Weixu Zhang, Ye Yuan, Changjiang Han, Yuxing Tian, Zipeng Sun, Linfeng Du, Jikun Kang, Hong Kang, Xue Liu, Haolun Wu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured information from unstructured text.Prior works typically represent information extraction as triplet-centric and use classical metrics such as precision and recall for evaluation.We reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights from various perspectives.We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP (AESOP) metric, designed to appropriately assess model performance.Later, we introduce a new Multistage Structured Entity Extraction (MuSEE) model that harnesses the power of LMs for enhanced effectiveness and efficiency by decomposing the extraction task into multiple stages.Quantitative and human side-by-side evaluations confirm that our model outperforms baselines, offering promising directions for future advancements in structured entity extraction.Our source code is available at https://github.com/microsoft/Structured- Entity-Extraction.

Digital twins are expected to play an important role in the widespread adaptation of AI-based networking solutions in the real world. The calibration of these virtual replicas is critical to ensure a trustworthy replication of the real environment. This work focuses on the input parameter calibration of radio access network (RAN) simulators using real network performance metrics as supervision signals. Usually, the RAN digital twin is considered a black-box function and each calibration problem is viewed as a standalone search problem. RAN simulators are slow and non-differentiable, often posing as the bottleneck in the execution time for these search problems. In this work, we aim to accelerate the search process by reducing the number of interactions with the simulator by leveraging RAN interactions from previous problems. We present a sequential Bayesian optimization framework that uses information from the past to warm-start the calibration process. Assuming that the network performance exhibits gradual and periodic changes, the stored information can be reused in future calibrations. We test our method across multiple physical sites over one week and show that using the proposed framework, we can obtain better calibration with a smaller number of interactions with the simulator during the search phase.

This paper advances the field of pedestrian localization by introducing a unifying framework for opportunistic positioning based on nonlinear factor graph optimization. While many existing approaches assume constant availability of one or multiple sensing signals, our methodology employs IMU-based pedestrian inertial navigation as the backbone for sensor fusion, opportunistically integrating Ultra- Wideband (UWB), Bluetooth Low Energy (BLE), and WiFi signals when they are available in the environment. The proposed PEOPLEx framework is designed to incorporate sensing data as it becomes available, operating without any prior knowledge about the environment (e.g. anchor locations, radio frequency maps, etc.), Our contributions are twofold: 1) we introduce an opportunistic multi-sensor and real-time pedestrian positioning framework fusing the available sensor measurements; 2) we develop novel factors for adaptive scaling and coarse loop closures, significantly improving the precision of indoor positioning. Experimental validation confirms that our approach achieves accurate localization estimates in real indoor scenarios using commercial smartphones.

With the global aim of reducing carbon emissions, energy saving for communication systems has gained tremendous attention. Efficient energy-saving solutions are not only required to accommodate the fast growth in communication demand but solutions are also challenged by the complex nature of the load dynamics. Recent reinforcement learning (RL)-based methods have shown promising performance for network optimization problems, such as base station energy saving. However, a major limitation of these methods is the requirement of online exploration of potential solutions using a high-fidelity simulator or the need to perform exploration in a real-world environment. We circumvent this issue by proposing an offline reinforcement learning energy saving (ORES) framework that allows us to learn an efficient control policy using previously collected data. We first deploy a behavior energy-saving policy on base stations and generate a set of interaction experiences. Then, using a robust deep offline reinforcement learning algorithm, we learn an energy-saving control policy based on the collected experiences. Results from experiments conducted on a diverse collection of communication scenarios with different behavior policies showcase the effectiveness of the proposed energy-saving algorithms.

