In this thesis, a modified Kalman filter is proposed for time varying channel estimation in orthogonal frequency division multiplexing (OFDM). The proposed scheme adopts pre-coding scheme and minimum mean squared error (MMSE) equalizer to improve system performance. By using pre-coding schemes, information can be protected by signal diversities, which prevent Kalman filter to disperse due to erroneous data signals. In this investigation, the proposed system architecture is verified by using simulation experiments. Simulation results demonstrate that the proposed schemes substantially improve system performances under verious channel conditions.

Multiple-input and multiple-output (MIMO) technology with antenna arrays is a vital solution to achieve the advertised features in the next generation wireless communication. Multiple antennas at the transmitter and receiver can achieve diversity as well as multiplexing gain during data transmission. In order to take advantage of the multiplexing gain of MIMO systems, two or more channel paths are required to send multiple signal streams simultaneously. Beamforming (BF) training using low resolution and high resolution array beams is already implemented in the IEEE 802.11ad standard, making hierarchical codebook design an attractive approach. In this thesis, our goal is to improve multi-path channel estimation and data transmission through BF training using hierarchical codebook design. Kaiser Window sector array design and restricted orthogonal projection are applied during the beam training phase. The pre-defined hybrid-implemented codewords selected after the BF training are used for data transmission directly. With these combined efforts, a 30\% higher spectral efficiency compared to the reference design [1] is achieved. / Graduate

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A radiolocation problem using DS-CDMA waveforms with array-based receivers is considered. It is assumed that M snapshots of N(s) Nyquist sample long data are available, with a P element antenna array. In the handshaking radiolocation protocol assumed here, data training sequences are available for all K users. As a result, the received spatial-temporal matrix R ∈ C^(MN(s)x P) is approximated by a sum of deterministic signal matrices S(k)^b ∈ C^(MN(s) N(s)) multiplied by unconstrained array response matrices A(k) ∈ C^(N(s)x P). The unknown delays are not estimated directly. Rather, the delays are implicitly approximated as part of the symbol-length long channel, and solutions sparse in the rows of A are thus sought. The resulting ML cost function is J = ||R - ∑(k=1)^K S(k)^bA(k)||(F). The Generalized Successive Interference Cancellation (GSIC) algorithm is employed to iteratively estimate and cancel multiuser interference. Thus, at the k-th GSIC iteration, the index p(k) = arg min(l ≠ p(1),...,p(k-1)) {min(A(l)) ||R^k-S(l)^bA(l)||(F)} is computed, where R^k = ∑(l=1)^(k-1) S(pl)^bÂ(pl). Matching pursuits is embedded in the GSIC iterations to compute sparse channel/steering vector solutions Â(l). Simulations are presented for DS-CDMA signals received over channels computed using a ray-tracing propagation model.

ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / OFDM is a preferred physical layer for an increasing number of telemetry and LAN applications. However, joint estimation of the multipath channel and frequency offset in OFDM remains a challenging problem. The Unscented Kalman Filter (UKF) is presented to solve the offset/channel tracking problem. The advantages of the UKF are that it is less susceptible to divergence than the EKF, and does not require computation of a Jacobian matrix. A hybrid analysis/simulation approach is developed to rapidly evaluate UKF performance in terms of symbol-error rate and channel/offset error for the 802.11a OFDM format.

ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Underwater acoustic (UWA) telemetry requires wideband waveforms for anti-multipath which are simultaneously easy to equalize and demodulate. The Walsh/m-sequence waveforms proposed here are robust to multipath and with appropriate time-guard bands do not require equalization. For example, in the UCSB prototype acoustic modem, a data rate of 133 bps is achieved using 8-ary Walsh signaling with an 11.2 msec. symbol duration. Demodulation is performed using noncoherent detection, and hence accurate phase tracking, which is difficult to achieve in the UWA channel, is not required. However, telemetry from unmanned underwater vehicles (UUVs) is more problematic due to large Doppler shifts resulting from platform motion. A new receiver algorithm based on Matching Pursuits is proposed which combines channel and Doppler shift estimation. Symbol-error rate (SER) simulation results are presented for the UWA modem under realistic Doppler/multipath conditions.

