As one of the key communication scenarios in the fifth-generation and also the sixth-generation (6G) mobile communication networks, ultrareliable and low-latency communications (URLLCs) will be central for the development of various emerging mission-critical applications. State-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLCs. In particular, a holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLCs in future 6G networks. This tutorial illustrates how domain knowledge (models, analytical tools, and optimization frameworks) of communications and networking can be integrated into different kinds of deep learning algorithms for URLLCs. We first provide some background of URLLCs and review promising network architectures and deep learning frameworks for 6G. To better illustrate how to improve learning algorithms with domain knowledge, we revisit model-based analytical tools and cross-layer optimization frameworks for URLLCs. Following this, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLCs and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning

Wireless body area network (WBAN) has become a promising technology, which can be widely applied in health monitoring, and so on. However, the performance of a practical WBAN may severely suffer from the degradation caused by dynamic nature of wireless channels with the movements of human body. Traditional communication frameworks cannot catch up with the channel variation of dynamic WBANs, which may severely degrade the performance, so an accurate channel prediction model is necessary for developing an efficient transmission strategy. In this paper, we propose a DeepBAN communication framework for dynamic WBANs. In our proposed framework, a temporal convolution network (TCN) based deep learning approach is adopted for channel prediction, the computationally intensive task of which is processed by mobile edge computing (MEC), to reduce the response time. Given the predicted channel conditions, we propose a joint power control, time-slot allocation, and relay selection algorithm to maximize the energy efficiency of the system, taking into account the transmission reliability and end-to-end latency requirements. We evaluate the performance of DeepBAN, and the results show that it can achieve energy-efficient, reliable, and low-latency data transmission in dynamic WBANs, which can improve the system energy efficiency by 15% compared with the stochastic scheduling scheme.

DeepBAN: A Temporal Convolution-Based Communication Framework for Dynamic WBANs

Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area.

Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective

As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. State-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLC. In particular, a holistic framework that takes into account latency, reliability, availability, scalability, and decision making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how domain knowledge (models, analytical tools, and optimization frameworks) of communications and networking can be integrated into different kinds of deep learning algorithms for URLLC. We first provide some background of URLLC and review promising network architectures and deep learning frameworks for 6G. To better illustrate how to improve learning algorithms with domain knowledge, we revisit model-based analytical tools and cross-layer optimization frameworks for URLLC. Following that, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLC and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

Wireless body area networks (WBAN) require long life links and energy efficient system. Besides the increasing commercialization of WBAN, health monitoring applications calls for enhanced quality of service (QoS). The establishment of the reliable and energy efficient link is crucial to support the improvement of the WBAN performance parameters. In this article, we propose a cross-layer routing mechanism for WBAN quality of service enhancement. The protocol uses a cost function, which linearly combines node energy ratio, link reliability, and specific absorption rate functions. The proposed algorithm initially maximizes network lifetime longevity by reducing node energy consumption with nearly reasonable throughput and the packet delivery ratio whereas the enhancement of the QoS focused on improving network throughput and the packet delivery success rate for WBAN applications. The algorithm is implemented in two stages, firstly by designing the energy efficient and reliable link routing policy in the network layer and secondly the adjustment of the contention window for QoS performance enhancement in the data link layer using IEEE 802.11 medium access control (MAC) protocol. We conduct parametric modeling of the cost function to analyze network performance in different parametric combinations and contention window adjustments. Simulation results show the proposed protocol improves network performance indicators such as energy efficiency, lifetime longevity maximization, throughput, and packet success delivery ratio.

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Link Reliability and Performance Optimization in Wireless Body Area Networks

The general non-stationarity of the wireless body area network (WBAN) narrowband radio channel makes long-term prediction very challenging. However, long short-term memory (LSTM) is a deep learning recurrent neural network (RNN) architecture that is proposed here to learn these atypical radio channel dynamics and make channel predictions. Thus, here we propose an LSTM-based RNN channel prediction framework providing long-term channel prediction up to 2s with low error. To address practical scenarios where information packets are transmitted continuously, we outline a timing scheme, which enables the LSTM predictor to operate online. We employ the proposed method in transmit power control for everyday on-body, measured, WBAN channels. When compared with existing approaches, the proposed channel prediction reduces circuit power consumption significantly while improving communications reliability.

