Sensing time minimization using pipelining in two stage spectrum sensing

The cognitive radio is an emerging adaptive radio technology promises to provide intelligence to sense empty spectrum slots or spectrum holes. These spectrum holes or white spaces of the spectrum may be used by the Secondary Users (SU) (also known as unlicensed users) without causing interference to the Primary Users (PU) (licensed users of the band). Cognitive Radio therefore intended to support dynamic spectrum access in wireless environment which is considered to be the future of wireless communication. One of the major problems for practical implementation of the same is sensing time minimization. There are a very convincing study to reduce the sensing time as it is directly related to practical accomplishment of the standard and QoS. A two-stage sensing technique is an appreciable technique which uses algorithmic approach to reduce sensing time. In this paper we proposed a pipelined based approach to further reduce the sensing time as compared to the conventional two stage spectrum sensing. This pipelined two-stage sensing technique reduces the latency and hence increases the probability of spectrum utilization. The result shows a considerable amount of saving of sensing time can be achieved by using proposed pipelined two-stage sensing algorithm over the conventional two-stage sensing time.

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

[2]  Thorsten Gerber,et al.  Handbook Of Mathematical Functions , 2016 .

[3]  Ian F. Akyildiz,et al.  Optimal spectrum sensing framework for cognitive radio networks , 2008, IEEE Transactions on Wireless Communications.

[4]  Hao Hu Cyclostationary Approach to Signal Detection and Classification in Cognitive Radio Systems , 2009 .

[5]  Ling Luo,et al.  A Two-Stage Sensing Technique for Dynamic Spectrum Access , 2008, 2008 IEEE International Conference on Communications.

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

[7]  Moshe T. Masonta,et al.  Spectrum Decision in Cognitive Radio Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[8]  Partha Pratim Bhattacharya,et al.  A Survey on Dynamic Spectrum Access Techniques for Cognitive Radio , 2011, ArXiv.

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

[10]  Robert N. McDonough,et al.  Detection of signals in noise , 1971 .

[11]  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..