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Bilişsel radyo ağlarında değişen paket boyutlarının enerji tüketimi üzerindeki etkisinin analizi

Yıl 2023, Cilt: 12 Sayı: 3, 644 - 655, 15.07.2023

Sercan Demirci Doğan Yıldız

https://doi.org/10.28948/ngumuh.1278682

Öz

Kablosuz iletişim teknolojileri, bu teknolojilere olan ihtiyaç ve taleple doğru orantılı olarak gelişmektedir. Dolayısıyla, bu doğrultuda yapılan çalışmalar günden güne önem kazanmaktadır. Sosyal ağlar, dosya paylaşımı ve çoklu ortam iletişimi gibi yoğun veri trafiği olan uygulamaların artışı, kablosuz iletişimde önemli bir yeri olan spektruma olan ihtiyacı günden güne artırmaktadır. Güncel kablosuz iletişim cihazları, sabit bir spektrum atama tekniği ile çalışmakta ve genellikle spektrumun belirli bölümlerini kullanmaktadır. Bilişsel Radyo Ağları (Cognitive Radio Networks, CRN'ler), spektrumun kullanılmayan ve boşta olan bölümlerine dinamik olarak erişerek en uygun durumdaki iletişim kanalının seçilmesini sağlamaktadır. CR kullanıcıları arasında frekans değişimi sonucu ortaya çıkan enerji tüketiminin azaltılabilmesi amacıyla, kullanıcılar arasındaki iletişimde kullanılan paket boyutunun belirlenmesi önem taşımaktadır. Çünkü, CR kullanıcılarının frekanslarını dinamik olarak değiştirmeleri, kendilerine veri iletişimi için ayrılan zaman dilimlerinde kalan süreyi azaltmaktadır. Azalan bu süre içerisinde en uygun paket boyutunun belirlenmesi önem arz etmektedir. Bu çalışmada, paket uzunluk değerlerinin CRN'lerdeki enerji verimliliğine etkileri, Pyhon benzetim ortamında farklı değişken parametreleri için nümerik analizler yoluyla incelenmiştir. Bu parametrelerle yapılan incelemeler; birim kanal değişim gücünün ve süresinin paket boyutlarına göre toplam enerjiye etkisi, farklı zaman dilimlerinin paket boyutlarına göre toplam enerjiye etkisi, başarısız gönderim oranın paket boyutlarına göre toplam enerjiye etkisi ve toplam bilişsel sürenin değişimine göre paket gönderim/alım veriminin değişimi şeklinde özetlenebilir.