To deal with notorious delays in communication systems, it is crucial to forecast key system characteristics, such as the communication load. Most existing studies aggregate data from multiple edge nodes for improving the forecasting accuracy. However, the bandwidth cost of such data aggregation could be unacceptably high from the perspective of system operators. To achieve both the high forecasting accuracy and bandwidth efficiency, this paper proposes an Adaptive Multi-Teacher Weighting in Teacher-Student Learning approach, namely AdaTeacher, for communication load forecasting of multiple edge nodes. Each edge node trains a local model on its own data. A target node collects multiple models from its neighbor nodes and treats these models as teachers. Then, the target node trains a student model from teachers via Teacher-Student (T-S) learning. Unlike most existing T-S learning approaches that treat teachers evenly, resulting in a limited performance, AdaTeacher introduces a bilevel optimization algorithm to dynamically learn an importance weight for each teacher toward a more effective and accurate T-S learning process. Compared to the state-of-the-art methods, Ada Teacher not only reduces the bandwidth cost by 53.85%, but also improves the load forecasting accuracy by 21.56% and 24.24% on two real-world datasets.

With the increasing use of data-intensive mobile applications and the number of mobile users, the demand for wireless data services has been increasing exponentially in recent years. In order to address this demand, a large number of new cellular base stations are being deployed around the world, leading to a significant increase in energy consumption and greenhouse gas emission. Consequently, energy consumption has emerged as a key concern in the fifth-generation (5G) network era and beyond. Reinforcement learning (RL), which aims to learn a control policy via interacting with the environment, has been shown to be effective in addressing network optimization problems. However, for reinforcement learning, especially deep reinforcement learning, a large number of interactions with the environment are required. This often limits its applicability in the real world. In this work, to better deal with dynamic traffic scenarios and improve real-world applicability, we propose a transfer deep reinforcement learning framework for energy optimization in cellular communication networks. Specifically, we first pre-train a set of RL-based energy-saving policies on source base stations and then transfer the most suitable policy to the given target base station in an unsupervised learning manner. Experimental results demonstrate that base station energy consumption can be reduced significantly using this approach.

The association of mobile devices with network resources (e.g., base stations, frequency bands/channels), known as load balancing, is critical to reduce communication traffic congestion and network performance. Reinforcement learning (RL) has shown to be effective for communication load balancing and achieves better performance than currently used rule-based methods, especially when the traffic load changes quickly. However, RL-based methods usually need to interact with the environment for a large number of time steps to learn an effective policy and can be difficult to tune. In this work, we aim to improve the data efficiency of RL-based solutions to make them more suitable and applicable for real-world applications. Specifically, we propose a simple, yet efficient and effective deep RL-based wireless network load balancing framework. In this solution, a set of good initialization values for control actions are selected with some cost-efficient approach to center the training of the RL agent. Then, a deep RL-based agent is trained to find offsets from the initialization values that optimize the load balancing problem. Experimental evaluation on a set of dynamic traffic scenarios demonstrates the effectiveness and efficiency of the proposed method.

In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and satisfy the UEs' service requirements. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate collaboration among the BSs (i.e., agents). In particular, to solve the Markov game and find a Nash equilibrium policy, we embrace the idea of adopting a nature agent to model the system uncertainty. Moreover, we utilize the self-attention mechanism, which encourages high-performance BSs to assist low-performance BSs. In addition, we consider two types of schemes, which can facilitate load balancing for both active UEs and idle UEs. We carry out extensive evaluations by simulations, and simulation results illustrate that, compared to the state-of-the-art MARL methods, Robust-MA3C scheme can improve the overall performance by up to 45%.

Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thousands of transmitting devices (cells). Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance. The effects of such changes are challenging to predict and will become even more so with the adoption of fifth-generation/sixth-generation (5G/6G) networks. Therefore, RAN monitoring is vital for network operators. We propose a self-supervised learning framework that leverages self-attention and self-distillation for this task. It works by detecting changes in Performance Measurement data, a collection of time-varying metrics which reflect a set of diverse measurements of the network performance at the cell level. Experimental results show that our approach outperforms the state of the art by 4% on a real-world based dataset consisting of about hundred thousands time series. It also has the merits of being scalable and generalizable. This allows it to provide deep insight into the specifics of mode of operation changes while relying minimally on expert knowledge.

