Load-Aware Cell Switching in Ultra-Dense Networks: An Artificial Neural Network Approach

Most online cell switching solutions are sub-optimal because they are computationally demanding, and thus adapt slowly to a dynamically changing network environments, leading to quality-of-service (QoS) degradation. This makes such solutions impractical for ultra-dense networks (UDN) where the number of base stations (BS) deployed is very large. In this paper, an artificial neural network (ANN) based cell switching solution is developed to learn the optimal switching strategy of BSs in order to minimize the total power consumption of a UDN. The proposed model is first trained offline, after which the trained model is plugged into the network for real-time decision making. Simulation results reveal that the performance of the proposed solution is very close to the optimal solution in terms of trade-off between the power consumption and QoS

The role of artificial intelligence driven 5G networks in COVID-19 outbreak: opportunities, challenges, and future outlook

There is no doubt that the world is currently experiencing a global pandemic that is reshaping our daily lives as well as the way business activities are being conducted. With the emphasis on social distancing as an effective means of curbing the rapid spread of the infection, many individuals, institutions, and industries have had to rely on telecommunications as a means of ensuring service continuity in order to prevent complete shutdown of their operations. This has put enormous pressure on both fixed and mobile networks. Though fifth generation mobile networks (5G) is at its infancy in terms of deployment, it possesses a broad category of services including enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC), that can help in tackling pandemic-related challenges. Therefore, in this paper, we identify the challenges facing existing networks due to the surge in traffic demand as a result of the COVID-19 pandemic and emphasize the role of 5G empowered by artificial intelligence in tackling these problems. In addition, we also provide a brief insight on the use of artificial intelligence driven 5G networks in predicting future pandemic outbreaks, and the development a pandemic-resilient society in case of future outbreaks

Energy optimization in ultra-dense radio access networks via traffic-aware cell switching

We propose a reinforcement learning based cell switching algorithm to minimize the energy consumption in ultra-dense deployments without compromising the quality of service (QoS) experienced by the users. In this regard, the proposed method can intelligently learn which small cells (SCs) to turn off at any given time based on the traffic load of the SCs and the macro cell. To validate the idea, we used the open call detail record (CDR) data set from the city of Milan, Italy, and tested our algorithm against typical operational benchmark solutions. With the obtained results, we demonstrate exactly when and how the proposed method can provide energy savings, and moreover how this happens without reducing QoS of users. Most importantly, we show that our solution has a very similar performance to the exhaustive search, with the advantage of being scalable and less complex

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

A survey of machine learning applications to handover management in 5G and beyond

Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed

Intelligent handover decision scheme using double deep reinforcement learning

Handovers (HOs) have been envisioned to be more challenging in 5G networks due to the inclusion of millimetre wave (mm-wave) frequencies, resulting in more intense base station (BS) deployments. This, by its turn, increases the number of HOs taken due to smaller footprints of mm-wave BSs thereby making HO management a more crucial task as reduced quality of service (QoS) and quality of experience (QoE) along with higher signalling overhead are more likely with the growing number of HOs. In this paper, we propose an offline scheme based on double deep reinforcement learning (DDRL) to minimize the frequency of HOs in mm-wave networks, which subsequently mitigates the adverse QoS. Due to continuous and substantial state spaces arising from the inherent characteristics of the considered 5G environment, DDRL is preferred over conventional -learning algorithm. Furthermore, in order to alleviate the negative impacts of online learning policies in terms of computational costs, an offline learning framework is adopted in this study, a known trajectory is considered in a simulation environment while ray-tracing is used to estimate channel characteristics. The number of HO occurrence during the trajectory and the system throughput are taken as performance metrics. The results obtained reveal that the proposed method largely outperform conventional and other artificial intelligence (AI)-based models

Edge intelligence in private mobile networks for next generation railway systems

The integration of Private Mobile Networks (PMN) with edge intelligence is expected to play an instrumental role in realizing the next generation of industry applications. This combination collectively termed as Intelligent Private Networks (IPN) deployed within the scope of specific industries such as transport systems can unlock several use-cases and critical applications that in turn can address rising business demands. This article presents a conceptual IPN that hosts intelligence at the network edge employing emerging technologies that satisfy a number of Next Generation Railway System (NGRS) applications. NGRS use-cases along with their applications and respective beyond 5G (B5G) enabling technologies have been discussed along with possible future research and development directions that will allow these promising technologies to be used and implemented widely