Anahtar Kelimeler

Bilişsel radyo ağları Kanal değiştirme maliyeti Paket boyutu Optimizasyon

Kaynakça

  • I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey, Computer Networks, 50 (13), 2127-2159, 2006. https://doi.org/10.1016/j.comnet.2006.05.001.
  • O. B. Akan, O. B. Karli, and O. Ergul, Cognitive radio sensor networks, IEEE Network, 23 ( 4), 34-40, 2009. 10.1109/MNET.2009.5191144
  • M. Song, C. Xin, Y. Zhao, and X. Cheng, Dynamic spectrum access: from cognitive radio to network radio, IEEE Wireless Communications, 19 (1), 23-29, 2012. 10.1109/MWC.2012.6155873
  • J. Mitola and G. Q. Maguire, Cognitive radio: making software radios more personal, IEEE Personal Communications, 6, (4), 13-18, 1999. 10.1109/98.788210
  • S. Haykin, Cognitive radio: brain-empowered wireless communications, IEEE Journal on Selected Areas in Communications, 23 (2), 201-220, 2005. 10.1109/JSAC.2004.839380
  • M. Zareei, A. M. Islam, S. Baharun, C. Vargas-Rosales, L. Azpilicueta, and N. Mansoor, Medium access control protocols for cognitive radio ad hoc networks: A survey, Sensors, 17 (9), 2136, 2017. https://doi.org/10.3390/s17092136
  • A. Jamal, C.-K. Tham, and W.-C. Wong, Dynamic packet size optimization and channel selection for cognitive radio sensor networks, IEEE Transactions on Cognitive Communications and Networking, 1 (4), 394-405, 2015. 10.1109/TCCN.2016.2531082
  • M. Awasthi, M. J. Nigam, and V. Kumar, Energy efficient sensing, transmitting time and transmission power for cognitive radio networks, in 2017 14th IEEE India Council International Conference (INDICON), pp. 1-5, Roorkee, India, 2017.
  • E. F. Orumwense, T. J. Afullo, and V. M. Srivastava, Secondary user energy consumption in cognitive radio networks, in AFRICON 2015, pp. 1-5, Addis Ababa, Ethiopia, 2015.
  • S. Agarwal and S. De, Impact of channel switching in energy constrained cognitive radio networks, IEEE Communications Letters, 19 (6), 977-980, 2015. 10.1109/LCOMM.2015.2416334
  • H. Ali, A. Khattab, and M. Fikri, Energy-efficient cooperative sensing for cognitive wireless sensor networks, in 5th International Conference on Energy Aware Computing Systems & Applications, pp. 1-4, Cairo, 2015.
  • T. Thanuja, K. A. Daman, and A. S. Patil, Optimized spectrum sensing techniques for enhanced throughput in cognitive radio network, in 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 137-141, Pune, India, 2020.
  • M. Yigit, H. U. Yildiz, S. Kurt, B. Tavli, and V. C. Gungor, A survey on packet size optimization for terrestrial, underwater, underground, and body area sensor networks, International Journal of Communication Systems, 31 (11), e3572, 2018. https://doi.org/10.1002/dac.3572
  • A. D. Sezer and S. Gezici, Average capacity maximization via channel switching in the presence of additive white Gaussian noise channels and switching delays, IEEE Transactions on Wireless Communications, 15 (9), 6228-6243, 2016. 10.1109/TWC.2016.2582150
  • X. Li, D. Wang, J. McNair, and J. Chen, Dynamic spectrum access with packet size adaptation and residual energy balancing for energy-constrained cognitive radio sensor networks, Journal of Network and Computer Applications, 41, 157-166, 2014. https://doi.org/10.1016/j.jnca.2013.11.001
  • Cheng, G., Liu, W., Li, Y., and Cheng, W., Spectrum aware on-demand routing in cognitive radio networks, In 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 571-574, IEEE, Dublin, Ireland, 2007.
  • Kim, H. and Shin, K.G., Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks, IEEE Transactions on Mobile Computing, 7(5), 533–545, 2010. 10.1109/TMC.2007.70751
  • Cheng, G., Liu, W., Li, Y., and Cheng, W., Joint on-demand routing and spectrum assignment in cognitive radio networks, In 2007 IEEE international conference on communications, pp. 6499-6503, IEEE, Glasgow, UK, 2007.
  • Namboodiri, V., Are cognitive radios energy efficient? A study of the wireless LAN scenario, In 2009 IEEE 28Th international performance computing and communications conference, pp. 437-442, IEEE, Scottsdale, AZ, USA, 2009.
  • Namboodiri, V. and Gao, L., Energy-Efficient VoIP over Wireless LANs, IEEE Transactions on Mobile Computing, 9(4), 566–581, 2010. 10.1109/TMC.2009.150
  • Bayhan, S., Zheng, L., Chen, J., Di Francesco, M., Kangasharju, J., and Chiang, M., Improving cellular capacity with white space offloading, In 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), pp. 1-8, IEEE, Paris, France, 2017.
