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Title: Topics in joint source-channel coding and multiuser detection
Author: Campo, A. T.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2011
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Abstract:
In the first part of this dissertation, we study the joint source-channel coding problem in the non-asymptotic regime. In particular, we derive new achievability and converse bounds on the error probability that characterize the performance limits of such model for arbitrary blocklengths. We also extend the random-coding analysis to study the JSCC error exponent and provide a formula that holds for discrete sources and general channels and recovers known results in the literature. Finally, our results enable us to numerically quantify the minimum gain of joint over separate source can channel coding. In the second part, we analyse multiuser detection under the assumption that the number of users accessing the channel is unknown by the receiver. Our main goal is to determine the performance loss caused by the need for estimating the identities of active users, which are not known a priori. We examine the performance of multiuser detectors when the number of potential users is large. We also study iterative multiuser joint decoding in large randomly spread code division multiple access systems under the same assumptions. In particular, we focus on the factor graph representation and iterative algorithms based on belief propagation. We study a suboptimal iterative scheme that jointly detects the encoded data and the users’ activity. Using density evolution, we provide a fixed-point equation of the overall iterative system where the nature of the exchanging probabilities depends on the users’ activity. Finally, when the number of users and the blocklength scale appropriately, we show that in the large limit a simple structure on the users’ codes yields a multiuser efficiency fixed-point equation that is equivalent to the case of all-active users with a system load scaled by the activity rate.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.597270  DOI: Not available
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