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Global and Local Optimization Algorithms for Optimal Signal Set Design

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ABSTRACT

The problem of choosing an optimal signal set for non-Gaussian detection was reduced to a smooth inequality constrained mini-max nonlinear programming problem by Gockenbach and Kearsley. Here we consider the application of several optimization algorithms, both global and local, to this problem. The most promising results are obtained when special-purpose sequential quadratic programming (SQP) algorithms are embedded into stochastic global algorithms.

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Model of a communication system.
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f1-j62kea: Model of a communication system.

Mentions: Consider the simple communication system model shown in Fig. 1. The goal is to transmit one of M possible symbols, i.e., an M-ary signaling system, over a memoryless additive noise channel. We will assume all signals are discrete-time with T samples. The transmitter assigns a unique signal sm : {1, …, T} → ℝ to each symbol m ∈ {1, …, M}. It is this signal that is sent through the channel. At the other end, the received signal isy[t]=sm[t]+n[t],t=1,…,T,where n : {1, …, T} → ℝ is a noise process, and the job of the receiver is to decide which symbol was transmitted. Our goal is to design a set of signals sm, m = 1, …, M, which maximize, subject to constraints on the signals, the probability of a correct decision by the receiver given a particular channel noise distribution.


Global and Local Optimization Algorithms for Optimal Signal Set Design
Model of a communication system.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4862810&req=5

f1-j62kea: Model of a communication system.
Mentions: Consider the simple communication system model shown in Fig. 1. The goal is to transmit one of M possible symbols, i.e., an M-ary signaling system, over a memoryless additive noise channel. We will assume all signals are discrete-time with T samples. The transmitter assigns a unique signal sm : {1, …, T} → ℝ to each symbol m ∈ {1, …, M}. It is this signal that is sent through the channel. At the other end, the received signal isy[t]=sm[t]+n[t],t=1,…,T,where n : {1, …, T} → ℝ is a noise process, and the job of the receiver is to decide which symbol was transmitted. Our goal is to design a set of signals sm, m = 1, …, M, which maximize, subject to constraints on the signals, the probability of a correct decision by the receiver given a particular channel noise distribution.

View Article: PubMed Central - PubMed

ABSTRACT

The problem of choosing an optimal signal set for non-Gaussian detection was reduced to a smooth inequality constrained mini-max nonlinear programming problem by Gockenbach and Kearsley. Here we consider the application of several optimization algorithms, both global and local, to this problem. The most promising results are obtained when special-purpose sequential quadratic programming (SQP) algorithms are embedded into stochastic global algorithms.

No MeSH data available.