  • Krishnamurthy, S., Thoppian, M., Venkatesan, S., and Prakash, R., Control channel based MAC-layer configuration, routing and situation awareness for cognitive radio networks, In MILCOM 2005-2005 IEEE Military Communications Conference, pp. 455-460, IEEE, Atlantic City, NJ, 2005.
  • Ma, H., Zheng, L., and Ma, X, Spectrum aware routing for multi-hop cognitive radio networks with a single transceiver, In 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008), pp. 1-6. IEEE, Singapore, 2008.
  • Marougi, S. D., Frequency-switching speed and post-tuning drift measurement of fast-switching microwave-frequency synthesisers, IET Science, Measurement & Technology, 1(2), 82-86, 2007. 10.1049/iet-smt:20060011
  • Gozupek, D., Buhari, S. and Alagoz, F., A spectrum switching delay-aware scheduling algorithm for centralized cognitive radio networks, IEEE Transactions on Mobile Computing, 12(7), 1270–1280, 2013. 10.1109/TMC.2012.101
  • Arkoulis, S., Anifantis, E., Karyotis, V., Papavassiliou, S., and Mitrou, N., On the optimal, fair and channel-aware cognitive radio network reconfiguration, Computer Networks, 57(8), 1739-1757, 2013. https://doi.org/10.1016/j.comnet.2013.03.004
  • Chen, J., Li, H., Wu, J., and Zhang, R., Starp: a novel routing protocol for multi-hop dynamic spectrum access networks, In Proceedings of the 1st ACM workshop on Mobile internet through cellular networks, pp. 49-54, Beijing China, 2009
  • Chowdhury, K. R., and Felice, M. D., Search: A routing protocol for mobile cognitive radio ad-hoc networks, Computer Communications, 32(18), 1983-1997, 2009. https://doi.org/10.1016/j.comcom.2009.06.011
  • Filippini, I., Ekici, E., and Cesana, M., Minimum maintenance cost routing in cognitive radio networks, In 2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems pp. 284-293, IEEE, Macau, China, 2009.
  • Guirguis, A., and ElNainay, M., Channel selection scheme for cooperative routing protocols in cognitive radio networks, In 2017 International Conference on Computing, Networking and Communications (ICNC), pp. 735-739, IEEE, Silicon Valley, CA, USA, 2017.
  • Demirci, S., and Gözüpek, D., Switching cost-aware joint frequency assignment and scheduling for industrial cognitive radio networks, IEEE Transactions on Industrial Informatics, 16(7), 4365-4377, 2019. 10.1109/TII.2019.2950563
  • Arat, F., and Demirci, S., Channel Switching Cost-Aware Energy Efficient Routing in Cognitive Radio-Enabled Internet of Things, Mobile Networks and Applications, 27(4), 1531-1550, 2022. https://doi.org/10.1007/s11036-022-02039-w
  • Agarwal, S., and De, S., Dynamic spectrum access for energy‐constrained CR: single channel versus switched multichannel, IET Communications, 10(7), 761-769, 2016. https://doi.org/10.1049/iet-com.2015.0523
  • Bayhan, S., and Alagoz, F., Scheduling in centralized cognitive radio networks for energy efficiency, IEEE Transactions on Vehicular Technology, 62(2), 582-595, 2012. 10.1109/TVT.2012.2225650
  • Celik, A., and Kamal, A. E., Green cooperative spectrum sensing and scheduling in heterogeneous cognitive radio networks, IEEE Transactions on Cognitive Communications and Networking, 2(3), 238-248, 2016. 10.1109/TCCN.2016.2608337
  • Shami, N., and Rasti, M., A joint multi-channel assignment and power control scheme for energy efficiency in cognitive radio networks, In 2016 IEEE Wireless Communications and Networking Conference,pp. 1-6, IEEE, Doha, Qatar, 2016.
  • D. Gözüpek and F. Alagöz, An interference aware throughput maximizing scheduler for centralized cognitive radio networks, in 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1527-1532, Istanbul, Turkey, 2010 .
  • Shu, T., Krunz, M., and Vrudhula, S., Joint optimization of transmit power-time and bit energy efficiency in CDMA wireless sensor networks, IEEE Transactions on Wireless Communications, 5(11), 3109-3118, 2006. 10.1109/TWC.2006.04738
  • C. Majumdar, D. Lee, A. A. Patel, S. Merchant, and U. B. Desai, Packet-size optimization for multiple-input multiple-output cognitive radio sensor networks-aided internet of things, IEEE Access, 5, 14419-14440, 2017. 10.1109/ACCESS.2017.2687083
  • Namdar M., Basgumus A., Outage Performance Analysis of Underlay Cognitive Radio Networks with Decode‐and‐Forward Relaying book chapter in Cognitive Radio (Editor: T. Trump), IntechOpen, ISBN: 978-953-51-3338-4, pp. 25-37, 2017.
  • Namdar M., Ilhan, H., Exact closed-form solution for detection probability in cognitive radio networks with switch-and-examine combining diversity, IEEE Transactions on Vehicular Technology, 67(9), 8215-8222, 2018. 10.1109/TVT.2018.2840227

