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Abstract Background To investigate whether radiomics models derived from pretreatment CT could help to predict response to immunotherapy in oral squamous cell carcinoma (OSCC). Methods Retrospectively, a total of 40 patients with measurable OSCC were included. The patients were divided into responder group and non-responder group according to the comparison of pre-treatment and post-treatment CT findings. Radiomics features were extracted from pre-treatment CT images, and optimal features were selected by univariate analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis. Neural network, support vector machine, random forest and logistic regression models were used to predict response to immunotherapy in OSCC, and leave-one-out cross validation was employed to assess the performance of the classifiers. The area under the curve (AUC), accuracy, sensitivity and specificity were calculated to quantify the predictive efficacy. Results A total of 7 features were selected to build models upon machine learning methods. By comparing different machine learning based models, the neural network model achieved the best predictive ability, with an AUC of 0.864, an accuracy of 82.5%, a sensitivity of 82.5%, and a specificity of 82.5%. Conclusions The pretreatment CT-based radiomics model showed good performance in predicting response to immunotherapy in OSCC. Pretreatment CT-based radiomics model might provide an alternative approach for the selection of patients who benefit from immunotherapy.

Package delivery via ridesharing provides appealing benefits of lower delivery cost and efficient vehicle usage. Most existing ridesharing systems operate the matching of ridesharing in a centralized manner, which may result in the single point of failure once the controller breaks down or is under attack. To tackle such problems, our goal in this paper is to develop a blockchain-based package delivery ridesharing system, where decentralization is adopted to remove intermediaries and direct transactions between the providers and the requestors are allowed. To complete the matching process under decentralized structure, an Event-Triggered Distributed Deep Reinforcement Learning (ETDDRL) algorithm is proposed to generate/update the real-time ridesharing orders for the new coming ridesharing requests from a local view. Simulation results reveal the vast potential of the ETDDRL matching algorithm under the blockchain framework for the promotion of the ridesharing profits. Finally, we develop an application for Android-based terminals to verify the ETDDRL matching algorithm.

Because of the conventional bioimaging methods, including ultrasound and X-ray examinations often lack accuracy and sensitivity in the early diagnosis, as a heterogeneous subtype of breast cancer, it remains an urgent need to develop efficaciously and sensitively diagnostic drivers of triple negative breast cancer (TNBC). The overexpression of c-mesenchymal epithelial transition-factor (c-Met) is associated with the basal subtype of breast carcinoma and linked with decreased survival rate of TNBC patients. Here, we have synthesized a dual-modal nanoprobe with luminescence imaging in the bio-window of near infrared II region (NIR II, 1000–1700) and magnetic resonance imaging (MRI) performances. After c-Met targeting binding protein decoration, this Fe3O4@mSiO2-ICG/cMBP had outstanding size stability and preeminent biocompatibility. Meaningfully, it presented effective TNBC cells recognition in vitro. Besides, after caudal-vein injection of this nanoprobe, both NIR II luminescence imaging and MRI demonstrated that it can more efficiently concentrated in metastatic TNBC tumors in comparison with Fe3O4@mSiO2-ICG and clinical used Gd-DTPA. Meanwhile, the core–shell nanoprobes also showed negligible toxicity in vivo. All results suggested that our cMBP modified nanocomposite could provide a fascinating and efficient non-invasive diagnostic tool for TNBC detection in clinic.

Abstract Background Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model. Methods In this retrospective study, we enrolled 127 patients with TED that received 4·5 g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms. Results The support vector machine (SVM) classifier had the best performance on the MRR model (AUC = 0·961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC = 0·766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC = 0·760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC = 0·916, LR) led to different conclusions. Conclusions The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result.

Abstract The system outage probability of two‐way hybrid satellite‐terrestrial relaying networks with hardware impairments is investigated, using a novel method which features appropriate signal‐to‐noise‐and‐distortion ratio expression adjustment. A concise closed‐form lower bound expression is derived, which is in good agreement with the exact outage probability in all signal‐to‐noise ratio regions. Based on the tight bound, an asymptotic expression is obtained, revealing that the hardware impairments coin the system outage probability ceiling. The deducing method, characterized by generality, can be applicable to various realistic fading channels. Simulation results validate the derived expressions.

Profound congenital sensorineural hearing loss (SNHL) prevents children from developing spoken language. Cochlear implantation and auditory brainstem implantation can provide partial hearing sensation, but language development outcomes can vary, particularly for patients with inner ear malformations and/or cochlear nerve deficiency (IEM&CND). Currently, the peripheral auditory structure is evaluated through visual inspection of clinical imaging, but this method is insufficient for surgical planning and prognosis. The central auditory pathway is also challenging to examine in vivo due to its delicate subcortical structures. Previous attempts to locate subcortical auditory nuclei using fMRI responses to sounds are not applicable to patients with profound hearing loss as no auditory brainstem responses can be detected in these individuals, making it impossible to capture corresponding blood oxygen signals in fMRI. In this study, we developed a new pipeline for mapping the auditory pathway using structural and diffusional MRI. We used a fixel-based approach to investigate the structural development of the auditory-language network for profound SNHL children with normal peripheral structure and those with IEM&CND under 6 years old. Our findings indicate that the language pathway is more sensitive to peripheral auditory condition than the central auditory pathway, highlighting the importance of early intervention for profound SNHL children to provide timely speech inputs. We also propose a comprehensive pre-surgical evaluation extending from the cochlea to the auditory-language network, showing significant correlations between age, gender, Cn.VIII median contrast value, and the language network with post-implant qualitative outcomes.

This study aimed to develop an apparent diffusion coefficient (ADC) ratio-based prognostic model to predict the recurrence and disease-free survival (DFS) of oral tongue squamous cell carcinoma (OTSCC). A total of 188 patients with cT1-2 oral tongue squamous cell carcinoma were enrolled retrospectively. Clinical and laboratory data were extracted from medical records. The ADC values were measured at the regions of interest of the tumor and non-tumor tissues of the MRI images, and the ADC ratio was used for comparison between the patient with recurrence (n = 83 case, 44%) and patients without recurrence (n = 105 cases, 56%). Cox proportional hazards models were generated to analyze the risk factors of cancer recurrence. A nomogram was developed based on significant risk factors to predict 1-, 5- and 10-year DFS. The receiver operator characteristic (ROC) curves of predictors in the multivariable Cox proportional hazards prognostic model were generated to predict the recurrence and DFS. The integrated areas under the ROC curve were calculated to evaluate discrimination of the models. The ADC ratio, tumor thickness and lymph node ratio were reliable predictors in the final prognostic model. The final model had a 71.1% sensitivity and an 81.0% specificity. ADC ratio was the strongest predictor of cancer recurrence in prognostic performance. Discrimination and calibration statistics were satisfactory with C-index above 0.7 for both model development and internal validation. The calibration curve showed that the 5- and 10-year DFS predicted by the nomogram agreed with actual observations.

