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Muhammad Sajjad Khan, Junsu Kim, Eung Hyuk Lee, Su Min Kim, "An Efficient Contention-Window Based Reporting for Internet of Things Features in Cognitive Radio Networks", Wireless Communications and Mobile Computing, vol. 2019, Article ID 8475020, 9 pages, 2019. https://doi.org/10.1155/2019/8475020
An Efficient Contention-Window Based Reporting for Internet of Things Features in Cognitive Radio Networks
Internet of things (IoT) is a new challenging paradigm for connecting heterogeneous networks. However, an explosive increase in the number of IoT cognitive users requires a mass of sensing reporting; thus, it increases complexity of the system. Moreover, bandwidth utilization, reporting time, and communication overhead arise. To realize spectrum sensing, how to collect sensing results by reducing the communication overhead and the reporting time is a problem of major concern in future wireless networks. On the other hand, cognitive radio is a promising technology to access the spectrum opportunistically. In this paper, we propose a contention-window based reporting approach with a sequential fusion mechanism. The proposed reporting scheme reduces the reporting time and the communication overhead by collecting sensing results from the secondary users with the highest reliability at a fusion center by utilizing Dempster-Shafer evidence theory. The fusion center broadcasts the sensing results once a global decision requirement is satisfied. Through simulations, we evaluate the proposed scheme in terms of percentage of the number of reporting secondary users, error probability, percentage of reporting, and spectral efficiency. As a result, it is shown that the proposed scheme is more effective than a conventional order-less sequential reporting scheme.
Wireless communication networks have tremendous progress in the last 30 years to support the growth of the application devices from 1G to 4G LTE-Advanced wireless network . Each generation has played its role to enhance data rate, reliability, latency, etc. During the past years, connecting each device with another device at anytime and anywhere is a big challenge in wireless communication networks. In a line of evolution, 5G will provide an unexpected contribution and a big step forward toward the spectrum management, public safety, energy efficiency, high data rate, low latency, and so on [2–5].
On the other hands, unmanned aerial vehicle (UAV) is expected to be an important component of the upcoming wireless network, i.e., 5G, because of its countless applications such as public safety, health, management, and remotely services [6, 7]. In , the authors deployed a UAV-based cognitive system to maximize energy efficiency by optimizing the transmit power. Similarly, in , the authors studied resource allocation and trajectory design for an energy-efficient secure UAV communication system, where a UAV base station serves multiple secondary users in the presence of the potential eavesdroppers. One of the potential UAV applications is to remotely deploy and monitor sensor devices for future Internet of things (IoT) networks.
IoT was first mentioned by Ashton, who introduces a technological revolution to bring heterogeneous networks under a single umbrella of the IoT . IoT is a promising subject of technical, social, and economic implications; it can be presumed that IoT has a strong and meaningful impact on daily life in the near future, such as automation, improvised learning, logistic, intelligent transportation, e-health care, and so on [11, 12]. Technically, the most focused area of paradigm is computing, communication, and connectivity in IoT. Among them, the connectivity and management spectrum are more challenging and of great concern. Additionally, it is noteworthy to mention that with the rapid increase in connecting devices, a lot of spectrum are required for coverage and capacity of these connecting devices in IoT. Femtocell can be a promising candidate to meet the demand for capacity and coverage of the growing IoT devices [13, 14]. As over 50 billion wireless devices will be connected by 2020, all of which will demand a lot of spectrum resources ; the authors in  argued the importance of the cognitive capability, that is, without comprehensive cognitive capability, IoT is just like an awkward stegosaurus: all brawn and no brains. The static management and allocation of spectrum resources are not efficient to meet requirements of wireless devices and applications. With static allocation of spectrum resources, some of them are heavily overloaded, whereas another part of the spectrum is rarely used. According to the report revealed by Federal Communication Commission (FCC), the spectrum usage varies from 15% to 85% in some cases .
One of tempting solutions against the spectrum shortage is cognitive radio technology (CRT), which has yield extensive studies on spectrum allocation and management for decades . CRT can be integrated with IoT to provide a more intelligent and efficient networking and resource utilization . CRT allows wireless devices to sense the spectrum bands, search for suitable frequency channels, and reconfigure their parameters to meet channel requirements while minimizing energy consumption . In CRT, spectrum sensing plays a vital role to find a spectrum hole and efficiently utilize the spectrum while avoiding interference to primary users (PUs) . In [22, 23], the authors have presented a detailed survey for the key enabling techniques, such as detection, localization, tracking, and controlling. A number of various detection techniques such as energy detection, matched filtering, and cyclostationary have been utilized to detect the existence of the PU in the network [24, 25]. Among these techniques, the energy detection is one of the most engaging techniques, thanks to its ease of implementation and no requirement on prior information of the PU. However, a major drawback of the energy detection is weakness of the received signal strength induced by fading, shadowing, and hidden terminal problem.