Analysis of the varying packet sizes effect on energy consumption in cognitive radio networks

Yıl 2023, Cilt: 12 Sayı: 3, 644 - 655, 15.07.2023

Sercan Demirci Doğan Yıldız

https://doi.org/10.28948/ngumuh.1278682

Öz

Wireless communication technologies are developing directly proportional to the need and demand for these technologies. Therefore, studies in this direction are gaining importance day by day. The increase in applications with heavy data traffic, such as social networks, file sharing, and multimedia communication, increases the need for spectrum, which is essential in daily wireless communication. Current wireless communication devices work with a fixed spectrum assignment technique and generally use certain parts of the spectrum. Cognitive Radio Networks (CRNs) dynamically access the unused and idle parts of the spectrum, allowing the selection of the most appropriate communication channel. It is crucial to determine the packet size used in communication between users in order to reduce the energy consumption resulting from frequency changes among CR users. Because CR users dynamically change their frequencies, reducing the time remaining in the time slots allocated to them for data communication. In this decreasing time, it is also crucial to determine the most suitable package size. In this study, the effects of packet length values on energy efficiency in CRNs were investigated through numerical analyzes for different variable parameters in a Python simulation environment. Investigations made with these parameters can be summarized as follows: The impact of unit channel change power and duration on the total energy according to the packet size, the impact of different time zones on the total energy according to the packet size, the impact of the unsuccessful transmission rate on the total energy according to the packet size, and the variation in the packet sending/receiving efficiency according to the change in the whole cognitive time.