Deep Learning Channel Prediction for Transmit Power Control in Wireless Body Area Networks

Recent advances in the Internet of Things (IoT) are reforming the health care industry by providing higher communication efficiency, lower costs, and higher mobility. Among the many IoT applications, wireless body area networks (BANs) are a remarkable solution caring for a rapidly growing aged population. Predictive transmit power control schemes improve BAN communications’ reliability and energy efficiency through long-term optimal radio resources allocation that supports consistent pervasive healthcare services. Here, we propose LSTM-based neural network (NN) prediction methods that provide long-term accurate channel gain prediction of up to 2 s over nonstationary BAN on-body channels. An incremental learning scheme, which enables the LSTM predictor to operate online, is also developed for dynamic scenarios. Our main contribution is a lightweight NN predictor, “LiteLSTM,” that has a compact structure and higher computational efficiency than other variants. We show that LiteLSTM remains functional under an incremental learning scheme, with only marginal performance degradation when implemented on hand-held devices. For optimal power allocation, we develop an interquartile range (IQR)-based power control for our channel prediction. When extensively tested using empirical channel measurements at different sampling rates, our proposed methods outperform the existing state-of-the-art methods in terms of prediction accuracy, power consumption, level crossing rate (LCR), and outage probability and duration.

Power Control for Body Area Networks: Accurate Channel Prediction by Lightweight Deep Learning

Penetration Testing plays a critical role in evaluating the security of a target network by emulating real active adversaries. Deep Reinforcement Learning (RL) is seen as a promising solution to automating the process of penetration tests by reducing human effort and improving reliability. Existing RL solutions focus on finding a specific attack path to impact the target hosts. However, in reality, a diverse range of attack variations are needed to provide comprehensive assessments of the target network’s security level. Hence, the attack agents must consider multiple objectives when penetrating the network. Nevertheless, this challenge is not adequately addressed in the existing literature. To this end, we formulate the automatic penetration testing in the Multi-Objective Reinforcement Learning (MORL) framework and propose a Chebyshev decomposition critic to find diverse adversary strategies that balance different objectives in the penetration test. Additionally, the number of available actions increases with the agent consistently probing the target network, making the training process intractable in many practical situations. Thus, we introduce a coverage-based masking mechanism that reduces attention on previously selected actions to help the agent adapt to future exploration. Experimental evaluation on a range of scenarios demonstrates the superiority of our proposed approach when compared to adapted algorithms in terms of multi-objective learning and performance efficiency.

Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach

The enabling of wireless body area networks (WBANs) coexistence by radio interference mitigation is very important due to a rapid growth in potential users, and a lack of a central coordinator among WBANs that are closely located. In this paper, we propose a TDMA based MAC layer Scheme, with a back-off mechanism that reduces packet collision probability; and estimate performance using a Markov chain model. Based on the MAC layer scheme, a novel non-cooperative game is proposed to jointly adjust sensor node's transmit power and rate. In comparison with the state-of-art, simulation that includes empirical data shows that the proposed approach leads to higher throughput and longer node lifespan as WBAN wearers dynamically move into each other's vicinity. Moreover, by adaptively tuning contention windows size an alternative game is developed, which significantly reduces the latency. Both proposed games provide robust transmission under strong inter-WBAN interferences, but are demonstrated to be applicable to different scenarios. The uniqueness and existence of Nash Equilibrium (NE), as well as close-to-optimum social efficiency, is also proven for both games.

Robust Wireless Body Area Networks Coexistence: A Game Theoretic Approach to Time-Division MAC

Transmit power control is vital to wireless body area networks (BANs), where due to their prevalence many BANs may be required to coexist reliably, with a requirement for reduced power consumption to significantly increase sensor battery lifetime. In this paper, we propose a socially optimal finite repeated non-cooperative transmit power control game, in order to mitigate radio interference amongst coexisting BANs, improve throughput and reduce power consumption. The game is shown to have a unique Nash equilibrium. We also prove that the outcome of the game is socially efficient, given reasonable constraints, across all players at the unique Nash equilibrium. Using a realistic channel model, the game is shown to be very energy-efficient, significantly reducing power consumption and improving packet delivery ratio (PDR) with respect to other potential schemes, consuming 67% less circuit power than transmitting constantly at 0 dBm.

Yizhou Yang

Papers

Deep Learning Channel Prediction for Transmit Power Control in Wireless Body Area Networks

Power Control for Body Area Networks: Accurate Channel Prediction by Lightweight Deep Learning

Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach

Robust Wireless Body Area Networks Coexistence: A Game Theoretic Approach to Time-Division MAC

Wireless body area networks: Energy-efficient, provably socially-efficient, transmit power control