With the continuous growth in communication network complexity and traffic volume, communication load balancing solutions are receiving increasing attention. Specifically, reinforcement learning (RL)-based methods have shown impressive performance compared with traditional rule-based methods. However, standard RL methods generally require an enormous amount of data to train, and generalize poorly to scenarios that are not encountered during training. We propose a policy reuse framework in which a policy selector chooses the most suitable pre-trained RL policy to execute based on the current traffic condition. Our method hinges on a policy bank composed of policies trained on a diverse set of traffic scenarios. When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training. Experiments demonstrate that this framework can outperform classical and adaptive rule-based methods by a large margin.

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G systems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixed-variable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using a-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.

Our work examines the way in which large language models can be used for robotic planning and sampling in the context of automated photographic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.

Advanced communication network functions, such as resource allocation and dynamic spectrum management, heavily rely on the accurate forecasting of traffic. Data-driven solutions, e.g., Neural Network (NN) based forecasting methods, have been proven to be effective only when sufficient data is available. However, Base Stations (BSs) have limited data in the real world, since big data for communication networks could be extremely expensive to collect, store, and migrate. Therefore, most existing traffic forecasting methods have limited accuracy in reality due to the lack of big data. To tackle this problem, our key observation is that, despite the data “amount” in a BS is limited, the data “source” is rich and diverse, i.e., in addition to Internet traffic logs, there are logs of Call and SMS. More importantly, our analysis shows a high correlation between different sources, which can be utilized to improve the forecasting accuracy. Motivated by this, we introduce AdaSource, a Multi-Source Adaptive Feature Boosting approach, which utilizes data source correlations for accurate traffic forecasting even on data-limited BSs. The core idea of AdaSource is a novel two-branch NN structure that adaptively trains multiple Encoder-Decoders for refining different data sources and multiple Encoder-Predictors for utilizing data source correlations to improve the accuracy. The experiments on a real-world dataset show that AdaSource improves the forecasting accuracy by up to 30.14%, compared to the state-of-the-art methods.

The anticipated huge number of devices and large traffic volumes impose new challenges on the communication system requirements and design. One of the main requirements of massive machine-type communication (mMTC) is to support network energy efficiency. Data compression is a widely adopted technique that enables higher energy efficiency, lower latency, and better bandwidth utilization. Unfortunately, the current compression techniques are mainly designed for human-type communications (HTC). Therefore, they consider the reconstruction fidelity, rather than the accuracy of inferred decisions, as the sole performance metric. In this work, we propose a novel encoder for data compression in mMTC communications, which is termed Distillation Encoder (DE). Unlike prior work, the design of the proposed DE aims to achieve high compression ratios while preserving the accuracy of the inferred decisions. DE inherits the knowledge of a large teacher model (trained on the raw data) through knowledge distillation. Evaluating the proposed framework on several public datasets shows a clear performance advantage compared with baseline models in terms of the inferred decision accuracy and generalizing to yet-unseen data. Moreover, the DE can be applied to learn efficient quantizers, as shown in the results.

Although reinforcement learning (RL) shows advantages in cellular network load balancing, it suffers from a low generalization ability, preventing it from real-world applications. Specifically, if network traffic pattern changes, the learned RL policy cannot adapt accordingly, resulting in system performance degradation. To address this issue, we propose a Multi-teacher MOdel BAsed Reinforcement Learning algorithm (MOBA), which leverages multi-teacher knowledge distillation theory to learn a generalized load balancing policy for adapting the real-world traffic pattern changes. The key is that different teachers represent different traffic patterns, and can learn various system models. By distilling and transferring the teacher knowledge, the student network is able to learn a generalized system model that covers different traffic patterns and unseen situations. Moreover, to improve the robustness of multi-teacher knowledge transfer, we learn a set of student models and use an ensemble method to jointly predict system dynamics. Results show that, compared with state-of-the-art RL methods, MOBA improves the minimal throughput and total throughput of a cellular network by up to 28.6% and 23.2%. Results also show that MOBA improves the training efficiency by up to 64%.

