# Approximate Networking for Universal Internet Access
## 4 Case Study: Approximate 5G Networks in Rural/Low Income Areas  
In this section, we investigate how approximate networking may be applied in the concrete setting focused on democratizing 5G wireless services universally, particularly in rural and low-income poor regions that are mostly found in developing countries. According to estimates, at least two billion people living mostly in _rural and low-income_ regions experience a complete lack of wireless network coverage. Figure 5 shows the cost of mobile broadband for developing and least developed regions is a big percentage of gross national income (GNI) per capita. As a result, a considerable population of these regions cannot afford a mere 100 MB mobile data per month (Figure 6). Without the enabling technology of the Internet and communication technology, these economically-backward areas suffer from a vicious cycle that pushes them even more backwards. Approximate networking services that can provide universal \"good-enough\" services can help bring the benefits of the Internet and communication to such disadvantaged communities. In contrast to the plethora of 5G research projects aiming for high performance, the coverage of rural and low-income areas in future 5G networks despite its great importance has received relatively little attention although that has begun to change with few recent works [3; 55; 56]. For rural areas, the main  
Figure 5: Mobile broadband prices as a percentage of Gross National Income (GNI) per capita for different regions [54]. challenge is cost-effective coverage rather than the urban focus on high data rates and low latency. The high-performance urban 5G requirements dictate the need of a complex and expensive network comprising inter alia high-capacity fronthaul and backhaul networks, dense heterogenous networks, and large datacenters. Supporting such an architecture requires significant revenues, which will be hard to obtain in rural environments that have very few inhabitants compared to urban environments. The weak business cases for telecommunication operators in serving rural areas can be observed from a previous study [57], which showed that the approximate revenue potential for operators in the least-densely populated areas is merely 262 USD per square mile of service, compared to 248,000 USD per square mile of service for highly-dense urban populations. Researchers are actively sketching out the details of the technologies that will drive the future 5G wireless networks. The unprecedented performance requirements of 5G will drive an increase in the overall energy consumption of cellular networks and in its carbon footprints. The evolution of cellular networks over its various generations, and the massive increase in performance as well as energy consumed by cellular Radio Access Networks (RANs) over time can be seen clearly in Figure 7. Satisfying these exacting requirements requires (1) greater _spectrum efficiency_, which may be defined as the _number of bits transferred per second per hertz or b/s/Hz_, as well as (2) better _energy efficiency_, which refers to _the energy consumed to communicate an information bit measured in Joules/bit_. The spectral efficiency (SE) and energy efficiency (EE) of wireless networks has been well studied in literature [59; 60]. Unfortunately, there is a trade-off between higher spectral efficiency and higher energy efficiency, which may be expressed by the following equation for the case of an Additive White Gaussian Noise (AWGN) Channel:  
\[\eta_{EE}=\frac{\eta_{SE}}{N_{o}(2^{\eta_{SE}}-1)} \tag{1}\]  
where \(N_{o}\) is the noise power spectral density. The SE vs EE tradeoff, however, is complex for practical systems and becomes bell shaped when circuit power \(P_{c}\) is considered. Figure 8 shows the tradeoff comparison for different values of circuit power.