Online group discussions play an important role in education reform by facilitating collaborative learning and knowledge sharing among participants. However, instructors face significant challenges in monitoring discussion progress, tracking student performance and understanding interaction dynamics due to overlapping conversations, time-varying participant behaviors, and hidden interaction patterns. To address these challenges, we propose GroupTrackVis, an interactive visual analytics system that incorporates both advanced algorithms and novel visualization designs, to help instructors analyze group discussions mainly from three perspectives: topic evolution, student performance, and interaction. GroupTrackVis proposes an enhanced topic segmentation algorithm by incorporating word vector weighting and reply relationship analysis, effectively disentangling overlapping discussions. It also extracts six key behavioral attributes from multimodal educational data, offering a comprehensive view of student performance and providing insights into the key factors driving learning outcomes. Additionally, a multi-layer tree network with edge bundling techniques is implemented to clearly visualize the dynamic evolution of student interactions. The integration of algorithms with interactive visualizations enables instructors to explore discussions quickly and dynamically adjust their analysis as the discussion evolves. The effectiveness of GroupTrackVis is demonstrated through two case studies, a user study, and expert interviews, highlighting its ability to support instructors in identifying engaged and disengaged students, and tracking discussion dynamics.

Recently, vehicle platooning has demonstrated its potential for improving traffic efficiency and enhancing the driving experience. When the onboard sensors of follower vehicles are unavailable, the leader vehicle typically needs to maintain the safety of platooning system through vehicle-to-vehicle (V2V) communications. Increasing communication resource utilization, such as transmission power, can enhance communication accuracy and ensure platooning safety but induce more communication costs. Balancing communication costs and system safety poses challenges in this dynamic system. In this paper, we propose a joint optimization method for transmission power control and system stability maintenance based on deep reinforcement learning (DRL), which can minimize the total transmission power of the leader vehicle over a period of time while maintaining the stability of the platooning system. To achieve this, we implement two advanced DRL algorithms, namely Deep Q-Network (DQN) and Actor-Critic, to derive task-oriented power control strategies for platooning stability. Experimental results show that the proposed strategy outperforms the conventional power control strategy. The transmission power gradually increases as the follower vehicle approaches the boundary of the safe distance range, indicating that the proposed strategy can adaptively adjust the transmission power to meet the requirements of the task.

In this paper, the situation in which a receiver has to execute a task from a quantized version of the information source of interest is considered. The task is modeled by the minimization problem of a general goal function$f(x;g)$for which the decision$x$has to be taken from a quantized version of the parameters$g$. This problem is relevant in many applications, e.g., for radio resource allocation (RA), high spectral efficiency communications, controlled systems, or data clustering in the smart grid. By resorting to high resolution (HR) analysis, it is shown how to design a quantizer that minimizes the gap between the minimum of$f$(which would be reached by knowing$g$perfectly) and what is effectively reached with a quantized$g$. The conducted formal analysis both provides quantization strategies in the HR regime and insights for the general regime and allows a practical algorithm to be designed. The analysis also allows one to provide some elements to the new and fundamental problem of the relationship between the goal function regularity properties and the hardness to quantize its parameters. The derived results are discussed and supported by a rich numerical performance analysis in which known RA goal functions are studied and allows one to exhibit very significant improvements by tailoring the quantization operation to the final task.

In this paper, the situation in which a receiver has to execute a task from a quantized version of the information source of interest is considered. The task is modeled by the minimization problem of a general cost function f(x;g) for which the decision x has to be taken from quantized parameters g. Especially, we focus on the particular scenario where the decision space is a convex polyhedron with cost function being convex. Furthermore, we propose a new goal-oriented quantization algorithm by combining the procedure of iteratively expanding and reinstating decision set together with Jensen’s inequality. Proposed method could also be extended to some non-convex scenarios, namely, weakly convex cost function whose eigenvalues of Hessian matrix w.r.t decision x are lower-bounded. Numerical results show that proposed algorithm can considerably reduce the optimality loss (OL) compared to conventional approaches or the required number of quantization bits to achieve a certain relative optimality loss.

