Intelligent reflecting surfaces (IRSs), active and/or passive, can be densely deployed in complex environments to significantly enhance wireless network coverage for both wireless information transfer (WIT) and wireless power transfer (WPT). In this letter, we study the downlink WIT/WPT from a multi-antenna base station to a single-antenna user over a multi-active/passive IRS (AIRS/PIRS)-enabled wireless link. In particular, we aim to optimize the location of the AIRS with those of the other PIRSs being fixed to maximize the received signal-to-noise ratio (SNR) and signal power at the user in the cases of WIT and WPT, respectively. We derive the optimal solutions for these two cases in closed-form, which reveals that the optimal AIRS deployment is generally different for WIT versus WPT. Furthermore, both analytical and numerical results are provided to show the conditions under which the proposed AIRS deployment strategy yields superior performance to other baseline deployment strategies as well as the conventional all- PIRS enabled WIT/WPT.
Intelligent reflecting surface (IRS) has emerged as a promising technology to realize smart radio environment for future wireless communication systems. Existing works in this line of research have mainly considered the conventional passive IRS that reflects wireless signals without power amplification, while in this article, we give an overview of a new type of IRS, called active IRS, which enables simultaneous signal reflection and amplification, thus significantly extending the signal coverage of passive IRS. We first present the fundamentals of active IRS, including its hardware architecture, signal and channel models, as well as practical constraints, in comparison with those of passive IRS. Then, we discuss new considerations and open issues in designing active-IRS-aided wireless communications, such as the reflection optimization, channel estimation, and deployment for active IRS, as well as its integrated design with passive IRS. Finally, numerical results are provided to show the potential performance gains of active IRS as compared to passive IRS and traditional active relay.
Aiming at the current problems of theory-oriented,practice-light,and lack of innovation ability in the teaching of postgraduate software engineering courses,a multi-stage feedback teaching mode for software engineering postgraduates based on competition project_driven is proposed. The model is driven by the competition project,and implementing suggestions are given in terms of stage allocation of software engineering course tasks and ability cultivation,competition case design and process evaluation improvement,etc. Through the implementation of this teaching mode,students enthusiasm and initiative are expected to be stimulated,and the overall development of students professional skills and comprehension ability would be improved to meet the demand of society for software engineering technical talents.
Embedding tables are usually huge in click-through rate (CTR) prediction models. To train and deploy the CTR models efficiently and economically, it is necessary to compress their embedding tables at the training stage. To this end, we formulate a novel quantization training paradigm to compress the embeddings from the training stage, termed low-precision training (LPT). Also, we provide theoretical analysis on its convergence. The results show that stochastic weight quantization has a faster convergence rate and a smaller convergence error than deterministic weight quantization in LPT. Further, to reduce the accuracy degradation, we propose adaptive low-precision training (ALPT) that learns the step size (i.e., the quantization resolution) through gradient descent. Experiments on two real-world datasets confirm our analysis and show that ALPT can significantly improve the prediction accuracy, especially at extremely low bit widths. For the first time in CTR models, we successfully train 8-bit embeddings without sacrificing prediction accuracy. The code of ALPT is publicly available.
Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the BS and its served users. In contrast, we propose in this paper a new integrated IRS BS architecture by deploying IRSs inside the BS antenna radome. Since the distance between the integrated IRSs and BS antenna array is practically small, the path loss among them is significantly reduced and the real time control of the IRS reflection by the BS becomes easier to implement. However, the resultant near field channel model also becomes drastically different. Thus, we propose an element wise channel model for IRS to characterize the channel vector between each single antenna user and the antenna array of the BS, which includes the direct (without any IRS reflection) as well as the single and double IRS-reflection channel components. Then, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum rate of the users. By considering two typical cases with/without perfect CSI at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS BS architecture over the conventional multi antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.
Regression trees are one of the oldest forms of AI models, and their predictions can be made without a calculator, which makes them broadly useful, particularly for high-stakes applications. Within the large literature on regression trees, there has been little effort towards full provable optimization, mainly due to the computational hardness of the problem. This work proposes a dynamic-programming-with-bounds approach to the construction of provably-optimal sparse regression trees. We leverage a novel lower bound based on an optimal solution to the k-Means clustering algorithm in 1-dimension over the set of labels. We are often able to find optimal sparse trees in seconds, even for challenging datasets that involve large numbers of samples and highly-correlated features.
In this paper, we propose to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channels at different locations. Based on the proposed channel model, we formulate the joint optimization problem of UAV three-dimensional (3-D) positioning and resource allocation, by power allocation, user association, and subcarrier allocation, to maximize the minimum achievable rate among users. To solve this non-convex combinatorial programming problem, we introduce a penalty term to relax it and develop a suboptimal solution via a penalty-based double-loop iterative optimization framework. The inner loop solves the penalized problem by employing the block successive convex approximation (BSCA) technique, where the UAV positioning and resource allocation are alternately optimized in each iteration. The outer loop aims to obtain proper penalty multipliers to ensure the solution of the penalized problem converges to that of the original problem. Simulation results demonstrate the superiority of the proposed algorithm over other benchmark schemes in terms of the minimum achievable rate.
Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface design, the recommendation page has evolved to a multi-list style. Separately employing traditional list-level reranking methods for different lists overlooks the inter-list interactions and the effect of different page formats, thus yielding suboptimal reranking performance. Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists. To this end, we propose to draw a bird's-eye view of \textbf{page-level reranking} and design a novel Page-level Attentional Reranking (PAR) model. We introduce a hierarchical dual-side attention module to extract personalized intra- and inter-list interactions. A spatial-scaled attention network is devised to integrate the spatial relationship into pairwise item influences, which explicitly models the page format. The multi-gated mixture-of-experts module is further applied to capture the commonalities and differences of user behaviors between different lists. Extensive experiments on a public dataset and a proprietary dataset show that PAR significantly outperforms existing baseline models.
Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the most challenging problems of this task is the existence of severe selection bias caused by the inherent self-selection behavior of users and the item selection process of systems. Currently, doubly robust (DR) learning approaches achieve the state-of-the-art performance for debiasing CVR prediction. However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice. Motivated by such analysis, we propose a generalized learning framework that not only unifies existing DR methods, but also provides a valuable opportunity to develop a series of new debiasing techniques to accommodate different application scenarios. Based on the framework, we propose two new DR methods, namely DR-BIAS and DR-MSE. DR-BIAS directly controls the bias of DR loss, while DR-MSE balances the bias and variance flexibly, which achieves better generalization performance. In addition, we propose a novel tri-level joint learning optimization method for DR-MSE in CVR prediction, and an efficient training algorithm correspondingly. We conduct extensive experiments on both real-world and semi-synthetic datasets, which validate the effectiveness of our proposed methods.