Anahtar Kelimeler

Cognitive radio networks Frequency switching cost Packet size Optimization

Kaynakça

  • I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey, Computer Networks, 50 (13), 2127-2159, 2006. https://doi.org/10.1016/j.comnet.2006.05.001.
  • O. B. Akan, O. B. Karli, and O. Ergul, Cognitive radio sensor networks, IEEE Network, 23 ( 4), 34-40, 2009. 10.1109/MNET.2009.5191144
  • M. Song, C. Xin, Y. Zhao, and X. Cheng, Dynamic spectrum access: from cognitive radio to network radio, IEEE Wireless Communications, 19 (1), 23-29, 2012. 10.1109/MWC.2012.6155873
  • J. Mitola and G. Q. Maguire, Cognitive radio: making software radios more personal, IEEE Personal Communications, 6, (4), 13-18, 1999. 10.1109/98.788210
  • S. Haykin, Cognitive radio: brain-empowered wireless communications, IEEE Journal on Selected Areas in Communications, 23 (2), 201-220, 2005. 10.1109/JSAC.2004.839380
  • M. Zareei, A. M. Islam, S. Baharun, C. Vargas-Rosales, L. Azpilicueta, and N. Mansoor, Medium access control protocols for cognitive radio ad hoc networks: A survey, Sensors, 17 (9), 2136, 2017. https://doi.org/10.3390/s17092136
  • A. Jamal, C.-K. Tham, and W.-C. Wong, Dynamic packet size optimization and channel selection for cognitive radio sensor networks, IEEE Transactions on Cognitive Communications and Networking, 1 (4), 394-405, 2015. 10.1109/TCCN.2016.2531082
  • M. Awasthi, M. J. Nigam, and V. Kumar, Energy efficient sensing, transmitting time and transmission power for cognitive radio networks, in 2017 14th IEEE India Council International Conference (INDICON), pp. 1-5, Roorkee, India, 2017.
  • E. F. Orumwense, T. J. Afullo, and V. M. Srivastava, Secondary user energy consumption in cognitive radio networks, in AFRICON 2015, pp. 1-5, Addis Ababa, Ethiopia, 2015.
  • S. Agarwal and S. De, Impact of channel switching in energy constrained cognitive radio networks, IEEE Communications Letters, 19 (6), 977-980, 2015. 10.1109/LCOMM.2015.2416334
  • H. Ali, A. Khattab, and M. Fikri, Energy-efficient cooperative sensing for cognitive wireless sensor networks, in 5th International Conference on Energy Aware Computing Systems & Applications, pp. 1-4, Cairo, 2015.
  • T. Thanuja, K. A. Daman, and A. S. Patil, Optimized spectrum sensing techniques for enhanced throughput in cognitive radio network, in 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 137-141, Pune, India, 2020.
  • M. Yigit, H. U. Yildiz, S. Kurt, B. Tavli, and V. C. Gungor, A survey on packet size optimization for terrestrial, underwater, underground, and body area sensor networks, International Journal of Communication Systems, 31 (11), e3572, 2018. https://doi.org/10.1002/dac.3572
  • A. D. Sezer and S. Gezici, Average capacity maximization via channel switching in the presence of additive white Gaussian noise channels and switching delays, IEEE Transactions on Wireless Communications, 15 (9), 6228-6243, 2016. 10.1109/TWC.2016.2582150
  • X. Li, D. Wang, J. McNair, and J. Chen, Dynamic spectrum access with packet size adaptation and residual energy balancing for energy-constrained cognitive radio sensor networks, Journal of Network and Computer Applications, 41, 157-166, 2014. https://doi.org/10.1016/j.jnca.2013.11.001
  • Cheng, G., Liu, W., Li, Y., and Cheng, W., Spectrum aware on-demand routing in cognitive radio networks, In 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 571-574, IEEE, Dublin, Ireland, 2007.
  • Kim, H. and Shin, K.G., Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks, IEEE Transactions on Mobile Computing, 7(5), 533–545, 2010. 10.1109/TMC.2007.70751
  • Cheng, G., Liu, W., Li, Y., and Cheng, W., Joint on-demand routing and spectrum assignment in cognitive radio networks, In 2007 IEEE international conference on communications, pp. 6499-6503, IEEE, Glasgow, UK, 2007.
  • Namboodiri, V., Are cognitive radios energy efficient? A study of the wireless LAN scenario, In 2009 IEEE 28Th international performance computing and communications conference, pp. 437-442, IEEE, Scottsdale, AZ, USA, 2009.
  • Namboodiri, V. and Gao, L., Energy-Efficient VoIP over Wireless LANs, IEEE Transactions on Mobile Computing, 9(4), 566–581, 2010. 10.1109/TMC.2009.150
  • Bayhan, S., Zheng, L., Chen, J., Di Francesco, M., Kangasharju, J., and Chiang, M., Improving cellular capacity with white space offloading, In 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), pp. 1-8, IEEE, Paris, France, 2017.
