High-Density and De-Densified Smart Campus Communications. Daniel Minoli
Connection density
In addition to traditional communications, evolving requirements for high‐density environments include wearables (for example, in augmented reality applications), M2M, and vehicular traffic in Intelligent Transportation Systems (ITSs) environments. For example, densities of 1 node per m2 have been identified for augmented reality applications, as with Personal Area Network (PAN) mechanisms [10]. For ITSs, vehicle density has been one of the main metrics used for assessing road traffic conditions: a high vehicle density usually indicates that the road or street is congested [11]; the communication traffic is comprised of beacon signals and user‐generated signals. A congested road with stopped vehicular traffic might have, say, 12 cars in an area of 2500 ft2, or a density of 1 car in about 200 ft2 – each car could have multiple user sessions. Beyond user counts, the requirements span data rates, as highlighted in Table 1.1; some M2M and process control applications have stringent reliability and latency requirements. Applications such as Ultra HD video Streaming Over The Top (OTT), augmented reality, and online gaming impose challenging requirements on bandwidth and latency; however, these applications are not expected, in the short term at least, to have major deployment in mobile environments, but more so in stationary domiciled environments.
Additional key factors to take into consideration when deploying a state‐of‐the‐art HDC system include spectrum utilization, energy consumption, and infrastructure and endpoint system cost [2]. Spectrum efficiency is measured as the data throughput per unit of spectrum resource per cell or per unit area (bps/Hz/cell or bps/Hz/km2); energy efficiency is quantified in terms of the number of bits that can be transmitted per unit of energy (bits/J); infrastructure cost efficiency can be defined by the number of bits that can be transmitted per unit cost as computed from network infrastructure amortization/allocation (bits/$); endpoint system costs are clearly the endsystem costs, especially for the air interface and the protocol stack resources, to support a given maximum throughput; applicable to human devices (e.g. smartphones) and M2M systems. Improvements in these metrics of one‐to‐two orders of magnitude are being sought compared with legacy environments.
A number of use cases follow.
1.2.1 Pre‐pandemic/Long‐term Requirements for Airports
Table 1.2 identifies some target design parameters for airport applications, including voice, video, data, IoT, IoT‐based security (video surveillance), IoT‐based automation, and wayfinding. Two characteristics of airports are as follow: (i) people at the airport are in a “slave” situation typically with nothing to do but to use their electronic devices – this is unlike a stadium or a school where other events and occurrences take up some of the person's time, thus likely diminishing the connection time of the individuals; (ii) multiple automation M2M‐like tasks may be at play in the airport including baggage handling, wayfinding/mobility/movement, and security. HDC requirements continue to be active, even, or especially, in emergency cases (these requirements were instituted in early 2020 and continued to be active as of press time [12]) – one example of a challenging airport environment even as the pandemic was already raging, is illustrated in Figure 1.2. Typically, the visitor's public airport communication support is completely separate and walled‐off from the high‐security airport operations networks – the discussion and network design considered in this book focus on the former and not the latter, although similar technologies may be at play. Another characteristic is that, unlike stadiums, there is a nearly continuous requirement for connectivity, especially in large hub airports; stadiums are only used for relatively short periods a few times a week (once, less than once, or a few times a week). In addition to visitors, there are stationary concession businesses in the airport that would often make use of the same network infrastructure as the public network, although some administratively secure slice (for example, separate Virtual LANs [VLANs] would be used).
TABLE 1.2 HDC KPIs for Airports
| Key Performance Indicators | Key Performance Indicators | Pre‐pandemic Requirements |
|---|---|---|
| Data/VoIP connection density, for people on smartphones, laptops, tablets | Data/VoIP connection density, for people on smartphones, laptops, tablets | 1 per 20 ft2 in terminals |
| User experienced data rate | 10–50 Mbps | |
| Peak data rate | 100 Mbps | |
| Traffic volume density | 5 Gbps per gate area (200 people per gate) | |
| End‐to‐end latency | 100 ms | |
| Wayfinding | Throughout airport and in adjacent spaces, garages, car rental locations | |
| Area of coverage | Entire airport and in adjacent spaces, garages, car rental locations | |
| Traditional telephony on DAS systems | Dialtone | 50 Erlangs per gate area (200 people per gate) |
| Call length | 10 minutes per call | |
| Connection density, IoT devices | Connection density, IoT devices | 1 per 10 ft2 throughout airport |
| User experienced data rate | 0.384 Mbps | |
| Peak data rate | 0.768 Mbps | |
| Traffic volume density | 100 Mbps per 1000 ft2 throughout airport and in adjacent spaces, garages, car rental locations | |
| End‐to‐end latency | 1–10 ms | |
| Area of coverage | Entire airport and in adjacent spaces, garages, car rental locations |
According to the National Plan of Integrated Airport Systems (NPIAS), there are approximately 19 700 airports in the United States. 5170 of these airports are open to the general public and 503 of them serve commercial flights. A typical gate area is 30 000 ft2 (which would equate to an area of 40 × 75 ft); however, not all of that space is usable