The fading and hidden terminal problems can be overcome by cooperative spectrum sensing (CSS) . CSS mainly consists of three steps: sensing, reporting, and global decision. In , the authors considered a centralized CSS mechanism to make a firm decision for the status of the channel and broadcast it to the others in the network; the authors also optimized the number of secondary users (SUs) and threshold to get the best result with minimum resources. In , the authors proposed a novel CoMAC-based CSS scheme that allow cooperative SUs to encode their local statistics in transmit power and to transmit sequence information of the modulated symbols to a fusion center (FC) for making a final decision on the existence of the PU. The authors in  have improved the sensing performance by adjusting parameters such as decision threshold, sensing frequency, and the number of sensing operations.
In this paper, we propose an ordered-sequential reporting mechanism based on contention window and D-S evidence theory IoTs in order to reduce the reporting time and communication overheads, which ultimately reduce costs such as control channel bandwidth and energy consumption. Once sensing is performed by utilizing energy detection techniques, SUs determine its basic probability assignment and reliability. Then, SUs wait for listening to the medium (control channel) and contents for the channel access. The SU with the highest reliability wins the contention and reserve time slots for transmitting sensing reports to the FC. Then, the FC broadcasts a burst of report messages in the whole medium (control channels) if the global requirement is satisfied. Through simulations, we demonstrate that the proposed scheme achieves better performance than a conventional order-less sequential reporting scheme. The main contributions of this paper are summarized as follows:(i)We propose a contention-window based mechanism, in which SUs content for the channel access. The SU with the highest reliability wins and access the channel for reporting sensing information to the FC.(ii)We propose an ordered-sequential reporting scheme instead of conventional order-less sequential reporting scheme, which ultimately enhances the performance of the system including reduced reporting time.(iii)To this end, we utilize Dempster-Shafer evidence theory in combining reports at the FC to decide the existence of PU in the network.(iv)We evaluate the proposed scheme in terms of percentage of number of sensing reports, error probability, percentage of reporting, and spectral efficiency. Through simulations, we demonstrate that the proposed scheme outperforms the order-less sequential reporting scheme.
The remainder of this paper is organized as follows. In Section 2, we present related work. In Section 3, we discuss the cooperative spectrum sensing and sequential fusion. In Section 4, we provide a detailed description of the proposed contention-window based reporting scheme. In Section 5, the numerical results are shown. Finally, the paper is concluded in Section 6.
2. Related Work
In recent years, reporting in CSS has drawn much more attention. In , the author proposed a random-access mechanism, in which the author discussed how to collect local sensing reports in CSS. A backward induction approach is applied to decide the optimal stopping time of the collection period. Similarly, in , the authors designed a reporting channel scheme based on random-access protocols including slotted ALOHA and reservation ALOHA to measure the performance of the probability of detection and probability of false alarm. In , the author proposed a CSS scheme for cognitive radio networks (CRN) with limited reporting. Two kinds of CSS approach with limited reporting in a centralized CRN have been proposed: a soft combination approach with threshold-based reporting and a soft combination approach with contention-based reporting. In , the authors designed a reporting channel structure based on a random-access protocol, which is introduced for SUs and FC; in addition, k-out-of-N rules are implemented at FC to determine the global detection.
A data fusion scheme for CSS based on Dempster-Shafer (D-S) theory was first proposed in . This scheme has significantly improved the probability of detection and the probability of false alarm. The performance of D-S evidence theory can be enhanced to obtain a larger gain of combination by utilizing the signal-to-noise-ratio (SNR) of the PU . However, the advantages of performance enhancement cost overhead in traffic control signaling; which results in consuming more communication resources such as reporting time delay, control channel bandwidth and transmission energy. The resource demands drastically increase with the increasing of the number of SUs. However, only few researchers have addressed these problems. In , the authors proposed a sequential test to control the number of reporting bits and average detection time. Similarly, in , the authors proposed a cooperative sequential detection scheme to reduce the sensing time. However, these schemes do not utilize an ordered-sequential approach for fast detection with a limited number of reports. Our proposed approach is ordered-sequential contention-window based reporting by the SU with the highest reliability, which significantly reduces reporting time and the error probability, and after all, it is more efficient than a conventional order-less sequential reporting scheme.
