![]() ![]() Abstract extensions of the main results are also discussed, where the theory of equitable graph coloring is employed to establish the utility of CS in settings where the test vectors exhibit more general statistical dependencies. It is shown here that time-domain probing of a multipath channel with a random binary sequence, along with utilization of CS reconstruction techniques, can provide significant improvements in estimation accuracy compared to traditional least-squares based linear channel estimation strategies. An immediate application is in wireless multipath channel estimation. An attempt is made to carry out a program for defining the concept of a random or patternless, finite binary sequence, and for subsequently defining arandom. It follows that CS can be effectively utilized in linear, time-invariant system identification problems provided the impulse response of the system is (approximately or exactly) sparse. This work extends CS theory to settings where the entries of the test vectors exhibit structured statistical dependencies. Existing results show that if the entries of the test vectors are independent realizations of certain zero-mean random variables, then with high probability the unknown signals can be recovered by solving a tractable convex optimization. In essence, CS enables the recovery of high-dimensional sparse signals from relatively few linear observations in the form of projections onto a collection of test vectors. In stochastic modeling, as in some computer simulations, the hoped-for randomness of potential input data can be verified, by a formal test for. They are bit sequences generated using maximal linear-feedback shift registers and are so called because they are periodic and reproduce every binary sequence (except the zero vector) that can be represented by the shift registers (i.e., for. A randomness test (or test for randomness ), in data evaluation, is a test used to analyze the distribution of a set of data to see whether it can be described as random (patternless). ![]() Abstract: Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. A maximum length sequence ( MLS) is a type of pseudorandom binary sequence.
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