this channel has enough capacity to handle all the messages required for coordination and sharing the sensing results among the SUs. Therefore, after synchronization, such as in , each SU has full knowledge of vacant channels available in the PU band.
where, y(t) is the PU’s signal and n(t) is the white noise.
Details of the proposed sensing scheme are discussed in the following section.
2.2 Proposed sensing scheme
The sensing and sharing phase of the proposed CRN MAC protocol are shown in Fig. 3, where one cycle (Tcycle) of a common control channel is divided into different phases, i.e, Tidle, Tps, Tshar, Tcont, and Ttran.
where, mod represents the modulo operation which gives the remainder and Uid is the user ID of the SU.
Temporary IDs assigned to SUs after joining the network.
Using MAC address as SU ID.
A unique temporary ID can be assigned to an SU when joining the network through coordination messages over the common control channel. However, this method is not viable as it saturates the common control channel due to the excessive messaging, when the number of SUs entering or leaving the network grow. Therefore, it is more appropriate to use MAC addresses as user IDs for secondary nodes in CRN, without extra burden on common control channel. Algorithm 1 in next the section also describes the process of parallel sensing scheme.
Reduced sensing phase.
Discovering more idle channels in PU band.
Energy efficient sensing.
2.2.1 Reduced sensing phase
where Tss is the sensing and sharing time for SMC-MAC protocol .
The reduced sensing time is adjusted in the data transmission time, thus increasing the throughput by a factor of N g .
2.2.2 Discovering more idle channels in PU band
The price of leaving out a vacant channel is too high in CRN, as the overall network performance is highly dependent on discovering the opportunities and then tapping them efficiently. So, ideally it is desirable to scan all the channels to find their availability or otherwise. However, this leads to more power consumption in the sensing phase, which is not desirable in a wireless network with nodes having limited battery at their disposal. Also, it extends the sensing phase time, leaving less time for actual data transmission in the transmission cycle Tcycle.
The proposed parallel sensing scheme has the capability to exploit the maximum available opportunities in the PU band, as each SU group simultaneously senses the specific portion of the PU band in sequence without skipping any PU channels. In the considered example of two groups of SUs, all members of an odd ID SU group will sense channels 1,3,5,7,...,Nch−1 and the members of the other group will sense channels 2,4,6,8,...,Nch, as shown in Fig. 3. In this case, the probability of an idle PU channel left out is low; only when all members fall in the same group. This probability diminishes as the number of SUs grow in the CRN. Increased number of users in a group increases the accuracy of the system, i.e., reduced false alarm and misdetection. However, even if there is a single member in a group, all channels assigned to the group will be sensed, thus, the proposed sensing scheme maximizes the pool of available resources in the CRN.
2.2.3 Energy efficient sensing
When an SU senses more channels in PU band it consumes more energy. The energy conservation during the sensing phase requires that fewer channels in the PU band be sensed. But at the same time, it is desirable that collectively all SUs sense all the primary channels to maximize the pool of idle channels available to SUs. In the proposed parallel sensing scheme, the energy conservation can be achieved by forming more SU groups. When we cerate more SU groups, then it implies that every SU has to sense less number of primary channels, thus saving energy during the parallel sensing phase. For example, if there are 5 SU groups in the CRN then primary band channels are also divided into 5 portions and each group has the onus of only 1/5th of the band, thus saving the SU energy by a factor of 5.
The parallel sensing scheme is more efficient as it saves time, energy and discovers more number of idle channels for CRN from the PU band; however, it requires a delicate trade-off among contradicting requirements.
2.3 Sharing scheme
Status of the sharing sub-slots in sharing phase
Channel sensed idle
Channel sensed busy
Channel not sensed
In our proposed protocol, contention phase (Tcont) and data transmission phase (Ttran) are similar to ; where, after parallel sensing (Tps) and sharing phase (Tshar), M slots are available in the contention phase for the SUs.
3 Performance analysis
To evaluate the performance of the proposed parallel sensing scheme, we analyze the number of idle channels sensed, energy consumption during sensing, and the throughput gain.