Abstract Background Dermatofibrosarcoma protuberans (DFSP) is a rare, low to intermediate-grade sarcoma, which needs imaging examination. Small series of ultrasound findings in DFSP have been published; however, the usefulness of elastography and contrast-enhanced ultrasound (CEUS) in DFSP has not been studied. We aim to study multimodal ultrasound findings and report the correlation between imagings and tiny extension in DFSP for preoperative evaluation. Methods Two-D ultrasound, 3-D color ultrasound, elastography, and CEUS findings were retrospectively evaluated. Forty histopathologically confirmed DFSPs were studied. Results On 2-D ultrasound, 26(65%) appeared as mostly hypoechoic lesions with occasional hyperechoic dots within the tumor matrix and lobulated lateral borders. Eight (20%) lesions were multilayered. Ninety-five percent of lesions showed increased vascularity. On 3-D ultrasound, DFSPs showed branch-shaped, striped, and wrapped color patterns. Power Doppler showed mainly artery of a moderate arterial peak systolic blood flow and low resistance index. DFSP is hard on elastography. On CEUS, DFSPs showed a long peak time, low peak and a small amount of perfusion around the tumor, 73.7% (14/19) of lesions showed a heterogeneous contrast enhancement and 89.5% (17/19) of lesions showed hyper-enhancement. CEUS showed better concordance than US with histology on the maximum diameter and depth (P

PurposeWe aimed to evaluate the spontaneous neuronal activity and functional connectivity pattern variations using resting-state functional magnetic resonance imaging (rs-fMRI) measures, such as amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), and functional connectivity (FC), in patients with thyroid-associated ophthalmopathy (TAO).MethodA total of 24 active TAO patients, 26 inactive TAO patients, and 27 matched healthy controls (HCs) were included. First, ALFF and fALFF were used to detect local neural activity changes, the MRI data were analyzed, and regions with group differences were taken as seeds. Second, FC analysis was performed to explore the altered connection between seeds and other brain regions. A correlation analysis was performed to assess the relationship between functional brain activity and clinical indices and neuropsychiatric behaviors.ResultsCompared to HCs, both active and inactive TAO patients exhibited significantly lower ALFF values in the right calcarine (Calcarine_R) and left postcentral gyrus (Postcentral_L). Active TAO patients also showed significantly higher ALFF values in the left caudate nucleus (Caudate_L) and increased fALFF values in the superior lobe of the right cerebellum (Cerebelum_Crus1_R). Moreover, both active and inactive TAO patients demonstrated decreased FC within the left postcentral gyrus (Postcentral_L) compared to HCs. Additionally, active TAO patients exhibited lower FC compared to inactive TAO patients. The ALFF values in the Calcarine_R of active TAO patients positively correlated with disease duration (r = 0.5892, p = 0.0049) and the Hamilton Anxiety Rating Scale (HARS; r = 0.5377, p = 0.0119). Furthermore, the ALFF value in the Calcarine_R of inactive TAO patients negatively correlated with visual functioning (r = −0.5449, p = 0.0072), while the ALFF values in the Caudate_L of active TAO patients positively correlated with visual functioning (r = 0.6496, p = 0.0014).ConclusionWe found that the Caudate_L and Cerebelum_Crus1_R related to motor control and coordination in active TAO patients exhibit significant compensatory mechanisms; whereas, the Calcarine_R and Postcentral_L related to visual and somatosensory cortices show varying degrees of impairment. Our findings complement the functional neural mechanism of TAO.

Both Internet use’s impact on depression and urban-rural disparities related to information and communication technologies (ICTs) are crucial topics in the information systems discipline. So far, limited studies have explored these topics in a comprehensive way. This study aims to explore the impact of Internet use on urban and rural older adults’ depression and provide insights into how ICTs play positive roles in human behaviors. Based on data from the China Health and Retirement Longitudinal Study, we used the panel-data regression approach to examine the relationships between older adults’ Internet use and depression, and adopted the propensity score matching and the difference-in-difference approach to test the robustness of our findings. We found that the influencing mechanisms behind Internet use’s impact on urban and rural older adults’ depression are different. Internet use not only directly reduces rural older adults’ depression but also indirectly reduces it via the mediation of social activity, while the impact of Internet use on urban older adults’ depression is fully mediated by social activity. We found that Internet use exerts different impacts on urban and rural older adults’ depression, and rural older adults can receive a greater benefit.

In order to solve the problems of 5G network handover authentication and key update mechanism， such as lacking of forward security， being vulnerable to bypass attack and having signaling congestion， for 5G enhanced （B5G） network， an efficient handover authentication and security key-updating mechanism based on no certificate was proposed.The certificateless key agreement mechanism was introduced in the network edge side， which made the mobile terminals in-itiate the key-updating request actively and complete the whole certificateless key-updating process on the fly.The security of the key-updating mechanism was proved theoretically based on the Diffie-Hellman problem under the eCK security model.Simulation results demonstrate that the proposed mechanism not only meets the forward security of mobile terminal key management， but also has lower communication and computing overheads compared with other similar handoff authentication.

Abstract Objective Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) histograms were used to investigate whether their parameters can distinguish between benign and malignant parotid gland tumors and further differentiate tumor subgroups. Materials and methods A total of 117 patients (32 malignant and 85 benign) who had undergone DCE-MRI for pretreatment evaluation were retrospectively included. Histogram parameters including mean, median, entropy, skewness, kurtosis and 10th, 90th percentiles were calculated from time to peak (TTP) (s), wash in rate (WIR) (l/s), wash out rate (WOR) (l/s), and maximum relative enhancement (MRE) (%) mono-exponential models. The Mann–Whitney U test was used to compare the differences between the benign and malignant groups. The diagnostic value of each significant parameter was determined on Receiver operating characteristic (ROC) analysis. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of the different tumor groups. Results For both the benign and malignant groups and the comparisons among the subgroups, the parameters of TTP and MRE showed better performance among the various parameters. WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors. Warthin’s tumors showed significantly lower values on 10th MRE and significantly higher values on skewness TTP and 10th WOR, and the combination of 10th MRE, skewness TTP and 10th WOR showed optimal diagnostic performance (AUC, 0.971) and provided 93.12% sensitivity and 96.70% specificity. After Warthin’s tumors were removed from among the benign tumors, malignant parotid tumors showed significantly lower values on the 10th TTP (AUC, 0.847; sensitivity 90.62%; specificity 69.09%; P

