A review on green caching strategies for next generation communication networks

© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching

Self-organised multi-objective network clustering for coordinated communications in future wireless networks

The fifth generation (5G) cellular system is being developed with a vision of 1000 times more capacity than the fourth generation (4G) systems to cope with ever increasing mobile data traffic. Interference mitigation plays an important role in improving the much needed overall capacity especially in highly interference-limited dense deployment scenarios envisioned for 5G. Coordinated multi-point (CoMP) is identified as a promising interference mitigation technique where multiple base stations (BS) can cooperate for joint transmission/reception by exchanging user/control data and perform joint signal processing to mitigate inter-cell interference and even exploit it as a useful signal. CoMP is already a key feature of long term evolution-advanced (LTE-A) and envisioned as an essential function for 5G. However, CoMP cannot be realized for the whole network due to its computational complexity, synchronization requirement between coordinating BSs and high backhaul capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters. This PhD thesis aims to investigate optimum dynamic CoMP clustering solutions in 5G and beyond wireless networks with massive small cell (SC) deployment. Truly self-organised CoMP clustering algorithms are investigated, aiming to improve much needed spectral efficiency and other network objectives especially load balancing in future wireless networks. Low complexity, scalable, stable and efficient CoMP clustering algorithms are designed to jointly optimize spectral efficiency, load balancing and limited backhaul availability. Firstly, we provide a self organizing, load aware, user-centric CoMP clustering algorithm in a control and data plane separation architecture (CDSA) proposed for 5G to maximize spectral efficiency and improve load balancing. We introduce a novel re-clustering algorithm for user equipment (UE) served by highly loaded cells and show that unsatisfied UEs due to high load can be significantly reduced with minimal impact on spectral efficiency. Clustering with load balancing algorithm exploits the capacity gain from increase in cluster size and also the traffic shift from highly loaded cells to lightly loaded neighbours. Secondly, we develop a novel, low complexity, stable, network-centric clustering model to jointly optimize load balancing and spectral efficiency objectives and tackle the complexity and scalability issues of user-centric clustering. We show that our clustering model provide high spectral efficiency in low-load scenario and better load distribution in high-load scenario resulting in lower number of unsatisfied users while keeping spectral efficiency at comparably high levels. Unsatisfied UEs due to high load are reduced by $68.5\%$ with our algorithm when compared to greedy clustering model. In this context, the unique contribution of this work that it is the first attempt to fill the gap in literature for multi-objective, network-centric CoMP clustering, jointly optimizing load balancing and spectral efficiency. Thirdly, we design a novel multi-objective CoMP clustering algorithm to include backhaul-load awareness and tackle one of the biggest challenges for the realization of CoMP in future networks i.e. the demand for high backhaul bandwidth and very low latency. We fill the gap in literature as the first attempt to design a clustering algorithm to jointly optimize backhaul/radio access load and spectral efficiency and analyze the trade-off between them. We employ 2 novel coalitional game theoretic clustering methods, 1-a novel merge/split/transfer coalitional game theoretic clustering algorithm to form backhaul and load aware BS clusters where spectral efficiency is still kept at high level, 2-a novel user transfer game model to move users between clusters to improve load balancing further. Stability and complexity analysis is provided and simulation results are presented to show the performance of the proposed method under different backhaul availability scenarios. We show that average system throughout is increased by 49.9% with our backhaul-load aware model in high load scenario when compared to a greedy model. Finally, we provide an operator's perspective on deployment of CoMP. Firstly, we present the main motivation and benefits of CoMP from an operator's viewpoint. Next, we present operational requirements for CoMP implementation and discuss practical considerations and challenges of such deployment. Possible solutions for these experienced challenges are reviewed. We then present initial results from a UL CoMP trial and discuss changes in key network performance indicators (KPI) during the trial. Additionally, we propose further improvements to the trialed CoMP scheme for better potential gains and give our perspective on how CoMP will fit into the future wireless networks