3.1 Idle channel analysis in parallel sensing scheme
where E[ Sidle] is the discovered idle channels in PU band by a single SU and E[ K] is the total number of idle channels in PU band.
3.2 Energy consumption analysis in parallel sensing scheme
where τps is the time spent by a single SU to sense a single channel and p τ is the power consumed during sensing of single channel. The sensing duration (τ ps ) for a single channel is 1 ms .
3.3 Throughput analysis in parallel sensing scheme
where τps is the sensing duration of each channel and N g is the number of parallel sensing groups.
where T(CR−RTS) and T(CR−CTS) is the time taken by SU to send and receive the CR-RTS and CR-CTS messages.
In general, transmission time can be increased by reducing the sensing, sharing, and contention phase time; however, reliable discovery requires increased sensing duration. This will increase the number of discovered idle channels, which in turn increases the number of contention slots (M) in the contention interval, thus decreasing the transmission time per cycle. So, there are trade-offs involved and require optimization of involved parameters to maximize the throughput. In our proposed sensing scheme, while keeping the same τps without compromising on reliability, the overall sensing time is reduced by forming more parallel SU groups (N g ). This in turn not only neutralizes the effect of increased contention phase time but also more than compensates by leaving more time for transmission, thereby, providing improved throughput.
4 Analytical and simulation results
In this section, we present the numerical and simulation results of the proposed parallel sensing scheme and compare it with the SMC-MAC protocol .
Number of primary channels (Nch)
Primary traffic load (γ)
Number of secondary users (Nsu)
Number of SU groups (N g )
Slot duration in sensing τps
Slot duration in sharing τshar
4.1 Sensed and discovered idle channels
Now, we present the simulation results of the proposed parallel sensing scheme and find out the average number of discovered idle channels by the SUs and then compare it with the sensing scheme discussed in the SMC-MAC  protocol.
Proposed and reference schemes are compared for different values of chmax and N g . Now, for the ease of analysis, we take only one scenario where number of secondary users are Nsu = 2. So, in Fig. 8a, with chmax=5 and N g =2, our proposed parallel sensing scheme discovers 66% more idle channels from the PU band than the reference sensing scheme at 0 load on primary. In Fig. 8b, with chmax=2 and N g =2 proposed parallel sensing scheme discovers 72% more idle channels. Similarly, in Fig. 8c, with chmax=2 and N g =5 proposed parallel sensing scheme discovers 32% more idle channels from the PU band. Moreover, in Fig. 8d, with chmax=2 and N g =5 proposed sensing scheme discovers 26% more idle channels from the PU band. It is observed that for each configuration given in Fig. 8, our scheme outperforms the reference scheme by discovering more idle channels from the PU band. However, in terms of average number of idle channels discovered, we can equate these two different sensing schemes by taking chmax=10 and N g =10 as shown in Fig. 9.
4.2 Energy consumption
The energy consumption comparison of parallel sensing scheme and sensing scheme of SMC-MAC  is also shown in Fig. 11. It is observed that when chmax = 10 (random channel selection scheme in SMC-MAC ) and N g = 10, then both the sensing schemes will consume same amount of energy during sensing phase. However, when we further increase the parallel number of groups beyond N g = 10 then our parallel sensing scheme outperforms the reference sensing scheme.
4.3 Throughput gain
It is apparent from the above results that the proposed parallel sensing scheme outperforms the reference sensing scheme because it discovers more idle channels, which leads to higher throughput. Also in the parallel sensing scheme, the sensing phase time is reduced by increasing the number of parallel groups which leaves more time for the transmission that also contribute towards higher throughput.
In this work, we have proposed a parallel sensing scheme for the cognitive radio ad hoc networks that efficiently discovers more idle channels from the primary user band, consequently leading to improved spectrum utilization, lower power consumption, and higher throughput. We have investigated the performance of proposed parallel sensing scheme through both mathematical analysis and simulations. Comparison with the SMC-MAC protocol  shows that the proposed parallel sensing scheme outperforms the SMC-MAC sensing scheme by finding more idle channels from the primary user band and also improves energy efficiency during the sensing phase by increasing the number of parallel groups. Most importantly, the proposed scheme provides higher system throughput as the duration of sensing phase is reduced through parallel sensing, resulting in increased transmission time for the SUs in a given cycle. The proposed parallel sensing scheme is particularly suitable for delay-sensitive applications in ad hoc cognitive radio networks as it discovers higher number of idle channels in less time, consumes lower energy, and affords enhanced throughput.
