Superallocation and Cluster‐Based Cooperative Spectrum Sensing in 5G Cognitive Radio Network

Consequently, the research and development for the 5G systems have already been started. This chapter presents an overview of potential system network architecture and highlights a superallocation technique that could be employed in the 5G cognitive radio network (CRN). A superallocation scheme is proposed to enhance the sensing detection performance by rescheduling the sensing and reporting time slots in the 5G cognitive radio network with a cluster-based cooperative spectrum sensing (CCSS). In the 4G CCSS scheme, first, all secondary users (SUs) detect the primary user (PU) signal during a rigid sensing time slot to check the availability of the spectrum band. Second, during the SU reporting time slot, the sensing results from the SUs are reported to the corresponding cluster heads (CHs). Finally, during CH reporting time slots, the CHs forward their hard decision to a fusion center (FC) through the common control channels for the global decision. However, the reporting time slots for the SUs and CHs do not contribute to the detection performance. In this chapter, a superallocation scheme that merges the reporting time slots of SUs and CHs by rescheduling the reporting time slots as a nonfixed sensing time slot for SUs to detect the PU signal promptly and more accurately is proposed. In this regard, SUs in each cluster can obtain a nonfixed sensing time slot depending on their reporting time slot order. The effectiveness of the proposed chapter that can achieve better detection performance under –28 to –10 dB environments and thus reduce reporting overhead is shown through simulations.

[1]  Aarne Mämmelä,et al.  Cooperative spectrum sensing using quantized soft decision combining , 2009, 2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[2]  V. I. Kostylev.  Characteristics of Energy Detection of Quasideterministic Radio Signals , 2000 .

[3]  J. I. Mararm,et al.  Energy Detection of Unknown Deterministic Signals , 2022 .

[4]  R.W. Brodersen,et al.  Implementation issues in spectrum sensing for cognitive radios , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[5]  Insoo Koo,et al.  Cooperative Spectrum Sensing using Kalman Filter based Adaptive Fuzzy System for Cognitive Radio Networks , 2012, KSII Trans. Internet Inf. Syst..

[6]  A. Ghasemi,et al.  Collaborative spectrum sensing for opportunistic access in fading environments , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[7]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[8]  Hua Li,et al.  Superposition-based cooperative spectrum sensing in cognitive radio networks , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[9]  Vera Stavroulaki,et al.  5G on the Horizon: Key Challenges for the Radio-Access Network , 2013, IEEE Vehicular Technology Magazine.

[10]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[11]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[12]  H. Mathis.  On the kurtosis of digitally modulated signals with timing offsets , 2001, 2001 IEEE Third Workshop on Signal Processing Advances in Wireless Communications (SPAWC'01). Workshop Proceedings (Cat. No.01EX471).

[13]  T. C. Aysal,et al.  Cooperative Spectrum Sensing with Noisy Hard Decision Transmissions , 2009, 2009 IEEE International Conference on Communications.

[14]  Teng Joon Lim,et al.  Composite Hypothesis Testing for Cooperative Spectrum Sensing in Cognitive Radio , 2009, 2009 IEEE International Conference on Communications.

[15]  T. C. Aysal,et al.  Cooperative Spectrum Sensing over Imperfect Channels , 2008, 2008 IEEE Globecom Workshops.

[16]  Michael S. Berger,et al.  Towards converged 5G mobile networks-challenges and current trends , 2014, Proceedings of the 2014 ITU kaleidoscope academic conference: Living in a converged world - Impossible without standards?.

[17]  Yonghong Zeng,et al.  Maximum-Minimum Eigenvalue Detection for Cognitive Radio , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[18]  Venugopal V. Veeravalli,et al.  The impact of fading on decentralized detection in power constrained wireless sensor networks , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[19]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[20]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[21]  S. Enserink,et al.  A cyclostationary feature detector , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[22]  Vladimir I. Kostylev,et al.  Energy detection of a signal with random amplitude , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[23]  Wenqing Chen,et al.  Cluster-Based Energy Efficient Cooperative Spectrum Sensing in Cognitive Radios , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[24]  Behrooz Makki,et al.  On the Ergodic Achievable Rates of Spectrum Sharing Networks with Finite Backlogged Primary Users and an Interference Indicator Signal , 2012, IEEE Transactions on Wireless Communications.

[25]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[26]  Wei Zhang,et al.  Cluster-Based Cooperative Spectrum Sensing in Cognitive Radio Systems , 2007, 2007 IEEE International Conference on Communications.

[27]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..