Visual analytics techniques are widely utilized to facilitate the exploration of online educational data. To help researchers better understand the necessity and the efficiency of these techniques in online education, we systematically review related works of the past decade to provide a comprehensive view of the use of visualization in online education problems. We establish a taxonomy based on the analysis goal and classify the existing visual analytics techniques into four categories: learning behavior analysis, learning content analysis, analysis of interactions among students, and prediction and recommendation. The use of visual analytics techniques is summarized in each category to show their benefits in different analysis tasks. At last, we discuss the future research opportunities and challenges in the utilization of visual analytics techniques for online education.

This paper investigates the uplink reception in the cloud radio access network (C-RAN) with finite-capacity fronthaul links. The latter is an emerging network that transfers the computing load from the radio heads (RHs) to the central processor (CP) unit. Due to the prohibitive complexity of computations, the most efficient uplink C-RAN schemes are challenging to be implemented in practical systems. Using deep neural networks (DNNs), we propose a new and low complex distributed processing for uplink C-RAN subject to some quantification rules. The objective of our architecture, called TDNet, is to optimize the processing jointly at the RHs and the CP side. Our goal is not to solve signal detection in multi-antenna systems. Instead, our work aims to find a helpful transformation scheme at the RH side before quantization. A correspondent decoding scheme at the CP side considers the quantization scheme. Inspired by the projected gradient descent algorithm, TDNet is designed as a distributed DNN with sparse connections. Numerical results are provided and show that our scheme outperforms linear receivers such as the zero-forcing (ZF). It also achieves near-optimal performance compared to the sphere decoder (SD) algorithm, especially for a low-to-moderate number of quantization bits.

Assuming that the number of possible decisions for a transmitter (e.g., the number of possible beam-forming vectors) has to be finite and is given, this paper investigates for the first time the problem of determining the best decision set when energy-efficiency maximization is pursued. We propose a framework to find a good (finite) decision set which induces a minimal performance loss w.r.t. to the continuous case. We exploit this framework for a scenario of energy-efficient MIMO communications in which transmit power and beamforming vectors have to be adapted jointly to the channel given under finite-rate feedback. To determine a good decision set we propose an algorithm which combines the approach of Invasive Weed Optimization (IWO) and an Evolutionary Algorithm (EA). We provide a numerical analysis which illustrates the benefits of our point of view. In particular, given a performance loss level, the feedback rate can by reduced by 2 when the transmit decision set has been designed properly by using our algorithm. The impact on energy-efficiency is also seen to be significant.

Cooperative schemes for energy efficient resource allocation usually require certain forms of communication (implicit or explicit) among the cooperating transmitters. These solutions are therefore impractical in the fast fading scenario where the channel gains change very quickly and there is no time for communication. Inspired by previous information theoretical results, in this paper we propose a heuristic resource allocation function which achieves cooperation without mutual communication among the transmitters. This function is a one shot decision function whose parameters are tuned offline for given channel statistics. It can be used online in a fast fading scenario as given the local channel state information (CSI), we can instantaneously find an energy efficient power allocation in a distributed manner. We compare the performance of the proposed function with other state of the art distributed power allocation schemes requiring communication. While some communicating schemes achieve better performances than the one proposed herein, it must be noted that they do so by using up timeslots for communication. By the virtue of not requiring communication, the proposed algorithm is superior for fast fading scenarios where communication might not be feasible as the coherence time of channels is only a few timeslots which will be used up for communication.

In this paper, we propose a distributed implementation of the opportunistic interference alignment (OIA) technique developed in [1]. Therein, global channel state information (CSI) is assumed at the secondary transmitter that is, the knowledge of all the channel transfer matrices is required, which may be difficult or impossible to obtain in practice. Therefore, we propose to relax this assumption by only assuming local CSI at the transmitters and that the covariance of the received secondary signal is available at the secondary transmitter; this setup is quite similar to the one assumed for the MIMO iterative water-filling algorithm [2]. One of the key ingredients which allows the secondary transmitter to implement the OIA condition of [1] by only having this reduced knowledge is that the primary transmitter reveals information about its local channels to the secondary transmitter by embedding (for one time-slot typically, supposedly among many others) in its pre-processing matrix the information needed at the secondary. The proposed distributed implementation is proved to be effective analytically but simulations are also provided to prove that it seems to be robust against imperfect covariance feedback.