3. Cooperative Sensing and Sequential Fusion
We consider a cooperative sensing scenario in a CRN, in which each SU conducts local sensing by utilizing energy detection technique and collects basic information about the PU status in the network. After local sensing, each SU measures its basic probability assignment (BPA) and reliability and reports this information to FC. Figure 1 shows the cooperative sensing scenario and a frame structure consisting of sensing period, reporting period, and transmission.
At the beginning, each SU performs local spectrum sensing in a distributed manner. The local spectrum sensing can be represented as a binary hypothesis testing of the presence or absence of PU in the network and is measured aswhere is the absence, is the presence of the PU, is the fading coefficient, is the additive white Gaussian noise (AWGN), is the PU signal, and is the signal received at the SU, respectively.
Each SU measures test statistics of the received signal by utilizing an energy detection technique, given aswhere NT = 2 TW, in which T is the sensing time and W is the bandwidth, and is the sample of the received signal. When NT is large enough, by central limit theorem (CLT), XEi can be well approximated as a Gaussian distribution .
After performing local sensing, each SU measures its self-assessed credibility, which is equivalent to BPA for and hypotheses. BPA is defined as a form of cumulative distribution function, given as where and are the BPAs of hypothesis , the absence of PU, and hypothesis , the presence of PU.
According to D-S evidence theory, the BPAs of each SU at the FC are combined aswhere is one of the elements of a set , in which is the ignorance hypothesis that either hypothesis can be true.
The main problem of D-S evidence theory as well as other schemes is that it requires a large amount of resources for reporting sensing results.
In order to reduce the overhead, processing time and reporting time, and energy consumption, we consider an ordered-sequential fusion CSS. After local sensing, each SU measures its BPA. At FC, the BPAs are combined sequentially according to their reliability.
It is noteworthy that the combined results of the proposed ordered-sequential fusion are equal to nonsequential one when all SUs send reports to FC. Therefore, instead of keeping threshold 0, we adopt a threshold (). The threshold is set to a large enough value so that the cooperation gain is equivalently maintained even if the number of combined sensing results is lower.
The final sequential decision is based on the following strategy.
The following two strategies are applied to FC to make a global decision. When the number of reports k at the FC is less than the total number of SUs, the global decision can be determined aswhere the condition denotes that the reports at the FC are not enough to declare a global decision and wait for more sensing reports. represents the global decision reliability at the report expressed aswhere is the order of sequential combination of the BPAs , and and are the and (k − 1)-th global BPA hypotheses Hj, respectively.
When the number of reports k is equal to the total number of SU at the FC, the global decision can be determined as
4. Proposed Contention-Window Based Reporting Scheme
In this section, we first describe the conventional order-less sequential fusion reporting scheme and then discuss the proposed contention-window based reporting scheme.
At the beginning, each SU performs spectrum sensing, measures its BPA information, and waits for the FC request to send their information to the FC.
In the conventional order-less sequential fusion reporting scheme, the FC sends a request to SUs in a predefined order. Then, the requested SU responds its own sensing report to the FC. The FC accumulates the sensing reports of the current SU with the previous SU and verifies whether the decision requirement is satisfied or not. If the required decision is satisfied, the FC sends the stop reporting message to all SUs, and further reporting is stopped. If the decision is not satisfied, FC sends a request to next SU in the predefined order, and the process is repeated until the decision requirement is satisfied. The overall process for the conventional order-less sequential reporting scheme is shown in Figure 2.
In the proposed reporting scheme, instead of reporting in a predefined manner, we consider a contention-window based reporting scheme, in which the SUs report to the FC according to their sensing data reliability given in (6). The sensing data reliability of each SU depends on the offset time for accessing the medium (control channel) for reporting to the FC for its turn. As in this mechanism, SUs content to access medium (control channel) for a specific time to report its sensing information to the FC; thus, it is named as the contention-window based reporting scheme.
In the reporting period, from the beginning of the contention slot, each SU listens to the medium (control channel). If there is no signal till for SUs, then it is assumed that SU wins the contention slot . The winner SU generates a burst signal to the medium (control channel) for reservation of the channel and reports in the next reporting slot. It is worth noting that the offset is in the range of . The first is reserved for the SU with higher priority for the transmission to the FC. Whenever the global decision requirement is satisfied, the FC sends a burst message signal in the whole medium (control channel). The overall proposed contention-based reporting scheme is shown in Figure 3.
