This letter studies the impact of network-coded multiple access (NCMA) on the outage performance of full-duplex cooperative non-orthogonal multiple access (NOMA). In particular, we propose a network-coded cooperative NOMA (NC-CNOMA) strategy, under which user devices utilize physical-layer network coding (PNC) to demodulate signals with greater probability. The exact analytical expression of the outage probability is derived to evaluate the performance of the proposed scheme. The provided simulation results validate our analysis and the proposed NC-CNOMA scheme outperforms the conventional cooperative NOMA with regard to outage performance.

In this letter, a model-driven deep learning (DL) decoder for irregular binary low-density parity-check (LDPC) codes is proposed via the alternating direction method of multipliers (ADMM) technique. Our technical contributions three twofold: (1) we formulate the maximum likelihood decoding problem as a non-convex quadratic program with a new penalty term and present an ADMM based decoder; (2) to alleviate hyper-parameter tuning, we employ the deep unfolding strategy to derive a model-driven ADMM-DL decoder; (3) to save the number of learning parameters, we propose an initialization strategy which can apply to different codeword structures. Specifically, the designing procedure for the DL network structure and DL network parameters training algorithm are presented in detail for efficient implementation. Numerical results demonstrate that the proposed model-driven ADMM-DL decoder for irregular binary LDPC codes is competitive in comparison with the state-of-the-arts.

Monitoring aircraft using synthetic aperture radar (SAR) images is a very important task. Given its coherent imaging characteristics, there is a large amount of speckle interference in the image. This phenomenon leads to the scattering information of aircraft targets being masked in SAR images, which is easily confused with background scattering points. Therefore, automatic detection of aircraft targets in SAR images remains a challenging task. For this task, this paper proposes a framework for speckle reduction preprocessing of SAR images, followed by the use of an improved deep learning method to detect aircraft in SAR images. Firstly, to improve the problem of introducing artifacts or excessive smoothing in speckle reduction using total variation (TV) methods, this paper proposes a new nonconvex total variation (NTV) method. This method aims to ensure the effectiveness of speckle reduction while preserving the original scattering information as much as possible. Next, we present a framework for aircraft detection based on You Only Look Once v8 (YOLOv8) for SAR images. Therefore, the complete framework is called SAR-NTV-YOLOv8. Meanwhile, a high-resolution small target feature head is proposed to mitigate the impact of scale changes and loss of depth feature details on detection accuracy. Then, an efficient multi-scale attention module was proposed, aimed at effectively establishing short-term and long-term dependencies between feature grouping and multi-scale structures. In addition, the progressive feature pyramid network was chosen to avoid information loss or degradation in multi-level transmission during the bottom-up feature extraction process in Backbone. Sufficient comparative experiments, speckle reduction experiments, and ablation experiments are conducted on the SAR-Aircraft-1.0 and SADD datasets. The results have demonstrated the effectiveness of SAR-NTV-YOLOv8, which has the most advanced performance compared to other mainstream algorithms.

In this paper, we study a downlink non-orthogonal multiple access (NOMA) transmission system, where a novel network-coded interference cancellation (NCIC) scheme is proposed to promote the outage performance. In order to further improve the outage performance, an adaptive NCIC (A-NCIC) scheme is also studied, which dynamically switches between successive interference cancellation (SIC) and the proposed NCIC schemes according to the power allocation factors and target data rates. The expressions of the outage probability are derived to evaluate the performance of the proposed scheme. The simulation results demonstrate the remarkable performance of the proposed NCIC scheme over the conventional SIC-based NOMA on the outage probability.

In this paper, we devise a new alternating direction method of multipliers (ADMM) decoder for non-binary low-density parity-check (LDPC) codes in Galois fields of characteristic two $\left( {{\mathbb{F}_{{2^q}}}} \right)$. Its main content are threefold: first, the procedure of formulating the maximum likelihood (ML) decoding problem in ${\mathbb{F}_{{2^q}}}$ to a linear integer problem in real space is presented; Second, after relaxing the integer problem to a continuous one, an efficient ADMM algorithm is customized to solve the latter, where all the entries of the variable vectors can be obtained in parallel; Third, we show that the proposed ADMM decoder satisfies the favorable codeword-independent property under some mild conditions and its computation complexity in each ADMM iteration is roughly $\mathcal{O}\left( {nq} \right)$, where n is code length of the considered non-binary LDPC code. Simulation results demonstrate that its performance, such as error-correction and decoding efficiency, is very competitive in comparison with state-of-the-art non-binary LDPC decoders.

In this letter, a new decoder based on a bit-embedding technique for non-binary low-density parity-check (LDPC) codes over Galois fields of characteristic two ( $\mathbb {F}_{2^{q}}$ ) is presented. The main content of this work is summarized in the following: first, we derive the equivalent binary codeword and binary parity-check matrix for non-binary LDPC codes in $\mathbb {F}_{2^{q}}$ ; second, a customized belief propagation algorithm utilizing a bit-embedding technique is proposed based on the formulated binary-check matrix; in the end, simulations results demonstrate that the proposed approach enjoys significant improvement on computational complexity, but almost similar error-correction performance in comparison with the state-of-the-art non-binary LDPC decoders.