Abstract Thyroid‐associated ophthalmopathy (TAO) is a serious, progressive, vision‐threatening and difficult‐to‐treat organ‐specific autoimmune disease. The course, therapeutic effects and prognosis of moderate to severe TAO vary greatly. High‐dose intravenous glucocorticoid (IVGC) therapy is considered a first‐line treatment for active moderate‐to‐severe TAO, but there is still insufficient evidence regarding the treatment duration. Long‐term IVGC therapy can influence the metabolism of glucose, lipids, and bone. This study was designed to compare changes in metabolic and immunological indexes as well as the magnetic resonance imaging apparent diffusion coefficient (ADC) of the extraocular muscles after 4 and 12 weeks of IVGC therapy. Forty‐eight patients with active moderate‐to‐severe TAO were included in this retrospective cohort study. Metabolism and immunological indexes were measured before and after therapy. The ADC and clinical activity score (CAS) were used to evaluate the efficacy of treatment in these patients. We found that the patients in the 12‐week group had increased fasting plasma glucose (p = 0.004), glycated hemoglobin (p = 0.028), total cholesterol (p

Abstract To evaluate the post-operative condylar bone remodeling after the treatment of Yang’s arthroscopic surgery. Consecutive cases from Jan 2017 to May 2018 that received Yang’s arthroscopic surgery were included in this study, the TMJ MRI examinations were performed preoperatively and postoperatively (follow up for 1 year or more), and condylar bone remodeling was estimated. A total of 229 patients (29 male and 200 female) were included in the study, 161 patients had new bone formation, and the average age was 17.5 ± 2.1a. There was no new bone formation in 68 patients with an average age of 24.5 ± 0.7a. The percentage of new bone formation patients in 10–15 years of age was 94.33% and decreases as the age increases. In the position of new bone formation, the posterior slope of condyle was the most (129 joints), the second was the top of condyle (54 joints), the third was around condyle (33 joints), only 25 joints had new bone on the anterior slope of condyle. After TMJ arthroscopic surgery, the condyle has the ability to form new bone, and the younger the age, the stronger the ability of new bone formation. The formation of new bone was most in posterior slope and least in anterior slope of condyle.

With the advancements of Software Defined Networking and Network Function Virtualization technologies, users can access the software-based service function chain (SFC), which is composed of multiple sequential virtual network function (VNF) nodes. Although SFC is more flexible and adaptive in terms of design and deployment, the security risks should not be underestimated. At present, there is a lack of security or risk assessment for SFC, and SFC deployments rarely take their security into account. However, vulnerabilities and risks can cause VNF node failure during operation, which can lead to issues such as disruptions in SFC service and user data leakage. This paper proposes the concept of SFC credibility, which quantifies the authenticity, availability, and reliability of the VNF nodes from both time and space dimensions. Then, a hierarchical credibility evaluation model is built such that VNF nodes can be selected for the user based on their trustworthiness. A credibility-based deployment strategy is further designed for SFC and the corresponding VNF forwarding graph. Furthermore, a comparative study with three existing deployment strategies has shown the advantages of the proposed method. The extensive experimental results demonstrate the improved trust degree and the acceptance rate of SFC with a limited budget.

The intelligent terminal will greatly expand the applications of 6G .However,it will also make 6G face with greater security threats.The reliable terminal identification technology is the premise to ensure the security of 6G networks.Firstly,based on the network architecture and terminal characteristics,the requirements and challenges of 6G intelligent terminal identification were summarized.Then,the development trend was put forward for 6G intelligent terminal identification.Finally,physical layer authentication was explored to achieve possible realization of intelligent terminal identity authentication mechanism in 6G networks.

The multiple access channel with confidential messages (MAC-CM) generalizes the traditional multiple access channel by allowing both users to receive channel outputs. Achievable secrecy rate region of this model has been established using random coding arguments, whereas practical codes design remains to be further discussed. In this paper, we develop an explicit polar coding scheme that achieves the secrecy rate region of the MAC-CM under strong secrecy. In particular, our scheme mainly relies on monotone chain rules and polar coding techniques for single-user channels. A proper chaining scheme is constructed to align polar indices and to provide strong secrecy. Finally, a rigorous analysis is provided to validate the performance of our scheme.

This paper studies a multi-objective optimization (MOO) problem in full-duplex (FD) networks with simultaneous wireless information and power transfer (SWIPT). In the considered networks, an FD base station (BS) communicates with multiple half-duplex (HD) uplink (UL) and downlink (DL) users simultaneously. The energy receivers (ERs) harvest energy from the ambient radio frequency (RF) signals and may act as potential eavesdroppers. Specifically, there exists two conflicting yet important system design objectives, i.e., secrecy energy efficiency (SEE) maximization and energy harvesting efficiency (EHE) maximization. An MOO design based on the weighted Tchebycheff approach is proposed to investigate the tradeoff between these two design objectives. The proposed design takes into account the quality-of-service (QoS) guarantees of secrecy rate and energy harvesting (EH) under the imperfect channel state information (CSI) of ERs. The formulated MOO problem is solved with a two-layer optimization algorithm, where the modified Dinkelbach method is applied to deal with the generalized fractional programming (FP) in the outer layer problem and semidefinite relaxation (SDR), successive convex approximation (SCA) as well as S-procedure methods are applied to transfer the inner layer problem into a convex iterative program. Moreover, we propose a suboptimal solution to the formulated problem and analyze the computational complexities. Finally, numerical results are provided to demonstrate the effectiveness of the proposed algorithms and reveal a tradeoff region between SEE and EHE.

Wireless networks have been generating a plethora of unstructured and highly-correlated big data with hidden anomalies. The anomalies may bring inaccurate predictions of network behaviors, which further lead to inefficient system designs such as proactive caching placement. Current Random Matrix Theory (RMT) approaches are unable to detect hidden anomalies with a satisfying tolerance of data correlation. We present a novel data Decomposition aided Random Matrix Theory (DC-RMT) framework, which enables a real-time anomaly detection of large scale multi-dimensional and highly-correlated data. The detection results show that the proposed DC-RMT methodology can detect anomalies with an accuracy of 28 times better than RMT applied without data decomposition. The prediction results present a 6 times higher accuracy than data with anomaly, which will facilitate the identification of regions of interests, and contribute to the improvement of resource allocation efficiency and user QoE.

Mobile edge computing (MEC) supports the internet of things (IoT) by leveraging computation offloading. It minimizes the delay and consequently reduces the energy consumption of the IoT devices. However, the consideration of static communication mode in most of the recent work, despite varying network dynamics and resource diversity, is the main limitation. An energy-efficient computation offloading method using deep reinforcement learning (DRL) is proposed. Both delay-tolerant and non-delay tolerant scenarios are considered using capillary machine type communication (MTC). Depending upon the type of service, an intelligent MTC edge server using DRL decides either process the incoming request at the MTC edge server or sends it to the cloud server. To control communication, we draft a markov decision problem (MDP). This minimizes the long-term power consumption of the system. The formulation of the optimization problem is considered under the constraint of computing power resources and delays. Simulation results delineate the significant performance gain of 12% in computation offloading through the proposed DRL approach. The effectiveness and superiority of the proposed model are compared with other baselines and are demonstrated numerically.

Encrypting information with pre-agreed key is an effective method to ensure information security. With the increasing requirement for key rate, this paper investigates the problem of simultaneously generating a secret key (SK) and a private key (PK) over three legitimate terminals with a trusted helper via public discussion that a passive eavesdropper completely access to. The legitimate terminals and the helper observe the correlated source sequences from the outputs of discrete memoryless source (DMS). The legitimate terminals wish to generate an SK that is concealed from the eavesdropper, and two designated legitimate terminals want to generate a PK that needs to be additionally keep secret from the third legitimate terminal. Under this model, the SK-PK capacity region is fully characterized, and the source coding scheme is further provided to show that the region is achievable. The security performance of the generated keys is analyzed in terms of key leakage rate.

ObjectivesTo assess computed tomography (CT) and magnetic resonance imaging (MRI) findings of intramuscular hemangiomas (IMHs) in oral and maxillofacial region and correlate them with the histopathological classifications for selecting optimum management.MethodsThe clinical data and pretreatment findings of 32 patients with pathologically proven IMHs on CT (n = 10), MRI (n = 27), or both (n = 5) were analyzed retrospectively. Correspondence of clinical and imaging characters with 3 different pathological classifications (cavernous, capillary, and mixed) of IMHs was studied. A number of pitfalls and overlap of imaging features can result in misdiagnosis of different IMHs lesions.ResultsFour patients had multi-muscular lesions, and 28 had single-muscular lesions. The predilection site were the tongue (11 cases, 34.4%) and the masseter muscle (10 cases, 31.2%). Cavernous type (17 cases, 53.1%) was the most common IMHs type. All patients showed slightly hypointense or isointense on T1-weighted imaging, 3 patients showed hyperintense on T2-weighted imaging and the others showed slightly hyperintense. The most common enhancement pattern was progressive (29 cases, 90.6%). The capillary type (9 cases, 28.1%) and mixed type (6 cases, 28.1%) of IMHs on imaging indicated characteristics of lesions with rich blood supply status, the cavernous type (17cases, 53.1%) of IMHs belonged to relatively poor blood supply lesions. A total of 5 patients (15.6%) were initially misdiagnosed, there were recurrences in 4 IMHs patients. Extra functional MRI (fMRI) was performed on these 5 misdiagnosed patients, the average ADC of the 5 patients was 1.50 × 10−3 mm2/s. The presence of vermicular vessels was different among these three types of IMHs.ConclusionsThe reason for the misdiagnosis in localized IMHs may be the obvious border of mass-like lesions and the lack of enlarged vessels. Combined evaluation of presence of vermicular vessels and fMRI might be more accurately for determining the IMHs and create a preoperative plan.

ABSTRACT: Background: The infiltrative nature and lymphatic metastasis of head and neck squamous cell carcinoma (HNSCC) are the main reasons leading to its poor prognosis. Methods: A multimodal surface-enhanced resonance Raman spectroscopy (SERRS) and magnetic resonance (MR) nanoprobe, in which paramagnetic chelators and heptamethine cyanine-based Raman reporter molecules were functionalized on a gold nanostar (AuS) surface was developed. Preoperative MRI and intraoperative SERRS-guided surgery were performed on rabbits bearing head and neck VX2 tumours to determine feasibility of the MR/SERRS probe in defining tumour marginal infiltration and lymph nodes metastasis. Findings: Preoperative T1-weighted MRI (T1W-MRI) unambiguously delineated the orthotopic head and neck VX2 tumour xenograft and detected the metastatic lymph nodes in rabbit models after intravenous administration of the probe. With the assistance of a hand-held Raman detector, the probe not only intra-operatively demarcated invasive tumour margins but also successfully distinguished metastatic lymph nodes via a remarkable attenuated Raman signal. Importantly, the group of rabbits subjected to the SERRS-guided surgery exhibited prolonged median survival time (78 days) compared with that of the control group without surgical intervention (29 days) or the group treated with conventional white-light-guided surgery (42 days) (P

Abstract Background Diffusion-weighted imaging (DWI) and ultrasound are commonly used methods to examine thyroid nodules, but their comparative value is rarely studied. We evaluated the utility of DWI and ultrasound in differentiating benign and malignant thyroid nodules. Methods A total of 100 patients with 137 nodules who underwent both DWI and ultrasound before operation were enrolled. The T1 and T2 signal intensity ratio (SIR) of each thyroid nodule was calculated by measuring the mean signal intensity divided by that of paraspinal muscle. The apparent diffusion coefficient (ADC) value and the SIR of benign and malignant thyroid nodules were analyzed by two-sample independent t tests. The sensitivity, specificity, and accuracy of DWI and ultrasound were compared with chi-square tests. Results There was no significant difference in the SIR between benign and malignant thyroid nodules. The ADC value was significantly different. At the threshold value was 1.12 × 10− 3 mm2/s, the maximum area under the curve was 0.944. The sensitivity, specificity, and accuracy were 84.9, 92.2, and 87.6% respectively. The corresponding values of ultrasound diagnosis were 90.1, 80.4, and 86.9%. Conclusions Ultrasound has high sensitivity in differentiating benign and malignant thyroid nodules, and the ADC value has high specificity, but there is no statistical difference in sensitivity or specificity between the two modalities. DWI and ultrasound each have their own advantages in differentiating benign and malignant thyroid nodules.

Abstract Background Susceptibility weighted imaging (SWI) is helpful for depicting hemorrhage, calcification, and increased vascularity in some neoplasms, which may reflect tumor grade. In this study, we aimed to apply SWI in patients with oral tongue squamous cell carcinomas (OTSCCs) and relate multi-parametric effect to tumor histological grade prediction. Methods Preoperative MR examinations were performed on a 1 .5T MRI scanner with T1-, T2- and contrast-enhanced (CE) T1-weighted imaging. In addition to routine head and neck MRI sequences, SWI was performed. Tumor thickness and volume were measured. Intratumoral susceptibility signal intensities (ITSSs), ITSS score and ITSS ratio on SWI were evaluated and recorded. Subjects were sub-grouped into low- and high-grade according to the histological findings post operation. Parameters such as tumor thickness, tumor volume and three ITSS related parameters were compared between low- and high-grade groups. ROC analysis was performed on above parameters to access the capability in predicting tumor histological grade. Different multi-parametric models were run to access multi-parametric combination effect. Results Thirty patients with OTSCC were finally included in the study. Twenty of them were categorized as low-grade SCC and the other ten subjects were high-grade SCC according to the pathologic findings. No significant difference was seen for tumor thickness or tumor volume between two sub-groups. ITSSs were seen in 23/30 patients. Significant difference of ITSS scores between low- and high-grade OTSCCs was observed, with mean value of 0.95 ± 0.83 and 1.70 ± 0.95, respectively. Univariate ROC analysis demonstrated ITSSs, ITSS score and ITSS ratio were valuable parameters for predicting tumor histological grade and ITSSs was superior to the other two parameters, with an area under ROC curve of 0.790. Multi-parametric model using combination of ITSSs and tumor thickness would greatly improve the predictive capability in comparison with a univariate approach, yielding the area under ROC curve of 0.84(0.69,0.99). On contrast-enhanced SWI (CE-SWI), ITSSs were shown more clearly delineated in comparison with non-contrast enhanced SWI. Conclusions In conclusion, SWI was superior in depiction of internal characteristics of OTSCCs, which would potentially provide more diagnostic information. Multi-parametric model using combination of ITSSs and tumor thickness would be valuable in predicting tumor histological grade.

To meet the ever-growing demand and service requirement, especially for delay-sensitive services like vehicular communications, small cells consisting of low-power base stations (BSs) are deployed to undertake the traffic pressure of BSs in macro cells. The vehicle may handover from the macro cell tier to the small cell tier when moving toward a small cell. In this paper, we mainly analyze and optimize the cross-tier handover performance in heterogeneous networks from the perspective of delay characteristics. Based on the stochastic geometry theory, we deduce the expression of the effective capacity which reflects the delay to the rate before and after the handover; moreover, a resource reservation scheme is proposed. Based on this scheme, a closed-form expression of the blocking probability for delay-sensitive users is derived. Finally, based on the convex optimization theory, a joint optimization of effective capacity and blocking probability is carried out. The simulations show that the proposed optimization algorithm will greatly reduce the blocking probability while keeping the effective capacity relatively constant, especially beneficial for the delay-sensitive users. The theoretical and simulation results can be applied to the handover strategy and resource reservation strategy selection in heterogeneous networks.

Machine type communication (MTC) is a fundamental technology to realize the concept of fully connected world in fifth generation (5G) Internet of Things (IoT). The massive roll out of the MTC devices is a serious challenge for cellular networks from operational and management perspective, including massive access and network congestion. Many proposals have been put forward by the research community to cater to the nuisance of massive MTC (mMTC) access in cellular networks. Recently, data aggregation has attracted a lot of research attention owing to its robust ability to resolve the above-mentioned challenges. In this paper, we review the recent development in data aggregation techniques, including their application scenarios, design, and limitations. Commencing with the application scenarios and current challenges in the MTC network, the classification of various proposed solutions for massive access along with the family of data aggregation techniques is discussed in detail. By doing so, it provides an insight about the future design trends that can propel the current research efforts to curb the mMTC access in a cellular network.

Encryption with shared key is one of the methods to ensure secure communication. To meet the requirements of keys, this paper investigates the problem of generating private key (PK) and group key (GK) using correlated sources and discrete memoryless wiretap broadcast channel (DM-WBC), in presence of one-way public communication. In the model considered, one transmitter (Alice) and three receivers (Bob, Carol and Eve) are connected via a DM-WBC, and a one-way communication over a public noiseless channel, initiated by Alice, is available. Alice and Bob wish to share a PK that is concealed from Carol and Eve, and they also want to generate a GK with Carol that needs to keep secret from Eve. Under this model, the key capacity regions of PK and GK are established by designing joint source-channel coding schemes to achieve these regions. In PK-generation, the considered model can be viewed as a one receiver two-eavesdropper model, and Alice and Bob have access to correlated source sequences. In GK-generation, the considered model can be viewed as a two-receiver one eavesdropper model, and the three legitimate parties (Alice, Bob and Carol) all have the source observations. Furthermore, the security performance of the generated keys are analyzed by analyzing key leakage rate of each key.

Regularized zero forcing (RZF) precoding is an efficient linear precoding scheme for combating interference in a single-cell massive multiple-input-multiple-output (MIMO) systems. Inaccurate channel state information (CSI) due to channel aging will reduce the performance of the precoder over time. The channel aging determines how often we need to estimate the channels, and thus how frequently we need to send pilots in order to maximize the overall data rate. Channel prediction is one way to improve the CSI accuracy in the downlink, without having to send new pilots but it requires frequent re-computation of the matrix inverse in the RZF precoder, which has high-computational complexity. In this paper, we consider massive MIMO-OFDM systems and propose an algorithm called inverse extrapolation that extrapolates the channel and inverse matrix coefficients separately. The RZF coefficients are then obtained with comparably low complexity with no need for matrix inversion. We compare this algorithm with the traditional way of computing the RZF coefficients through prediction of the channel matrix followed by matrix inversion. The simulation results show that the two predictors have the same performance when the number of antennas is large, and thus the proposed scheme is preferable since it can reduce the complexity. For example, a scenario is shown, where the complexity is reduced by 61.84% without a significant degradation in performance.

The enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC) are the two main scenarios of 5th generation (5G) mobile communication system networks. There is an obvious difference in service requirements between different scenarios. When multi-scenario services coexist in the 5G networks, exploring optimized resource scheduling and allocation strategies become a critical issue. The 5G New Radio (NR) and numerology technologies have been standardized, which lay the foundation for flexible frame structure and adaptive scheduling. In this paper, we propose the self-adaptive flexible transmission time interval (TTI) scheduling (SAFE-TS) strategy in the eMBB and uRLLC coexistence scenario. Machine learning (ML) is applied to achieve flexible TTI scheduling. Moreover, we design the random forest-based ensemble TTI decision algorithm (RF-ETDA) to accomplish the TTI selection for each service. Compared with the existing ML methods, the proposed algorithm has a performance improvement in selecting TTI, especially for the uRLLC services. Then, the TTI selection results will be the basis of system resource scheduling and allocation. The simulation results prove that the proposed SAFE-TS effectively reduce the delay and packet loss rate of the uRLLC services while guaranteeing the eMBB requirements. Therefore, it is highly recommended that flexible TTI scheduling should be applied in the construction of the 5G networks to achieve superior network performances.

Physical layer security (PLS) is a promising technology to enhance security performance of wireless communication systems, while the analyzing of PLS abnormal user detection is an important aspect of PLS research. Considering random matrix theory (RMT) is a time-efficient and theory-mature method, we will utilize RMT to analyze the abnormal user detection problem from the perspective of physical layer data analysis, where the carrier frequency offset (CFO) data is used as an indicator for abnormal user detection. Specially, the ring law theory and empirical spectral analysis of RMT are adopted for the analysis of CFO data, which is time-efficient and can be implanted in other detection methods. The proposed abnormal user detection method can provide a guidance for the appropriate choice of security enhancement technologies, so as to improve the utilization efficiency of different PLS technologies.

Unmanned aerial vehicle (UAV)-enabled mobile relaying technique is a promising approach in providing high data rate transmission. Limitations of the UAV's hardware and the line-of-sight link of the air-to-ground channel make UAV ground communication more vulnerable to wiretap and spoof threats. This paper tackles the secure transmission problem with physical layer security (PLS) technologies. A four-node wiretap channel with UAV-enabled relay is considered and eavesdroppers' location information is partially known that they are in a certain area with variable densities. We put forward the secrecy rate maximization problem aiming at optimizing the source/relay transmission power. The objective function and constraints are proved to be non-convex and we convert the non-convex problem to a convex one by introducing the difference-of-concave (DC) programming and slack variables. For a special case, a closed-form solution is obtained which greatly reduces the complexity of the algorithm. The simulation result shows that with the proposed algorithm, the system with partial eavesdroppers' location information can achieve a pretty good secrecy performance close to that in the case with perfect location information.

Background Whether statin treatment can improve hemodynamic status of coronary atherosclerotic plaque remains unknown. It is of clinical interest to explore the hemodynamic change of coronary lesions after statin treatment. Methods and Results Consecutive patients with intermediate pre‐test probability of coronary artery disease were prospectively enrolled and underwent baseline coronary computed tomography angiography (CCTA) as well as follow‐up CCTA. The primary end point was to determine the lesion‐specific change of △computed tomography‐derived fractional flow reserve (△CT‐FFR, defined as the change of CT‐FFR value across each lesion) after rosuvastatin treatment. The secondary end point was to compare the change of other plaque characteristics according to serial CCTA findings. 152 patients (mean age: 67.1±9.7 years, 100 men, mean follow‐up duration of 13.9±2.5 months) were finally included. In non‐calcified plaque subgroup, △CT‐FFR was significantly lower at follow‐up compared with baseline (0.051±0.010 versus 0.035±0.012, P=0.013). All other parameters were not found to be significantly different between baseline and follow‐up CCTA measurements. In calcified plaque and mixed plaque subgroups, all parameters showed no significant differences between baseline and follow‐up CCTA groups (P>0.05 for all). According to multivariate regression analysis, non‐calcified plaque was >2 times more likely than calcified plaque to observe the decrease of △CT‐FFR (adjusted hazard ratio: 2.05 [1.03–4.09], P=0.042). Conclusions In patients with mild to intermediate coronary stenosis, rosuvastatin treatment resulted in a reduction in lesion‐specific △CT‐FFR at mid‐term follow‐up. This hemodynamic improvement was mainly observed for non‐calcified lesions.

Abstract Background The overlap of morphological feature and mean ADC value restricted clinical application of MRI in the differential diagnosis of orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP). In this paper, we aimed to retrospectively evaluate the combined diagnostic value of conventional magnetic resonance imaging (MRI) and whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in the differentiation of the two lesions. Methods In total, 18 patients with orbital lymphoma and 22 patients with IOIP were included, who underwent both conventional MRI and diffusion weighted imaging before treatment. Conventional MRI features and histogram parameters derived from ADC maps, including mean ADC (ADCmean), median ADC (ADCmedian), skewness, kurtosis, 10th, 25th, 75th and 90th percentiles of ADC (ADC10, ADC25, ADC75, ADC90) were evaluated and compared between orbital lymphoma and IOIP. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating. Differential model was built upon the selected variables and receiver operating characteristic (ROC) analysis was also performed to determine the differential ability of the model. Results Multivariate logistic regression showed ADC10 (P = 0.023) and involvement of orbit preseptal space (P = 0.029) were the most promising indexes in the discrimination of orbital lymphoma and IOIP. The logistic model defined by ADC10 and involvement of orbit preseptal space was built, which achieved an AUC of 0.939, with sensitivity of 77.30% and specificity of 94.40%. Conclusions Conventional MRI feature of involvement of orbit preseptal space and ADC histogram parameter of ADC10 are valuable in differential diagnosis of orbital lymphoma and IOIP.

The accommodation of massive machine-type communication (mMTC) in cellular networks brings up serious technical challenges due to concurrent massive access of MTC devices. These challenges may further be aggravated by the presence of delay tolerant and intolerant services in an MTC network. This paper proposes a cooperative data aggregation (CDA) scheme by employing fixed data aggregator (FDA) and multiple mobile data aggregators (MDAs) to cater MTC devices having variable quality of service (QoS) requirements. In this vein, a distributed MDA selection algorithm is also proposed to designate appropriate user equipment as aggregator. The proposed CDA scheme effectively caters the massive access and provides ubiquitous availability of the aggregating devices in the MTC network. In addition, the limited channel resources impel an FDA to schedule resources besides data aggregation. Therefore, a resource allocation scheme is also proposed to dynamically allocate channels to the MTC devices subject to their QoS requirements. The proposed resource scheduling scheme ensures that transmission requests from delay intolerant MTC devices are contented on priority basis. The proposed CDA and dynamic resource scheduling schemes are analyzed and compared with the existing data aggregation and resource scheduling schemes, respectively. The numerical results corroborate that our proposed CDA scheme in conjunction with dynamic resource allocation improves the outage probability, energy efficiency, and system capacity by 30%, 25%, and 44%, respectively, as compared to the single FDA scheme.

In this paper, we investigate the secure transmission in two-way untrusted amplify-and-forward relay networks with simultaneous wireless information and power transfer (SWIPT). Two secure SWIPT relaying strategies, namely, secure power splitting (SPS) and secure time switching (STS), are studied with the objective of secrecy rate (SR) and secure energy efficiency (SEE) maximization. First, the high signal-to-noise ratio approximations of SR and SEE are established, based on which non-convex SPS-based and convex STS-based SR maximization problems are formulated to jointly optimize the source transmit power and resource division ratio. Suboptimal solutions are obtained by employing Dinkelbach's method and Newton's method, respectively. Furthermore, to guarantee both the energy efficiency and secure quality of service (QoS) performance, SEE maximization problems are formulated with the consideration of the energy harvesting activation constraint and the target SR requirement. The intractable non-convex SEE maximization problems are reformulated as parameter programming, transformed to Lagrange dual problems, and finally solved by two SEE resource allocation algorithms with low complexity. Numerical results verify the convergence and effectiveness of the proposed algorithms and provide new insights into relaying strategy design and secure relay placement.

Ultra-dense networks (UDNs) provide a promising paradigm to cope with exponentially increasing mobile traffic. However, a little work has to date considered unsaturated traffic with Qualityof-Service (QoS) requirements. This paper presents a new cross-layer analytical model to capture the unsaturated traffic of a UDN in the presence of QoS requirements. The effective capacity (EC) of the UDN is derived, taking into account small-scale channel fading and possible interference. Key properties of the EC are revealed. The amount of traffic impacts EC of the UDN due to the sophisticated interactions among small base stations operating in the same frequency. The maximization of total EC is formulated as a non-cooperative game in this paper. The best-response function is derived, iteratively searching the Nash equilibrium point. System simulation results indicate that our proposed model is accurate. The simulations also show the maximum allowed arrival rate with the QoS guarantee, compared with the full interference model.

User equipment (UE)-to-network relay is a promising approach to assist UEs, especially cell-edge UEs, to obtain a better connectivity experience. By leveraging the proximity communication, specific UEs can serve as relays to assist transmissions between UEs and base stations (BSs). However, it also imposes a significant challenge on backhaul pressure. On the other hand, caching is considered as an efficient way to reduce data traffic and alleviate the burden of the backhaul networks. In this paper, we jointly consider UE-to-network relay and caching. We propose a tractable analytical framework to model caching enabled UE-to-network relay in cellular networks. Besides, we employ a four-level hierarchical mode selection scheme, where a request is satisfied with priority order by: 1) self-request mode, 2) device request mode, 3) UE-to-network relay mode, and 4) cellular request mode. Based on stochastic geometry, we derive tractable expressions for performance metrics, such as cache hit probability, successful transmission probability, and cache-aided throughput. The accuracy of analytical results is validated by simulations with the 3rd generation partnership project evaluation methodology. The results also show that the distance threshold should be set carefully to satisfy user communication requirements. Moreover, we find that the UE intensity and content popularity jointly have significant impacts on caching policy choices.

User mobility brings about the spatial correlation in user association (or handover) over a period of multi slots. The correlation makes the classic user association methodology, i.e., typical user method in stochastic geometry, invalid. In addition, the impact of signal to interference plus noise ratio (SINR) on handover cannot be overlooked due to the strong interference caused by the dense base station deployment. To address the above issues, we aim to explore a methodology to analyze the multi-slot network performance for a mobile user considering the SINR effect at the user's location before it moves and the user's location after it moves. The main idea is to find the correlated association (and interference) regions in different time slots. On this basis, the expression of multi-slot coverage probability, SINR-based horizontal, and vertical handover rate are derived under maximum average received power (MARP) strategy. Simulation results prove the positive correlation in the associated distance as well as the multi-slot coverage probability, and the impact of SINR on horizontal and vertical handover rate over a period of multi slots. Moreover, MARP strategy always outperforms the nearest distance strategy on multi-slot coverage probability at the almost same cost of handovers. Finally, we found that increasing the picocell density, transmission powers, and biasing can lead substantial loads offloading to picocell but make no significant contribution to the overall coverage.

The recent advancement in intelligent transportation system makes it crucial to equip vehicles with intelligent onboard system (IOS). With the help of IOSs, vehicles can obtain varieties of convenient services from both remote service centers and other vehicles. However, the deficiency of the existing IOS makes the upgrade process complex and costly for the reason of its closed architecture. In order to solve this problem, network function virtualization (NFV) is introduced to make the IOS open by virtualizing services as software applications that can be executed on standard IT platforms. Furthermore, the NFV-based IOS can build an ordered service chain, consisting of virtualized network functions (VNFs), among different IOSs, and support VNF reuse. Thus, it is imperative to optimize the chaining scheme to obtain a shorter average service time (AST). In this paper, we propose a clustered VNF chaining scheme, which deploys VNFs in clusters according to the cluster head of each vehicle clusters, and derive the expression of the AST in a relative static scenario. Based on this scheme, the dynamic AST of a given cluster in a moving scenario is also analyzed on the grounds of both macroscopic mobility model and microscopic mobility model. The analyses show that both of the vehicle density and distance-headway variations have influence on the AST of one given cluster, which is verified by numerical results. In addition, the experiment results also show that the proposed scheme has a better performance compared with other two schemes, which reduces the AST 61% and 43%, respectively.

In this paper, we study the coverage probability and average data rate of user pairing in cooperative non-orthogonal multiple access (NOMA) networks. With fixed locations of the source and typical user, the candidate users for pairing follow homogeneous Poisson point process. Considering the geometric distance between nodes, close-to-user pairing (CUP) and close-to-source pairing (CSP) schemes are investigated with the near user acting as half-duplex relay or full-duplex (FD) relay. Lower bounds on the coverage probability and average data rate are approximately obtained using stochastic geometry and Gaussian-Chebyshev quadrature. Numerical and simulation results corroborate the accuracy of the analytical results and reveal that CUP-based cooperative NOMA outperforms CSP-based cooperative NOMA in terms of the data rate performance. With typical user close to the source, CUP-based FD NOMA is the best transmission scheme (among CUP-based and CSP-based cooperative NOMA, non-cooperative NOMA, and OMA schemes) that maximizes the sum data rate and the minimum user rate.

Conventional compressive sensing-based data gathering (CS-DG) algorithms require a large number of sensors for each compressive sensing measurement, thereby resulting in high energy consumption in clustered wireless sensor networks (WSNs). To solve this problem, we propose a novel energy-efficient CS-DG algorithm, which exploits the better reconstruction accuracy of the adjacency matrix of an unbalanced expander graph. In the proposed CS-DG algorithm, each measurement is the sum of a few sensory data, which are jointly determined by random sampling and random walks. Through theoretical analysis, we prove that the constructed M × N sparse binary sensing matrix is the adjacency matrix of a (k, ε) unbalanced expander graph when M = O (k log N/k) and t = O (Nc/(kq)) for WSNs with Nc clusters, where 0 ≤ q ≤ 1 and Nc > k. Simulation results show our proposed CS-DG has better performance than existing algorithms in terms of reconstruction accuracy and energy consumption. When hybrid energy-efficient distributed clustering algorithm is used, to achieve the same reconstruction accuracy, our proposed CS-DG can save energy by at least 27.8%.

Accompanied by the rapid development of mobile video service requirements, the dramatic increase in video streaming traffic causes a heavy burden for mobile networks. Mobile edge computing (MEC) has become a promising paradigm to enhance the mobile networks by providing cloudcomputing capabilities within the radio access network (RAN). With the ability of content caching and context awareness, MEC could provide low-latency and adaptive-bitrate video streaming to improve service providing ability of the RAN. In this paper, we propose an MEC enhanced adaptive bitrate (ABR) video delivery scheme, which combines content caching and ABR streaming technology together. The MEC server acts as a controlling component to implement the video caching strategy and adjust the transmitted bitrate version of videos flexibly. A Stackelberg game is formulated to deal with the storage resources occupied by each base station. The joint cache and radio resource allocation (JCRA) is tackled into a matching problem. We propose the JCRA algorithm to solve the matching problem in order to make the cooperation between cache and radio resources. Simulation results reveal that the proposed scheme could improve both the cache hit ratio and the system throughput over other schemes.

