In the first part of the thesis, we study the trade-off between the transmission reliability and data rate in high signal-to-noise ratio regime in ad-hoc wireless networks. Bandwidth allocation plays a significant role in this trade-off, since dividing bandwidth reduces the number of users on each band and consequently decreases the interference level, however it also decreases the data rate. Noting that the interference power is substantially influenced by the network density, this trade-off introduces a measure for appropriate bandwidth allocation among users considering the network density. The diversity-multiplexing trade-off is derived for a one-dimensional regular ad-hoc network. In the second part of the thesis, we study the performance of point-to-point and broadcast systems with partial channel state information at the transmitter in a time-varying environment. First, the capacity of time-varying channels with periodic feedback at the transmitter is evaluated. It is assumed that the channel state information is perfectly known at the receiver and is fed back to the transmitter at the regular time-intervals. The system capacity is investigated in two cases: i) finite state Markov channel, and ii) additive white Gaussian noise channel with time-correlated fading. In a multiuser scenario, we consider a downlink system in which a single-antenna base station communicates with single antenna users, over a time-correlated fading channel. It is assumed that channel state information is perfectly known at each receiver, while the rate of channel variations and the fading gain at the beginning of each frame are known to the transmitter. The asymptotic throughput of the scheduling that transmits to the user with the maximum signal to noise ratio is examined applying variable code rate and/or variable codeword length signaling. It is shown that by selecting a fixed codeword length for all users, the order of the maximum possible throughput (corresponding to quasi-static fading) is achieved.

In the first part of the thesis, we study the trade-off between the transmission reliability and data rate in high signal-to-noise ratio regime in ad-hoc wireless networks. Bandwidth allocation plays a significant role in this trade-off, since dividing bandwidth reduces the number of users on each band and consequently decreases the interference level, however it also decreases the data rate. Noting that the interference power is substantially influenced by the network density, this trade-off introduces a measure for appropriate bandwidth allocation among users considering the network density. The diversity-multiplexing trade-off is derived for a one-dimensional regular ad-hoc network. In the second part of the thesis, we study the performance of point-to-point and broadcast systems with partial channel state information at the transmitter in a time-varying environment. First, the capacity of time-varying channels with periodic feedback at the transmitter is evaluated. It is assumed that the channel state information is perfectly known at the receiver and is fed back to the transmitter at the regular time-intervals. The system capacity is investigated in two cases: i) finite state Markov channel, and ii) additive white Gaussian noise channel with time-correlated fading. In a multiuser scenario, we consider a downlink system in which a single-antenna base station communicates with single antenna users, over a time-correlated fading channel. It is assumed that channel state information is perfectly known at each receiver, while the rate of channel variations and the fading gain at the beginning of each frame are known to the transmitter. The asymptotic throughput of the scheduling that transmits to the user with the maximum signal to noise ratio is examined applying variable code rate and/or variable codeword length signaling. It is shown that by selecting a fixed codeword length for all users, the order of the maximum possible throughput (corresponding to quasi-static fading) is achieved.

