|dc.description.abstract||Information-theoretic arguments focus on modeling the reliability of information transmission, assuming availability of infinite data at sources, thus ignoring randomness in message generation times at the respective sources. However, in information transport networks, not only is reliable transmission important, but also stability, i.e., finiteness of mean delay in-
curred by messages from the time of generation to the time of successful reception. Usually, delay analysis is done separately using queueing-theoretic arguments, whereas reliable information transmission is studied using information theory. In this thesis, we investigate these
two important aspects of data communication jointly by suitably combining models from
these two fields. In particular, we model scheduled communication of messages , that arrive in a random process, (i) over multiaccess channels, with either independent decoding or joint decoding, and (ii) over degraded broadcast channels. The scheduling policies proposed permit up to a certain maximum number of messages for simultaneous transmission.
In the ﬁrst part of the thesis, we develop a multi-class discrete-time processor-sharing
queueing model, and then investigate the stability of this queue. In particular, we model the queue by a discrete-time Markov chain defined on a countable state space, and then establish (i) a sufficient condition for c-regularity of the chain, and hence positive recurrence and finiteness of stationary mean of the function c of the state, and (ii) a sufficient condition
for transience of the chain. These stability results form the basis for the conclusions drawn in the thesis.
The second part of the thesis is on multiaccess communication with random message
arrivals. In the context of independent decoding, we assume that messages can be classified into a fixed number of classes, each of which specifies a combination of received signal power, message length, and target probability of decoding error. Each message is encoded independently and decoded independently. In the context of joint decoding, we assume that messages can be classified into a fixed number of classes, each of which specifies a message
length, and for each of which there is a message queue. From each queue, some number of messages are encoded jointly, and received at a signal power corresponding to the queue. The messages are decoded jointly across all queues with a target probability of joint decoding error.
For both independent decoding and joint decoding, we derive respective discrete-
time multiclass processor-sharing queueing models assuming the corresponding information-theoretic models for the underlying communication process. Then, for both the decoding schemes, we (i) derive respective outer bounds to the stability region of message arrival rate vectors achievable by the class of stationary scheduling policies, (ii) show for any mes-
sage arrival rate vector that satisfies the outer bound, that there exists a stationary “state-independent” policy that results in a stable system for the corresponding message arrival process, and (iii) show that the stability region of information arrival rate vectors, in the
limit of large message lengths, equals an appropriate information-theoretic capacity region for independent decoding, and equals the information-theoretic capacity region for joint de-coding. For independent decoding, we identify a class of stationary scheduling policies, for which we show that the stability region in the limit of large maximum number of simultane-ous transmissions is independent of the received signal powers, and each of which achieves a
spectral efficiency of 1 nat/s/Hz in the limit of large message lengths.
In the third and last part of the thesis, we show that the queueing model developed for
multiaccess channels with joint decoding can be used to model communication over degraded
broadcast channels, with superposition encoding and successive decoding across all queues. We then show respective results (i), (ii), and (iii), stated above.||en_US