Master of Engineering by Research / As a multi-carrier modulation scheme, Orthogonal Frequency Division Multiplexing (OFDM) technique can achieve high data rate in frequency-selective fading channels by splitting a broadband signal into a number of narrowband signals over a number of subcarriers, where each subcarrier is more robust to multipath. The wireless communication system with multiple antennas at both the transmitter and receiver, known as multiple-input multiple-output (MIMO) system, achieves high capacity by transmitting independent information over different antennas simultaneously. The combination of OFDM with multiple antennas has been considered as one of most promising techniques for future wireless communication systems. The challenge in the detection of a space-time signal is to design a low-complexity detector, which can efficiently remove interference resulted from channel variations and approach the interference-free bound. The application of iterative parallel interference canceller (PIC) with joint detection and decoding has been a promising approach. However, the decision statistics of a linear PIC is biased toward the decision boundary after the first cancellation stage. In this thesis, we employ an iterative receiver with a decoder metric, which considerably reduces the bias effect in the second iteration, which is critical for the performance of the iterative algorithm. Channel state information is required in a MIMO-OFDM system signal detection at the receiver. Its accuracy directly affects the overall performance of MIMO-OFDM systems. In order to estimate the channel in high-delay-spread environments, pilot symbols should be inserted among subcarriers before transmission. To estimate the channel over all the subcarriers, various types of interpolators can be used. In this thesis, a linear interpolator and a trigonometric interpolator are compared. Then we propose a new interpolator called the multi-tap method, which has a much better system performance. In MIMO-OFDM systems, the time-varying fading channels can destroy the orthogonality of subcarriers. This causes serious intercarrier interference (ICI), thus leading to significant system performance degradation, which becomes more severe as the normalized Doppler frequency increases. In this thesis, we propose a low-complexity iterative receiver with joint frequency- domain ICI cancellation and pilot-assisted channel estimation to minimize the effect of time-varying fading channels. At the first stage of receiver, the interference between adjacent subcarriers is subtracted from received OFDM symbols. The parallel interference cancellation detection with decision statistics combining (DSC) is then performed to suppress the interference from other antennas. By restricting the interference to a limited number of neighboring subcarriers, the computational complexity of the proposed receiver can be significantly reduced. In order to construct the time variant channel matrix in the frequency domain, channel estimation is required. However, an accurate estimation requiring complete knowledge of channel time variations for each block, cannot be obtained. For time- varying frequency-selective fading channels, the placement of pilot tones also has a significant impact on the quality of the channel estimates. Under the assumption that channel variations can be approximated by a linear model, we can derive channel state information (CSI) in the frequency domain and estimate time-domain channel parameters. In this thesis, an iterative low-complexity channel estimation method is proposed to improve the system performance. Pilot symbols are inserted in the transmitted OFDM symbols to mitigate the effect of ICI and the channel estimates are used to update the results of both the frequency domain equalizer and the PICDSC detector in each iteration. The complexity of this algorithm can be reduced because the matrices are precalculated and stored in the receiver when the placement of pilots symbols is fixed in OFDM symbols before transmission. Finally, simulation results show that the proposed MIMO-OFDM iterative receiver can effectively mitigate the effect of ICI and approach the ICI-free performance over time-varying frequency-selective fading channels.

This dissertation deals with the channel estimation techniques for orthogonal frequency division multiplexing (OFDM) systems such as in IEEE 802.11. Although there has been a great amount of research in this area, characterization of typical wireless indoor environments and design of channel estimation schemes that are both robust and practical for such channel conditions have not been thoroughly investigated. It is well known that the minimum mean-square-error (MMSE) estimator provides the best mean-square-error (MSE) performance given a priori knowledge of channel statistics and operating signal-to-noise ratio (SNR). However, the channel statistics are usually unknown and the MMSE estimator has too much computational complexity to be realized in practical systems. In this work, we propose two simple channel estimation techniques: one that is based on modifying the channel correlation matrix from the MMSE estimator and the other one with averaging window based on the LS estimates. We also study the characteristics of several realistic indoor channel models that are of potential use for wireless local area networks (LANs). The first method, namely MMSE-exponential-Rhh, does not depend heavily on the channel statistics and yet offer performance improvement compared to that of the LS estimator. The simulation results also show that the second method, namely averaging window (AW) estimator, provides the best performance at moderate SNR range.

This dissertation deals with the channel estimation techniques for orthogonal frequency division multiplexing (OFDM) systems such as in IEEE 802.11. Although there has been a great amount of research in this area, characterization of typical wireless indoor environments and design of channel estimation schemes that are both robust and practical for such channel conditions have not been thoroughly investigated. It is well known that the minimum mean-square-error (MMSE) estimator provides the best mean-square-error (MSE) performance given a priori knowledge of channel statistics and operating signal-to-noise ratio (SNR). However, the channel statistics are usually unknown and the MMSE estimator has too much computational complexity to be realized in practical systems. In this work, we propose two simple channel estimation techniques: one that is based on modifying the channel correlation matrix from the MMSE estimator and the other one with averaging window based on the LS estimates. We also study the characteristics of several realistic indoor channel models that are of potential use for wireless local area networks (LANs). The first method, namely MMSE-exponential-Rhh, does not depend heavily on the channel statistics and yet offer performance improvement compared to that of the LS estimator. The simulation results also show that the second method, namely averaging window (AW) estimator, provides the best performance at moderate SNR range.

In this thesis, we propose a modified MT-CDMA system, which can improve channel estimation accuracy by using transmit diversity of pilot signals. We not only expound the principles and structures of the system we proposed, but analyze its performance in slow Rayleigh fading channel environment. According to different ways to assign data symbols of transmitted signals, we have two different bit error rate results. At the same transmit power, the simulation results show that when we combine comb-type pilot signals of two parallel channels to estimate channel gains, we can recover the drawbacks of comb-type pilot arrangement, which can not perform well in frequency selective fading channel. Finally, the numerical results will be also shown.

A complex array for constructing perfect sequences is presented in this paper. The row sequences and their discrete Fourier transform form two sets of perfect sequences. The column sequences are orthogonal to each other for any cyclic shift. In addition, any combination of the column sequences with complex weighting coefficients of equal amplitude is also a perfect sequence. In addition, a superimposed training scheme is also proposed for channel estimation in OFDM systems. The perfect sequence is adopted since it has a constant magnitude in both the time domain and the frequency domain. Although the derived channel estimator has a slightly worse performance since the unknown data contributes extra noise, the effective data throughput is substantially increased. In addition, the proposed scheme is shown to have a much better peak-to-average power ratio (PAPR) because the added perfect sequence has a constant magnitude in the time domain.