# Approximate Networking for Universal Internet Access
## 4 Case Study: Approximate 5G Networks in Rural/Low Income Areas
Some of the emerging 5G trends such as small cells can be good for energy efficiency as it has been shown that reducing the cell size can increase the number of bits delivered per unit energy for given user density and total power in the service area; similarly, the HetNet arrangement of overlaying a macrocell with micro-/pico-cells at the edge can also help save energy [61]. Figure 9 illustrates a better SE vs EE tradeoff for LTE pico cells as compared to LTE micro cells and Global System for Mobile Communication (GSM). Figure 6: Percentage of population who can afford 500 MB and 100 MB pre-paid mobile data per month [58]. Researchers have demonstrated that 5G has a rich design space with many potential trade-offs [60], such as _deployment efficiency_ vs. _energy efficiency_; _spectrum efficiency_ vs. _energy efficiency_; _bandwidth vs. power_; and _latency vs. power_. A comparison of parameters for the various 5G use cases are shown in Figure 10. Mainstream 5G research has taken up the challenge of providing high-quality 5G service with gusto, but such an approach will result in an over-engineered system that will be too expensive to install and maintain for low-income and/or rural areas. By suitably scaling the level of service, 5G can encompass a broader coverage base. As an example, the requirement for supporting high-definition (HD) video and tactile Internet may be dropped in low-income scenarios but may be supported for urban and high-income rural environments. Since the rural users would likely prefer \"good-enough\" service to no service, researchers can explore using the traditional 5G trade-offs in conjunction with the newly developed flexibility of approximate computing and approximate networking to produce cost effective solutions for low-income and/or rural areas. Figure 8: Spectral energy vs. energy efficiency trade-offs for different circuit powers (Adapted from [63]). Figure 7: A timeline of historical evolution of cellular networks and the expected electricity consumption of Radio Access Network(RAN) [62]. We can also use techniques that bring us more flexibility and convenience in reducing the cost of networking with techniques such as using (1) renewable energy sources such as solar power; (2) base station sleep modes; (3) the building blocks proposed in approximate computing literature (such as the digital baseband processors proposed in [16]) for 5G infrastructure such as base stations; (4) virtualization of network components using techniques such as network functions virtualization (NFV); (5) exploitation of commodity hardware; (6) using community networking and resource pooling principles; (7) achieving flexibility using a separation of control and data planes; and (8) achieving flexibility by integrating Software Defined Networks (SDN) and NFV [64]. Using such technologies, coupled with a well-thought out approximate networking design, it is feasible to provide contextually-appropriate \"good-enough\" services in a sustainable fashion at a low cost (_in the order_ of 1 USD for low-income regions and 10 USD for rural regions). [PERSON] et al. [65] have proposed a mix of low-cost solutions and techniques to bring 5G to rural and low-income regions. For instance, remote radio heads (RRHs) can be mounted on unmanned aerial vehicle and balloons, low cost solar powered large cells (with coverage radius in the order of 50 km), and delay tolerant networks for 5G to provide cellular access in rural areas. This mixture was proposed considering the application context of rural areas, which comprised basic delay tolerant connectivity that could support applications such as e-health, e-learning, and emergency services. An economic analysis of these techniques in [56] showed a monthly subscription fee of 11 euros for rural areas of Italy and Cook Islands and a monthly subscription fee of 1  
Figure 10: The different requirements of the various 5G use cases.