In the contention slot, the value of the offset time is calculated as follows:where is the slot time corresponding to the time required by the radio layer carrier sensing to function and is the length of the contention slot. and are the maximum and minimum values of the data sensing reliability at the contention slot, respectively. The values of and are first time defined by the FC and then updated automatically after every reporting slot.
The average reporting time for the order-less sequential reporting scheme can be determined aswhere M is the number of SUs and is the percentage of the number of reporting SUs.
The average reporting time for the proposed contention-window based reporting scheme, , is determined aswhere is the percentage of the number of reporting SUs for the proposed contention-window based reporting scheme.
The overall flowchart of the proposed contention-window based reporting scheme is shown in Figure 4.
5. Numerical Evaluation
In this section, we present simulation results for the proposed contention-window based reporting scheme in consideration with the ordered-sequential fusion and compare its performance with the conventional order-less sequential reporting scheme. We consider IEEE 802.22 standards; it is assumed that the existence of the PU is 0.5 and the used bandwidth is 6 MHz. The simulation environment is developed by utilizing MATLAB as an implementation tool. The parameters for the numerical evaluation are summarized in Table 1.
Figure 5 shows the percentage of the number of reporting SUs against the different value of SNRs (−20 to −10 dB). It can be observed from Figure 5 that as the number of SUs in the network increases, the percentage of the number of reporting SUs decreases for the proposed contention-based ordered-sequential fusion for a specific threshold value (threshold = 15). Specifically, it is shown in Figure 5 that with the order-less sequential reporting scheme, approximately 37% of the number of reporting SUs is required to satisfy the global decision requirement, whereas the proposed scheme requires approximately 14% of the number of reporting SUs, when 100 SUs are considered in the network. The reason is obvious that instead of the SU reporting in an order-less manner, only SU with the highest reliability reports to FC to satisfy the global decision requirement, which ultimately reduces the number of reports. The proposed contention-window based reporting scheme requires less percentage of the number of reports compared with the order-less sequential reporting scheme. It is noted that as the SNR value increases, the reports for the proposed scheme decrease. Also, it is worth noting that as the value of the threshold increases, the percentage of the number of reporting SUs increases.
Figure 6 shows the performance comparison of the proposed scheme in terms of error probability for varying average number of reporting SUs. It can be observed from Figure 6 that error probability of the proposed contention-window based reporting scheme is smaller than the order-less sequential reporting scheme, and it becomes identical to that of the order-less scheme as the average number of reporting SUs increases. It is obvious that in the beginning of the proposed contention-window based reporting, the highly reliable SUs report to FC; thus, its global requirements converge and the error probability is smaller than the order-less sequential fusion scheme. However, as the average number of reporting SUs increases, more SUs will report to FC to meet the required global decision and converges, and thus the error probability decreases. As a result, at a large average number of reporting SUs, the error probability is low and almost the identical for both schemes.
Figure 7 shows the percentage of reporting for varying threshold value. It is worth noting that the proposed contention-window based reporting scheme requires a smaller number of SUs for reporting than the conventional order-less reporting scheme. Also, it is important to mention that as the number of threshold value increases, the percentage of reporting increases. It is well justified because more SUs report to FC in order to satisfy the global decision requirement. After all, it is shown that the proposed contention-window based reporting scheme is more effective than the conventional order-less sequential fusion reporting scheme.
Figure 8 shows the spectral efficiency for varying detection probability. As the detection probability increases, spectrum availability for the SUs decreases; thus, the spectral efficiency of the system decreases. However, the proposed scheme achieves better performance than the conventional order-less scheme. As a result, the proposed scheme is also more effective in the perspective of spectral efficiency.
The integration of cognitive radio technology and the Internet of things seems to shift future wireless networks (5G). Cognitive radio technology has the potential to efficiently utilize the spectrum via cooperative sensing. However, the rise in cooperative sensing users increases the average reporting time, bandwidth utilization, and communication overhead. In this paper, we proposed an ordered-sequential fusion scheme with contention-window based reporting for Internet of things to reduce the reporting time and the communication overhead, which ultimately reduces cost and bandwidth utilization. Secondary users content to access the shared control channel and report their information to FC based on reliability to reduce the reporting time duration by satisfying the global decision requirement. The effectiveness of the proposed contention-window based reporting scheme is shown through simulations by considering percentage of the number of reporting SUs, error probability, percentage of reporting, and spectral efficiency. The results showed that the proposed ordered-sequential contention-window based reporting scheme outperforms the conventional order-less sequential reporting scheme in various aspects.
