With the explosive growth in demand for mobile traffic, one of the promising solutions is to offload cellular traffic to small base stations for better system efficiency. Due to increasing system complexity, network operators are facing severe challenges and looking for machine learning-based solutions. In this work, we propose an energy-aware mobile traffic offloading scheme in the heterogeneous network jointly apply deep Q network (DQN) decision making and advanced traffic demand forecasting. The base station control model is trained and verified on an open dataset from a major telecom operator. The performance evaluation shows that DQN with traffic forecasting outperforms others at all levels of mobile traffic demands. Also, the advantage of accurate traffic prediction is more significant under higher traffic loads.

Massive multiple-input-multiple-output (MIMO) systems support advanced applications with high data rates, low latency, and high reliability in next-generation mobile networks. However, using machine learning in massive MIMO resource allocation is challenging due to quality of service (QoS) and network complexity across layers. This work presents a novel framework for adapting the scheduling and antenna allocation in massive MIMO systems using deep reinforcement learning (DRL). Rather than directly assigning execution parameters, the proposed framework utilizes DRL to select combinations of algorithms based on the current traffic conditions. The DRL model is trained using a specialized Markov decision process (MDP) model with a componentized action structure and is evaluated in realistic traffic scenarios. The results show that the proposed framework increases satisfied users by 7.2% and 12.5% compared to static algorithm combinations and other cross-layer adaptation methods. This demonstrates the effectiveness of the framework in managing and optimizing resource allocation in a flexible and adaptable manner.

Sixth-generation (6G) wireless communication networks will provide larger coverage and capacity with lower energy consumption and hardware costs than 5G. Intelligent reflecting surface (IRS)-aided millimeter-wave massive MIMO OFDM communication is a new technology that intelligently manipulates electromagnetic waves. This has recently attracted much attention given its potential to manage the wireless propagation environment at low hardware costs and with minimal energy usage. However, channel prediction is complicated by the fact that IRS is rarely equipped with power amplifiers, various radio frequency chains, or a significant number of reflecting components. In this paper, we propose a convolutional denoising autoencoder model and investigate a joint attention mechanism for channel prediction. Then, we employ the attention mechanism to identify features of channel subcarrier interference to improve the channel prediction performance. Long-range dependent specificity is captured through the attention mechanism to generate useful features from the input signal. The encoder-decoder design of the autoencoder serves as a dimensionality reduction method that enables the autoencoder to predict the spatial and temporal distribution features of continuous signals by exploiting the extraction of sequence features from the model. Numerical results show that the proposed algorithm significantly improves the performance of IRS-aided millimeter-wave massive MIMO OFDM communication systems compared with previous methods.

Broadcasting is an essential operation for Wireless Ad Hoc Networks to perform routing path discovery, and it has become the predominant technology for message dissemination in Vehicular Ad Hoc Networks (VANETs). In the reactive routing schemes of wireless ad hoc network, Routing Request (RREQ) packets are broadcast from the source, and once received the destination traces back the broadcasting path to build an on-demand route. In VANETs, different broadcasting schemes have been proposed for safety, comfort, and commercial applications. However, broadcasting usually generates a lot of redundant messages which would cause excessive channel contention and packet collisions, especially when the density of the network becomes higher. In this survey article, we first introduce the most common data dissemination techniques used in wireless ad hoc networks. After that, we discuss techniques that have been proposed to mitigate the broadcast storm problem. Finally, we introduce an effective model for analyzing the broadcasting schemes, and based on the model we propose an adaptively adjusted probabilistic scheme that highly improves the reliability of broadcasting in dense ad hoc networks.

Managing cellular networks in the Beyond 5G (B5G) era is a complex and challenging task requiring advanced deep learning approaches. Traditional models focusing on internet traffic (INT) analysis often fail to capture the rich temporal and spatial contexts essential for accurate INT predictions. Furthermore, these models do not account for the influence of external factors such as weather, news, and social trends. This study proposes a multi-source CNN-RNN (MSCR) model that leverages a rich dataset, including periodic, weather, news, and social data to address these limitations. This model enables the capture and fusion of diverse data sources for improved INT prediction accuracy. An advanced deep learning model, the transformer-enhanced CNN-RNN (TE-CNN-RNN), has been introduced. This model is specifically designed to predict INT data only. This model demonstrates the effectiveness of transformers in extracting detailed temporal-spatial features, outperforming conventional CNN-RNN models. The experimental results demonstrate that the proposed MSCR and TE-CNN-RNN models outperform existing state-of-the-art models for traffic forecasting. These findings underscore the transformative power of transformers for capturing intricate temporal-spatial features and the importance of multi-source data and deep learning techniques for optimizing cell site management in the B5G era.