Interference Avoidance Algorithms in Cognitive Radio Networks and Performance Analysis

Mohammadbeiki, Akram | 2012

351 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44444 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Nasiri Kenari, Masoumeh
  7. Abstract:
  8. Cognitive radio faces specific challenges due to its special spectrum access scheme. Providing required QoS and preventing irrecoverable interference on the primary users is among the crucial requirements. Beam forming is one of the interference avoidance schemes utilized in MIMO networks. Most of the studies on this scheme assume that each symbol is transmitted via vectors in order to grant desired beam forming and specific combinations in the receiver. Since transmitting multiple symbols at every transmission can provide higher data rates, it seems necessary to exploit beam forming through matrixes instead of vectors. In this thesis, by neglecting the interference of the secondary user, power allocation and beam forming matrixes for the primary users are determined first, and also transmission power is allocated equally among different secondary symbols. In the next step, by considering the prevention of irrecoverable interference on the primary network, three different algorithms for determination of secondary beam forming matrixes with the goal of maximizing the SINR ratio are studies. The performance is also analyzed in the case of perfect and imperfect channels state information. In the last chapter of this thesis, spectrum sensing is also considered in the transmission of the secondary users and optimal sensing time with the objective of maximizing spectrum efficiency is studied. It is demonstrated that applying the optimal duration for spectrum sensing can considerably enhance the performance of the secondary users in the case of light primary traffic.
  9. Keywords:
  10. Cognitive Radio ; Interference Avoidance ; Beamforming ; Optimal Sensing Time

 Digital Object List