In this paper, we propose simple one-shot power control functions and assess their performance both through analytical and numerical results. The proposed functions only assume individual channel state information (CSI) at each transmitter and are based on channel inversion and more importantly on thresholding; a transmitter uses zero power if the channel gain is below a threshold. Although the idea of thresholding has been used for maximizing spectral efficiency, it has not been used for maximizing (the total network) energy-efficiency (EE), which is measured here in terms of sum-EE. More specifically, we prove the optimality of the proposed policy in asymptotic regimes such as the low and high interference scenarios. We also prove that the expected sum-energy is individually quasi-concave with respect to each of the thresholds; this allows us to provide a low-complexity algorithm which can be run offline to find good thresholds. Through numerical simulations, we show that the simple idea of thresholding provides very appreciable gains.

In this paper, we propose a new perspective for quantizing a signal and more specifically the channel state information (CSI). The proposed point of view is fully relevant for a receiver which has to send a quantized version of the channel state to the transmitter. Roughly, the key idea is that the receiver sends the right amount of information to the transmitter so that the latter be able to take its (resource allocation) decision. More formally, the decision task of the transmitter is to maximize a utility function f(x;g) with respect to x (e.g., a power allocation vector) given the knowledge of a quantized version of the function parameters g. We exhibit a special case of an energy-efficient power control (PC) problem for which the optimal task oriented CSI quantizer (TOCQ) can be found analytically. For more general utility functions, we propose to use neural networks (NN) based learning. Simulations show that the compression rate obtained by adapting the feedback information rate to the function to be optimized may be significantly increased.

In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task. More specifically, we restrict our attention to how the source should quantize information so that the destination can maximize a utility function which represents the task to be executed only knowing the quantized information. For example, for utility functions under the form u (x; g), x might represent a decision in terms of using some radio resources and g the system state which is only observed through its quantized version Q(g). Both in the case where the utility function is known and the case where it is only observed through its realizations, we provide solutions to determine such a quantizer. We show how this approach applies to energy-efficient power allocation. In particular, it is seen that quantizing the state very roughly is perfectly suited to sum-rate-type function maximization, whereas energy-efficiency metrics are more sensitive to imperfections.

In many resource allocation problems, optimal allocation strategies must be determined when only a quantized version of the relevant parameters are available, for instance, power allocation in wireless communications. The contribution of this work is threefold. First, the quantization problem is revisited and a framework which encompasses the classical problem of quantization is proposed. Instead of minimizing the distortion, the goal is to minimize the gap between the maximum of a general payoff function (which would be reached by knowing all parameters of the function) and what is effectively reached when only the quantized version of the parameters is available. Then, to determine such a quantizer, the well-known Lloyd-Max algorithm is generalized. At last, we show how this framework can be applied to the problem of power control in wireless communications; the obtained numerical results clearly show the potential of such a framework.

A novel technique is proposed, which enables each transmitter to acquire global channel state information (CSI) from the sole knowledge of individual received signal power measurements, which makes dedicated feedback or inter-transmitter signaling channels unnecessary. To make this possible, we resort to a completely new technique whose key idea is to exploit the transmit power levels as symbols to embed information and the observed interference as a communication channel the transmitters can use to exchange coordination information. Although the used technique allows any kind of low-rate information to be exchanged among the transmitters, the focus here is to exchange local CSI. The proposed procedure also comprises a phase which allows local CSI to be estimated. Once an estimate of global CSI is acquired by the transmitters, it can be used to optimize any utility function which depends on it. While algorithms, which use the same type of measurements, such as the iterative water-filling algorithm, implement the sequential best-response dynamics (BRD) applied to individual utilities, here, thanks to the availability of global CSI, the BRD can be applied to the sum-utility. Extensive numerical results show that significant gains can be obtained and, this, by requiring no additional online signaling.