  • Krishnamurthy, S., Thoppian, M., Venkatesan, S., and Prakash, R., Control channel based MAC-layer configuration, routing and situation awareness for cognitive radio networks, In MILCOM 2005-2005 IEEE Military Communications Conference, pp. 455-460, IEEE, Atlantic City, NJ, 2005.
  • Ma, H., Zheng, L., and Ma, X, Spectrum aware routing for multi-hop cognitive radio networks with a single transceiver, In 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008), pp. 1-6. IEEE, Singapore, 2008.
  • Marougi, S. D., Frequency-switching speed and post-tuning drift measurement of fast-switching microwave-frequency synthesisers, IET Science, Measurement & Technology, 1(2), 82-86, 2007. 10.1049/iet-smt:20060011
  • Gozupek, D., Buhari, S. and Alagoz, F., A spectrum switching delay-aware scheduling algorithm for centralized cognitive radio networks, IEEE Transactions on Mobile Computing, 12(7), 1270–1280, 2013. 10.1109/TMC.2012.101
  • Arkoulis, S., Anifantis, E., Karyotis, V., Papavassiliou, S., and Mitrou, N., On the optimal, fair and channel-aware cognitive radio network reconfiguration, Computer Networks, 57(8), 1739-1757, 2013. https://doi.org/10.1016/j.comnet.2013.03.004
  • Chen, J., Li, H., Wu, J., and Zhang, R., Starp: a novel routing protocol for multi-hop dynamic spectrum access networks, In Proceedings of the 1st ACM workshop on Mobile internet through cellular networks, pp. 49-54, Beijing China, 2009
  • Chowdhury, K. R., and Felice, M. D., Search: A routing protocol for mobile cognitive radio ad-hoc networks, Computer Communications, 32(18), 1983-1997, 2009. https://doi.org/10.1016/j.comcom.2009.06.011
  • Filippini, I., Ekici, E., and Cesana, M., Minimum maintenance cost routing in cognitive radio networks, In 2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems pp. 284-293, IEEE, Macau, China, 2009.
  • Guirguis, A., and ElNainay, M., Channel selection scheme for cooperative routing protocols in cognitive radio networks, In 2017 International Conference on Computing, Networking and Communications (ICNC), pp. 735-739, IEEE, Silicon Valley, CA, USA, 2017.
  • Demirci, S., and Gözüpek, D., Switching cost-aware joint frequency assignment and scheduling for industrial cognitive radio networks, IEEE Transactions on Industrial Informatics, 16(7), 4365-4377, 2019. 10.1109/TII.2019.2950563
  • Arat, F., and Demirci, S., Channel Switching Cost-Aware Energy Efficient Routing in Cognitive Radio-Enabled Internet of Things, Mobile Networks and Applications, 27(4), 1531-1550, 2022. https://doi.org/10.1007/s11036-022-02039-w
  • Agarwal, S., and De, S., Dynamic spectrum access for energy‐constrained CR: single channel versus switched multichannel, IET Communications, 10(7), 761-769, 2016. https://doi.org/10.1049/iet-com.2015.0523
  • Bayhan, S., and Alagoz, F., Scheduling in centralized cognitive radio networks for energy efficiency, IEEE Transactions on Vehicular Technology, 62(2), 582-595, 2012. 10.1109/TVT.2012.2225650
  • Celik, A., and Kamal, A. E., Green cooperative spectrum sensing and scheduling in heterogeneous cognitive radio networks, IEEE Transactions on Cognitive Communications and Networking, 2(3), 238-248, 2016. 10.1109/TCCN.2016.2608337
  • Shami, N., and Rasti, M., A joint multi-channel assignment and power control scheme for energy efficiency in cognitive radio networks, In 2016 IEEE Wireless Communications and Networking Conference,pp. 1-6, IEEE, Doha, Qatar, 2016.
  • D. Gözüpek and F. Alagöz, An interference aware throughput maximizing scheduler for centralized cognitive radio networks, in 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1527-1532, Istanbul, Turkey, 2010 .
  • Shu, T., Krunz, M., and Vrudhula, S., Joint optimization of transmit power-time and bit energy efficiency in CDMA wireless sensor networks, IEEE Transactions on Wireless Communications, 5(11), 3109-3118, 2006. 10.1109/TWC.2006.04738
  • C. Majumdar, D. Lee, A. A. Patel, S. Merchant, and U. B. Desai, Packet-size optimization for multiple-input multiple-output cognitive radio sensor networks-aided internet of things, IEEE Access, 5, 14419-14440, 2017. 10.1109/ACCESS.2017.2687083
  • Namdar M., Basgumus A., Outage Performance Analysis of Underlay Cognitive Radio Networks with Decode‐and‐Forward Relaying book chapter in Cognitive Radio (Editor: T. Trump), IntechOpen, ISBN: 978-953-51-3338-4, pp. 25-37, 2017.
  • Namdar M., Ilhan, H., Exact closed-form solution for detection probability in cognitive radio networks with switch-and-examine combining diversity, IEEE Transactions on Vehicular Technology, 67(9), 8215-8222, 2018. 10.1109/TVT.2018.2840227
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı, Elektrik Mühendisliği
Bölüm Bilgisayar Mühendisliği
Yazarlar

