Driven by the demand to accommodate today’s growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the ever-increasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning. We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications.

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Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

Configuration tuning is vital to optimize the performance of database management system (DBMS). It becomes more tedious and urgent for cloud databases (CDB) due to the diverse database instances and query workloads, which make the database administrator (DBA) incompetent. Although there are some studies on automatic DBMS configuration tuning, they have several limitations. Firstly, they adopt a pipelined learning model but cannot optimize the overall performance in an end-to-end manner. Secondly, they rely on large-scale high-quality training samples which are hard to obtain. Thirdly, there are a large number of knobs that are in continuous space and have unseen dependencies, and they cannot recommend reasonable configurations in such high-dimensional continuous space. Lastly, in cloud environment, they can hardly cope with the changes of hardware configurations and workloads, and have poor adaptability. To address these challenges, we design an end-to-end automatic CDB tuning system, CDBTune, using deep reinforcement learning (RL). CDBTune utilizes the deep deterministic policy gradient method to find the optimal configurations in high-dimensional continuous space. CDBTune adopts a try-and-error strategy to learn knob settings with a limited number of samples to accomplish the initial training, which alleviates the difficulty of collecting massive high-quality samples. CDBTune adopts the reward-feedback mechanism in RL instead of traditional regression, which enables end-to-end learning and accelerates the convergence speed of our model and improves efficiency of online tuning. We conducted extensive experiments under 6 different workloads on real cloud databases to demonstrate the superiority of CDBTune. Experimental results showed that CDBTune had a good adaptability and significantly outperformed the state-of-the-art tuning tools and DBA experts.

An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning

Database knob tuning is important to achieve high performance (e.g., high throughput and low latency). However, knob tuning is an NP-hard problem and existing methods have several limitations. First, DBAs cannot tune a lot of database instances on different environments (e.g., different database vendors). Second, traditional machine-learning methods either cannot find good configurations or rely on a lot of high-quality training examples which are rather hard to obtain. Third, they only support coarse-grained tuning (e.g., workload-level tuning) but cannot provide fine-grained tuning (e.g., query-level tuning).To address these problems, we propose a query-aware database tuning system QTune with a deep reinforcement learning (DRL) model, which can efficiently and effectively tune the database configurations. QTune first featurizes the SQL queries by considering rich features of the SQL queries. Then QTune feeds the query features into the DRL model to choose suitable configurations. We propose a Double-State Deep Deterministic Policy Gradient (DS-DDPG) model to enable query-aware database configuration tuning, which utilizes the actor-critic networks to tune the database configurations based on both the query vector and database states. QTune provides three database tuning granularities: query-level, workload-level, and cluster-level tuning. We deployed our techniques onto three real database systems, and experimental results show that QTune achieves high performance and outperforms the state-of-the-art tuning methods.

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QTune: a query-aware database tuning system with deep reinforcement learning

This paper describes a wireless mesh network testbed for research in rapid deployment and autoconfiguration of mesh nodes. Motivated by the needs of first responders and military personnel arriving to an incident area, we developed and tested an automated deployment algorithm that indicates when a mesh node needs to be deployed as the coverage area grows. Conventional radios can experience severe coverage limitations inside structures such as hi-rise buildings, subterranean buildings, caves, and underground mines. The approach examined here is to deploy wireless relays that extend coverage through multihop communication using a deployment algorithm that employs physical layer measurements. A flexible platform based on IEEE 802.11 radios has been implemented and tested in a subterranean laboratory complex where conventional public safety radios have no coverage. Applications tested include two-way voice, data, and location information. This paper describes the testbed, presents experimental results, and recommends areas for further study and development in rapidly-deployable multihop networks.

Rapidly-Deployable Mesh Network Testbed

Information Networking

Wireless Local Area Networks (WLANs) are commonly deployed in various environments. The WLAN data packets are not transmitted continuously but often worst-case exposure of WLAN is assessed, assuming 100% activity and leading to huge overestimations. Actual duty cycles of WLAN are thus of importance for time-averaging of exposure when checking compliance with international guidelines on limiting adverse health effects. In this paper, duty cycles of WLAN using Wi-Fi technology are determined for exposure assessment on large scale at 179 locations for different environments and activities (file transfer, video streaming, audio, surfing on the internet, etc.). The median duty cycle equals 1.4% and the 95th percentile is 10.4% (standard deviation SD = 6.4%). Largest duty cycles are observed in urban and industrial environments. For actual applications, the theoretical upper limit for the WLAN duty cycle is 69.8% and 94.7% for maximum and minimum physical data rate, respectively. For lower data rates, higher duty cycles will occur. Although counterintuitive at first sight, poor WLAN connections result in higher possible exposures. File transfer at maximum data rate results in median duty cycles of 47.6% (SD = 16%), while it results in median values of 91.5% (SD = 18%) at minimum data rate. Surfing and audio streaming are less intensively using the wireless medium and therefore have median duty cycles lower than 3.2% (SD = 0.5–7.5%). For a specific example, overestimations up to a factor 8 for electric fields occur, when considering 100% activity compared to realistic duty cycles.

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Determination of the duty cycle of WLAN for realistic radio frequency electromagnetic field exposure assessment.

