A histopathological analysis performed by pathologists plays a key role in the diagnosis of breast cancer. A novel approach based on an image processing technique is proposed to help pathologists efficiently produce accurate diagnoses, that is composed of two modules, namely, anomaly detection with a support vector machine (ADSVM) method and a resolution adaptive network (RANet) model. The ADSVM method screens mislabeled patches to improve the training performance of the RANet model. In the RANet model, subnetworks with variable resolutions and depths are utilized to classify images according to the classification difficulty. This adaptive mechanism potentially increases the computational efficiency and prediction accuracy. The proposed RANet-ADSVM approach is evaluated using two public datasets: the BreaKHis and BACH 2018 datasets. Binary and multiclass classifications of patient and image levels at different magnification factors are conducted on the BreaKHis dataset. The best accuracies of 98.83% and 99.14% are obtained for the binary classification at $200\times $ magnification at the patient and image levels, respectively. For the BACH 2018 dataset, binary and multiclass classifications on patch and image levels are performed. Experimental results show that the best accuracies for multiclass and binary classifications at the image level are 97.75% and 99.25%, respectively. Additionally, comparative experiments are performed and indicated that the proposed approach achieves significant improvements in both the classification accuracy and computational efficiency. Compared with similar networks (ResNet and DenseNet), the computational time is reduced by approximately 50%.

In this letter, we investigate the non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system, where multiple tasks with heterogeneous latency requirements are offloaded simultaneously. In particular, previous works assumed that the task streams are transmitted un-intermittently for simplicity, which resulted in suboptimal performance. To address this problem, an energy-efficient rate allocation scheme is proposed when only statistical channel state information (CSI) is available. And the energy minimization problem under latency and outage constraints is formulated. Then the optimal SIC order is obtained and an iterative water-filling (IWF)-based rate allocation algorithm is developed to solve this problem. Numerical results indicate that the proposed scheme improves the energy efficiency.

With the rapid development of wireless communication technology and the emergence of intelligent applications, higher requirements have been put forward for data communication and computing capacity. Multi-access edge computing (MEC) can handle highly demanding applications by users by sinking the services and computing capabilities of the cloud to the edge of the cell. Meanwhile, the multiple input multiple output (MIMO) technology based on large-scale antenna arrays can achieve an order-of-magnitude improvement in system capacity. The introduction of MIMO into MEC takes full advantage of the energy and spectral efficiency of MIMO technology, providing a new computing paradigm for time-sensitive applications. In parallel, it can accommodate more users and cope with the inevitable trend of continuous data traffic explosion. In this paper, the state-of-the-art research status in this field is investigated, summarized and analyzed. Specifically, we first summarize a multi-base station cooperative mMIMO-MEC model that can easily be expanded to adapt to different MIMO-MEC application scenarios. Subsequently, we comprehensively analyze the current works, compare them to each other and summarize them, mainly from four aspects: research scenarios, application scenarios, evaluation indicators and research issues, and research algorithms. Finally, some open research challenges are identified and discussed, and these indicate the direction for future research on MIMO-MEC.

With the widespread application of light emitting diodes (LEDs) and the rapid evolution of visible light communication (VLC), visible light-based positioning (VLP) is envisioned as a promising solution for indoor positioning due to its high accuracy and environmental friendliness. However, the performance of VLP is significantly affected by the unknown orientation of the user equipment (UE) and system parameters uncertainty. Besides, high computation complexity and vulnerability to hardware degradation of the VLP algorithms are also the challenges to meet. To handle the above issues, a novel cone geometry-based simultaneous position and orientation estimation (SPO) scheme with low complexity is proposed. By modeling the field of view (FOV) of the photodiode (PD) as a cone, the geometry relationship between the sensed LEDs and the UE position and orientation is established. The binary values of whether the LEDs signals are received or not are exploited, and an alternating flashing strategy for the LED arrays is designed to guarantee normal illumination and reduce bandwidth consumption. Moreover, the theoretical lower bound derived from Kanatani-Cramer-Rao bound (KCRB) is provided to analyze optimality of the estimator. Simulation results demonstrate the proposed scheme achieve an excellent performance gain with low complexity over two classical SPO baseline, and the critical factors that affect the performance are investigated.

Rate splitting multiple access (RSMA), as a flexible multiple access and interference management strategy, bridges the nonorthogonal multiple access (NOMA) and the space division multiple acces (SDMA). In RSMA, users share a layer of common stream whose achievable rate is limited by the user with lowest channel quality. In this paper, a framework based on RSMA with user separation scheme is proposed, wherein the users are clustered into groups based on channel gain and the inter-group common streams are superimposed in power domain. Taking precoding and rate allocation into account, an optimization problem is formulated to maximize the sum rate, which is solved based on the successive convex approximation (SCA) method. To further enhance the performance, a user regrouping strategy is proposed by considering inter-group interferences, which is tackled with a simulated annealing (SA) method. Numerical results demonstrate that the proposed schemes flexibly allocate the rates to mitigate the effect of the above issue and achieve a significant sum rate gain in overloaded systems.

For indoor visible light communication (VLC) systems, multiuser spatial multiplexing (SMP) has become a crucial technology to enhance the network capacity by using multiple light-emitting diodes (LEDs). However, the performance of a VLC system based on multiuser SMP is dynamic-range-limited since the dynamic range of some LEDs is usually underutilized in this case. To solve this problem, an opportunistic non-orthogonal multiple access (NOMA) is put forward to utilize the unsaturated dynamic range by allowing more user connections and as a result the spectral-efficiency is improved. Therein, the effects of user location distribution and successive interference cancellation (SIC) error propagation are investigated. In order to further improve the spectral-efficiency, we have developed an adaptive dynamic-range utilization scheme. Simulation results demonstrate that the proposed scheme effectively improves the spectral-efficiency under the primary users' quality of service (QoS) requirement.