A.1 SVRG and SCSG Here we provide the pseudocode for SVRG (Algorithm 2) and SCSG (Algorithm 3) seen in Lei et al. [35]. The idea of SVRG (Algorithm 2) is to reuses past full gradient computations (line 3) to reduce the variance of the current stochastic gradient estimate (line 7) before the parameter update (line 8). Note that N = 1 corresponds to a GD step (i.e., v SVRG achieves linear convergence O(1/T) using the semi-stochastic gradient. The key difference is that SCSG (Algorithm 3) considers a sequence of time-varying batch sizes (Bt and bt) and employs geometric sampling to generate the number of parameter update steps Nt in each iteration (line 6), instead of fixing the batch sizes and the number of updates as done in SVRG. Particularly when finding an -approximate solution (Definition 1) for optimizing smooth non-convex objectives, Lei et al. [35] proves that SCSG is never worse than SVRG in convergence rate and significantly outperforms SVRG when the requiredis small.

The 3rd Generation Partnership Project started the study of Release 18 in 2021. Artificial intelligence (AI)-native air interface is one of the key features of Release 18, where AI for channel state information (CSI) feedback enhancement is selected as the representative use case. This article provides an overview of AI for CSI feedback enhancement in 5G-Advanced. Several representative non-AI and AI-enabled CSI feedback frameworks are first introduced and compared. Then, the standardization of AI for CSI feedback enhancement in 5G-advanced is presented in detail. First, the scope of the AI for CSI feedback enhancement in 5G-Advanced is presented and discussed. Then, the main challenges and open problems in the standardization of AI for CSI feedback enhancement, especially focusing on performance evaluation and the design of new protocols for AI-enabled CSI feedback, are identified and discussed. This article provides a guideline for the standardization study of AI-based CSI feedback enhancement.

Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression algorithms are statistical and machine learning algorithms that can provide probabilistic forecasts in a straightforward way, and have not been applied so far for urban water demand forecasting. In this work, we aim to fill this gap by automating and extensively comparing several quantile-regression-based practical systems for probabilistic one-day ahead urban water demand forecasting. For designing the practical systems, we use five individual algorithms (i.e., the quantile regression, linear boosting, generalized random forest, gradient boosting machine and quantile regression neural network algorithms), their mean combiner and their median combiner.

Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression algorithms are statistical and machine learning algorithms that can provide probabilistic forecasts in a straightforward way, and have not been applied so far for urban water demand forecasting. In this work, we aim to fill this gap by automating and extensively comparing several quantile-regression-based practical systems for probabilistic one-day ahead urban water demand forecasting. For designing the practical systems, we use five individual algorithms (i.e., the quantile regression, linear boosting, generalized random forest, gradient boosting machine and quantile regression neural network algorithms), their mean combiner and their median combiner. The comparison is conducted by exploiting a large urban water flow dataset, as well as several types of hydrometeorological time series (which are considered as exogenous predictor variables in the forecasting setting). The results mostly favour the practical systems designed using the linear boosting algorithm, probably due to the presence of trends in the urban water flow time series. The forecasts of the mean and median combiners are also found to be skilful in general terms.

To improve the system performance towards the Shannon limit, advanced radio resource management mechanisms play a fundamental role. In particular, scheduling should receive much attention, because it allocates radio resources among different users in terms of their channel conditions and QoS requirements. The difficulties of scheduling algorithms are the tradeoffs need to be made among multiple objectives, such as throughput, fairness and packet drop rate. We propose a smart scheduling scheme based on deep reinforcement learning (DRL). We not only verify the performance gain achieved, but also provide implementation-friend designs, i.e., a scalable neural network design for the agent and a virtual environment training framework. With the scalable neural network design, the DRL agent can easily handle the cases when the number of active users is time-varying without the need to redesign and retrain the DRL agent. Training the DRL agent in a virtual environment offline first and using it as the initial version in the practical usage helps to prevent the system from suffering from performance and robustness degradation due to the time-consuming training. Through both simulations and field tests, we show that the DRL-based smart scheduling outperforms the conventional scheduling method and can be adopted in practical systems. The wireless communication industry has been keeping a fast growing and updating speed for several decades. About every ten years, new generations of mobile communication system were standardized with lots of new features and supported scenarios. Thanks to the evolution of wireless communications technologies, we are now enjoying diverse services and applications conveniently. It is well known that the fifth generation (5G) mobile communications system supports three major categories of services, i.e., enhanced mobile broadband (eMBB), ultrareliable and low-latency communications (uRLLC) and massive machine-type communications (mMTC). Meanwhile, new applications and scenarios have never stopped coming up, which sets up new requirements including even higher throughput, more connected devices, faster access with lower latency and higher efficiency for wireless communication systems. With all these requirements in mind, designing a new generation of mobile communications system becomes a quite challenging work.