The data used to support the findings of this study are included within the article.
Conflicts of Interest
The authors declare no conflicts of interest.
This work was supported in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2018-0-01426) supervised by the IITP (Institute for Information and Communication Technology Planning & Evaluation) and in part by the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. 2019R1F1A1059125).
- A. Agarwal, G. Misra, and K. Agarwal, “The 5th generation mobile networks-key concepts, network architecture and challenges,” American Journal of Electrical and Electronic Engineering, vol. 3, no. 2, pp. 22–28, 2015.
- H. Shakhatreh, A. Sawalmeh, A. Al-Fuqaha et al., Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges, IEEE Access, Piscataway, NJ, USA, 2019.
- Z. Kaleem and M. H. Rehmani, “Amateur drone monitoring: state-of-the-art architectures, key enabling technologies, and future Research directions,” IEEE Wireless Communications, vol. 25, no. 2, pp. 150–159, 2018.
- M. A. Khan, I. M. Qureshi, and F. Khanzada, “A hybrid communication scheme for efficient and low-cost deployment of future flying ad-hoc network (FANET),” Drones, vol. 3, no. 1, p. 16, 2019.
- Z. Kaleem, Y. Li, and K. Chang, “Public safety users’ priority-based energy and time-efficient device discovery scheme with contention resolution for ProSe in third generation partnership project long-term evolution-advanced systems,” IET Communications, vol. 10, no. 15, pp. 1873–1883, 2016.
- Z. Kaleem, M. H. Rehmani, E. Ahmed et al., “Amateur drone surveillance: applications, architectures, enabling technologies, and public safety issues: part 1,” IEEE Communications Magazine, vol. 56, no. 1, pp. 14-15, 2018.
- Z. Kaleem, M. Yousaf, A. Qamar et al., “UAV-empowered disaster-resilient edge architecture for delay-sensitive communication,” 2019, https://arxiv.org/abs/1809.09617v2.
- L. Sboui, H. Ghazzai, Z. Rezki, and M. Alouini, “Energy-Efficient power allocation for UAV cognitive radio systems,” in Proceedings of the IEEE 86th Vehicular Technology Conference (VTC-Fall), IEEE, Toronto, ON, Canada, September 2017.
- Y. Cai, Z. Wei, R. Li, D. W. K. Ng, and J. Yuan, “Energy-efficient resource allocation for secure UAV communication systems,” 2019, http://arxiv.org/abs/1901.09308.
- K. Ashton, “That “internet of things”: in the real world, things matter more than ideas,” RFID Journal, vol. 22, 2009.
- S. Chatterjee, R. Mukherjee, S. Ghosh, D. Gosh, S. Gosh, and A. Mukherjee, “Internet of things and cognitive radio-issues and challenges,” in Proceedings of the IEEE International Conference on Opto-Electronics and Applied Optics (Optronix), IEEE, Kolkata, India, November 2017.
- A. A. Khan, M. H. Rehmani, and A. Rachedi, “When cognitive radio meets the internet of things?” in Proceedings of the IEEE International Wireless Communications and Computing Conference (IWCMC), IEEE, Paphos, Cyprus, September 2016.
- F. Al-Turjman, E. Ever, and H. Zahmatkesh, “Green femtocells in the IoT era: traffic modeling and challenges—an overview,” IEEE Network, vol. 31, no. 6, pp. 48–55, 2017.
- F. Al-Turjman, E. Ever, and H. Zahmatkesh, “Small cells in the forthcoming 5G/IoT: traffic modeling and deployment overview,” IEEE Communications Surveys and Tutorials, vol. 21, no. 1, pp. 28–65, 2018.
- A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: a survey on enabling technologies, protocols, and applications,” IEEE Communications Surveys and Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.
- Q. Wu, G. Ding, Y. Xu et al., “Cognitive internet of things: a new paradigm beyond connection,” IEEE Internet of Things Journal, vol. 1, no. 2, pp. 129–143, 2014.
- FCC, “Notice of proposed rule-making and order,” FCC, Washington, DC, USA, 2003, ET Docket No. 03-222.
- P. Rawat, K. D. Singh, and J. M. Bonnin, “Cognitive radio for M2M and internet of things: a survey,” Computer Communications, vol. 94, pp. 1–29, 2016.
- S. Aslam, W. Ejaz, and M. Ibnkahla, “Energy and spectral efficient cognitive radio sensor networks for internet of things,” IEEE Internet of Things Journal, vol. 5, no. 4, pp. 3220–3233, 2018.