The modern power system is transitioning towards increasing penetration of renewable energy generation and demand from different types of electrical appliances. With this transition, residential load forecasting, especially short-term load forecasting (STLF), is becoming more and more challenging and important. Accurate short-term load forecasting can help improve energy dispatching efficiency and, as a consequence, reduce overall power system operation cost. Most current load forecasting algorithms assume that there is a large amount of training data available upon which to learn a reliable load forecasting model. However, this assumption can be challenging for real-world applications. In this work, we first propose the use of transfer learning and an attention mechanism to improve short-term load forecasting for a target domain with only a limited amount of available data. Furthermore, we extend the proposed method to utilize heterogeneous features which enables the approach to deal with more complex scenarios in the real world. Experimental results using real-world data sets show that the proposed methods can improve forecasting accuracy by a large margin over several existing baselines.

Prediction of future traffic load is a crucial task to support the automatic Operations, Administration, and Management (OAM) of communication networks. Existing Machine Learning (ML) models require big data to accomplish this task. However, large data sets are not always available, due to the limited storage capacity and the high storage cost at Base Stations (BSs). To solve the problem, we leverage the spatial-temporal correlation among different BSs, which allows other BSs’ data to be used for the prediction of the target BS. One major challenge in realizing this idea is the imbalance of data amounts between neighbor BSs and a prediction target BS. If one simply aggregates the data from both neighbors and target, the target’s traffic features would be overwhelmed by the neighbors’ data. To address this challenge, we propose a Spatial-Temporal Transfer (STT) framework, which trains a base model with an aggregated data set from multiple BSs, and then carefully refines the base model to serve a target BS. To strike a perfect balance between general tendency and individual features, STT adopts an advanced transfer learning technique that exploits regularization on model parameters. Experiments show the efficiency of the proposed STT framework.

Accurate traffic volume estimation and prediction are essential for advanced communication network functions, such as automatic operations and predictive resource allocation. Although machine learning (ML)-based approaches achieve great success in accomplishing this goal, existing approaches suffer from two drawbacks that limit their real-world applications. First, the ML-based prediction models developed in the past might be obsolete now, since the communication traffic patterns and volumes keep changing in the real world, leading to prediction errors. Second, most Base Stations (BSs) can only save a small amount of data due to the limited storage capacity and high storage costs, which prevents from training an accurate prediction model. In this paper, we propose a novel framework that adapts the prediction model to the constantly changing traffic with only a few current traffic data. Specifically, the framework first learns the knowledge of historical traffic data as much as possible by using a proposed two-branch neural network design, which includes a prediction and a reconstruction module. Then, the framework transfers the knowledge from an old (past) prediction model to a new (current) model for the model update by using a proposed continual knowledge distillation technique. Evaluations on a real-world dataset show that the proposed framework reduces the Mean Absolute Error (MAE) of traffic prediction by up to 9.62% compared to the state-of-the-art prediction methods.

Due to the rapid increase in wireless communication traffic in recent years, load balancing is becoming increasingly important for ensuring the quality of service. However, variations in traffic patterns near different serving base stations make this task challenging. On one hand, crafting a single control policy that performs well across all base station sectors is often difficult. On the other hand, maintaining separate controllers for every sector introduces overhead, and leads to redundancy if some of the sectors experience similar traffic patterns. In this paper, we propose to construct a concise set of controllers that cover a wide range of traffic scenarios, allowing the operator to select a suitable controller for each sector based on local traffic conditions. To construct these controllers, we present a method that clusters similar scenarios and learns a general control policy for each cluster. We use deep reinforcement learning (RL) to first train separate control policies on diverse traffic scenarios, and then incrementally merge together similar RL policies via knowledge distillation. Experimental results show that our concise policy set reduces redundancy with very minor performance degradation compared to policies trained separately on each traffic scenario. Our method also outperforms handcrafted control parameters, joint learning on all tasks, and two popular clustering methods.

Mobile edge computing (MEC) networks have been recently adopted to accommodate the fast-growing number of mobile devices performing complicated tasks with limited hardware capability. Recently, edge nodes with communication, computation, and caching capacities are starting to be deployed in MEC networks. Due to the physical separation of these resources, efficient coordination and scheduling are important for efficient resource utilization and optimal network performance. In this paper, we study mobility load balancing for communication, computation, and caching-enabled heterogeneous MEC networks. Specifically, we propose to tackle this problem via a multi-agent deep reinforcement learning-based framework. Users served by overloaded edge nodes are handed over to less loaded ones, to minimize the load in the most loaded base station in the network. In this framework, the handover decision for each user is made based on the user’s own observation which comprises the user’s task at hand and the load status of the MEC network. Simulation results show that our proposed multi-agent deep reinforcement learning-based approach can reduce the time-average maximum load by up to 30% and the end-to-end delay by 50% compared to baseline algorithms.

