Thesis Supervisor: Gregory W. Wornell
Title: ProfessorCommunication over interference channels poses challenges not present for the more traditional
additive white Gaussian noise (AWGN) channels. In order to approach the information
limits of an interference channel, interference mitigation techniques need to be
integrated with channel coding and decoding techniques. This thesis develops such practical
schemes when the transmitter has no knowledge of the channel.
The interference channel model we use is described by r = Hx + w, where r is the
received vector, H is an interference matrix, x is the transmitted vector of data symbols
chosen from a finite set, and w is a noise vector. The objective at the receiver is to
detect the most likely vector x that was transmitted based on knowledge of r, H, and
the statistics of w. Communication contexts in which this general integer programming
problem appears include the equalization of intersymbol interference (ISI) channels, the
cancellation of multiple-access interference (MAI) in code-division multiple-access (CDMA)
systems, and the decoding of multiple-input multiple-output (MIMO) systems in fading
environments.
We begin by introducing mode-interleaved precoding, a transmitter precoding technique
that conditions an interference channel so that the pairwise error probability of any two
transmit vectors becomes asymptotically equal to the pairwise error probability of the same
vectors over an AWGN channel at the same signal-to-noise ratio (SNR).
While mode-interleaved precoding dramatically increases the complexity of exact ML detection,
we develop iterated-decision detection to mitigate this complexity problem. Iterateddecision
detectors use optimized multipass algorithms to successively cancel interference
from r and generate symbol decisions whose reliability increases monotonically with each iteration.
When used in uncoded systems with mode-interleaved precoding, iterated-decision
detectors asymptotically achieve the performance ofML detection (and thus the interferencefree
lower bound) with considerably lower complexity. We interpret these detectors as
low-complexity approximations to message-passing algorithms.
The integration of iterated-decision detectors into communication systems with coding
is also developed to approach information rates close to theoretical limits. We present
joint detection and decoding algorithms based on the iterated-decision detector with modeinterleaved
precoding, and also develop analytic tools to predict the behavior of such systems.
We discuss the use of binary codes for channels that support low information rates,
and multilevel codes and lattice codes for channels that support higher information ratesHewlett-Packard under the MIT/HPAlliance, the National Science Foundation, the Semiconductor Research Corporation, Texas Instruments through the Leadership Universities Program, and the Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarship Program