Browsing by Author "Falowo, Olabisi"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Open Access
    Efficient energy management in ultra-dense wireless networks
    (2019) Wambi, Paul James; Falowo, Olabisi
    The increase in demand for more network capacity has led to the evolution of wireless networks from being largely Heterogeneous (Het-Nets) to the now existing Ultra-dense (UDNs). In UDNs, small cells are densely deployed with the goal of shortening the physical distance between the base stations (BSs) and the UEs, so as to support more user equipment (UEs) at peak times while ensuring high data rates. Compared to Het-Nets, Ultra-dense networks (UDNs) have many advantages. These include, more network capacity, higher flexibility to routine configurations, and more suitability to achieve load-balancing, hence, fewer blind spots as well as lower call blocking probability. It should be noted that, in practice, due to the high density of deployed small cells in Ultra-Dense Networks, a number of issues, or rather concerns, come with this evolution from Het-Nets. Among these issues include problems with efficient radio resource management, user cell association, inter- and intra-cell interference management and, last but not least, efficient energy consumption. Some of these issues which impact the overall network efficiency are largely due to the use of obsolete algorithms, especially those whose resource allocation is based solely on received signal power (RSSP). In this paper, the focus is solely on the efficient energy management dilemma and how to optimally reduce the overall network energy consumption. Through an extensive literature review, a detailed report into the growing concern of efficient energy management in UDNs is provided in Chapter 2. The literature review report highlights the classification as well as the evolution of some of the Mobile Wireless Technologies and Mobile Wireless Networks in general. The literature review report provides reasons as to why the energy consumption issue has become a very serious concern in UltraDense networks as well as the various techniques and measures taken to mitigate this. It is shown that, due to the increasing Mobile Wireless Systems’ carbon footprint which carries serious negative environmental impact, and the general need to lower operating costs by the network operators, the management of energy consumption increases in priority. By using the architecture of a Fourth Generation Long Term Evolution (4G-LTE) UltraDense Network, the report further shows that more than 65% of the overall energy consumption is by the access network and base stations in particular. This phenomenon explains why most attention in energy efficiency management in UDNs is largely centred on reducing the energy consumption of the deployed base stations more than any other network components like the data servers or backhauling features used. Furthermore, the report also provides detailed information on the methods/techniques, their classification, implementation, as well as a critical analysis of the said implementations in literature. This study proposes a sub-optimal algorithm and Distributed Cell Resource Allocation with a Base Station On/Off scheme that aims at reducing the overall base station power consumption in UDNs, while ensuring that the overall Quality of Service (QoS) for each User Equipment (UE) as specified in its service class is met. The modeling of the system model used and hence formulation of the Network Energy Efficiency (NEE) optimization problem is done viii using stochastic geometry. The network model comprises both evolved Node B (eNB) type macro and small cells operating on different frequency bands as well as taking into account factors that impact NEE such as UE mobility, UE spatial distribution and small cells spatial distribution. The channel model takes into account signal interference from all base stations, path loss, fading, log normal shadowing, modulation and coding schemes used on each UE’s communication channels when computing throughout. The power consumption model used takes into account both static (site cooling, circuit power) and active (transmission or load based) base station power consumption. The formulation of the NEE optimization problem takes into consideration the user’s Quality-of-service (QoS), inter-cell interference, as well as each user’s spectral efficiency and coverage/success probability. The formulated NEE optimization problem is of type Nondeterministic Polynomial time (NP)-hard, due to the user-cell association. The proposed solution to the formulated optimization problem makes use of constraint relaxation to transform the NP-hard problem into a more solvable, convex and linear optimization one. This, combined with Lagrangian dual decomposition, is used to create a distributed solution. After cellassociation and resource allocation phases, the proposed solution in order to further reduce power consumption performs Cell On/Off. Then, by using the computer simulation tools/environments, the “Distributed Resource Allocation with Cell On/Off” scheme’s performance, in comparison to four other resource allocation schemes, is analysed and evaluated given a number of different network scenarios. Finally, the statistical and mathematical results generated through the simulations indicate that the proposed scheme is the closest in NEE performance to the Exhaustive Search algorithm, and hence superior to the other sub-optimal algorithms it is compared to.
  • Thumbnail Image
    Item
    Open Access
    Enhanced bicasting and buffering
    (2012) Mphatsi, Lebajoa Anthony; Falowo, Olabisi
    Includes abstract. Includes bibliographical references.