Energy efficiency in next generation cellular networks

There is an exponential growth in the energy consumption of cellular networks due to the surge in data traffic, explosion of handheld and Internet-of-Things (IoT) devices, development of data-hungry mobile applications, increasing support for new and emerging use cases, the introduction of ultra-dense networks, and aerial base stations (BSs). This will create the challenge of increased energy consumption for next-generation cellular networks that is bound to escalate, if not properly managed. This thesis seeks to address this challenge and make future cellular networks more energy- and cost-efficient, and environmentally sustainable. To achieve this, analytical methods, conventional approaches, and machine learning solutions are utilized to develop novel optimization frameworks that can minimize the energy consumption in heterogeneous cellular networks (HetNets) while satisfying quality of service (QoS) constraints. First, energy optimization in ultra-dense heterogeneous networks (UDHNs) through cell switching and traffic offloading is studied. Though dynamic cell switching is a common technique for reducing energy consumption in UDHNs, most current methods are computationally demanding, making them unsuitable for practical applications in UDHNs with a large number of BSs. As a result, scalable and computationally efficient cell switching and traffic offloading frameworks using Q-learning, a reinforcement learning algorithm, and artificial neural networks (ANN), a supervised learning algorithm, is initially developed. However, these solutions are effective only in small- to medium-sized networks. Subsequently, a lightweight cell switching scheme called Threshold-based Hybrid cEll SwItching Scheme (THESIS), which combines the benefits of multi-level clustering (MLC) and exhaustive search (ES) algorithms is proposed. In addition, the two components of the THESIS algorithm, k-means and ES, are used for benchmarking. The performance evalaution reveals that THESIS algorithm is able to find a good trade-off between optimal energy saving performance and computational complexity. Hence, it is suitable for cell switching purposes in real networks with large dimension. Second, the cell switching solution is extended to include spectrum leasing. Spectrum leasing involves leasing out unused spectrum for a fee (in this case, those originally occupied by switched off BSs). A solution to enable mobile network operators (MNOs) gain additional revenue from leasing dormant spectrum, in addition to reducing energy consumption (electricity bills) via cell switching, is proposed. In this direction, a network scenario comprising primary network (PN) operators, who hold the spectrum license, and secondary network (SN) operators, who need to lease the spectrum is considered. Moreover, both non-delaytolerant (NDT), and delay-tolerant (DT) spectrum demand scenarios are also considered. A cell switching and spectrum leasing framework based on the simulated annealing (SA) algorithm is developed to maximize the revenue of the PN while satisfying the QoS constraints. The simulation results reveal that the DT spectrum demand is more beneficial to both PN and SN operators as it results in 19% increase in the revenue generated by the PN, while leading to a 21% surge in the amount of spectrum that can be accessed by the SN. Third, energy consumption has been identified as one of the major factors limiting the adoption of unmanned ariel vehicles (UAVs) in cellular networks (e.g., for providing additional offloading capacity during cell switching, and spectrum leasing operations), hence, the quest for green UAV-based cellular communications. To this end, a comprehensive survey on energy optimization techniques in UAVbased cellular networks is conducted, which revealed that it is energy-inefficient to continuously make UAV-BSs hover or fly to provide wireless coverage. Thus, an alternative deployment scheme where UAV-BSs land on designated locations, known as landing stations (LSs), is considered, and the appropriate separation distances (∆) between LSs and the optimal hovering position (OHP) are evaluated. Mathematical frameworks using stochastic geometry are developed to model the relationship between power consumption, coverage probability, throughput, and ∆. Numerical results reveal about 95% reduction in energy consumption, which results in more than 20 times increase in the service time of UAV-BS when the LSs are exploited compared to OHP. However, this energy reduction is obtained at the expense of some degradation in coverage probability and throughput, which can be compensated for by increasing the transmit power of the UAV-BS as ∆ increases. This leads to a slight increase in the energy consumption of UAV at LS which is significantly lesser than that of the UAV at OHP. In summary, this thesis presents scalable and computationally efficient energy and revenue optimization frameworks for terrestrial and aerial cellular networks that can be applied to large-scale networks, which are typical in next generation of cellular networks. The proposed solutions would lead to a reduction in operating cost, increased profitability, and the achievement of net-zero emission target

A lightweight cell switching and traffic offloading scheme for energy optimization in ultra-dense heterogeneous networks

One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in a tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network, but most of the proposed methods are computationally demanding, which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that THESIS significantly reduces the energy consumption of the UDHN and can reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity