The integration of reconfigurable intelligent surface (RIS) into millimeter wave (mmWave) vehicular communications offers the possibility to unleash the potential of future proliferating vehicular applications. However, the high-mobility-induced rapidly varying channel state information (CSI) has been making it challenging to obtain the accurate instantaneous CSI (I-CSI) and to cope with the incurable high signaling overhead. The situation may become worse when the RIS with a large number of passive reflecting elements is deployed. To overcome this challenge, we investigate in this paper a robust transmission scheme for the time-varying RIS-aided mmWave vehicular communications, in which, specifically, a multi-antenna base station (BS) serves vehicle user equipments (VUEs) with the help of RIS at the mmWave frequency. The uplink average achievable rate is maximized relying only upon the imperfect knowledge of statistical CSI. Considering the time-varying characteristics, we first propose an effective transmission protocol by reasonably configuring the time-scale of CSI acquisition in order to significantly relax the frequency of channel information updates, which constitutes one of the most critical issues in RIS-aided vehicular communications. Then, the formulated resource allocation problem is discussed in the single- and multi-VUE case, respectively. To be specific, for the single-VUE case, a closed-form expression of the average rate is derived by extracting the statistical characteristics of mmWave channels, and an alternating optimization (AO)-based algorithm is proposed. For the multi-VUE case, we develop an efficient algorithm, called JAPMC, to circumvent the unavailability of the closed-form of the objective function and probabilistic constraint by constructing quadratic surrogates of that. Simulation results confirm the effectiveness and robustness of our proposed algorithms as compared to benchmark schemes.

Network slicing has been considered as a promising candidate to provide customized services for vehicular applications that have extremely high requirements of latency and reliability. However, the high mobility of vehicles poses significant challenges to resource management in such a stochastic vehicular environment with time-varying service demands. In this paper, we develop an online network slicing scheduling strategy for joint resource block (RB) allocation and power control in vehicular networks. The long-term time averaged total system capacity is maximized while guaranteeing strict ultra-reliable and low-latency requirements of vehicle communication links, subject to stability constraints of task queues. The formulated problem is a mixed integer nonlinear stochastic optimization problem, which is decoupled into three subproblems by leveraging Lyapunov optimization. In order to tackle this problem, we propose an online algorithm, namely JRPSV, to obtain the optimal RB allocation and power control at each time slot according to the current network state. Furthermore, rigorous theoretical analysis is conducted for the proposed JRPSV algorithm, indicating that the system capacity and the system average latency obey a [O(1/V),O(1/V)] trade-off with the control parameter V. Extensive simulation results are provided to validate the theoretical analysis and demonstrate the effectiveness of JRPSV as well as the impacts of various parameters.

Medical applications challenge today’s text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap forward in regard to accuracy, this leap comes at the sacrifice of interpretability. In this paper, we introduce a text categorization approach that leverages the recently introduced Tsetlin Machine to address this accuracy-interpretability challenge. Briefly, we represent the terms of a text as propositional variables. From these variables, we capture categories using simple propositional formulae, such as: IF “rash” AND “reaction” AND “penicillin” THEN Allergy. The Tsetlin Machine learns these formulae from labeled text, utilizing conjunctive clauses to represent the particular facets of each category. Therefore, also the absence of terms (negated features) can be used for categorization purposes. Our empirical comparisons with Naïve Bayes classifiers, decision trees, linear support vector machines (SVMs), random forest, long short-term memory (LSTM) neural networks, and other techniques, are quite conclusive. Using relatively simple propositional formulae, the accuracy of the Tsetlin Machine either outperforms or performs approximately on par with the best evaluated methods on both the 20 Newsgroups and IMDb datasets, as well as on a clinical dataset containing authentic electronic health records (EHRs). On average, the Tsetlin Machine delivers the best recall and precision scores across the datasets. The main merit of the proposed approach is thus its capacity for producing human-interpretable rules, while at the same time achieving acceptable accuracy. We believe that our novel approach can have a significant impact on a wide range of text analysis applications, providing a promising starting point for deeper natural language understanding with the Tsetlin Machine.

With the implementation of channel assembling (CA) techniques, higher data rate can be achieved for secondary users in multi-channel cognitive radio networks. Recent studies which are based on loss systems show that maximal capacity can be achieved using dynamic CA strategies. However the channel allocation schemes suffer from high blocking and forced termination when primary users become active. In this paper, we propose to introduce queues for secondary users so that those flows that would otherwise be blocked or forcibly terminated could be buffered and possibly served later. More specifically, in a multi-channel network with heterogeneous traffic, two queues are separately allocated to real-time and elastic users and channel access opportunities are distributed between these two queues in a way that real-time services receive higher priority. Two queuing schemes are introduced based on the delay tolerance of interrupted elastic services. Furthermore, continuous time Markov chain models are developed to evaluate the performance of the proposed CA strategy with queues, and the correctness as well as the preciseness of the derived theoretical models are verified through extensive simulations. Numerical results demonstrate that the integration of queues can further increase the capacity of the secondary network and spectrum utilization while decreasing blocking probability and forced termination probability.

The Tsetlin Machine (TM) is a recent machine learning algorithm with several distinct properties, such as interpretability, simplicity, and hardware-friendliness. Although numerous empirical evaluations report on its performance, the mathematical analysis of its convergence is still open. In this article, we analyze the convergence of the TM with only one clause involved for classification. More specifically, we examine two basic logical operators, namely, the “IDENTITY”- and “NOT” operators. Our analysis reveals that the TM, with just one clause, can converge correctly to the intended logical operator, learning from training data over an infinite time horizon. Besides, it can capture arbitrarily rare patterns and select the most accurate one when two candidate patterns are incompatible, by configuring a granularity parameter. The analysis of the convergence of the two basic operators lays the foundation for analyzing other logical operators. These analyses altogether, from a mathematical perspective, provide new insights on why TMs have obtained state-of-the-art performance on several pattern recognition problems.