Performance Analysis of Wireless Systems with Doubly Selective Rayleigh Fading

Theoretical error performances of wireless communication systems suffering from both doubly selective (time varying and frequency selective) Rayleigh fading and sampler timing offset are analyzed in this paper. Single-input-single-output systems with doubly selective fading channels are equivalently represented as discrete-time single-input-multiple-output (SIMO) systems with correlated frequency-flat fading channels, with the correlation information being determined by the combined effects of sampler timing phase, maximum Doppler spread, and power delay profile of the physical fading. Based on the equivalent SIMO system representation, closed-form error-probability expressions are derived as tight lower bounds for linearly modulated systems with fractionally spaced equalizers. The information on the sampler timing offset and the statistical properties of the physical channel fading, along with the effects of the fractionally spaced equalizer, are incorporated in the error-probability expressions. Simulation results show that the new analytical results can accurately predict the error performances of maximum-likelihood sequence estimation and maximum a posteriori equalizers for practical wireless communication systems in a wide range of signal-to-noise ratio. Moreover, some interesting observations about receiver oversampling and system timing phase sensitivity are obtained based on the new analytical results

Cooperative MIMO Systems in Wireless Sensor Networks

28 page(s

Reducing Multiple Access Interference in Broadband Multi-User Wireless Networks

This dissertation is devoted to developing multiple access interference (MAI) reduction techniques for multi-carrier multi-user wireless communication networks. In multi-carrier code division multiple access (MC-CDMA) systems, a full multipath diversity can be achieved by transmitting one symbol over multiple orthogonal subcarriers by means of spreading codes. However, in frequency selective fading channels, orthogonality among users can be destroyed leading to MAI. MAI represents the main obstacle to support large number of users in multi-user wireless systems. Consequently, MAI reduction becomes a main challenge when designing multi-carrier multi-user wireless networks. In this dissertation, first, we study MC-CDMA systems with different existing MAI reduction techniques. The performance of the studied systems can be further improved by using a fractionally spaced receivers instead of using symbol spaced receivers. A fractionally spaced receiver is obtained by oversampling received signals in a time domain. Second, a novel circular-shift division multiple access (CSDMA) scheme for multi-carrier multi-user wireless systems is developed. In CSDMA, each symbol is first spread onto multiple orthogonal subcarriers in the frequency domain through repetition codes. The obtained frequency-domain signals are then converted to a time-domain representation. The time-domain signals of different users are then circularly shifted by different numbers of locations. The time-domain circular shifting enables the receiver to extract signals from different users with zero or a small amount of MAI. Our results show that the CSDMA scheme can achieve a full multipath diversity with a performance outperforms that of orthogonal frequency division multiple access (OFDMA). Moreover, multipath diversity of CSDMA can be further improved by employing the time-domain oversampling. Performance fluctuations due to a timing offset between transmitter and receiver clocks in MC-CDMA and CSDMA systems can be removed by employing the time-domain oversampling. Third, we study the theoretical error performance of high mobility single-user wireless communication system with doubly selective (time-varying and frequency-selective) fading channel under impacts of imperfect channel state information (CSI). Throughout this dissertation, intensive computer simulations are performed under various system configurations to investigate the obtained theoretical results, excellent agreements between simulation and theoretical results were observed in this dissertation