With the increasing adoption of renewable energy generation and electric devices, electric load forecasting, especially short-term load forecasting (STLF), is becoming more and more important. The widespread adoption of smart meters makes it possible to utilize complex machine learning models for both aggregated load and single-home residential load forecasting. Similar homes in nearby locations are likely to have similar load consumption patterns and this similarity can be used to improve the overall forecasting performance. However, most current work on load forecasting focuses on single learning task without exploiting the benefit of joint learning. In this paper, we propose the use of the multi-task learning (MTL) framework with long short-term memory (LSTM) recurrent neural networks for both aggregated and single home STLF. We propose a MTL-based forecasting algorithm for aggregated load forecasting in which single home forecasting is formulated as a single learning task within the MTL framework. This algorithm is extended for single home load forecasting in which load forecasting for a particular home becomes the primary learning task. Experimental results on real-world data sets demonstrate that residential load forecasting for both aggregated load and a single home can be improved within the MTL framework.

With the increasing popularity of electric vehicles and the growing trend of working from home, electricity consumption in the residential sector is expected to continue to grow rapidly over the next few years. As a consequence, short-term residential load forecasting is becoming even more vital for the reliability and sustainability of the smart grid. Although deep learning models have shown impressive success in different areas including short-term electric load forecasting, such models require a large amount of training data. For many real-world load forecasting cases, we may not have enough training data to learn a reliable forecasting model. In this paper, we address this challenge through the use of boosting-based transfer learning with multiple sources. We first train a set of deep regression models on source houses that can provide relatively abundant data. We then transfer these learned models via the boosting framework to support data-scarce target houses. The transfer process is selective and customized for each target house to minimize the potential for negative transfer. Experimental results, based on real-world residential data sets, show that the proposed method can significantly improve forecasting accuracy.

We propose an approach that enables simultaneous interpretable learning of a high-level discrete behaviour and its low-level rhythmic sub-behaviour. We do this though a unified reward function, where a reward function that only describes low-level behaviour, with less impact on learning of other behaviours is recovered from few-shot motion demonstrations. To this end, we first extract local behaviour motifs from state-only human demonstrations and random driving samples using an adaptive motif discovery approach derived from the Matrix Profile algorithm. We then optimize parameters for motif discovery by maximizing the sum and entropy over motif sizes. Interacting Multiple Model (IMM) estimators are constructed on top of linear-Gaussian dynamics of discovered motifs, the cumulative distributions over motifs estimated by IMMs serve as the basis of the reward function. By combining the recovered reward with the terrain type signal gathered from the environment, we are able to train a dual-objective off-road vehicle controller that demonstrates both terrain selection and human-like driving behaviours. Compared with related approaches across 10 people, our rhythmic behaviour reward recovery approach enables the controller to produce higher preference over human driving demonstrations. In addition to performing more stable across different people with 87% less variance than the best baseline in rhythmic behaviour indicator, our method reduces the negative effects on higher-level behaviour learning while maintaining high interpretability at all stages of the algorithm.

