Polynomial matrix decompositions and semi-blind channel estimation for MIMO frequency-selective channels

Polynomial eigenvalue decomposition (PEVD) and polynomial QR decomposition (PQRD) are generalisation of eigenvalue decomposition (EVD) and QR decomposition (QRD), and they are suitable for decoupling and precoding of frequencyselective (FS) multiple-input multiple-output (MIMO) channels. Precoding and decoding of communication channels however require reliable estimation of the channel which is normally achieved through use of pilot signals. The pilot transmission will reduce spectral efficiency due to lower data throughput – not particularly attractive for FS-MIMO channels. In this paper, we therefore introduce a new method that utilises a semi-blind channel estimation (semi-BCE) scheme coupled with PQRD/PEVDbased MIMO-channel decomposition to enable efficient communications over wireless MIMO channels. The proposed semi-BCE algorithm is a generalisation of a recently developed single-input single output (SISO) BCE method to MIMO systems. A new class of PQRD algorithms is introduced, which is based on the recently-developed sequential matrix diagonalization (SMD). The decoders produced by the proposed SMD-based PQRD algorithm are shown to be more suitable (efficient) for MIMO-channel equalisation than those generated by the prior art. Computer simulations show that the proposed MIMO-channel coding strategy compares favourably to state-of-the-art MIMO systems, in terms of bit error rate (BER) performance, while reducing the overhead

Polynomial GSVD beamforming for two-user frequency-selective MIMO channels

In this paper, we propose a generalized singular value decomposition (GSVD) for polynomial matrices, or polynomial GSVD (PGSVD). We then consider PGSVD-based beamforming for two-user, frequency-selective, multiple-input multiple-output (MIMO) multicasting. The PGSVD can jointly factorize two frequency-selective MIMO channels, producing a set of virtual channels (VCs), split into: private channels (PCs) and common channels (CCs). An important advantage of the proposed PGSVD-based beamformer, over the application of GSVD independently to each frequency bin of the orthogonal frequency division multiplexing (OFDM) scheme, is that it can facilitate different modulation and/or access schemes to various users. Using computer simulations, we characterize the bit error rate performance of our two-user MIMO multicasting system for different PCs/CCs configurations. Here, we also propose an OFDM-GSVD benchmark system, and show that our PGSVDbased beamformer compares favorably to this benchmark under erroneous and uncertain MIMO channel conditions, in addition to its advantage of facilitating heterogeneous modulation and access for various users

Kurdish News Dataset Headlines (KNDH) through multiclass classification

The rapid growth of technology has massively increased the amount of text data. The data can be mined and utilized for numerous natural language processing (NLP) tasks, particularly text classification. The core part of text classification is collecting the data for predicting a good model. This paper collects Kurdish News Dataset Headlines (KNDH) for text classification. The dataset consists of 50000 news headlines which are equally distributed among five classes, with 10000 headlines for each class (Social, Sport, Health, Economic, and Technology). The percentage ratio of getting the channels of headlines is distinct, while the numbers of samples are equal for each category. There are 34 distinct channels that are used to collect the different headlines for each class, such as 8 channels for economics, 14 channels for health, 18 channels for science, 15 channels for social, and 5 channels for sport. The dataset is preprocessed using the Kurdish Language Processing Toolkit (KLPT) for tokenizing, spell-checking, stemming, and preprocessing