Doppler Spread Estimation in MIMO Frequency-Selective Fading Channels

One of the main challenges in high-speed mobile communications is the presence of large Doppler spreads. Thus, accurate estimation of maximum Doppler spread (MDS) plays an important role in improving the performance of the communication link. In this paper, we derive the data-aided (DA) and non-data-aided (NDA) Cramér-Rao lower bounds (CRLBs) and maximum likelihood estimators (MLEs) for the MDS in multiple-input multiple-output (MIMO) frequency-selective fading channel. Moreover, a low-complexity NDA-moment-based estimator (MBE) is proposed. The proposed NDA-MBE relies on the second- and fourth-order moments of the received signal, which are employed to estimate the normalized squared autocorrelation function of the fading channel. Then, the problem of MDS estimation is formulated as a non-linear regression problem, and the least-squares curve-fitting optimization technique is applied to determine the estimate of the MDS. This is the first time in the literature, when DA- and NDA-MDS estimation is investigated for MIMO frequency-selective fading channel. Simulation results show that there is no significant performance gap between the derived NDA-MLE and NDA-CRLB, even when the observation window is relatively small. Furthermore, the significant reduced-complexity in the NDA-MBE leads to low root-mean-square error over a wide range of MDSs, when the observation window is selected large enough

Index modulation for next generation wireless communications.

Doctoral Degree. University of KwaZulu-Natal, Durban.A multicarrier index modulation technique in the form of quadrature spatial modulation (QSM) orthogonal frequency division multiplexing (QSM-OFDM) is proposed, in which transmit antenna indices are employed to transmit additional bits. Monte Carlo simulation results demonstrates a 5 dB gain in signal-to-noise ratio (SNR) over other OFDM schemes. Furthermore, an analysis of the receiver computational complexity is presented. A low-complexity near-ML detector for space-time block coded (STBC) spatial modulation (STBC-SM) with cyclic structure (STBC-CSM), which demonstrate near-ML error performance and yields significant reduction in computational complexity is proposed. In addition, the union-bound theoretical framework to quantify the average bit-error probability (ABEP) of STBC-CSM is formulated and validates the Monte Carlo simulation results. The application of media-based modulation (MBM), to STBC-SM and STBC-CSM employing radio frequency (RF) mirrors, in the form of MBSTBC-SM and MBSTBC-CSM is proposed to improve the error performance. Numerical results of the proposed schemes demonstrate significant improvement in error performance when compared with STBC-CSM and STBC-SM. In addition, the analytical framework of the union-bound on the ABEP of MBSTBC-SM and MBSTBC-CSM for the ML detector is formulated and agrees well with Monte Carlo simulations. Furthermore, a low-complexity near-ML detector for MBSTBC-SM and MBSTBC-CSM is proposed, and achieves a near-ML error performance. Monte Carlo simulation results demonstrate a trade-off between the error performance and the resolution of the detector that is employed. Finally, the application of MBM, an index modulated system to spatial modulation, in the form of spatial MBM (SMBM) is investigated. SMBM employs RF mirrors located around the transmit antenna units to create distinct channel paths to the receiver. This thesis presents an easy to evaluate theoretical bound for the error performance of SMBM, which is validated by Monte Carlo simulation results. Lastly, two low-complexity suboptimal mirror activation pattern (MAP) optimization techniques are proposed, which improve the error performance of SMBM significantly

Iterative deep learning receiver

DeepRx is a deep learning receiver which replaces much of the functionality of a traditional 5G receiver. It is a deep model which uses residual connections and a fully convolutional architecture to process an incoming signal, and it outputs log-likelihood ratios for each bit. However, the deep model can be computationally too heavy to use in a real environment. Nokia Bell Labs has recently developed an iterative version of the DeepRx, where a model with fewer layers is used iteratively. This thesis focuses on developing a neural network which determines how many iterations the iterative DeepRx needs to use. We trained a separate neural network, the stopping condition neural network, which will be used together with the iterative model. It predicts the number of iterations the model requires to process the input correctly, with the aim that each inference uses as few iterations as possible. The model also stops the inference early if it predicts that the required number of iterations is greater than the maximum amount. Our results show that an iterative model with a stopping condition neural network has significantly fewer parameters than the deep model. The results also show that while the stopping condition neural network could predict with a high accuracy which samples could be decoded, using it also increased the uncoded bit error rate of the iterative model slightly. Therefore, using a stopping condition neural network together with an iterative model seems to be a flexible lightweight alternative to the DeepRx model