This paper investigates the achievable performance of cell-free massive multiple-input multiple-output (MIMO) systems with low resolution analog-to-digital converters (ADCs) at both the access points (APs) and users. A closed-form expression for the achievable rate is derived, which enables the study of the effects of AP number, antenna number per AP, user number and ADC resolution on the achievable rate. In addition, a simple asymptotic approximation for the individual user rate is presented, which shows that the user rate is mainly constrained by the ADC resolution at the user. Moreover, we propose an ADC resolution bits allocation scheme aiming at maximizing the sum rate subject to the total ADC resolution bits constraint, which substantially outperforms the equal ADC resolution bits allocation scheme. Furthermore, a max-min power control scheme is proposed, which not only ensures user fairness, but also improves the achievable rate. Finally, simulation results are provided to corroborate the analytical results.

Defect detection is to locate and classify the possible defects in an image, which plays a key role in the quality inspection link in the manufacturing process of industrial products. Defects in industrial products are generally very small and extremely uneven in scale, resulting in poor detection results. Therefore, we propose an efficient scale-aware network (ES-Net) to improve the effect of defect detection. By addressing the information loss of tiny targets and the mismatch between the receptive field of detection head and the scale of targets, ES-Net improves the overall defect detection effect, especially for tiny defects. Considering that existing works directly use an integrated feature to enhance features at all levels, it may cause confusion in the direction of network optimization. Therefore, we propose the aggregated feature guidance module (AFGM), which first performs efficient cascading fusion of multi-level features to filter cross-layer conflicts. Then the split and aggregation enhancement (SAE) module is designed to further optimize the integrated feature map, and the result is used to guide the shallow features. Moreover, we also introduce the multi-receptive field fusion (MFF) module to generate multi-receptive field information to supplement the shallow features after dimensionality reduction. The efficient stair pyramid (ESP) is a further improvement of feature pyramid network (FPN)-based network. In particular, we propose the dynamic scale-aware head (DSH) in shallow detection layer, which can adaptively select the best detection receptive field according to different scales of targets, thereby improving the detection performance of tiny targets. Extensive experimental results on Aliyun Tianchi fabric dataset (76.2% mAP), NEU-DET (79.1% mAP), and printed circuit board (PCB) defect dataset of Peking University (97.5% mAP) demonstrate the proposed ES-Net achieves competitive results compared to the state-of-the-art (SOTA) methods. Moreover, the high efficiency of ES-Net makes it more applicable in scenarios with high real-time requirements.

This paper investigates the power allocation problems for non-orthogonal multiple access with coordinated direct and relay transmission (CDRT-NOMA), where a base station (BS) communicates with its nearby user directly, while communicating with its far user only through a dedicated relay node (RN). The RN is assumed to operate in either half-duplex relaying (HDR) mode or full-duplex relaying (FDR) mode. Based on instantaneous channel state information (CSI), the dynamic power allocation problems under HDR and FDR schemes are formulated respectively, with the objective of maximizing the minimum user achievable rate. After demonstrating the quasi-concavity of the considered problems, we derive the optimal closed-form power allocation policies under the HDR scheme and the FDR scheme. Then, a hybrid relaying scheme dynamically switching between HDR and FDR schemes is further designed. Moreover, we also study the fixed power allocation problems for the considered CDRT-NOMA systems based on statistical CSI so as to optimize the long-term system performance. The simulations show that our proposed power allocation policies can significantly enhance the performance of CDRT-NOMA systems.

This paper investigates the performance of angle-domain millimeter-wave (mmWave) multi-input multi-output nonorthogonal multiple access systems in the presence of angular estimation error. A closed-form expression for the achievable rate is presented, based on which an asymptotic approximation is derived. The analytical findings indicate that, as the number of BS antennas goes to infinity, the achievable rate is mainly determined by the antenna number to beam number ratio (ANTBNR) as well as the spatial direction distance. In particular, a large ANTBNR would cause a severe rate loss, and the achievable rate is an increasing function with respect to the spatial direction distance. Capitalizing on this key observation, a novel cluster grouping scheme is designed to mitigate the intercluster interference, which shows a great advantage over a random cluster grouping scheme. Furthermore, a max-min power control scheme is proposed, aiming at enhancing user fairness. Finally, simulation results are provided to corroborate the analytical results.

Intelligent reflecting surface (IRS) is a newly emerged technology that can increase the energy and spectral efficiency of wireless communication systems. This paper considers an IRS-aided multi-user multiple-input single-output (MISO) communication system, and presents a detailed analysis and optimization framework for the weighted sum-rate (WSR) of the downlink transmission over Rician fading channels. Unlike most of the prior works where the active beamformer at the base station (BS) and passive beamformer at the IRS are jointly designed based on the instantaneous channel state information (CSI), this paper proposes a low-complexity transmission protocol where the IRS passive beamforming and BS power allocation coefficient vector are optimized in the large timescale based on the statistical CSI, and the BS transmit beamforming is designed in the small timescale based on only the instantaneous CSI of the effective BS-user channels. Therefore, the channel training overhead in each channel coherence interval under our proposed protocol is independent of the number of IRS reflecting elements, which is in sharp contrast to most of the prior works. By considering maximum-ratio transmit beamforming at the BS, we derive a lower bound of the ergodic WSR in closed-form. Then, we propose an efficient algorithm to jointly optimize the IRS passive beamforming and BS power allocation coefficient vector for maximizing the ergodic WSR lower bound. Numerical results validate the tightness of our derived WSR bound and show that the proposed scheme outperforms various existing schemes in terms of complexity or capacity performance.

Defect detection is a task to locate and classify the possible defects in an image. However, unlike common object detection tasks, defect detection often needs to deal with images with relatively complex backgrounds, for example, in industrial product quality inspection scenario. The complex background can greatly interfere with the feature of the target objects in the multiscale feature fusion process and therefore puts great challenge on the defect detector. In this work, a channel-space adaptive enhancement feature pyramid network (CA-FPN) is proposed to eliminate this interference from the complex background. By extracting the inner relationship of different scale features, CA-FPN realizes adaptive fusion of multiscale features to enhance the semantic information of the defect while avoiding background interference as much as possible. In particular, CA-FPN is very lightweight. Moreover, considering that defects are often of varying sizes and can be extremely tiny or slender, a flexible anchor-free detector CA-AutoAssign is proposed by combining CA-FPN and an anchor-free detection strategy AutoAssign. Based on the Alibaba Cloud Tianchi Fabric dataset and NEU-DET, CA-AutoAssign is compared with the state-of-the-art (SOTA) detectors. The experimental results show that CA-AutoAssign has the best detection performance with AP50 [mean average precision (mAP) with the intersection over union (IOU) threshold of 50%] reaching 89.1 and 82.7, respectively. Despite the improvement in accuracy, the processing time has barely increased. Furthermore, CA-FPN is applied to other classical detectors, and the experimental results demonstrate the competitiveness and generalization ability of CA-FPN. The code is available at https://github.com/EasonLuht/CA-AutoAssign.git .