Although the Internet ecosystem in East Africa has experienced significant growth as evidenced by the 115% increment in Internet users in Sub-Sharan Africa, we understand very little about the infrastructure's performance which is crucial, particularly in DNS resolution, ISP peering, and the use of IXPs. Previous studies highlighting the importance of IXPs and their positive impact on connectivity are qualitative with limited participants, limiting their completeness. To achieve a nuanced understanding of the East African Internet landscape, a more extensive and diverse sample size is needed, integrating both context from qualitative with the scale from quantitative methods. Additionally, prior studies focus on the period following the installation of submarine fiber-optic cables, which may not fully capture the current state of the Internet ecosystem. This study aims to use a mixed methods approach to provide a comprehensive assessment of the Internet infrastructure in East Africa.

Mobile traffic demand has been increasing exponentially over the last few years and forecasts show that this trend will continue in the foreseeable future. As a result, operators are forced to densify and upgrade their networks to meet this demand. This has created concerns, such as increasing greenhouse gas emissions, high capital expenditures, and associated energy costs. This paper uses tools from stochastic geometry to analyze and formulate energy-efficient deployment strategies for multi-tier heterogeneous networks (HetNets) using various user association schemes. We use simple approximations to combine the required base station (BS) density and associated transmit power per tier subject to both coverage probability and average user rate constraints. In this paper, this combination is called the deployment factor and it can be expressed in closed form for unbiased HetNets. We then formulate area power consumption (APC) minimization framework, which optimizes the deployment factor to derive specific optimal BS density and transmit power values. Furthermore, we perform a comprehensive study of the effect of biasing on the APC performance of biased HetNets. Our results show that for HetNets using the maximum average-biased-received-power association scheme, significant energy savings are possible with appropriate biasing.

Traffic demand is increasing exponentially and operators will need to densify their networks with small cells to meet this demand. This has created economic and ecological concerns due to the high cost of energy and the associated greenhouse gas emissions. In this paper, we apply tools from stochastic geometry to design a deployment strategy for multi-tier heterogeneous networks (HetNets). Using appropriate approximations, we derive simple expressions that allow insights into the energy efficiency (EE) performance of biased and unbiased HetNets. We perform a comprehensive analysis on the effect of biasing on the EE performance of HetNets. We also formulate an optimization framework that minimizes the area power consumption (APC) of the HetNet subject to appropriate performance constraints. Our results show that significant energy savings can be made by appropriately biasing the HetNet and optimizing the BS densities and their transmit powers subject to the performance constraints.

Data traffic continues to increase exponentially and operators are continuously upgrading their networks to meet this demand. The resulting capital and operational expenditures have limited revenues and the associated energy costs and CO2emissions have raised economic and ecological concerns. In this paper, we use the stochastic geometry approach to investigate different sleep mode mechanisms that can address both capacity and energy efficiency (EE) objectives in dense small cell networks. We derive a multi-user connectivity model that facilitates the study of sleep mode mechanisms and manages the blocking rate of the network. We formulate an optimization framework that minimizes area power consumption using appropriate constraints. Numerical results show that sleep mode mechanisms enhance the EE of dense small cell networks and that the selection criterion of sleep mode candidate base stations is very important.

Forecasts show that traffic demand will continue to grow exponentially over the foreseeable future and mobile operators will have to upgrade and densify their networks to support this traffic demand. However, due to the spatiotemporal variation of traffic, some base stations (BSs) may remain idle during low traffic conditions. In this paper, we use tools from stochastic geometry to analyze the capacity and energy efficiency (EE) performance of a dense heterogeneous network (HetNet). We derive a multi-user connectivity model based on the LTE standard to determine realistic BS density configurations. We implement conventional sleep mode and investigate the effect of biasing on the capacity and EE performance of the HetNet. Our results show that the prevailing user density influences the HetNet performance, and that conventional sleep mode significantly enhances the EE performance. Appropriate biasing of the HetNet results in further EE performance enhancement.

Mobile network operators are currently faced with exponentially-increasing data traffic demand which necessitates significant investment in network expansion and upgrades. The required capital expenditure (CAPEX) and operational expenditure (OPEX) have reduced revenues and the associated CO2 emissions continue to raise ecological concerns. In this paper, we investigate the effect of varying user densities on the energy efficiency (EE) performance of the network. Using Poisson point process (PPP) theory, we derive simple analytical approximations that allow important insights to be made on the spectral efficiency (SE) and EE performance of a typical dense small cell network. We also study the impact of the sleep mode power consumption on the network EE especially in dense networks where numerous base stations may be idle simultaneously.

Data traffic demand in cellular networks continues to increase exponentially leading to high capital expenditure and operational costs such as electricity cost. Femtocells have been proposed as a solution to enhance network capacity without significantly increasing energy consumption and associated network deployment costs. In this paper, we analyze the network capacity and energy consumption aspects of a joint macrocell and femtocell network based on the LTE standard. Femtocells and macrocells are operated in the same frequency band to enhance spectral efficiency. Femtocell users are secondary users who only access the channel opportunistically when macrocell users are idle. We propose the use of cooperative spectrum sensing to manage adverse interference emanating from the co-channel operation of both macrocells and femtocells. Simulation results show that cooperative sensing is very effective at managing this cross-tier interference to maximize the throughput of both the macrocell and femtocell layers. This throughput maximization allows the lowest energy consumption ratio of such a heterogeneous network.