Sercan Demirci 0000-0001-6739-7653

Doğan Yıldız 0000-0001-9670-4173

Erken Görünüm Tarihi 11 Temmuz 2023
Yayımlanma Tarihi 15 Temmuz 2023
Gönderilme Tarihi 7 Nisan 2023
Kabul Tarihi 21 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 12 Sayı: 3

Kaynak Göster

APA Demirci, S., & Yıldız, D. (2023). Bilişsel radyo ağlarında değişen paket boyutlarının enerji tüketimi üzerindeki etkisinin analizi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12(3), 644-655. https://doi.org/10.28948/ngumuh.1278682
AMA Demirci S, Yıldız D. Bilişsel radyo ağlarında değişen paket boyutlarının enerji tüketimi üzerindeki etkisinin analizi. NÖHÜ Müh. Bilim. Derg. Temmuz 2023;12(3):644-655. doi:10.28948/ngumuh.1278682
Chicago Demirci, Sercan, ve Doğan Yıldız. “Bilişsel Radyo ağlarında değişen Paket boyutlarının Enerji tüketimi üzerindeki Etkisinin Analizi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12, sy. 3 (Temmuz 2023): 644-55. https://doi.org/10.28948/ngumuh.1278682.
EndNote Demirci S, Yıldız D (01 Temmuz 2023) Bilişsel radyo ağlarında değişen paket boyutlarının enerji tüketimi üzerindeki etkisinin analizi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12 3 644–655.
IEEE S. Demirci ve D. Yıldız, “Bilişsel radyo ağlarında değişen paket boyutlarının enerji tüketimi üzerindeki etkisinin analizi”, NÖHÜ Müh. Bilim. Derg., c. 12, sy. 3, ss. 644–655, 2023, doi: 10.28948/ngumuh.1278682.
ISNAD Demirci, Sercan - Yıldız, Doğan. “Bilişsel Radyo ağlarında değişen Paket boyutlarının Enerji tüketimi üzerindeki Etkisinin Analizi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12/3 (Temmuz 2023), 644-655. https://doi.org/10.28948/ngumuh.1278682.
JAMA Demirci S, Yıldız D. Bilişsel radyo ağlarında değişen paket boyutlarının enerji tüketimi üzerindeki etkisinin analizi. NÖHÜ Müh. Bilim. Derg. 2023;12:644–655.
MLA Demirci, Sercan ve Doğan Yıldız. “Bilişsel Radyo ağlarında değişen Paket boyutlarının Enerji tüketimi üzerindeki Etkisinin Analizi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, c. 12, sy. 3, 2023, ss. 644-55, doi:10.28948/ngumuh.1278682.
Vancouver Demirci S, Yıldız D. Bilişsel radyo ağlarında değişen paket boyutlarının enerji tüketimi üzerindeki etkisinin analizi. NÖHÜ Müh. Bilim. Derg. 2023;12(3):644-55.
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