Emerging Internet of Things (IoT) applications, such as cashier-less shopping, mobile ads targeting, and geo-based augmented reality (AR), are expected to bring us much more convenience and infotainment. To realize this amazing future, we need to feed these applications with user locations of (sub)meter-level resolution anytime and anywhere. Unfortunately, many widely used location sources are either unavailable indoor (e.g., global positioning system) or coarse grained (e.g., user check-ins). In order to provide ubiquitous localization services, the widespread WiFi signals are being leveraged to establish (sub)meter-level localization systems. Fine-grained WiFi propagation characteristics, which are sensitive to human body locations, have been employed to create location fingerprints. However, these WiFi characteristics are also sensitive to: 1) the body shapes of different users and 2) the objects in the background environment. Consequently, systems based on WiFi fingerprints are vulnerable in the presence of: 1) new users with different body shapes and 2) daily changes of the environment, e.g., opening/closing doors. To tackle this issue, this article proposes a WiFi-based localization system based on domain-adaptation with cluster assumption, named Fidora. Fidora is able to: 1) localize different users with labeled data from only one or two example users and 2) localize the same user in a changed environment without labeling any new data. To achieve these, Fidora integrates two major modules. It first adopts a data augmenter that introduces data diversity using a variational autoencoder (VAE). It then trains a domain-adaptive classifier that adjusts itself to newly collected unlabeled data using a joint classification-reconstruction structure. We conducted real-world experiments to evaluate Fidora against the state of the art. It is demonstrated that when tested on an unlabeled user, Fidora increases the average$F1$score by 17.8% and improves the worst case accuracy by 20.2%. Moreover, when applied in a varied environment, Fidora outperforms the state of the art by 23.1%.

Load balancing has become a key technique to handle the increasing traffic demand and improve the user experience. It evenly distributes the traffic across network resources by offloading users from overloaded base stations or channels to less crowded ones. Load balancing is a multi-objective optimization problem involving the automatic adjustment of several parameters to simultaneously maximize multiple network performance indicators. However, the existing methods mostly rely on single-objective approaches which lead to sub-optimal solutions. In this paper, we introduce the first multi-objective reinforcement learning (MORL) framework for load balancing. Specifically, we propose a solution based on meta-reinforcement learning (meta-RL) to learn a general policy capable of quickly adapting to new trade-offs between the objectives. We further enhance the generalization of our proposed solution using policy distillation techniques. To showcase the effectiveness of our framework, experiments are conducted based on real-world traffic scenarios. Our results show that our load balancing framework can (i) significantly outperform the existing rule-based and single-objective solutions, (ii) compute better Pareto front approximations compared to MORL baselines, and (iii) quickly adapt to new objective trade-offs.

By placing the computing, storage and networking resources close to the end users, distributed edge computing greatly benefits the performance of 5G communication systems. However, as a tradeoff, resources on the edge are usually limited and imbalanced among the heterogeneous edge nodes. To overcome this drawback, this paper proposes a Transfer Learning based Prediction (TLP) framework that allows the edge nodes to share their resources and data in an efficient manner. In particular, the TLP framework focuses on the prediction of the future traffic load, which is a key reference for many automated network functions. To enhance the efficiency of data and bandwidth, TLP first learns a base model on a data-abundant edge node (the source), and then transfers this model (instead of data) to other data-limited nodes (the targets). To achieve a delicate balance between maintaining common features and learning target-specific features, we develop a new transfer learning technique named Similarity-based Elastic Weight Con-solidation (SEWC), and integrate it into TLP. Experiments on real-world data illustrate that, compared to the state-of-the-art methods, TLP-SEWC reduces the Mean Absolute Error (MAE) of traffic prediction by up to 57.9%.

Prediction of key system characteristics, such as the communication load, is required to overcome the delays in wireless communication systems. State-of-The-Art (SOTA) approaches mostly apply existing Neural Network (NN) structures, and extract latent features purely based on their sensitivity to the forecasting accuracy. This way of feature extraction may neglect some non-obvious yet informative dimensions in the model input, leading to inaccurate forecasting results. In this paper, we present an Adaptive Feature Boosting (AFB) approach, which integrates multiple AutoEncoders (AEs) to automatically extract robust and comprehensive latent features for communication load forecasting. The recurrent and residual connections among the AEs make sure that the extracted latent features are representative for all input dimensions. With more comprehensive information extracted from the history, the forecasting accuracy is thus improved. We evaluate AFB against existing approaches on a real-world dataset that contains Call Detail Records (CDRs) of the Milan city over a period of two months. The evaluation shows that our AFB-based approach achieves 35.2% more accurate load forecasting results than the SOTA deep approaches.