During the last years, the growth of wireless traffic pushed the wireless community to search for solutions that can assist in a more efficient management of the spectrum. Toward this direction, the operation of long term evolution (LTE) in unlicensed spectrum (LTE-U) has been proposed. Targeting a global solution that respects the regional regulations worldwide, 3GPP has published the LTE licensed assisted access (LAA) standard. According to LTE LAA, a listen before talk (LBT) procedure must precede any LTE transmission burst in the unlicensed spectrum. However, the proposed standard may cause coexistence issues between LTE and Wi-Fi, especially in the case that the latter does not use frame aggregation. Toward the provision of a balanced channel access, we have proposed mLTE-U that is an adaptive LTE LBT scheme. According to mLTE-U, LTE uses a variable transmission opportunity (TXOP), followed by a variable muting period. This muting period can be exploited by co-located Wi-Fi networks to gain access to the medium. In this paper, the system model of the mLTE-U scheme in coexistence with Wi-Fi is studied. In addition, mLTE-U is enhanced with a Q-learning technique that is used for autonomous selection of the appropriate combinations of TXOP and muting period that can provide fair coexistence between co-located mLTE-U and Wi-Fi networks. Simulation results showcase the performance of the proposed model and reveal the benefit of using Q-learning for self-adaptation of mLTE-U to the changes of the dynamic wireless environment, toward fair coexistence with Wi-Fi. Finally, the Q-learning mechanism is compared with conventional selection schemes showing the superior performance of the proposed model over less complex mechanisms.

https://biblio.ugent.be/publication/8566955/file/8566956.pdf

A Q-Learning Scheme for Fair Coexistence Between LTE and Wi-Fi in Unlicensed Spectrum

Over the last years, the ever-growing wireless traffic has pushed the mobile community to investigate solutions that can assist in more efficient management of the wireless spectrum. Towards this direction, the long-term evolution (LIE) operation in the unlicensed spectrum has been proposed. Targeting a global solution that respects the regional requirements, 3GPP announced the standard of LIE licensed assisted access (LAA). However, LIE LAA may result in unfair coexistence with Wi-Fi, especially when Wi-Fi does not use frame aggregation. Targeting a technique that enables fair channel access, the mLTE-U scheme has been proposed. According to mLTE-U, LTE uses a variable transmission opportunity, followed by a variable muting period that can be exploited by other networks to transmit. For the selection of the appropriate mLTE-U configuration, information about the dynamically changing wireless environment is required. To this end, this paper proposes a convolutional neural network (CNN) that is trained to perform identification of LIE and Wi-Fi transmissions. In addition, it can identify the hidden terminal effect caused by multiple LTE transmissions, multiple Wi-Fi transmissions, or concurrent LIE and Wi-Fi transmissions. The designed CNN has been trained and validated using commercial off-the-shelf LIE and Wi-Fi hardware equipment and for two wireless signal representations, namely, in-phase and quadrature samples and frequency domain representation through fast Fourier transform. The classification accuracy of the two resulting CNNs is tested for different signal to noise ratio values. The experimentation results show that the data representation affects the accuracy of CNN. The obtained information from CNN can be exploited by the mLTE-U scheme in order to provide fair coexistence between the two wireless technologies.

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Enhancing the coexistence of LTE and Wi-Fi in unlicensed spectrum through convolutional neural networks

In a firefighter emergency mission it is essential for the members of a fire brigade to get an intelligent and reliable overview of the complete situation, presented according to the role of each member In this paper we report on the design and development of a system to support a fire brigade on site with a set of mobile services that offers a role-based focus+context user interface It provides the required overview over the emergency situation according to the user task and context, while life-saving information is emphasized The implementation of a context-rule-based decision module enhances the visualization of required information Interaction with the user interface is designed for use in the wild; which in this case comes down to providing a “fat finger” interface that allows firemen to interact with the user interface on site with his gloves on.

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A situation-aware mobile system to support fire brigades in emergency situations

During a crisis situation, it is essential for emergency services to have a reliable communication network. Because one cannot trust on existing infrastructure, wireless mesh technology can be a good networking mechanism to provide high broadband communication while performing rescue operations. In this paper, we'll describe the concept and implementation of a monitoring and planning tool that can assist an intervention team in deploying the network and providing a real-time overview of the status of the network. To minimize overhead on the network, we use the packet pair probing technique for the estimation of the link capacity. The information is disseminated throughout the network to estimate end-to-end throughput. Our distributed monitoring technique is based on a layer 3 approach, which is transparent to underlying layers. To assist the intervention team in determining the best location for adding new nodes, our planning tool will continuously monitor signal strength in the environment. The applicability of the system is proven by experiment in a real testing environment.

Dries Naudts

Papers

Determination of the duty cycle of WLAN for realistic radio frequency electromagnetic field exposure assessment.

A Q-Learning Scheme for Fair Coexistence Between LTE and Wi-Fi in Unlicensed Spectrum

Enhancing the coexistence of LTE and Wi-Fi in unlicensed spectrum through convolutional neural networks

A situation-aware mobile system to support fire brigades in emergency situations

A Wireless Mesh Monitoring and Planning Tool for Emergency Services