The direct sequence (DS) code division multiple access (CDMA) is one of the most promising multiplexing technologies for wireless communications. It is also a core technology used in the wideband CDMA (WCDMA) system for the third generation (3G) wireless communication systems. In practice, in the CDMA systems the incomplete orthogonal of the spreading codes between users may introduce the so-called multiple access interference (MAI). Usually, the near-far problem exists when the interfering users are assigned powers much higher than the desired user. Such that the system performance might degrade, dramatically, and thus limits the system capacity. To circumvent the above-mentioned problems many effective adaptive multiuser detectors, based on the minimum mean square error (MMSE) and the minimum output energy (MOE) criteria subject to certain constraints have been proposed. In addition, to mitigate multipath fading effect, RAKE receiver was adopted due to the advantages of path diversity, thus, enhances the system performance. To implement the blind adaptive multiuser detector the linearly constrained minimum variance (LCMV), which is the constrained version of MOE, has been suggested. Further, the LCMV-based receivers exhibit high sensitivity to the channel mismatch caused by the unreliable estimation. To deal with this problem the constant modulus (CM) criterion was considered. In this dissertation, to deal with diverse phenomena encountered in practical channels, we first propose new blind adaptive multi-user detectors, based on the Min/Max criterion associated with the LCCM approach. For implementation the LC exponential window (EW) recursive least-square (RLS) algorithm is derived, and is referred to as the EW LCCM-RLS receiver. It can be used to effectively suppress the MAI and ISI, simultaneously, over multipath fading channels and are robust to mismatch problem caused by inaccuracies in the acquisition of timing and spreading code of the desired user. To reduce the complexity of the above-mentioned blind adaptive multi-user receiver with the LCCM-RLS algorithm, the so-called generalized sidelobe-canceller (GSC) structure is adopted, results in obtaining new CM-GSC-RLS algorithm. Moreover, to further improve the system performance for multipath fading and time-varying channel, the sliding window (SW) LCCM-RLS and SW CM-GSC-RLS algorithms are developed. It can be employed for multipath fading channel with the rapidly changing strong narrowband interference (NBI), which is joined suddenly to the CDMA systems. To look more inside the effect of selecting the initial value of the input signals autocorrelation matrix, some theoretical analyses for the SW LC-RLS as well as EW LC-RLS are provided. Since, unfortunately, the LCCM criterion is known to highly depend on the exact knowledge of the desired user amplitude that is not known exactly at receiver. In the final of this dissertation, a novel linearly constrained adaptive constant modulus RLS (LC-ACM-RLS) algorithm for blind DS-CDMA receiver is proposed. With this new proposed LC-ACM-RLS algorithm, the amplitude variation of the desired user, due to changing characteristics of the channel, can be tracked adaptively. Thus, better performance achievement, in terms of output signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER), over the conventional LCCM-LMS and LCCM-RLS algorithms can be expected.

This thesis presents a comparison between two transmit diversity schemes, namely space-time coding and time-switched transmit diversity (TSTD) over block-fading and time-varying multi-input single-output (MISO) channels with different channel parameters. The schemes are concatenated with outer channel codes in order to achieve spatio-temporal diversity. The analytical results are derived for the error performances of the systems and the simulation results as well as outage probabilities are provided. Besides, the details of the pilot-symbol-aided modulation (PSAM) technique are investigated and the error performances of the systems are analyzed when the channel state information is estimated with PSAM. It is demonstrated using the analytical and simulation results that TSTD have a comparable error performance with the space-time coding techniques and it even outperforms the space-time codes for some channel parameters. Our results indicate that TSTD can be suggested as an alternative to space-time codes in some time-varying channels especially due to the implementation simplicity.

Orthogonal Frequency Division Multiplexing (OFDM) techniques have been used in many wireless communication systems to improve the system capacity and achieve high data-rate. It possesses good spectral efficiency and robustness against interferences. The OFDM system has been adopted in many communication standards, such as the 802.11a/g standards for the high-speed WLAN, HIPERLAN2, and IEEE 802.16 standard, and meanwhile, it is also employed in the European DAB and DVB systems. To avoid the inter-block interference (IBI), usually, in the transmitter of OFDM systems the redundancy with sufficient length is introduced, it allows us to overcome the IBI problem, due to highly dispersive channel. Many redundancy insertion methods have been proposed in the literatures, there are cyclic prefix (CP), zero padding (ZP) and the pseudorandom postfix (PRP). Under such system we have still to know the correct channel state information for equalizing the noisy block signal. Especially, in time-varying channel, the incorrect channel state information may introduce serious inter-symbol interference (ISI), if the channel estimation could not perform correctly. In this thesis, the PRP-OFDM system is considered. According to the PRP-OFDM scheme, the redundancy with pseudorandom postfix (PRP) approach is employed to make semi-blind channel estimation with order-one statistics of the received signal. But these statistic characteristics may not be available under time-varying channel. Hence, in this thesis, we propose a modified PRP-OFDM system with assistant training sequences, which is equipped with minimum mean-square-error equalizer and utilize Kalman filter algorithm to implement time-varying channel estimation. To do so, we first model time-varying channel estimation problem with a dynamic system, and adopt the Kalman filter algorithm to estimate the true channel coefficients. Unfortunately, since most parameters in dynamic system are random and could not to be known in advance. We need to apply effective estimation schemes to estimate the statistics of true parameters for implementing the Kalman filter algorithm. When the channel state information is known, MMSE equalizer follows to suppress the inter-symbol interference (ISI). Moreover, after making decision the binary data can be used to re-modulate PRP-OFDM symbol and to be re-used in Kalman filter to obtain more accurate CSI to improve the effectiveness of the equalizer. Via computer simulations, we verify that desired performance in terms of bit error rate (BER), can be achieved compared with the CP-OFDM systems.

