In spread spectrum systems employing parity bit selected spreading sequences, parity bits generated from a linear block encoder are used to select a spreading code from a set of mutually orthogonal spreading sequences. In this thesis, turbo receivers for SS-PB systems are proposed and investigated. In the transmitter, data bits are rst convolutionally encoded before being fed into SS-PB modulator. In fact, the parity bit spreading code selection technique acts as an inner encoder in this system without allocating any transmit energy to the additional redundancy provided by this technique. The receiver implements a turbo processing by iteratively exchanging the soft information on coded bits between a SISO detector and a SISO decoder. In this system, detection is performed by incorporating the extrinsic information provided by the decoder in the last iteration into the received signal to calculate the likelihood of each detected bit in terms of LLR which is used as the input for a SISO decoder. In addition, SISO detectors are proposed for MC-CDMA and MIMO-CDMA systems that employ parity bit selected and permutation spreading. In the case of multiuser scenario, a turbo SISO multiuser detector is introduced for SS-PB systems for both synchronous and asynchronous channels. In such systems, MAI is estimated from the extrinsic information provided by the SISO channel decoder in the previous iteration. SISO multiuser detectors are also proposed for the case of multiple users in MC-CDMA and MIMO-CDMA systems when parity bit selected and permutation spreading are used. Simulations performed for all the proposed turbo receivers show a signi cant reduction in BER in AWGN and fading channels over multiple iterations.

In spread spectrum systems employing parity bit selected spreading sequences, parity bits generated from a linear block encoder are used to select a spreading code from a set of mutually orthogonal spreading sequences. In this thesis, turbo receivers for SS-PB systems are proposed and investigated. In the transmitter, data bits are rst convolutionally encoded before being fed into SS-PB modulator. In fact, the parity bit spreading code selection technique acts as an inner encoder in this system without allocating any transmit energy to the additional redundancy provided by this technique. The receiver implements a turbo processing by iteratively exchanging the soft information on coded bits between a SISO detector and a SISO decoder. In this system, detection is performed by incorporating the extrinsic information provided by the decoder in the last iteration into the received signal to calculate the likelihood of each detected bit in terms of LLR which is used as the input for a SISO decoder. In addition, SISO detectors are proposed for MC-CDMA and MIMO-CDMA systems that employ parity bit selected and permutation spreading. In the case of multiuser scenario, a turbo SISO multiuser detector is introduced for SS-PB systems for both synchronous and asynchronous channels. In such systems, MAI is estimated from the extrinsic information provided by the SISO channel decoder in the previous iteration. SISO multiuser detectors are also proposed for the case of multiple users in MC-CDMA and MIMO-CDMA systems when parity bit selected and permutation spreading are used. Simulations performed for all the proposed turbo receivers show a signi cant reduction in BER in AWGN and fading channels over multiple iterations.

In spread spectrum systems employing parity bit selected spreading sequences, parity bits generated from a linear block encoder are used to select a spreading code from a set of mutually orthogonal spreading sequences. In this thesis, turbo receivers for SS-PB systems are proposed and investigated. In the transmitter, data bits are rst convolutionally encoded before being fed into SS-PB modulator. In fact, the parity bit spreading code selection technique acts as an inner encoder in this system without allocating any transmit energy to the additional redundancy provided by this technique. The receiver implements a turbo processing by iteratively exchanging the soft information on coded bits between a SISO detector and a SISO decoder. In this system, detection is performed by incorporating the extrinsic information provided by the decoder in the last iteration into the received signal to calculate the likelihood of each detected bit in terms of LLR which is used as the input for a SISO decoder. In addition, SISO detectors are proposed for MC-CDMA and MIMO-CDMA systems that employ parity bit selected and permutation spreading. In the case of multiuser scenario, a turbo SISO multiuser detector is introduced for SS-PB systems for both synchronous and asynchronous channels. In such systems, MAI is estimated from the extrinsic information provided by the SISO channel decoder in the previous iteration. SISO multiuser detectors are also proposed for the case of multiple users in MC-CDMA and MIMO-CDMA systems when parity bit selected and permutation spreading are used. Simulations performed for all the proposed turbo receivers show a signi cant reduction in BER in AWGN and fading channels over multiple iterations.

In spread spectrum systems employing parity bit selected spreading sequences, parity bits generated from a linear block encoder are used to select a spreading code from a set of mutually orthogonal spreading sequences. In this thesis, turbo receivers for SS-PB systems are proposed and investigated. In the transmitter, data bits are rst convolutionally encoded before being fed into SS-PB modulator. In fact, the parity bit spreading code selection technique acts as an inner encoder in this system without allocating any transmit energy to the additional redundancy provided by this technique. The receiver implements a turbo processing by iteratively exchanging the soft information on coded bits between a SISO detector and a SISO decoder. In this system, detection is performed by incorporating the extrinsic information provided by the decoder in the last iteration into the received signal to calculate the likelihood of each detected bit in terms of LLR which is used as the input for a SISO decoder. In addition, SISO detectors are proposed for MC-CDMA and MIMO-CDMA systems that employ parity bit selected and permutation spreading. In the case of multiuser scenario, a turbo SISO multiuser detector is introduced for SS-PB systems for both synchronous and asynchronous channels. In such systems, MAI is estimated from the extrinsic information provided by the SISO channel decoder in the previous iteration. SISO multiuser detectors are also proposed for the case of multiple users in MC-CDMA and MIMO-CDMA systems when parity bit selected and permutation spreading are used. Simulations performed for all the proposed turbo receivers show a signi cant reduction in BER in AWGN and fading channels over multiple iterations.

Monte Carlo algorithms can be used to estimate the state of a system given relative observations. In this dissertation, these algorithms are applied to physical layer communications system models to estimate channel state information, to obtain soft information about transmitted symbols or multiple access interference, or to obtain estimates of all of these by joint estimation. Initially, we develop and analyze a multiple access technique utilizing mutually orthogonal complementary sets (MOCS) of sequences. These codes deliberately introduce inter-chip interference, which is naturally eliminated during processing at the receiver. However, channel impairments can destroy their orthogonality properties and additional processing becomes necessary. We utilize Monte Carlo algorithms to perform joint channel and symbol estimation for systems utilizing MOCS sequences as spreading codes. We apply Rao-Blackwellization to reduce the required number of particles. However, dense signaling constellations, multiuser environments, and the interchannel interference introduced by the spreading codes all increase the dimensionality of the symbol state space significantly. A full maximum likelihood solution is computationally expensive and generally not practical. However, obtaining the optimum solution is critical, and looking at only a part of the symbol space is generally not a good solution. We have sought algorithms that would guarantee that the correct transmitted symbol is considered, while only sampling a portion of the full symbol space. The performance of the proposed method is comparable to the Maximum Likelihood (ML) algorithm. While the computational complexity of ML increases exponentially with the dimensionality of the problem, the complexity of our approach increases only quadratically. Markovian structures such as the one imposed by MOCS spreading sequences can be seen in other physical layer structures as well. We have applied this partitioning approach with some modification to blind equalization of frequency selective fading channel and to multiple-input multiple output receivers that track channel changes. Additionally, we develop a method that obtains a metric for quantifying the convergence rate of Monte Carlo algorithms. Our approach yields an eigenvalue based method that is useful in identifying sources of slow convergence and estimation inaccuracy.

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