There are no other participants in the research except those in the authors’ list.
There is no funding involved in this research.
ILK and RH conceptualized the idea and designed the experiments. AS, AI, and SA provided the data curation. JA and QuH contributed in writing and draft preparation and SAM supervised the research. All authors read and approved the final manuscript.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- A De Domenico, E Calvanese Strinati, M-G Di Benedetto, A survey on mac strategies for cognitive radio networks. IEEE Commun. Surv. Tutorials. 14(1), 21–44 (2012).View ArticleGoogle Scholar
- M Powell, JS Adelstein, in Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies. Notice of proposed rule making and order (Washington D.C., 2003), pp. 1–53.Google Scholar
- MA McHenry, NSF spectrum occupancy measurements project summary. Shared Spectr. Co. Rep., 1–58 (2005).Google Scholar
- S Haykin, Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005).View ArticleGoogle Scholar
- AL Sukkar, GMN Al-Damluji, I Mansour, A MAC protocol with an improved connectivity for cognitive radio networks. EURASIP J. Wirel. Commun. Netw. 2016(1), 116 (2016).View ArticleGoogle Scholar
- IF Akyildiz, W-Y Lee, MC Vuran, S Mohanty, Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Comput. Netw. 50(13), 2127–2159 (2006).View ArticleMATHGoogle Scholar
- IEEE Standard for Information technology– Local and metropolitan area networks– Specific requirements– Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Policies and procedures for operation in the TV Bands, in IEEE Std 802.22-2011, (2011), pp. 1–680. https://doi.org/10.1109/IEEESTD.2011.5951707.Google Scholar
- A De Domenico, E Calvanese Strinati, MG Di Benedetto, A survey on mac strategies for cognitive radio networks. IEEE Commun. Surv. Tutorials. 14(1), 21–44 (2012).View ArticleGoogle Scholar
- Z Qing, T Lang, A Swami, in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on. Decentralized cognitive mac for dynamic spectrum access (Elsevier North-Holland, Inc.New York, 2005), pp. 224–232.View ArticleGoogle Scholar
- J Han, Y Ma, Y Xu, Z Wang, in 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). Sensing order optimization in wideband spectrum sensing (IEEEXi’an, 2016), pp. 1043–1047.Google Scholar
- A Ewaisha, A Sultan, T ElBatt, in Wireless Communications and Networking Conference (WCNC), 2011 IEEE. Optimization of channel sensing time and order for cognitive radios (IEEECancun, 2011), pp. 1414–1419.View ArticleGoogle Scholar
- H Shokri-Ghadikolaei, C Fischione, in 2014 IEEE International Conference on Communications (ICC). Distributed random sensing order analysis and optimization in cognitive radio systems (IEEESydney, 2014), pp. 1543–1548.View ArticleGoogle Scholar
- S Park, J Kim, G Hwang, JK Choi, in 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN). Joint optimal access and sensing policy on distributed cognitive radio networks with channel aggregation (IEEEVienna, 2016), pp. 253–258.View ArticleGoogle Scholar
- S Senthilmurugan, TG Venkatesh, Optimal channel sensing strategy for cognitive radio networks with heavy-tailed idle times. IEEE Trans. Cogn. Commun. Netw. 3(1), 26–36 (2017).View ArticleGoogle Scholar
- Y Lu, D Wang, M Fattouche, Cooperative spectrum-sensing algorithm in cognitive radio by simultaneous sensing and BER measurements. EURASIP J. Wirel. Commun. Netw. 2016(1), 136 (2016).View ArticleGoogle Scholar
- LT Tan, LB Le, Joint cooperative spectrum sensing and MAC protocol design for multi-channel cognitive radio networks. EURASIP J. Wirel. Commun. Netw. 2014(1), 101 (2014).View ArticleGoogle Scholar
