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This paper describes a low-power processor tailored for fast Fourier transform computations where transport triggering template is exploited. The processor is software-programmable while retaining an energy-efficiency comparable to existing fixed-function implementations. The power savings are achieved by compressing the computation kernel into one instruction word. The word is stored in an instruction loop buffer, which is more power-efficient than regular instruction memory storage.

Polar codes have drawn much attention and been adopted in 5G New Radio (NR) due to their capacity-achieving performance. Recently, as the emerging deep learning (DL) technique has breakthrough achievements in many fields, neural network decoder was proposed to obtain faster convergence and better performance than belief propagation (BP) decoding. However, neural networks are memory-intensive and hinder the deployment of DL in communication systems. In this work, a low-complexity recurrent neural network (RNN) polar decoder with codebook-based weight quantization is proposed.

Dual-polarized (DP) faster-than-Nyquist (FTN) transmission using higher-order modulation (HoM) schemes can significantly increase the spectral efficiency (SE) of the existing wireless backhaul links. However, FTN transmissions introduce inter-symbol interference (ISI), antenna polarization multiplexing suffers from cross-polarization interference (XPI) and HoM makes a communication system vulnerable to phase noise (PN) distortions.

The Wavelet Transform (WT) in JPEG 2000 is using a ‘separable’ lifting structure, where the one-dimensional (1D) transform is put into multidimensional image signal of its spatial and temporal dimensions. A ‘non-separable’ three-dimensional (3D) structure as the existing method is used to minimize its lifting steps. The ‘non-separable’ 3D structure in the (5,3) type transform for lossless coding is proved to reduce the rounding noise inside it. However, in the (9,7) type transform for lossy coding, the rounding noise inside the ‘non-separable’ 3D structure has increased.

Successive-cancellation list (SCL) decoding is an algorithm that provides very good error-correction performance for polar codes. However, its hardware implementation requires a large amount of memory, mainly to store intermediate results. In this paper, a partitioned SCL algorithm is proposed to reduce the large memory requirements of the conventional SCL algorithm. The decoder tree is broken into partitions that are decoded separately.

Successive-cancellation list (SCL) decoding is an algorithm that provides very good error-correction performance for polar codes. However, its hardware implementation requires a large amount of memory, mainly to store intermediate results. In this paper, a partitioned SCL algorithm is proposed to reduce the large memory requirements of the conventional SCL algorithm. The decoder tree is broken into partitions that are decoded separately.

In the 1960s, Marshall McLuhan published the book entitled, The Extensions of Man focusing primarily on television, an electronic media as being the outward extension of human nervous system, which from contemporary interpretation marks the previous stage of Big Data.