In this paper, we consider downlink power-domain non-orthogonal multiple access (NOMA) in heterogeneous networks (HetNets) and propose resource allocation algorithms for subchannels and transmit powers to improve the sum rate performance while satisfying a minimum data-rate requirement. The proposed subchannel allocation scheme is an iterative algorithm to achieve NOMA gain by selecting the best subchannel from the viewpoint of each user, without the constraint of the number of NOMA users on each subchannel. The proposed power allocation scheme for NOMA is a deep neural network (DNN)-based unsupervised learning algorithm, where the output of the subchannel allocation scheme is used, and unsupervised learning is adopted to reduce the training complexity, as compared to supervised learning. Through simulation, we show that the proposed subchannel allocation scheme provides better sum rates compared to the conventional two-sided matching scheme, and the proposed power allocation scheme achieves a comparable sum rate to the interior point method (IPM).

Most of the studies on power line communication (PLC) are focused on single-input–single output (SISO)-PLC. There have been recent empirical and/or theoretical research with the focus on multiple-input–multiple-output (MIMO)-PLC. This article analyzes the effects of correlation between different paths in MIMO-PLC on the performance of bit error rate (BER) and the channel capacity in the PLC system. Orthogonal frequency division multiplexing (OFDM) is an effective method to mitigate the effect of the frequency selective multipath fading channel in the PLC system. However, OFDM is susceptible to the imperfect orthogonality among its subcarriers caused by the frequency offset at the receiver. Intercarrier interference cancellation technique such as parallel cancellation (PC) scheme is implemented in MIMO-PLC scenario to study the effect of correlation based on the latest research in MIMO-PLC. 2 × 1 and 2 × 2 space–time (ST)-OFDM and 2 × 2 STPC-OFDM channel models are investigated for the performance of the BER and channel capacity in additive impulsive Gaussian noise via computer simulations. It shows that the BER and channel capacity of both 2 × 1 and 2 × 2 STPC-OFDM outperforms 2 × 1 and 2 × 2 ST-OFDM, respectively, regardless of the correlation between different paths in MIMO-PLC.

In recent years, the polarization domain has attracted significant attention for the 4th and 5th generation (4G/5G) wireless communications owing to its benefit in channel capacity, symbol/bit error rate, and energy efficiency. One driving technology to fully utilize the polarization domain is the polarization-agile antennas that allow flexible reconfiguration of the transmitter (Tx) and receiver (Rx) polarization. The current state-of-the-art polarization-agile antennas can take several discrete polarization angles. This article proposes and analyzed a practical scheme to determine feasible optimal Tx and Rx polarization among the limited number of polarization angles to converge to theoretical channel capacity supported by the discrete polarization-agile antennas with single-RF front-end systems. Both the theoretical and simulation-based analyses for the capacity outage caused by adopting practical discrete polarization-agile antennas are provided in this article to complement communication system design in balancing channel capacity maximization and hardware cost. Comprehensive simulation results demonstrate that the multipolarization multi-input multi-output communication system toward the late revisions of 5G and beyond-5G, so called, 6G wireless communications can successfully reach the theoretically achievable channel capacity with the practical discrete polarization-agile antennas.

The 5th generation (5G) new radio (NR) access technology and the beyond-5G future wireless communication require extremely high data rate and spectrum efficiency. Energy-efficient transmission/reception schemes are also regarded as an important component. The polarization domain has attracted substantial attention in this aspects. This paper is the first to propose multi-polarization superposition beamforming (MPS-Beamforming) with cross-polarization discrimination (XPD) and cross-polarization ratio (XPR)-aware transmit power allocation utilizing the 5G NR antenna panel structure. The appropriate orthogonal frequency division multiplexing (OFDM) subcarrier assignment algorithm is also proposed to verify the theoretical schemes via simulations. The detailed theoretical derivation along with comprehensive simulation results illustrate that the proposed novel scheme of MPS-Beamforming is significantly beneficial to the improvement of the performance in terms of the symbol error rate (SER) and signal-to-noise ratio (SNR) gain at the user equipment (UE). For instance, a provided practical wireless channel environment in the simulations exhibits 8 dB SNR gain for 10^-4 SER in a deterministic channel, and 4 dB SNR gain for 10^-5 SER in abundant statistical channel realizations.

Advanced algorithms of image quality enhancement have been attracting substantial attention recently due to the successful business model of video streaming services. The extremely high image quality in video streaming demands a significant increase in the transmit data rate. In turn, the required ultrahigh data rate causes the saturation of the video streaming service network if there is no remedy for this situation. Compression algorithms have contributed to the energy-efficient transmission of data; however, they have almost reached the upper bound. The demand for ultrahigh image quality by the user is significantly increasing. Meanwhile, minimizing data transmission is inevitable in energy-efficient communications. Therefore, to improve energy efficiency, we propose to decrease the image resolution at the transmitter (Tx) and upscale the image at the receiver (Rx). However, standard upscaling does not yield ultrahigh-quality images. Deep machine learning contributes to image super-resolution techniques with the cost of enormous time and resources at the user end. Hence, it is inappropriate for real-time applications. With this motivation, this paper proposes a deep machine learning-based real-time image super-resolution with a residual neural network on the prevalent resources at the user end. The proposed scheme provides better quality than conventional image upscaling such as interpolation. The comprehensive simulation verifies that our scheme substantially outperforms the conventional methods, utilizing the seven-layer residual neural network.

Further developing the idea of combining spatial diversity and parallel cancellation (PC) scheme together for mitigating intercarrier interference (ICI), we develop both 4x1 and 4x2 orthogonal frequency division multiplexing (OFDM) architectures with two-pair two-path PC schemes. First, the two-path PC scheme is integrated with the space-time-frequency (STF) block code OFDM transceiver to develop 4x1 and 4x2 STFPC-OFDM architectures in downlink. Given the same bandwidth efficiency and the same symbol size in modulation for transmission, the bit error rate (BER) of 4x1 and 4x2 STFPC-OFDM systems at a full-rate outperforms that of 4x1 and 4x2 STF-OFDM systems at a half-rate using both QPSK and 16-QAM modulations in high-speed mobile channels. This two-pair STFPC-OFDM architecture provides both diversity and block coding gains to mitigate ICI and lower BER. Additionally, the total number of computational requirements for the full-rate 4x1 STFPC-OFDM transceiver is half that of the rate half 4x1 STF-OFDM transceiver. Moreover, the orthogonal Walsh–Hadamard (WH) transform is employed as a pre-coder to develop 4x1 and 4x2 WHSTF-OFDM and WHSTFPC-OFDM systems. The BER of WHSTFPC-OFDM systems at a full-rate outperforms that of WHSTF-OFDM systems at a half-rate in QPSK and 16-QAM modulations. The 4x1 and 4x2 WHSTFPC-OFDM schemes are simple and can be generalized to multiple input and multiple output (MIMO), such as 4x4 and 4x8 MIMO-OFDM ones. They may serve as basic modules for the fifth generation, and future broadcast systems.