We present a generative adversarial method that uses deep learning to identify network load traffic conditions in which network optimization algorithms under-perform other known algorithms: the Deep Convolutional Failure Generator (DCFG). The spatial distribution of network load presents challenges for network operators for tasks such as load balancing, in which a network optimizer attempts to maintain high quality communication while at the same time abiding capacity constraints. Testing a network optimizer for all possible load distributions is challenging if not impossible. We propose a novel method that searches for load situations where a target network optimization method underperforms baseline, which are key test cases that can be used for future refinement and performance optimization. By modeling a realistic network simulator's quality assessments with a deep network and, in parallel, optimizing a load generation network, our method efficiently searches the high dimensional space of load patterns and reliably finds cases in which a target network optimization method under-performs a baseline by a significant margin.

Within a cellular network, load balancing between different cells is of critical importance to network performance and quality of service. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. These rule-based meth-ods are difficult to adapt quickly to traffic changes in real-world environments. Given the success of Reinforcement Learning (RL) algorithms in many application domains, there have been a number of efforts to tackle load balancing for communication systems using RL-based methods. To our knowledge, none of these efforts have addressed the need for data efficiency within the RL framework, which is one of the main obstacles in applying RL to wireless network load balancing. In this paper, we formulate the communication load balancing problem as a Markov Decision Process and propose a data-efficient transfer deep reinforcement learning algorithm to address it. Experimental results show that the proposed method can significantly improve the system performance over other baselines and is more robust to environmental changes.

Due to the uneven demographic distribution and people’s daily activities, communication systems usually experience highly imbalanced load across different cells. This imbalance leads to unsatisfied users in the congested cells and under-utilized resources in the less-loaded cells. To deal with this issue, existing work migrates the load from heavily loaded cells to lightly loaded cells, by either handing over active mode User Equipment (UEs) to other serving cells, or re-selecting the camping cells for idle mode UEs. In this paper, we further advance the research on Load Balancing (LB) with a hybrid control of both active and idle UEs. This task is challenging, due to the conflicts between Active-UE LB (AULB) and Idle-UE LB (IULB) policies. To overcome this challenge, we propose a Hierarchical Policy Learning (HPL) framework, which coordinates the actions between LB policies with a two-level learning structure. In this way, HPL produces AULB and IULB policies that are better aligned with each other. Extensive simulation results illustrate the efficiency and efficacy of the proposed HPL.

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays.To eliminate the need of a complex space scanning, this paper presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge to apply this idea in real-world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a few number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUSIC (MF-MUSIC) algorithm, which extends the classic MUSIC algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. By doing this, our framework uses only 3 antennas to achieve an accurate angle estimation, which is merely 0.15° different from the state-of-the-art 16-antenna method.

Although existing cellular network base stations are typically immobile, the recent development of small form factor base stations and self driving cars has enabled the possibility of deploying a team of continuously moving base stations that can reorganize the network infrastructure to adapt to changing network traffic usage patterns. Given such a system of mobile base stations (MBSes) that can freely move on the road, how should their path be planned in an effort to optimize the experience of the users? This paper addresses this question by modeling the problem as a Markov Decision Process where the actions correspond to the MBSes deciding which direction to go at traffic intersections; states corresponds to the position of MBSes; and rewards correspond to minimization of packet loss in the network. A Monte Carlo Tree Search (MCTS)-based anytime algorithm that produces path plans for multiple base stations while optimizing expected packet loss is proposed. Simulated experiments in the city of Verdun, QC, Canada with varying user equipment (UE) densities and random initial conditions show that the proposed approach consistently outperforms myopic planners, and is able to achieve near-optimal performance.

Passive sensing with ambient WiFi signals is a promising technique that will enable new types of human-robot interactions while preserving users' privacy. Here, we present PresSense, a system for human respiration sensing in noisy environments. Unlike existing WiFi-based respiration sensors, we employ a human presence detector, improving the robustness in scenarios where no human is present in an Area Of Interest (AOI). We also integrate our novel feature, Peak Distance Histogram (PDH), with other classic WiFi features to achieve better accuracy when someone is present in the AOI. We tested our system using commodity WiFi devices in an office room. Our PresSense outperforms the state of the arts in both respiration rate estimation and presence detection.