The multiple-input-multiple-output (MIMO) Gaussian wireless network with K users and an intermediate relay is investigated. In this network, each user with available local channel state information (CSI) intends to convey a multicast message to all other users while receiving all messages from other users via the relay. This model is termed the MIMO K-way relay channel with distributed CSI. For this channel, the sum capacity is shown as MK/(K-1)log(SNR)+o(SNR) where each user and the relay is equipped with M antennas. Achievability is based on the signal space alignment strategy with a K-1 time slot extension. A most general case is then considered, in which each user intends to recover all messages required within T time slots. We provide an improved scheme called fractional signal space alignment which achieves MK/(K-1) degrees of freedom in the general case and the feasibility condition is also explored.

This letter analyzes the outage probability of limited feedback beamforming systems with receive antenna selection. Tight analytical closed-form expressions of outage performance are derived for both cases, with and without spatial fading correlation, which allow for evaluation of the performance as a function of the codebook size, the level of fading correlation, and the number of transmit and receive antennas. Simulation results are also provided to verify the analysis.

Prior studies on limited feedback (LFB) beamforming in multiple-antenna orthogonal frequency division multiplexing (OFDM) have resorted to Monte-Carlo simulations to evaluate the system performance. This letter proposes a novel analytical framework, based on which the averaged signal-to-noise ratio and the ergodic capacity performance of clustering-based LFB beamforming in multiple-antenna OFDM systems are studied. Simulations are also provided to verify the analysis.

Multipath energy capture and inter-symbol interference (ISI) are two intractable problems in high-data-rate Ultra-wideband (UWB) systems. To tackle the problems and simplify the receiver, we propose an adaptive interference avoidance scheme based on Pre-RAKE combining technique. The symbol repetition period (SRP) is regarded a changeable parameter in an ordered set to avoid severe interference paths and guarantee high data-rate. The set is known to both the transmitter and receiver. The index of the selected SRP is then sent to the receiver to coordinate the transmitter and receiver. The SRP can be updated adaptively according to the variations of the channels. Both theoretical analysis and simulations show that the ISI is mitigated and the transmission rate is improved simultaneously compared to the constant SRP transmission scheme.

In this paper, we study the problem of joint resource allocation in the two-way relay system, where a pair of multi-antenna users wish to exchange information via multi-antenna amplify-and-forward relay under orthogonal frequency-division multiplexing (OFDM) modulation. We formulate a sum-rate maximization problem subject to a limited power constraint for each user and relay. Our resource allocation strategy aims at finding the best pairing scheme and optimal power allocation over subchannels in frequency and space domains. This turns out to be a mixed integer programming problem. We then derive an asymptotically optimal solution though the Lagrange dual decomposition approach. Finally, simulation results are provided to demonstrate the performance gain of the proposed algorithms.

The haze situation will seriously affect the quality of license plate recognition and reduce the performance of the visual processing algorithm. In order to improve the quality of haze pictures, a license plate recognition algorithm based on haze weather is proposed in this paper. The algorithm in this paper mainly consists of two parts: The first part is MPGAN image dehazing, which uses a generative adversarial network to dehaze the image, and combines multi-scale convolution and perceptual loss. Multi-scale convolution is conducive to better feature extraction. The perceptual loss makes up for the shortcoming that the mean square error (MSE) is greatly affected by outliers; the second part is to recognize the license plate, first we use YOLOv3 to locate the license plate, the STN network corrects the license plate, and finally enters the improved LPRNet network to get license plate information. Experimental results show that the dehazing model proposed in this paper achieves good results, and the evaluation indicators PSNR and SSIM are better than other representative algorithms. After comparing the license plate recognition algorithm with the LPRNet algorithm, the average accuracy rate can reach 93.9%.

A multiple-antenna receiving and combining scheme is proposed for high-data-rate transmitted-reference (TR) Ultra-Wideband (UWB) systems. The nonlinearity of the inter-symbol interference (ISI) model is alleviated via simple antenna combining. Under the simplified ISI model, frequency domain equalization (FDE) is adopted and greatly reduces the complexity of the equalizer. A simple estimation algorithm for the simplified ISI model is presented. Simulation results demonstrate that compared to the single receive antenna scheme, the proposed method can obtain a significant diversity gain and eliminate the BER floor effect. Moreover, compared to the complex second-order time domain equalizer, FDE showed better performance robustness in the case of imperfect model estimation.

Prior studies have shown that the performance of amplify-and-forward (AF) relay systems can be considerably improved by using multiple antennas and low complexity linear processing at the relay nodes. However, there is still a lack of performance analysis for the cases where the processing is based on limited feedback (LFB). Motivated by this, we derive the closed-form expression of the outage probability of AF relay systems with LFB beamforming in this letter. Simulation results are also provided to confirm the analytical studies.

In order to solve the low-quality problems such as low brightness, poor contrast, noise interference and color imbalance in night images, a night image enhancement algorithm based on MDIFE-Net curve estimation is presented. This algorithm mainly consists of three parts: Firstly, we design an illumination estimation curve (IEC), which adjusts the pixel level of the low illumination image domain through a non-linear fitting function, maps to the enhanced image domain, and effectively eliminates the effect of illumination loss; Secondly, the DCE-Net is improved, replacing the original Relu activation function with a smoother Mish activation function, so that the parameters can be better updated; Finally, illumination estimation loss function, which combines image attributes with fidelity, is designed to drive the no-reference image enhancement, which preserves more image details while enhancing the night image. The experimental results show that our method can not only effectively improve the image contrast, but also make the details of the target more prominent, improve the visual quality of the image, and make the image achieve a better visual effect. Compared with four existing low illumination image enhancement algorithms, the NIQE and STD evaluation index values are better than other representative algorithms, verify the feasibility and validity of the algorithm, and verify the rationality and necessity of each component design through ablation experiments.