  • Thumbnail Image
    Item
    Open Access
    Machine learning based heuristic BBU-RRH switching scheme for C-RAN in 5G
    (2019) Liu, Jiamo; Falowo, Olabisi
    The immense increase in bandwidth demand by various services such as high definition video streaming, online gaming, and virtual reality has made it increasingly challenging for operators to provide satisfactory services to the end users while making a profit. Cloud Radio Access Network (C-RAN) is a new architecture that has been proposed to facilitate the mobile networks' ability to meet the increase in bandwidth demand. C-RAN consists of three parts, namely Remote Radio Head (RRH), the front haul link, and Baseband Processing Units (BBU) pool. Many RRHs are associated with one BBU pool, and all RRHs within the pool are logically connected to every BBU in the pool. Thus, a BBU-RRH switching algorithm needs to be developed as it is able to enhance the performance of such architecture while managing the resource efficiently. This work mainly focuses on developing a traffic profile prediction-based BBU-RRH switching algorithm using a real life dataset. In the literature, there are related works that have proposed algorithms to achieve this purpose. However some of the existing algorithms suffer from high switching complexity while others fall short in QoS provision. Therefore, this work develops a BBU-RRH algorithm that to enhance the QoS while reducing the switching complexity, with the aid of machine learning techniques. The algorithm developed consists of three parts. The first part consists of an efficient RRH clustering mechanism that determines which RRHs are associated with a specific BBU pool. The second part utilizesrecurrent neural networks (RNN) to predict the daily traffic profile of RRHs, so that a relatively accurate traffic profile prediction can be obtained to facilitate the switching algorithm. Finally, the third part comprises the BBU-RRH switching scheme that works in conjunction with the predicted traffic profile to make an informed decision about the associations between RRHs and BBUs within the BBU pool. The performance of the proposed algorithm has been evaluated through simulations. The simulation results show that the proposed algorithm reduces the number of BBUs used and therefore save on energy. In addition, the algorithm reduces the occurrence of congestion and failure states, and thus improve the quality of the service of the network. Finally, the developed switching algorithm also reduces the switching complexity when compared with existing algorithms.
  • Thumbnail Image
    Item
    Open Access
    Multiple-RAT selection for reducing call blocking/dropping probability in cooperative heterogeneous wireless networks
    (Springer, 2012) Falowo, Olabisi; Chan, H Anthony
    There is an increasing demand for high bandwidth-consuming services such as real-time video and video streaming over wireless access networks. A single radio access technology (RAT) in a heterogeneous wireless network may not always have enough radio resource to admit high bandwidth-consuming calls, such as video calls. Existing joint call admission control (JCAC) algorithms designed for heterogeneous wireless networks block/drop an incoming call when none of the available individual RATs in the network has enough bandwidth to admit the incoming call. Consequently, video calls experience high call blocking/dropping probability in the network. However, some calls such as multi-layer coded (scalable) video can be transmitted/received over one or multiple RATs. This article proposes a JCAC algorithm that selects a single or multiple RATs for scalable video calls in heterogeneous wireless networks, depending on availability of radio resources in available RATs. Non scalable calls are always admitted into a single RAT by the algorithm. The aim of the proposed algorithm is to reduce call blocking/dropping probability for both scalable and non-scalable calls. An analytical model is developed for the proposed JCAC algorithm, and its performance is evaluated. Simulation results show that the proposed algorithm reduces call blocking/dropping probability in heterogeneous wireless networks.
  • Thumbnail Image
    Item
    Open Access
    SDN-Enabled Data Offloading and Load Balancing In WLAN and Cellular Networks
    (2022) Ashipala, Aili Kanee; Falowo, Olabisi
    Networking is an interesting field that is always evolving as new technologies that connect the world are adopted. There are many distinct types of networks, each of which is classified in a different way. One categorization is based on cellular wireless network generations, which have progressed from 1G to 5G. Several additional interconnected technologies have emerged as networking has progressed. SDN (Software Defined Networking) is a significant networking technology that represents a new paradigm. SDN distinguishes between the data and control planes and allows software-controlled networking for a wide range of applications. Cellular networks are critical for sending digital data from mobile or stationary senders to mobile or stationary receivers in wireless networks. Cellular networks are currently experiencing a data explosion because of the ever-increasing bandwidth demands of today's mobile applications. This has resulted in traffic congestion and a scarcity of resources. The network must handle a high volume of traffic and serve many customers, which may result in poor service quality for users. A single access network struggles to handle such a tremendous volume of traffic. Operators of cellular networks are attempting to alleviate the problem by offloading mobile data from cellular networks to complementary networks like Wi-Fi. However, without centralized control, traffic offloading may not significantly improve overall network load balancing, network usage, or users' quality of experience. This dissertation proposes a traffic offloading and load balancing algorithm between cellular and Wi-Fi networks, to enhance the overall utilization of cellular network. The proposed algorithm uses an SDN controller for making decisions of offloading users from a cellular network to a Wi-Fi network and to balance the load across access points. The algorithm makes use of the SDN controller's view in making decisions. Simulation results obtained show that the proposed data offloading scheme improves load distribution and throughput.