Mobility management associating all user equipments (UEs) to proper base stations (BSs) (also known as handover, HO) to achieve the designated performance optimization is one of the most crucial functions in mobile networks. Although effective mobility management has received considerable research attentions, existing schemes follow event-driving operations, in which HO decisions are made based on events of performance degradation that BSs/UEs passively suffer from or proactively foresee. However, the performance and decisions of these schemes are highly subject to the identified events, to lose generalization and better performance under unidentified events. To address this issue, we propose a foundation model (FM) based mobility management for the sixth generation (6G) mobile networks inherently supporting artificial intelligence (AI) computing, in which the FM generates the HO decisions for all UEs to maximize the overall throughput under the constraints of ping-pong rate and HO failure (HOF) rate by implicitly taking moving trajectories, traffic demands, channel conditions of all UEs and available resources of BSs into account. To this end, a hierarchical model structure composed of Long Short-Term Memory (LSTM) networks with multi-head attention (MHA) is adopted, which is trained by emulated datasets with augmentation. The performance evaluation results show that the proposed scheme outperforms the state-of-the-art schemes in terms of the average throughput over all UEs while satisfying the required ping-pong rate and HOF rate, and justify the robustness of the proposed FM under different network deployment scenarios.

We study the problem of Non-Stationary Reinforcement Learning (NS-RL) without prior knowledge about the system’s non-stationarity. A state-of-the-art, black-box algorithm, known as MASTER, is considered, with a focus on identifying the conditions under which it can achieve its stated goals. Specifically, we prove that MASTER’s non-stationarity detection mechanism is not triggered for practical choices of horizon, leading to performance akin to a random restarting algorithm. Moreover, we show that the regret bound for MASTER, while being order optimal, stays above the worst-case linear regret until unreasonably large values of the horizon. To validate these observations, MASTER is tested for the special case of piecewise stationary multi-armed bandits, along with methods that employ random restarting, and others that use quickest change detection to restart. A simple, order optimal random restarting algorithm, that has prior knowledge of the non-stationarity is proposed as a baseline. The behavior of the MASTER algorithm is validated in simulations, and it is shown that methods employing quickest change detection are more robust and consistently outperform MASTER and other random restarting approaches.

THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD. A finite-horizon variant of the quickest change detection problem is investigated, which is motivated by a change detection problem that arises in piecewise stationary bandits. The goal is to minimize the latency, which is smallest threshold such that the probability that the detection delay exceeds the threshold is below a desired low level, while controlling the false alarm probability to a desired low level. When the pre- and post-change distributions are unknown, two tests are proposed as candidate solutions. These tests are shown to attain order-optimality in terms of the horizon. Furthermore, the growth in their latencies with respect to the false alarm probability and late detection probability satisfies a property that is desirable in regret analysis for piecewise stationary bandits. Numerical results are provided to validate the theoretical performance results.

THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD. A finite horizon variant of the quickest change detection problem is studied, in which the goal is to minimize a delay threshold (latency), under constraints on the probability of false alarm and the probability that the latency is exceeded. In addition, the horizon is not known to the change detector. A variant of the cumulative sum (CuSum) test with a threshold that increasing logarithmically with time is proposed as a candidate solution to the problem. An information-theoretic lower bound on the minimum value of the latency under the constraints is then developed. This lower bound is used to establish certain asymptotic optimality properties of the proposed test in terms of the horizon and the false alarm probability. Some experimental results are given to illustrate the performance of the test.

Sequential learning using transformer has achieved state-of-the-art performance in natural language tasks and many others. The key to this success is the multi-head self attention which encodes and gathers the features from individual tokens of an input sequence. The mapping or decoding is performed to produce an output sequence via cross attention. There are threefold weaknesses by using such an attention framework. First, since the attention would mix up the features of different tokens in input and output sequences, it is likely that redundant information exists in sequence data representation. Second, the patterns of attention weights among different heads tend to be similar. The model capacity is bounded. Third, the robustness in an encoder-decoder network against the model uncertainty is disregarded. To handle these weaknesses, this paper presents a Bayesian semantic and disentangled mask attention to learn latent disentanglement in multi-head attention where the redundant features in transformer are compensated with the latent topic information. The attention weights are filtered by a mask which is optimized through semantic clustering. This attention mechanism is implemented according to Bayesian learning for clustered disentanglement. The experiments on machine translation and speech recognition show the merit of Bayesian clustered disentanglement for mask attention.

This study designed an an online course Application of Data Science in the Realm of Medicine in which 33 students of medical university and the staff of a medical university hospital enrolled. and Artificial Intelligence and Law of Technology course in which 59 national university students enrolled. A massive open online course platform was used with the online class mentor diversification course operation method and teaching strategy to provide solutions to problems existing in the industry and to encourage a spirit of teamwork in the physical class. Through diverse cross-domain channels, students learned how to use the technology that was integrated with artificial intelligence and relevant to their department. Students cultivated the cross-domain ability required in the contemporary digital economy. The results of this study revealed that after medical university students and general university students received this cultivation-model training, the students who did not study information and communications technology (ICT) significantly outperformed the students who studied ICT. Moreover, no significant differences were found between the learning performance of male and female students. This cultivation model proved to be effective for cross-domain learners. The model also reduced the achievement gap in science learning asssociated with gender differences. In the after-class survey, average score of satisfaction reached 7 and higher in the total of 9 points.

A cooperative spectrum sharing, power allocation and user scheduling method is studied in this work under the framework of LTE self-organizing femtocell networks. Making use of dynamic channel and power allocation, and interference control, all femto access points (APs) in a self-organizing network (SON) can share the channels available to the SON with guaranteed quality of service. Simulation results show that the effective channel usage ratio can reach 75% in average even with no explicit cellular planning inside the service area of an SON. This result supports the feasibility of randomly deploying large numbers of femto APs in metropolitan areas based on the concept of SON.