Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

Energy sustainable paradigms and methods for future mobile networks: A survey

In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.Comment: Accepted by Elsevier Computer Communications, 21 pages, 9 figure

Wearable Communications in 5G: Challenges and Enabling Technologies

As wearable devices become more ingrained in our daily lives, traditional communication networks primarily designed for human being-oriented applications are facing tremendous challenges. The upcoming 5G wireless system aims to support unprecedented high capacity, low latency, and massive connectivity. In this article, we evaluate key challenges in wearable communications. A cloud/edge communication architecture that integrates the cloud radio access network, software defined network, device to device communications, and cloud/edge technologies is presented. Computation offloading enabled by this multi-layer communications architecture can offload computation-excessive and latency-stringent applications to nearby devices through device to device communications or to nearby edge nodes through cellular or other wireless technologies. Critical issues faced by wearable communications such as short battery life, limited computing capability, and stringent latency can be greatly alleviated by this cloud/edge architecture. Together with the presented architecture, current transmission and networking technologies, including non-orthogonal multiple access, mobile edge computing, and energy harvesting, can greatly enhance the performance of wearable communication in terms of spectral efficiency, energy efficiency, latency, and connectivity.Comment: This work has been accepted by IEEE Vehicular Technology Magazin

Robust Schemes to Enhance Energy Consumption Efficiency for Millimeter Wave-Based Microcellular Network in Congested Urban Environments

Future wireless communication networks will be largely characterized by small cell deployments, typically on the order of 200 meters of radius/cell, at most. Meanwhile, recent studies show that base stations (BS) account for about 80 to 95 % of the total network power. This simply implies that more energy will be consumed in the future wireless network since small cell means massive deployment of BS. This phenomenon makes energy-efficient (EE) control a central issue of critical consideration in the design of future wireless networks. This paper proposes and investigates (the performance of) two different energy-saving approaches namely, adaptive-sleep sectorization (AS), adaptive hybrid partitioning schemes (AH) for small cellular networks using smart antenna technique. We formulated a generic base-model for the above-mentioned schemes and applied the spatial Poisson process to reduce the system complexity and to improve flexibility in the beam angle reconfiguration of the adaptive antenna, also known as a smart antenna (SA). The SA uses the scalable algorithms to track active users in different segments/sectors of the microcell, making the proposed schemes capable of targeting specific users or groups of users in periods of sparse traffic, and capable of performing optimally when the network is highly congested. The capabilities of the proposed smart/adaptive antenna approaches can be easily adapted and integrated into the massive MIMO for future deployment. Rigorous numerical analysis at different orders of sectorization shows that among the proposed schemes, the AH strategy outperforms the AS in terms of energy saving by about 52 %. Generally, the proposed schemes have demonstrated the ability to significantly increase the power consumption efficiency of micro base stations for future generation cellular systems, over the traditional design methodologies

Robust Schemes to Enhance Energy Consumption Efficiency for Millimeter Wave-Based Microcellular Network in Congested Urban Environments

Future wireless communication networks will be largely characterized by small cell deployments, typically on the order of 200 meters of radius/cell, at most. Meanwhile, recent studies show that base stations (BS) account for about 80 to 95 % of the total network power. This simply implies that more energy will be consumed in the future wireless network since small cell means massive deployment of BS. This phenomenon makes energy-efficient (EE) control a central issue of critical consideration in the design of future wireless networks. This paper proposes and investigates (the performance of) two different energy-saving approaches namely, adaptive-sleep sectorization (AS), adaptive hybrid partitioning schemes (AH) for small cellular networks using smart antenna technique. We formulated a generic base-model for the above-mentioned schemes and applied the spatial Poisson process to reduce the system complexity and to improve flexibility in the beam angle reconfiguration of the adaptive antenna, also known as a smart antenna (SA). The SA uses the scalable algorithms to track active users in different segments/sectors of the microcell, making the proposed schemes capable of targeting specific users or groups of users in periods of sparse traffic, and capable of performing optimally when the network is highly congested. The capabilities of the proposed smart/adaptive antenna approaches can be easily adapted and integrated into the massive MIMO for future deployment. Rigorous numerical analysis at different orders of sectorization shows that among the proposed schemes, the AH strategy outperforms the AS in terms of energy saving by about 52 %. Generally, the proposed schemes have demonstrated the ability to significantly increase the power consumption efficiency of micro base stations for future generation cellular systems, over the traditional design methodologies

A Framework for Enhancing the Energy Efficiency of IoT Devices in 5G Network

A wide range of services, such as improved mobile broadband, extensive machine-type communication, ultra-reliability, and low latency, are anticipated to be delivered via the 5G network. The 5G network has developed as a multi-layer network that uses numerous technological advancements to provide a wide array of wireless services to fulfil such a diversified set of requirements. Several technologies, including software-defined networking, network function virtualization, edge computing, cloud computing, and tiny cells, are being integrated into the 5G networks to meet the needs of various requirements. Due to the higher power consumption that will arise from such a complicated network design, energy efficiency becomes crucial. The network machine learning technique has attracted a lot of interest from the scientific community because it has the potential to play a crucial role in helping to achieve energy efficiency. Utilization factor, access latency, arrival rate, and other metrics are used to study the proposed scheme. It is determined that our system outperforms the present scheme after comparing the suggested scheme to these parameters

Scalable base station switching framework for green cellular networks

With the recent unprecedented growth in the wireless market, network operators are obliged not only to find new techniques including dense deployment of base stations (BSs) in order to support high data rate services and high user density, but also to reduce the operating costs and energy consumption of various network elements. To solve these challenges, powering down certain BSs during low-traffic periods, so-called BS sleeping, has emerged as an effective green communications paradigm. While BS sleeping offers the potential to significantly lower energy consumption, it also raises many challenges, since when a BS is switched off, this can lead to, for example, coverage holes, sudden degradation in quality of service (QoS), higher transmit power dissipation in off-cell mobile stations (MSs), an inability to rapidly power up/down equipment and finally, a failure to uphold regulatory requirements. In order to realise greener network designs which both maximise energy savings whilst guaranteeing QoS, innovative BS switching mechanisms need to be developed. This thesis presents a novel BS switching framework which improves energy efficiency (EE) in comparison with existing approaches, while guaranteeing the minimum QoS and seamless services. The major technical contributions in this framework are: i) a new BS to relay station (RS) switching model where certain BSs are switched to RS mode rather than being turned off, firstly using a fixed threshold based switching algorithm utilizing temporal traffic diversity, and ii) then subsequently by means of an adaptive threshold by exploiting the inherently asymmetric traffic profile between cells, i.e., by exploiting both the temporal and spatial traffic diversity; iii) a traffic-and-interference-aware BS switching strategy that considers the impact of inter-cell interference in the decision making process to dynamically determine the best BS set to be kept active for improved EE; and finally iv) a novel scalable multimode BS switching model which enables each BS to operate in different power modes i.e., macro/micro/sleep to explore energy savings potential even at higher traffic conditions. The thesis findings conclusively confirm this new BS switching framework provides significant EE improvements from both BS and MS perspectives, under diverse network conditions and represents a notable step towards greener communications