Blind Identification Of MIMO Space-time/Frequency Schemes

Blind identification of information signals' parameters of a transmitter from received signals has important applications both in military surveillance and civilian cognitive radios systems,and will be a promising alternative scheme in the next-generation wireless communications.Multiple-input multiple-output(MIMO)and orthogonal frequency division multiplexing(OFDM)technologies are adopted in cellular and Wi Fi standards because they achieve high spectral efficiency.Different from the identification of single-antenna systems,the blind identification of MIMO or MIMO-OFDM signals requires the enumeration of the number of transmit antennas and identification of MIMO schemes.For the blind identification of SFBC-OFDM signals,the sole time-domain cross-correlation based algorithm can only identify two MIMO schemes and does not perform well for a short observation period.To the best of our knowledge,no method exists in the literature for the joint blind identification,although it helps to reduce the system complexity.Therefore,this dissertation will focus on these two issues.After introducing the system model and conventional algorithms,we propose three novel blind identification algorithms for space-frequency block codes(SFBC)to extend the SFBC pool and enhance performance with a short observation period,and a novel joint blind identification algorithm of the number of transmit antennas and MIMO schemes.The time-domain cross-correlation based blind identification of SFBC only considered identifying Alamouti and spatial multiplexing transmission schemes.In this dissertation,we propose a novel algorithm to identify SFBCs by analyzing discriminating features for different SFBCs,calculated by separating the signal subspace and noise subspace of the received signals at different adjacent OFDM sub-carriers.Relying on random matrix theory,this algorithm utilizes a serial hypothesis test to determine the decision boundary according to the maximum eigenvalue in the noise subspace.Then,a decision tree of a special distance metric is employed for decision making.The proposed algorithm can identify 7 MIMO schemes and does not require prior knowledge of the signal parameters such as the number of transmit antennas,channel coefficients,modulation mode and noise power.Simulation results verify the viability of the proposed algorithm for a reduced observation period with an acceptable computational complexity.Previous approaches for blind identification of SFBC do not perform well for short observation periods due to their inefficient utilization of frequency-domain redundancy.This dissertation proposes a hypothesis test(HT)-based algorithm and a support vector machine(SVM)-based algorithm for SFBC signals identification over frequency-selective fading channels to exploit two-dimensional space-frequency domain redundancy.Based on the central limit theorem,space-domain redundancy is used to construct the cross-correlation function of the estimator and frequency-domain redundancy is incorporated in the construction of the statistics.The difference between the two proposed algorithms is that the HT-based algorithm constructs a chi-square statistic and employs an HT to make the decision,while the SVM-based algorithm constructs a non-central chi-square statistic with unknown mean as a strongly-distinguishable statistical feature and uses SVM to make the decision.In addition,we propose a decision tree to identify 4 MIMO schemes.Both algorithms do not require knowledge of the channel coefficients,modulation type or noise power,and the SVM-based algorithm does not require timing synchronization.Simulation results verify the superior performance of the proposed algorithms for short observation periods with comparable computational complexity to conventional algorithms,as well as their acceptable identification performance in the presence of transmission impairments.Moreover,the HT-based algorithm performs better than the SVM-based algorithm in the low signal-to-noise ratio regime with successful timing synchronization.In this dissertation,we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO scheme simultaneously.By restructuring the received signals,we derive three subspace-rank features based on the signal subspace-rank to determine the number of transmit antennas and identify space-time redundancy.Then,a Gerschgorin radii-based method and a feed-forward neural network are employed to calculate these three features,and a minimal weighted norm-1 distance metric is utilized for decision making.In particular,our approach can identify additional MIMO schemes,which most previous works have not considered,and is compatible with both single-carrier and orthogonal frequency division multiplexing(OFDM)systems.Simulation results verify the viability of our proposed approach for single-carrier and OFDM systems and demonstrate its favorable identification performance for a short observation period with acceptable complexity.To sum up,this dissertation proposes three blind identification of SFBC signals and a joint blind identification of the number of transmit antennas and MIMO schemes.Proposed algorithms have superior performance for short observation periods with comparable computational complexity to conventional algorithms,as well as their acceptable identification performance in the presence of transmission impairments.This dissertation fills the research gap in this area and makes important contributions to engineering applications.