abstract: Underwater acoustic communications face significant challenges unprecedented in radio terrestrial communications including long multipath delay spreads, strong Doppler effects, and stringent bandwidth requirements. Recently, multi-carrier communications based on orthogonal frequency division multiplexing (OFDM) have seen significant growth in underwater acoustic (UWA) communications, thanks to their well well-known robustness against severely time-dispersive channels. However, the performance of OFDM systems over UWA channels significantly deteriorates due to severe intercarrier interference (ICI) resulting from rapid time variations of the channel. With the motivation of developing enabling techniques for OFDM over UWA channels, the major contributions of this thesis include (1) two effective frequencydomain equalizers that provide general means to counteract the ICI; (2) a family of multiple-resampling receiver designs dealing with distortions caused by user and/or path specific Doppler scaling effects; (3) proposal of using orthogonal frequency division multiple access (OFDMA) as an effective multiple access scheme for UWA communications; (4) the capacity evaluation for single-resampling versus multiple-resampling receiver designs. All of the proposed receiver designs have been verified both through simulations and emulations based on data collected in real-life UWA communications experiments. Particularly, the frequency domain equalizers are shown to be effective with significantly reduced pilot overhead and offer robustness against Doppler and timing estimation errors. The multiple-resampling designs, where each branch is tasked with the Doppler distortion of different paths and/or users, overcome the disadvantages of the commonly-used single-resampling receivers and yield significant performance gains. Multiple-resampling receivers are also demonstrated to be necessary for UWA OFDMA systems. The unique design effectively mitigates interuser interference (IUI), opening up the possibility to exploit advanced user subcarrier assignment schemes. Finally, the benefits of the multiple-resampling receivers are further demonstrated through channel capacity evaluation results. / Dissertation/Thesis / Ph.D. Electrical Engineering 2011

In this dissertation, channel estimation and channel prediction are studied for wireless communication systems. Wireless communication for time-variant channels becomes more important by the fast development of intelligent transportation systems which motivates us to propose a reduced rank channel estimator for time-variant frequency-selective high-speed railway (HSR) systems and a reduced rank channel predictor for fast time-variant flat fading channels. Moreover, the potential availability of large bandwidth channels at mm-wave frequencies and the small wavelength of the mm-waves, offer the mm-wave massive multiple-input multiple-output (MIMO) communication as a promising technology for 5G cellular networks. The high fabrication cost and power consumption of the radio frequency (RF) units at mm-wave frequencies motivates us to propose a low-power hybrid channel estimator for mm-wave MIMO orthogonal frequency-division multiplexing (OFDM) systems. The work on HSR channel estimation takes advantage of the channel's restriction to low dimensional subspaces due to the time, frequency and spatial correlation of the channel and presents a low complexity linear minimum mean square error (LMMSE) estimator for MIMO-OFDM HSR channels. The channel estimator utilizes a four-dimensional (4D) basis expansion channel model obtained from band-limited generalized discrete prolate spheroidal (GDPS) sequences. Exploiting the channel's band-limitation property, the proposed channel estimator outperforms the conventional interpolation based least square (LS) and MMSE estimators in terms of estimation accuracy and computational complexity, respectively. Simulation results demonstrate the robust performance of the proposed estimator for different delay, Doppler and angular spreads. Channel state information (CSI) is required at the transmitter for improving the performance gain of the spatial multiplexing MIMO systems through linear precoding. In order to avoid the high data rate feedback lines, which are required in fast time-variant channels for updating the transmitter with the rapidly changing CSI, a subframe-wise channel tracking scheme is presented. The proposed channel predictor is based on an assumed DPS basis expansion model (DPS-BEM) for exploiting the variation of the channel coefficients inside each sub-frame and an autoregressive (AR) model of the basis coefficients over each transmitted frame. The proposed predictor properly exploits the channel's restriction to low dimensional subspaces for reducing the prediction error and the computational complexity. Simulation results demonstrate that the proposed channel predictor out-performs the DPS based minimum energy (ME) predictor for different ranges of normalized Doppler frequencies and has better performance than the conventional Wiener predictor for slower time-variant channels and almost the similar performance to it for very fast time-variant channels with the reduced amount of computational complexity. The work on the hybrid mm-wave channel estimator considers the sparse nature of the mm-wave channel in angular domain and leverages the compressed sensing (CS) tools for recovering the angular support of the MIMO-OFDM mm-wave channel. The angular channel is treated in a continuous framework which resolves the limited angular resolution of the discrete sparse channel models used in the previous CS based channel estimators. The power leakage problem is also addressed by modeling the continuous angular channel as a multi-band signal with the bandwidth of each sub-band being proportional to the amount of power leakage. The RF combiner is designed to be implemented using a network of low-power switches for antenna subset selection based on a multi-coset sampling pattern. Simulation results validate the effectiveness of the proposed hybrid channel estimator both in terms of the estimation accuracy and the RF power consumption. / Graduate