Resource Allocation in Energy Cooperation Enabled 5G Cellular Networks

PhD thesisIn fifth generation (5G) networks, more base stations (BSs) and antennas have been deployed to meet the high data rate and spectrum efficiency requirements. Heterogeneous and ultra dense networks not only pose substantial challenges to the resource allocation design, but also lead to unprecedented surge in energy consumption. Supplying BSs with renewable energy by utilising energy harvesting technology has became a favourable solution for cellular network operators to reduce the grid energy consumption. However, the harvested renewable energy is fluctuating in both time and space domains. The available energy for a particular BS at a particular time might be insufficient to meet the traffic demand which will lead to renewable energy waste or increased outage probability. To solve this problem, the concept of energy cooperation was introduced by Sennur Ulukus in 2012 as a means for transferring and sharing energy between the transmitter and the receiver. Nevertheless, resource allocation in energy cooperation enabled cellular networks is not fully investigated. This thesis investigates resource allocation schemes and resource allocation optimisation in energy cooperation enabled cellular networks that employed advanced 5G techniques, aiming at maximising the energy efficiency of the cellular network while ensuring the network performance. First, a power control algorithm is proposed for energy cooperation enabled millimetre wave (mmWave) HetNets. The aim is to maximise the time average network data rate while keeping the network stable such that the network backlog is bounded and the required battery capacity is finite. Simulation results show that the proposed power control scheme can reduce the required battery capacity and improve the network throughput. Second, resource allocation in energy cooperation enabled heterogeneous networks (Het- Nets) is investigated. User association and power control schemes are proposed to maximise the energy efficiency of the whole network respectively. The simulation results reveal that the implementation of energy cooperation in HetNets can improve the energy efficiency and the improvement is apparent when the energy transfer efficiency is high. Following on that, a novel resource allocation for energy cooperation enabled nonorthogonal multiple access (NOMA) HetNets is presented. Two user association schemes which have different complexities and performances are proposed and compared. Following on that, a joint user association and power control algorithm is proposed to maximise the energy efficiency of the network. It is confirmed from the simulation results that the proposed resource allocation schemes efficiently coordinate the intra-cell and inter-cell interference in NOMA HetNets with energy cooperation while exploiting the multiuser diversity and BS densification. Last but not least, a joint user association and power control scheme that considers the different content requirements of users is proposed for energy cooperation enabled caching HetNets. It shows that the proposed scheme significantly enhances the energy efficiency performance of caching HetNets

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio

Energy Efficient Network Function Virtualisation in 5G Networks

Once the dust settled around 4G, 5G mobile networks become the buzz word in the world of communication systems. The recent surge of bandwidth-greedy applications and the proliferation of smart phones and other wireless connected devices has led to an enormous increase in mobile traffic. Therefore, 5G networks have to deal with a huge number of connected devices of different types and applications, including devices running life-critical applications, and facilitate access to mobile resources easily. Therefore given the increase in traffic and number of connected devices, intelligent and energy efficient architectures are needed to adequately and sustainably meet these requirements. In this thesis network function virtualisation is investigated as a promising paradigm that can contribute to energy consumption reduction in 5G networks. The work carried out in this thesis considers the energy efficiency mainly in terms of processing power consumption and network power consumption. Furthermore, it considers the energy consumption reduction that can be achieved by optimising the locations of virtual machines running the mobile 5G network functions. It also evaluates the consolidation and pooling of the mobile resources. A framework was introduced to virtualise the mobile core network functions and baseband processing functions. Mixed integer linear programming optimisation models and heuristics were developed minimise the total power consumption. The impact of virtualisation in the 5G front haul and back haul passive optical network was investigated by developing MILP models to optimise the location of virtual machines. A further consideration is caching the contents close to the user and its impact on the total power consumption. The impact of a number of factor on the power consumption were investigated such as the total number of active users, the backhaul to the fronthaul traffic ratio, reduction/expansion in the traffic due to baseband processing, and the communication between virtual machines. Finally, the integration of network function virtualisation and content caching were introduced and their impact on improving the energy efficiency was investigated

Integrated Data and Energy Communication Network: A Comprehensive Survey

OAPA In order to satisfy the power thirsty of communication devices in the imminent 5G era, wireless charging techniques have attracted much attention both from the academic and industrial communities. Although the inductive coupling and magnetic resonance based charging techniques are indeed capable of supplying energy in a wireless manner, they tend to restrict the freedom of movement. By contrast, RF signals are capable of supplying energy over distances, which are gradually inclining closer to our ultimate goal – charging anytime and anywhere. Furthermore, transmitters capable of emitting RF signals have been widely deployed, such as TV towers, cellular base stations and Wi-Fi access points. This communication infrastructure may indeed be employed also for wireless energy transfer (WET). Therefore, no extra investment in dedicated WET infrastructure is required. However, allowing RF signal based WET may impair the wireless information transfer (WIT) operating in the same spectrum. Hence, it is crucial to coordinate and balance WET and WIT for simultaneous wireless information and power transfer (SWIPT), which evolves to Integrated Data and Energy communication Networks (IDENs). To this end, a ubiquitous IDEN architecture is introduced by summarising its natural heterogeneity and by synthesising a diverse range of integrated WET and WIT scenarios. Then the inherent relationship between WET and WIT is revealed from an information theoretical perspective, which is followed by the critical appraisal of the hardware enabling techniques extracting energy from RF signals. Furthermore, the transceiver design, resource allocation and user scheduling as well as networking aspects are elaborated on. In a nutshell, this treatise can be used as a handbook for researchers and engineers, who are interested in enriching their knowledge base of IDENs and in putting this vision into practice