Linear Precoder Design for PAPR Reduction of GFDM Signals Using Gradient Descent Methods

This paper addresses linear precoder design for Peak-to-Average Power Ratio (PARP) reduction of Generalized Frequency Division Multiplexing (GFDM). A general framework, which is composed of four different scenarios and utilizes Gradient-based iterative methods to reduce PAPR through minimizing statistical parameters of the instantaneous power of GFDM signal including variance, power, and third moment, is suggested. Numerical results confirm when the step-size of the Gradient method is dynamically computed using the Wolf line search rule, the suggested algorithm circumvents drawbacks of existing studies and converges to a precoder providing advantages in design speed, obtained PAPR, symbol error rate, and out-of-band emission.<br>

Linear Precoder Design for PAPR Reduction of GFDM Signals Using Gradient Descent Methods

This paper addresses linear precoder design for Peak-to-Average Power Ratio (PARP) reduction of Generalized Frequency Division Multiplexing (GFDM). A general framework, which is composed of four different scenarios and utilizes Gradient-based iterative methods to reduce PAPR through minimizing statistical parameters of the instantaneous power of GFDM signal including variance, power, and third moment, is suggested. Numerical results confirm when the step-size of the Gradient method is dynamically computed using the Wolf line search rule, the suggested algorithm circumvents drawbacks of existing studies and converges to a precoder providing advantages in design speed, obtained PAPR, symbol error rate, and out-of-band emission.<br>

Broad Coverage Precoding for 3D Massive MIMO with Huge Uniform Planar Arrays

In this paper, we propose a novel broad coverage precoder design for three-dimensional (3D) massive multi-input multi-output (MIMO) equipped with huge uniform planar arrays (UPAs). The desired two-dimensional (2D) angle power spectrum is assumed to be separable. We use the per-antenna constant power constraint and the semi-unitary constraint which are widely used in the literature. For normal broad coverage precoder design, the dimension of the optimization space is the product of the number of antennas at the base station (BS) and the number of transmit streams. With the proposed method, the design of the high-dimensional precoding matrices is reduced to that of a set of low-dimensional orthonormal vectors, and of a pair of low-dimensional vectors. The dimensions of the vectors in the set and the pair are the number of antennas per column and per row of the UPA, respectively. We then use optimization methods to generate the set of orthonormal vectors and the pair of vectors, respectively. Finally, simulation results show that the proposed broad coverage precoding matrices achieve nearly the same performance as the normal broad coverage precoder with much lower computational complexity.