What’s Next for Mobile Telephony?

What’s Next for Mobile Telephony?

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Many factors will define the evolution and adoption of commercial wireless technology. From the huge range of possible outcomes, it is useful to consider which factors are most relevant in predict­ing the future. Figure 1 shows the evolution of wireless since the start of the digital era in the early 1990s.

Much industry speculation and debate exists attempting to predict the next-generation market winners with the focus often being on the peak data rates possible. To take a more complete view, however, other important criteria should also be considered:

  • Achievable data densities given the constraints of interfer­ence and deployment costs
  • The consequences of format proliferation and spectrum fragmentation on system complexity and cost
  • Customer-centric issues such as compelling end-user services and Quality of Experience (QoE)

This article will examine the continuing growth in peak data rates and consider implications on achievable data densities, system cost and customer QoE. The issue of interference and the grow­ing gap between peak and average data rates will be considered.

At this point, it would not be unreasonable to conclude that Moore’s law was predicting this growth in data rates, although with the doubling occurring every 18 months rather than two years. That said, Moore’s law is an observation of the semi­conductor industry. Although it is very tempting to hope, it is unlikely to be just a matter of time before we are able to download a 1 GByte operating system upgrade in 25 seconds at 326.4 Mbps from a cellular system to our laptop while riding an elevator on the way to a meeting.

Probing the Shannon-Hartley Theorem

Techniques such as interference cancellation (IC) and spatial diversity with multi-stream transmission appear to get around the theorem but looking a bit closer this is not strictly true. The potential of IC in cellular systems is due to most noise not being truly Gaussian as assumed by the theorem. If information can be extracted from this “noise” then it is “others’ signal” and can be removed, thereby improving capacity. The challenge is in the processing power and advanced algorithms required to track, decode and then remove the dynamic interference from multiple users. This puts a practical and modest upper limit on what can be achieved. For spatial diversity, the theorem still indicates the capacity of each channel and it is the correlation between the channels that would determine the overall improvement possible using multiple-input/multiple-output (MIMO) techniques.

Examining the Trend Towards Higher Data Rates

Over the last 20 years, mobile wireless systems have evolved from expensive, low-tech niche markets into one of the world’s biggest high-tech industries. Subscriber numbers this year will exceed 3 billion — or half the planet — with more to come. In addition to subscriber growth, Table 1 shows a parallel growth in cellular peak data rates of four orders of magnitude.

Pinpointing the Origins of Higher Data Rates

If we take a closer look at the evolution of data rates and spectral efficiency for each system, we discover six technical factors that explain the growth:

  • Allocating more time (TDMA duty cycle)
  • Allocating more bandwidth
  • Improving frequency reuse
  •  Reducing channel coding protection
  • Using higher order modulation
  • Taking advantage of spatial diversity (MIMO)

Accounting for Interference

Unlike wired communication channels such as copper or fiber which largely isolate signals from each other, electromagnetic propagation in free space knows no boundaries. On first inspec­tion, the Shannon-Hartley theorem predicts that for LTE to deliver

100 Mbps in a single channel would require an SNR of better than 30 dB. This is a crucial point: In a typical environment, how often does the SNR reach such levels?

In the limit case of an isolated cell (e.g., a hotspot), demonstrat­ing peak performance is straightforward and only limited at the cell boundaries when the self-noise of the system becomes dom­inant. However, when the cells become closer to the point where coverage is continuous, the interference situation is very differ­ent. A common measure for system interference is the geometry factor or “G factor” defined as:

G = Îor / Ioc where

Îor = received power of desired signal

Ioc = all other co-channel received power

Looking to the Future

The physical and commercial constraints on implementing high-

data-rate cellular services must be considered. As with trans­portation systems where there is a wide mix of vehicle types to match the range of channels, so it will be with wireless. The cellular industry needs to continue to optimize performance for the average user as defined by the statistics of the radio channel and not risk getting distracted by peak performance. Cellular’s focus should move from technology towards the development of compelling services with high QoE. The battle for the high-da­ta-rate home and enterprise wireless markets will continue, and whether the winner is based on Wi-Fi or the more advanced femtocells, Keysight Technologis, Inc. is ready to provide the tools the industry needs to design and optimize the right wireless technology for the future