- J. Mitola and G. Q. Maguire, “Cognitive radio: making software radio more personal,” IEEE Personal Communication, vol. 6, no. 4, pp. 13–18, 1999.
- Y. Arjoune and N. Kaabouch, “A comprehensive survey on spectrum sensing in cognitive radio netowks: recent advances, new challenges, and future research direction,” Sensors, vol. 19, no. 1, p. 126, 2019.
- G. Ding, Q. Wu, L. Zhang, Y. Lin, T. A. Tsiftsis, and Y. Yao, “An amateur drone surveillance system based on cognitive internet of things,” IEEE Communications Magazine, vol. 56, no. 1, pp. 29–35, 2018.
- Y. Saleem, M. H. Rehmani, and S. Zeadally, “Integration of cognitive radio technology with unmanned aerial vehicles: issues, opportunities, and future research challenges,” Journal of Network and Computer Applications, vol. 50, pp. 15–31, 2015.
- A. Ranjan, Anurag, and B. Singh, “Design and analysis of spectrum sensing in cognitive radio based on energy detection,” in Proceedings of the IEEE International Conference on Signal and Information Processing (IConSIP), IEEE, Nanded, India, October 2016.
- I. Ilyas, S. Paul, A. Rahman, and R. K. Kundu, “Comparative evaluation of cyclo-stationary detection based cognitive spectrum sensing,” in Proceedings of the IEEE 7th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), IEEE, New York, NY, USA, October 2016.
- I. F. Akilidz, B. F. Lo, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: a survey,” Physical Communication, vol. 4, no. 1, pp. 40–62, 2011.
- S. C. Shinde and A. N. Jadhav, “Centralized cooperative spectrum sensing with energy detection in cognitive radio and optimization,” in Proceedings of the IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT), IEEE, Bangalore, India, May 2016.
- M. Zheng, C. Xu, W. Liang, H. Yu, and L. Chin, “A novel CoMAC based cooperative spectrum sensing scheme in cognitive radio networks,” in Proceedings of the IEEE International Conference on Communication Workshop (ICCW), IEEE, London, UK, June 2015.
- H. Hu, Y. Xu, Z. Liu, N. Li, and H. Zhang, “Optimal strategies for cooperative spectrum sensing in multiple cross over cognitive radio networks,” KSII Transaction on Internet and Information System, vol. 6, no. 12, pp. 3061–3080, 2012.
- D. Lee, “Adaptive random access for cooperative spectrum sensing in cognitive radio network,” IEEE Transaction on Wireless Communications, vol. 14, no. 2, pp. 831–840, 2015.
- R. Alhamad, H. Wang, and Y. Yao, “Cooperative spectrum sensing with random access reporting channels in cognitive radio networks,” IEEE Transaction on Vehicular Technology, vol. 66, no. 8, pp. 7249–7261, 2017.
- J. So, “Cooperative spectrum sensing for cognitive radio networks with limited reporting,” KSII Transaction on Internet and Information Systems, vol. 9, no. 8, pp. 2755–2773, 2015.
- R. Alhamad, H. Wang, and Y. Yao, “Reporting channel design and analysis in cooperative spectrum sensing for cognitive radio networks,” in Proceedings of the IEEE 82nd Vehicular Technology Conference (VTC-2015-Fall), IEEE, Boston, MA, USA, September 2015.
- Q. Peng, K. Zeng, W. Jun, and S. Li, “A distributed spectrum sensing scheme based on credibility and evidence theory in cognitive radio networks,” in Proceedings of the 17th IEEE International Symposium on Personal, Indoor and Mobile Radio Communication, pp. 1–5, IEEE, Helsinki, Finland, September 2006.
- N. Nguyen-Thanh and I. Koo, “An enhanced cooperative spectrum sensing scheme based on evidence theory and reliability source evaluation in cognitive radio context,” IEEE Communications Letters, vol. 13, no. 7, pp. 492–494, 2009.
- S. Yeelin and Y. T. Su, “A sequential test based cooperative spectrum sensing scheme for cognitive radios,” in Proceedings of the IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–5, IEEE, Cannes, France, September 2008.
- Z. Qiyue, Z. Songfeng, and A. H. Sayed, “Cooperative spectrum sensing via sequential detection for cognitive radio networks,” in Proceedings of the IEEE 10th Workshop on Signal Processing Advances in Wireless Communication, pp. 121–125, IEEE, Perugia, Italy, June 2009.
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