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With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous networks, mobile broadband users are generating massive volumes of data that undergo fast processing and computing to obtain actionable insights. While analyzing this huge amount of data typically involves machine and deep learning-based data-driven Artificial Intelligence (AI) models, a key challenge arises in terms of providing privacy assurances for user-generated data. Even though data-driven techniques have been widely utilized for network traffic analysis and other network management tasks, researchers have also identified that applying AI techniques may often lead to severe privacy concerns. Therefore, the concept of privacy-preserving data-driven learning models has recently emerged as a hot area of research to facilitate model training on large-scale datasets while guaranteeing privacy along with the security of the data. In this paper, we first demonstrate the research gap in this domain, followed by a tutorial-oriented review of data-driven models, which can be potentially mapped to privacy-preserving techniques. Then, we provide preliminaries of a number of privacy-preserving techniques (e.g., differential privacy, functional encryption, Homomorphic encryption, secure multi-party computation, and federated learning) that can be potentially adopted for emerging communication networks. The provided preliminaries enable us to showcase the subset of data-driven privacy-preserving models, which are gaining traction in emerging communication network systems. We provide a number of relevant networking use cases, ranging from the B5G core and Radio Access Networks (RANs) to semantic communications, adopting privacy-preserving data-driven models. Based on the lessons learned from the pertinent use cases, we also identify several open research challenges and hint toward possible solutions. 

WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. The high-frequency bands can provide high transmission rates, but their high amplitude makes it so the signal cannot go through any walls or obstacles. The signal also has a strong path loss element caused by the high frequency, significantly limiting the reach of connections because the signal is too weak at moderate distances. Due to these issues, users can easily lose connection with the access point while moving and need to connect to a new device, making WiGig systems unstable as they need to rely on frequent handovers to maintain a high-quality service. However, this solution is problematic as it forces users into bad connections and downtime before they are switched to a better access point. In this work, we use machine learning to identify patterns in user behaviors and predict user actions. This prediction is used to do proactive handovers, switching users to access points with better future transmission rates and a more stable environment based on the future state of the user. Results show that not only the proposal is effective at predicting channel data, but the use of such predictions improves system performance and avoids unnecessary handovers. 

Recent advancements in wireless local area network (WLAN) technology include IEEE 802.11be and 802.11ay, often known as Wi-Fi 7 and WiGig, respectively. The goal of these developments is to provide Extremely High Throughput (EHT) and low latency to meet the demands of future applications like as 8K videos, augmented and virtual reality, the Internet of Things, telesurgery, and other developing technologies. IEEE 802.11be includes new features such as 320 MHz bandwidth, multi-link operation, Multi-user Multi-Input Multi-Output, orthogonal frequency-division multiple access, and Multiple-Access Point (multi-AP) coordination (MAP-Co) to achieve EHT. With the increase in the number of overlapping APs and inter-AP interference, researchers have focused on studying MAP-Co approaches for coordinated transmission in IEEE 802.11be, making MAP-Co a key feature of future WLANs. Moreover, similar issues may arise in EHF bands WLAN, particularly for standards beyond IEEE 802.11ay. This has prompted researchers to investigate the implementation of MAP-Co over future 802.11ay WLANs. Thus, in this article, we provide a comprehensive review of the state-of-the-art MAP-Co features and their shortcomings concerning emerging WLAN. Finally, we discuss several novel future directions and open challenges for MAP-Co. 

The mmWave WiGig frequency band can support high throughput and low latency emerging applications. In this context, accurate prediction of channel gain enables seamless connectivity with user mobility via proactive handover and beamforming. Machine learning techniques have been widely adopted in literature for mmWave channel prediction. However, the existing techniques assume that the indoor mmWave channel follows a stationary stochastic process. This paper demonstrates that indoor WiGig mmWave channels are non-stationary where the channel’s cumulative distribution function (CDF) changes with the user’s spatio-temporal mobility. Specifically, we show significant differences in the empirical CDF of the channel gain based on the user’s mobility stage, namely, room entering, wandering, and exiting. Thus, the dynamic WiGig mmWave indoor channel suffers from concept drift that impedes the generalization ability of deep learning-based channel prediction models. Our results demonstrate that a state-of-the-art deep learning channel prediction model based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) recurrent neural network suffers from a deterioration in the prediction accuracy by 11–68% depending on the user’s mobility stage and the model’s training. To mitigate the negative effect of concept drift and improve the generalization ability of the channel prediction model, we develop a robust deep learning model based on an ensemble strategy. Our results show that the weight average ensemble-based model maintains a stable prediction that keeps the performance deterioration below 4%.