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In this paper, we conduct the joint design of transmitting sequence(s) and receiving filters subject to the Peak-to-Average Ratio (PAR) constraint in radar and communications applications. We consider optimizing the worst-case performance and the resulting optimization problem takes a maximin format. We propose two algorithms based on the MM (Majorization-Minimization or Minorization-Maximization) method as opposed to the traditional epigraph-based smooth reformulation. On top of that, both algorithms are guaranteed to converge to a B(oulingand)-stationary solution, and B-stationarity is the appropriate stationarity condition for problems with a nonconvex constraint set. The proposed algorithms successively solve a series of simple convex problems that enjoy low computational complexity. Numerical simulations have shown that the proposed algorithms empirically achieve slightly higher objective values and converge faster in terms of CPU time than the existing methods. © 2016 IEEE.

In this paper, we conduct the joint design of transmitting sequence(s) and receiving filters subject to the Peak-to-Average Ratio (PAR) constraint in radar and communications applications. We consider optimizing the worst-case performance and the resulting optimization problem takes a maximin format. We propose two algorithms based on the MM (Majorization-Minimization or Minorization-Maximization) method as opposed to the traditional epigraph-based smooth reformulation. On top of that, both algorithms are guaranteed to converge to a B(oulingand)-stationary solution, and B-stationarity is the appropriate stationarity condition for problems with a nonconvex constraint set. The proposed algorithms successively solve a series of simple convex problems that enjoy low computational complexity. Numerical simulations have shown that the proposed algorithms empirically achieve slightly higher objective values and converge faster in terms of CPU time than the existing methods. © 2016 IEEE.

In this paper, we conduct the joint design of transmitting sequence(s) and receiving filters subject to the Peak-to-Average Ratio (PAR) constraint in radar and communications applications. We consider optimizing the worst-case performance and the resulting optimization problem takes a maximin format. We propose two algorithms based on the MM (Majorization-Minimization or Minorization-Maximization) method as opposed to the traditional epigraph-based smooth reformulation. On top of that, both algorithms are guaranteed to converge to a B(oulingand)-stationary solution, and B-stationarity is the appropriate stationarity condition for problems with a nonconvex constraint set. The proposed algorithms successively solve a series of simple convex problems that enjoy low computational complexity. Numerical simulations have shown that the proposed algorithms empirically achieve slightly higher objective values and converge faster in terms of CPU time than the existing methods. © 2016 IEEE.