Communication plays a significant role in terms of providing connectivity for urban users as well as sensors in smart cities. It has been shown that better communication capability for vehicular users can be obtained by introducing moving relays (MRs). With MRs, it is possible for macro base stations and moving relays to serve non-vehicular macro-users by performing coordinated multipoint (CoMP) joint transmission (JT). A bias-based CoMP scheme for MR enabled cellular network is analyzed in this paper. Motivated by antenna design constraints, in this paper, we assume that the outdoor antenna system of the MR can only be used for the moving backhaul link, i.e., the in-vehicle antenna system is also serving the nonvehicular macro-users. Using the stochastic geometry approach, a tractable model of the network is proposed. Based on the proposed model, integral expressions for CoMP-JT probability and coverage probability of nonvehicular macro user equipment are derived. Simulations verify the accuracy of the derived expressions. The results show that the probability for macro user equipment to be served with CoMP-JT is up to 70% when the intensity of MRs is ten times that of MBSs. This paper also includes a performance comparison among the analyzed scheme and related works. CoMP-JT with MRs provides better coverage performance for nearby macro user equipment. It can be found that the coverage gain of CoMP increases when the intensity of MRs increases within a certain range. Simulation results provide insights for practical system design in smart cities, such as the optimal MR intensity and the feasibility for opening access of MRs to macro user equipment.

As the technique that determines the position of a target device based on wireless measurements, Wi-Fi localization is attracting increasing attention due to its numerous applications and the widespread deployment of Wi-Fi infrastructure. In this paper, we propose ConFi, the first convolutional neural network (CNN)-based Wi-Fi localization algorithm. Channel state information (CSI), which contains more position related information than traditional received signal strength, is organized into a time-frequency matrix that resembles image and utilized as the feature for localization. The ConFi models localization as a classification problem and addresses it with a five layer CNN that consists of three convolutional layers and two fully connected layers. The ConFi has a training stage and a localization stage. In the training stage, the CSI is collected at a number of reference points (RPs) and used to train the CNN via stochastic gradient descent algorithm. In the localization stage, the CSI of the target device is fed to the CNN and the localization result is calculated as the weighted centroid of the RPs with high output value. Extensive experiments are conducted to select appropriate parameters for the CNN and demonstrate the superior performance of the ConFi over existing methods.

In this article, different from the traditional Device-to-Device caching wireless cellular networks, we consider the scalable video coding performance in cache-based machine-type communication network, where popular videos encoded by scalable video coding method can be cached at machine-type devices with limited memory space. We conduct a comprehensive analysis of the caching hit probability using stochastic geometry, which measures the probability of requested video files cached by nearby local devices and the user satisfaction index, which is essential to delay sensitive video streams. Simulation results prove the derivation of the performance metrics to be correct, using Random cache method and Popularity Priority cache method. It is also demonstrated that scalable video coding–based caching method can be applied according to different user requirements as well as video-type requests, to achieve a better performance.

A widely held belief in network epidemiology is that information diffusion makes individuals aware of the epidemic and thus drives them to seek protections from nonpharmaceutical or pharmaceutical resources, which can help suppress its spread. However, as the COVID-19 pandemic has revealed, excessive information diffusion can trigger irrational acquisition and hoarding behaviors, which can lead to shortages of resources even for those in urgent need, consequently, worsening disease spreading. To develop a quantitative understanding of the effect of information diffusion on epidemic spreading, subject to allocations of limited resources, has become an urgently important problem with broad implications. We construct a multiplex network framework to characterize the complex interplay among resource allocation, information diffusion, and epidemic spreading, and develop a microscopic Markov chain theory to analyze their coevolution dynamics. There are two main findings. Firstly, if infected individuals have a large recovery probability, information diffusion plays the expected “normal” role of suppressing the epidemic. However, if the recovery probability is low, information diffusion can anomalously worsen the spread, regardless of the available resources insofar as they are limited. Secondly, different types of resources can lead to distinct phase transitions underlying the epidemic outbreak when the recovery probability is low: with limited cure-focused resources, the phase transition is of the second order, but if resources are of the protection type, the transition becomes first order, and a hysteresis loop emerges. The generality of the findings is established through simulations of synthetic and empirical three-layer networks with results in agreement with the theoretical predictions.

To alleviate the backhaul burden and reduce user-perceived latency, content caching at base stations has been identified as a key technology. However, the caching strategy design at the wireless edge is challenging, especially when both wired backhaul condition and wireless channel quality are considered in the optimization. In this paper, taking into account the conditions of the backhaul in terms of delay and wireless channel quality, joint design and optimization of the caching and user association policy to minimize the average download delay is studied in a cache-enabled heterogeneous network. We first prove the joint caching and association optimization problem is NP-hard based on a reduction to the facility location problem. Furthermore, in order to reduce the complexity, a distributed algorithm is developed by decomposing the NP-hard problem into an assignment problem solvable by the Hungarian method and two simple linear integer subproblems, with the aid of McCormick envelopes and the Lagrange partial relaxation method. Simulation results reveal a near-optimal performance that performs up to 22% better in term of delay compared with those in the literatures at a low complexity of O(nm3/ε2).

Non-orthogonal multiple access is an essential promising solution to support large-scale connectivity required by massive machine-type communication scenario defined in the fifth generation (5G) mobile communication system. In this article, we study the problem of energy minimization in non-orthogonal multiple access–based massive machine-type communication network. Focusing on the massive machine-type communication scenario and assisted by grouping method, we propose an uplink cooperative non-orthogonal multiple access scheme with two phases, transmission phase and cooperation phase, for one uplink cooperative transmission period. Based on uplink cooperative non-orthogonal multiple access, the machine-type communication device with better channel condition and more residual energy will be selected as a group head, which acts as a relay assisting other machine-type communication devices to communicate. In the transmission phase, machine-type communication devices transmit data to the group head. Then, the group head transmits the received data with its own data to base station in the cooperation phase. Because the massive machine-type communication devices are low-cost dominant with limited battery, based on uplink cooperative non-orthogonal multiple access, we propose a joint time and power allocation algorithm to minimize the system energy consumption. Furthermore, the proposed joint time and power allocation algorithm includes dynamic group head selection and fractional transmit time allocation algorithms. Simulation results show that the proposed solution for uplink cooperative non-orthogonal multiple access–based massive machine-type communication network outperforms other schemes.