Offline multi-agent reinforcement learning (MARL) addresses key limitations of online MARL, such as safety concerns, expensive data collection, extended training intervals, and high signaling overhead caused by online interactions with the environment. In this work, we propose an offline MARL algorithm for radio resource management (RRM), focusing on optimizing scheduling policies for multiple access points (APs) to jointly maximize the sum and tail rates of user equipment (UEs). We evaluate three training paradigms: centralized, independent, and centralized training with decentralized execution (CTDE). Our simulation results demonstrate that the proposed offline MARL framework outperforms conventional baseline approaches, achieving over a$15\%$improvement in a weighted combination of sum and tail rates. Additionally, the CTDE framework strikes an effective balance, reducing the computational complexity of centralized methods while addressing the inefficiencies of independent training. These results underscore the potential of offline MARL to deliver scalable, robust, and efficient solutions for resource management in dynamic wireless networks.

This paper introduces the minimum error entropy (MEE) criterion as an advanced information-theoretic loss function tailored for deep learning applications in wireless communications. The MEE criterion leverages higher-order statistical properties, offering robustness in noisy scenarios like Rayleigh fading and impulsive interference. In addition, we propose a less complex, matrix based version of the MEE function to enhance practical usability in wireless communications. The method is evaluated through simulations on two critical applications: over-the-air regression and indoor localization. Results indicate that the MEE criterion outperforms conventional loss functions, such as mean squared error (MSE) and mean absolute error (MAE), achieving significant performance improvements in terms of accuracy, over 20% gain over traditional methods, and convergence speed across various channel conditions. This work establishes MEE as a promising alternative for wireless communication tasks in deep learning models, enabling better resilience and adaptability.

Smart Campus is one of the essential use cases in the Internet of Things (acrshort IoT). This work describes a long-range wide area network (LoRaWAN)-based smart campus data set comprising measurements of several sensors in hundreds of acrshort IoT devices. In addition, the data set contains information about the PHY and MAC layers of the LoRaWAN network. Therefore, we first describe the LoRa network, the connection between devices and gateway, and the gateway and network server. As the wireless network is prone to errors, e.g., outages, collisions, and interference, among other factors, the collected data may contain missing transmissions. To alleviate the problem of missing values, we resort to a$k$-nearest neighbor approach. For example, once the data imputation phase is complete, we employ an long short-term memory (LSTM) architecture to predict future sensor readings. Then, we build a deep neural network (DNN) to predict the room occupancy based on the selected sensor’s readings. Our results show that our model achieves an accuracy of 95% in predicting the number of people in a room. Furthermore, the data set is openly available and described in detail, which is an opportunity to explore other features and applications in Smart Campus.

The age of information (AoI) is used to measure the freshness of the data. In IoT networks, the traditional resource management schemes rely on a message exchange between the devices and the base station (BS) before communication which causes high AoI, high energy consumption, and low reliability. Unmanned aerial vehicles (UAVs) as flying BSs have many advantages in minimizing the AoI, energy-saving, and throughput improvement. In this paper, we present a novel learning-based framework that estimates the traffic arrival of IoT devices based on Markovian events. The learning proceeds to optimize the trajectory of multiple UAVs and their scheduling policy. First, the BS predicts the future traffic of the devices. We compare two traffic predictors: 1) the forward algorithm (FA) and 2) the long short-term memory (LSTM). Afterward, we propose a deep reinforcement learning (DRL) approach to optimize the optimal policy of each UAV. Finally, we manipulate the optimum reward function for the proposed DRL approach. Simulation results show that the proposed algorithm outperforms the random-walk (RW) baseline model regarding the AoI, scheduling accuracy, and transmission power.

In many massive IoT communication scenarios, the IoT devices require coverage from dynamic units that can move close to the IoT devices and reduce the uplink energy consumption. A robust solution is to deploy a large number of UAVs (UAV swarm) to provide coverage and a better line of sight (LoS) for the IoT network. However, the study of these massive IoT scenarios with a massive number of serving units leads to high dimensional problems with high complexity. In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices. The target is to minimize the overall age of information in the IoT network. The results reveal that both cooperative and partially cooperative multi-agent deep reinforcement learning approaches are able to outperform the high-complexity centralized deep reinforcement learning approach, which stands helpless in large-scale networks.

The current random access (RA) allocation techniques suffer from congestion and high signaling overhead while serving massive machine-type communication (mMTC) applications. To this end, third-generation partnership project introduced the need to use fast uplink grant (FUG) allocation in order to reduce latency and increase reliability for smart Internet of Things (IoT) applications with strict Quality-of-Service constraints. We propose a novel FUG allocation based on support vector machine (SVM). First, machine-type communication (MTC) devices are prioritized using an SVM classifier. Second, a long short-term memory architecture is used for traffic prediction and correction techniques to overcome prediction errors. Both results are used to achieve an efficient resource scheduler in terms of the average latency and total throughput. A coupled Markov modulated Poisson process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques. In addition, an extended traffic model-based CMMPP is used to evaluate the proposed algorithm in a more dense network. We test the proposed scheme using real-time measurement data collected from the Numenta anomaly benchmark (NAB) database. Our simulation results show the proposed model outperforms the existing RA allocation schemes by achieving the highest throughput and the lowest access delay of the order of 1 ms by achieving prediction accuracy of 98 % when serving the target massive and critical MTC applications with a limited number of resources.

In this work, we present a novel traffic prediction and fast uplink (FU) framework for IoT networks controlled by binary Markovian events. First, we apply the forward algorithm with hidden Markov models (HMMs) in order to schedule the available resources to the devices with maximum likelihood activation probabilities via the FU grant. In addition, we evaluate the regret metric as the number of wasted transmission slots to evaluate the performance of the prediction. Next, we formulate a fairness optimization problem to minimize the Age of Information (AoI) while keeping the regret as minimum as possible. Finally, we propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) in a real-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models, such as time-division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and AoI.