- S Xie, Y Liu, Y Zhang, R Yu, A parallel cooperative spectrum sensing in cognitive radio networks. IEEE Trans. Veh. Technol. 59(8), 4079–4092 (2010).View ArticleGoogle Scholar
- B Shahrasbi, N Rahnavard, in Military Communications Conference, 2011-MILCOM 2011. Cooperative parallel spectrum sensing in cognitive radio networks using bipartite matching (IEEEBaltimore, 2011), pp. 19–24re.View ArticleGoogle Scholar
- K Hyoil, KG Shin, Efficient discovery of spectrum opportunities with mac-layer sensing in cognitive radio networks. IEEE Trans. Mob. Comput. 7(5), 533–545 (2008).View ArticleGoogle Scholar
- HT Cheng, W Zhuang, Simple channel sensing order in cognitive radio networks. IEEE J. Sel. Areas Commun. 29(4), 676–688 (2011).View ArticleGoogle Scholar
- R Misra, AP Kannu, in Communications (ICC), 2012 IEEE International Conference on. Optimal sensing-order in cognitive radio networks with cooperative centralized sensing (IEEEOttawa, 2012), pp. 1566–1570.View ArticleGoogle Scholar
- Z Khan, JJ Lehtomaki, LA DaSilva, M Latva-aho, Autonomous sensing order selection strategies exploiting channel access information. IEEE Trans. Mob. Comput. 12(2), 274–288 (2013).View ArticleGoogle Scholar
- J Juncheng, Z Qian, S Xuemin, Hc-mac: A hardwareconstrained cognitive mac for efficient spectrum management. IEEE J. Sel. Areas Commun. 26(1), 106–117 (2008).View ArticleGoogle Scholar
- Z Yanxiao, S Min, X Chunsheng, in INFOCOM, 2013 Proceedings IEEE. Fmac: A fair mac protocol for coexisting cognitive radio networks (IEEETurin, 2013), pp. 1474–1482.Google Scholar
- S Debroy, S De, M Chatterjee, Contention based multichannel mac protocol for distributed cognitive radio networks. IEEE Trans. Mob. Comput. 13(12), 2749–2762 (2014).View ArticleGoogle Scholar
- C Wenchi, Z Xi, Z Hailin, Full-duplex spectrumsensing and mac-protocol for multichannel nontime-slotted cognitive radio networks. IEEE J. Sel. Areas Commun. 33(5), 820–831 (2015).View ArticleGoogle Scholar
- L Seyoun, L Tae-Jin, A self-scheduling multi-channel cognitive radio mac protocol based on cooperative communications. IEICE Trans. Commun. 94(6), 1657–1668 (2011).Google Scholar
- S Pandit, G Singh, Backoff algorithm in cognitive radio mac protocol for throughput enhancement. IEEE Trans. Veh. Technol. 64(5), 1991–2000 (2015).View ArticleGoogle Scholar
- JSP Singh, MK Rai, Cognitive radio intelligentmac (cr-i-mac): channel-diverse contention free approach for spectrum management. Telecommun. Syst, 1–14 (2016).Google Scholar
- D Willkomm, S Machiraju, J Bolot, A Wolisz, Primary user behavior in cellular networks and implications for dynamic spectrum access. IEEE Commun. Mag. 47(3), 88–95 (2009).View ArticleGoogle Scholar
- EF Orumwense, TJ Afullo, VM Srivastava, in AFRICON, 2015. Secondary user energy consumption in cognitive radio networks (IEEEAddis Ababa, 2015), pp. 1–5.Google Scholar
- C Cordeiro, M Ghosh, D Cavalcanti, K Challapali, in Cognitive radio oriented wireless networks and communications, 2007. CrownCom 2007. 2nd International Conference on. Spectrum sensing for dynamic spectrum access of tv bands (IEEEOrlando, 2007), pp. 225–233.Google Scholar
- IEEE Standard for Information technology–Telecommunications and information exchange between systems Local and metropolitan area networks–Specific requirements - Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, in IEEE Std 802.11-2016 (Revision of IEEE Std 802.11-2012), (2016), pp. 1–3534. https://doi.org/10.1109/IEEESTD.2016.778699.Google Scholar