Multicarrier DS-CDMA communication systems using smart antennas

In this thesis the benefits of smart antennas are investigated in the context of a generalized MC DS-CDMA system, which includes the subclasses of both multitone DS-CDMA and orthogonal MC DS-CDMA as special cases. Firstly, in Chapter 2 the philosophy of the generalized multicarrier DS-CDMA system invoking smart antennas was described and characterized, where a (M x L)-dimensional antenna array was employed by the generalized MC DS-CDMA system considered for the sake of achieving both SNR gain and spatial diversity. Four optimum combining schemes, which are based on the Minimum Variance Distortionless Response (MVDR), the Maximum Signal-to-Interference-plus-Noise Ratio (MSINR), the Minimum Mean-Square Error (MMSE) and the Minimum Power Distortionless Response (MPDR) principles, were introduced. In the context of optimum combining, the signals received by the antennas are appropriately weighted and combined in order to combat the effect of the multipath fading on the desired signal and for the sake of mitigating the effects of the interfering signals. Secondly, four downlink space-time transmitter processing schemes based on the principles of beamforming, Beam Selection Transmit Diversity (BSTD), Space-Time Transmit Diversity (STTD) and Steered Space-Time Spreading (SSTS) were invoked for the downlink of generalized MC DS-CDMA systems in Chapter 3, in order to enhance the achievable performance. Specific discussions concerning the downlink beamforming-aided generalized Multicarrier DS-CDMA system were first provided. Then a BSTD scheme constituted by an amalgam of beamforming and Selection Transmit Diversity (STD) was discussed and analyzed, followed by the characterization of the STTD scheme. Finally, SSTS, based on both STTD and beamforming, was adopted for employment in the generalized MC DS-CDMA system. The novel contribution of the second part of this thesis is that the four downlink space-time transmitter processing schemes are investigated comparatively in the context of the smart antennas aided MC DS-CDMA system. Thirdly, a range of adaptive space-time processing schemes are investigated in the context of a generalized MC DS-CDMA system supported by a (M x L)-dimensional antenna array in Chapter 4, where knowledge of the Direction of Arrival (DOA), of the channel amplitudes or of the channel-induced phase-rotations is required. A brief introduction to the literature of adaptive space-time processing is provided first, followed by portraying the philosophy of the Least-Mean-Square (LMS) and Recursive-Least-Square (RLS) algorithms. Then the LMS-based Adaptive Space-Time Detector (ASTD) and the RLS-based ASTD are developed for the adaptive uplink of the generalized MC DS-CDMA system. Furthermore, the Parallel Interference Canceller (PIC) technique is employed for improving the convergence rate of the ASTDs. The simulation results demonstrate that the RLS-based ASTD has a higher convergence rate than that of the LMS-based ASTD, which is achieved at the expense of a higher computational complexity. Finally, in Chapter 5 the performance of subspace-based blind and group-blind space-time Multi-User Detection (MUD) invoked for a generalized MC DS-CDMA system is studied, which does not require any training sequence and hence achieves an increased spectrum efficiency. After a detailed discourse on subspace-based both totally blind and group-blind MUDs, we concentrated our investigations on blind and group-blind space-time MUD invoked for a smart antenna aided generalized MC DS-CDMA system. Two adaptive subspace tracking algorithms, namely the Projection Approximation Subspace Tracking deflation (PASTd) algorithm and the Noise-Averaged Hermitian-Jacobi Fast Subspace Tracking (NAHJ-FST) algorithm, are employed for the sake of reducing the computational complexity imposed. As expected, the group-blind MUD benefitting from the knowledge of more intracell users' signature waveforms attained a better BER performance. The NAHJ-FST tracking algorithm exhibits a faster convergence and a better steady-state performance than that of the PASTd tracking algorithm.