This paper develops a family of irregular convolutional codes for bit-interleaved coded modulation (BICM) systems under iterative detection and decoding. Irregular convolutional codes are constructed through irregular puncturing over multiple mother codes of different memory. Strategies based on fixed- and variable-size trellises are proposed to connect different memory mother codes. The use of irregular puncturing and code memory yields improved coding efficiency with the aid of extrinsic information transfer charts. Under ergodic and quasi-static fading conditions, multiantenna BICM systems using the proposed codes outperform comparable turbo-coded systems and multilevel coding strategies.

The Bell-Labs Layered Space-time (BLAST) architecture is a simple and efficient multi-antenna coding structure that can achieve high-spectral efficiency. Many BLAST detectors require more receiver antennas than transmitter antennas. We propose a novel turbo-processing BLAST detector based on a group detection strategy that can operate in systems with fewer receiver antennas than transmitter antennas. A maximum a posteriori (MAP) decision is made using a group of transmitted symbols and the remaining signal contribution is treated as interference. The interference is characterized as a non-zero mean colored noise source that is whitened before a decision is made. The proposed detector, the reduced dimension MAP (RDMAP) detector, is a generalization of both the MAP detector and the turbo-processing minimum mean squared error (MMSE) detector (Sellathurai et al. (2002); Abe et al. (2001)). Simulation is used to compare the GMAP detector with the MAP detector and MMSE detector.

Pulse shaping is examined as a means to improve the performance of a differential offset quadrature phase-shift keying system in a bandwidth-constrained environment. Through optimization with respect to a composite Nyquist criterion, the derived pulse shapes have comparable performance to a pi/4-differential quadrature phase-shift keying in an additive white Gaussian noise (AWGN) channel and better performance in a hard-limited AWGN channel

Bayesian symbol-by-symbol detection using a finite sequence observation space has been the subject of renewed research interest. The Bayesian transverse equalizer (BTE) and Bayesian decision feedback equalizer (BDFE) are two common Bayesian detectors. It is often difficult to evaluate the bit-error rate (BER) performance of these Bayesian detectors since the BER cannot be analytically evaluated and the high complexity of these detectors makes simulation techniques computationally prohibitive, especially at low BERs. We propose a framework to evaluate the BER for the BTE and a lower bound on the BER for the BDFE. This framework is based on finding an approximation of the conditional error probability for each of the noiseless channel states in the observation space. The optimal Bayesian decision boundary is approximated by a set of hyperplanes, and each hyperplane is rotated some minimal angle to make them mutually orthogonal/parallel. The conditional probability of error can be readily evaluated on the topology of orthogonal/parallel rotated hyperplanes. Our BER evaluation is accurate and does not require simulations. A reduced complexity approach to evaluate the BER is also developed.

The Bell-Labs layered space-time (BLAST) architecture is a simple and efficient multi-antenna coding structure that can achieve high spectral efficiency (Foschini, G. and Gans, M., Wireless Personal Commun., vol.6, p.311-35, 1998). Many BLAST detectors require more receiver antennas than transmitter antennas. We propose a novel turbo-processing BLAST detector based on a group detection strategy that can operate in systems with fewer receiver antennas than transmitter antennas. A maximum a posteriori (MAP) decision is made using a group of transmitted symbols and the remaining signal contribution is treated as interference. The interference is characterized as a non-zero mean colored noise source that is whitened before a decision is made. The proposed detector, the group MAP (GMAP) detector, is a generalization of both the MAP detector and the turbo-processing minimum mean squared error (MMSE) detector (Sellathurai, M. and Haykin, S., 2002; Abe, T. and Matsumoto, T., 2001). A novel grouping algorithm is proposed for the GMAP detector. Simulation is used to compare the GMAP detector with the MAP detector and MMSE detector.