Signal processing has been playing a key role in providing solutions to key problems encountered in communications, in general, and in wireless communications, in particular. Time-Frequency Signal Processing (TFSP) provides eective tools for analyzing nonstationary signals where the frequency content of signals varies in time as well as for analyzing linear time-varying systems. This research aimed at exploiting the advantages of TFSP, in dealing with nonstationary signals, into the fundamental issues of signal processing, namely the signal estimation and signal separation. In particular, it has investigated the problems of (i) the Instantaneous Frequency (IF) estimation of Linear Frequency-Modulated (LFM) signals corrupted in complex-valued zero-mean Multiplicative Noise (MN), and (ii) the Underdetermined Blind Source Separation (UBSS) of LFM signals, while focusing onto the fast-growing area of Wireless Communications (WCom). A common problem in the issue of signal estimation is the estimation of the frequency of Frequency-Modulated signals which are seen in many engineering and real-life applications. Accurate frequency estimation leads to accurate recovery of the true information. In some applications, the random amplitude modulation shows up when the medium is dispersive and/or when the assumption of point target is not valid; the original signal is considered to be corrupted by an MN process thus seriously aecting the recovery of the information-bearing frequency. The IF estimation of nonstationary signals corrupted by complex-valued zero-mean MN was investigated in this research. We have proposed a Second-Order Statistics approach, rather than a Higher-Order Statistics approach, for IF estimation using Time-Frequency Distributions (TFDs). The main assumption was that the autocorrelation function of the MN is real-valued but not necessarily positive (i.e. the spectrum of the MN is symmetric but does not necessary has the highest peak at zero frequency). The estimation performance was analyzed in terms of bias and variance, and compared between four dierent TFDs: Wigner-Ville Distribution, Spectrogram, Choi-Williams Distribution and Modified B Distribution. To further improve the estimation, we proposed to use the Multiple Signal Classification algorithm and showed its better performance. It was shown that the Modified B Distribution performance was the best for Signal-to-Noise Ratio less than 10dB. In the issue of signal separation, a new research direction called Blind Source Separation (BSS) has emerged over the last decade. BSS is a fundamental technique in array signal processing aiming at recovering unobserved signals or sources from observed mixtures exploiting only the assumption of mutual independence between the signals. The term "blind" indicates that neither the structure of the mixtures nor the source signals are known to the receivers. Applications of BSS are seen in, for example, radar and sonar, communications, speech processing, biomedical signal processing. In the case of nonstationary signals, a TF structure forcing approach was introduced by Belouchrani and Amin by defining the Spatial Time- Frequency Distribution (STFD), which combines both TF diversity and spatial diversity. The benefit of STFD in an environment of nonstationary signals is the direct exploitation of the information brought by the nonstationarity of the signals. A drawback of most BSS algorithms is that they fail to separate sources in situations where there are more sources than sensors, referred to as UBSS. The UBSS of nonstationary signals was investigated in this research. We have presented a new approach for blind separation of nonstationary sources using their TFDs. The separation algorithm is based on a vector clustering procedure that estimates the source TFDs by grouping together the TF points corresponding to "closely spaced" spatial directions. Simulations illustrate the performances of the proposed method for the underdetermined blind separation of FM signals. The method developed in this research represents a new research direction for solving the UBSS problem. The successful results obtained in the research development of the above two problems has led to a conclusion that TFSP is useful for WCom. Future research directions were also proposed.