for making regulatory decisions which can have significant ... paper [1] from the FCC Technological Advisory ... space, and that connections between technology.
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TOPICS IN RADIO COMMUNICATIONS
From Protocol Stack to Technology Circle: Exploring Regulation, Efficiency Metrics, and the High-Dimensional Design Space of Wireless Systems Petri Mähönen, Ljiljana Simi´ c, and Marina Petrova, RWTH Aachen University J. Pierre de Vries, University of Colorado
ABSTRACT Considerations of efficiency often play an important role in debates on how to allocate new spectrum. Regulators want to maximize the common good, and try to use efficiency as a tool to do so; consequently, all the stakeholders strive to show that their proposal leads to efficient use of resources. However, often the discussion on particular methods or allocations becomes deadlocked. In this article we explore what is meant by efficiency metrics in wireless networks and what underlies the widely divergent claims for the relative efficiency of competing solutions. Our treatment attempts to take into account the regulatory and policy viewpoints on the development of system efficiency metrics. We will argue that it is important to define system optimization considerations and design goals for future wireless systems more clearly than has been done to date. We will show that although this may sound simple, it is far from it. We argue that efficiency needs to be analyzed within a full N-dimensional design space, and that connections between technology and policy need to be stated explicitly.
INTRODUCTION A lively debate on how to allocate limited spectrum resources is raging. Discussion has been reinvigorated lately due to the success of mobile broadband services, switching from analog to digital TV systems, and the first real-world application of dynamic spectrum access concepts in recent regulatory rulings allowing secondary access to so-called TV white space (TVWS). In order to justify their decisions, policy makers and regulators invite arguments to prove that proposed new radio operations will use spectrum in an efficient manner. An interrelated issue has been the relative merit of spectrum allocated for licensed or unlicensed use. This decades-long discussion has taken on renewed importance due
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to the new possibility of deploying additional services in TVWS. Consistent and coherent metrics and predictable evaluations are imperative for making regulatory decisions which can have significant economical and societal impact. However, the ongoing debate suggests that it is not always clear what those metrics should be. At first glance, the reader might believe that a precise definition of spectrum use efficiency is surely well established and all too obvious to the engineering community and regulators. However, this is not the case. Indeed, the recent white paper [1] from the FCC Technological Advisory Council (TAC) alone refers to 25 different spectrum efficiency metrics, and this is not an exhaustive list. Moreover, such quantitative technical metrics do not capture the many further commercial and societal factors that strongly influence technology development. Our aim in this article is to provide a more holistic view of efficiency and the merits of different spectrum allocations. We use the debate over unlicensed vs. licensed access to illustrate some of our points; we consider the performance potential of Wi-Fi in TVWS, and contrast this with the performance of an LTE-like licensed cellular system. We believe that this serves as a good example of how discussions about supporting efficiency, innovation, and the public good can be very difficult to conduct without a proper framework. We start our discussion by summarizing the conventional notions of efficiency in wireless communication systems. We present technical case studies to show why any given metric might be problematic when characterizing efficiency. We consider what regulators and policy makers would need to do to conduct a reasoned discussion, and what the research community can do to help speed up and improve the decision making process by using more holistic evaluation metrics. We hope that removing the confusion engendered by single efficiency metrics will lead to better-informed policymaking.
IEEE Communications Magazine • December 2012
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TOWARD EFFICIENT WIRELESS SYSTEMS Without a doubt, every engineer wants to design an efficient wireless system, and every regulator wants to enable as much value to be generated as possible by radio operation while incorporating considerations of the public good. However, as wireless systems become more complex and diverse, the time has come to stop and ask: what does “efficiency” actually mean in this context? How do we define the efficiency of wireless systems and spectrum allocations, and is this familiar metric in fact meaningful and appropriate? We start by noting that cellular system design has historically emphasized detailed design of whole systems and very careful optimization of the physical (PHY) and medium access control (MAC) layers. Spectral efficiency, especially in bits per second per Hertz at the link level, has always been one of the key parameters. By contrast, Wi-Fi technology was originally driven by the computer communications community emphasizing ease of use and cost minimization. One of our aims is to ask how different these systems are today and whether spectral efficiency should still be a key deciding factor for regulators. It is always tempting to try to characterize system efficiency with a single or very few quantitative parameters. Not only does this simplify technical optimization problems for the engineering researcher, it also greatly facilitates marketing. In the regulatory domain, single quantitative efficiency metrics are a holy grail, as they lend an air of cold, impartial judgment to policy decisions and thus greatly smooth decision making, advocacy, and defending new rules against appeal. In this vein, two often used performance metrics for wireless systems are user bit rate (or throughput, or capacity), measured in bits per second, and spectral efficiency, usually measured in bits per second per Hertz (sometimes extended to consider covered area to bits per second per Hertz per square meter). These measures are consistently used in technology marketing and lobbying, regardless of their appropriateness for a particular scenario. There is also a temptation for engineers to use these numbers for convenience, even if they might often be vaguely uneasy about doing so. The inadequacy of these parameters used in isolation ought to be obvious. Throughput is only one particular aspect of network efficiency, and the term itself can be defined in various ways. Any given system throughput number is necessarily based on a number of assumptions (e.g., traffic load and number of users), and the number itself may have different meanings (e.g., maximum or average user throughput). The familiar spectral efficiency metric of bits per second per Hertz is often no more credible, especially if used outside the very simple case of characterizing the physical layer under some specific channel assumptions. The advocates for specific solutions, and even some researchers, are given to building their case around the bestsounding metric, such as aggregate maximum
IEEE Communications Magazine • December 2012
theoretical PHY-layer throughput, regardless of its applicability. Although this is clear to wireless systems engineers, these metrics are often used in inappropriate contexts, such as in the debate on the relative merit of licensed vs. unlicensed approaches to spectrum access; policy decision makers, in particular, should be alert to this danger. It is difficult to characterize any complex technical system that is also under regulatory and economic constraints, and it is likely to require a multidimensional representation to properly represent the relative strengths of competing solutions. Of course, as it is natural for policy makers to seek succinct guidance in nontechnical terms from engineers and economists, simplistic metrics will always be with us, not least in radio regulation. Given this reality, merely stating that the system is complex will not go far toward solving the problem. Instead, engineers must face the challenge of developing a better understanding of the highly multidimensional design space of modern wireless systems and extracting more meaningful metrics. Engineering researchers need to also educate regulators, policy makers, and economists to use them: indeed, the engineering community should insist they are used as a part of public discourse. Before tackling the question of how this challenge might be met, we illustrate the difficulty of using the traditional approaches discussed above to choose the “most efficient” system in the context of the licensed vs. unlicensed debate.
Merely stating that the system is complex will not go far toward solving the problem. Instead, engineers must face the challenge of developing a better understanding of the highly multidimensional design space of modern wireless systems and extracting more meaningful metrics.
CASE STUDY: WI-FI AND LTE IN TVWS In this section we present a technical case study of the use of TVWS frequencies by either unlicensed (Wi-Fi type) or licensed (cellular type) systems to illustrate the problem with using simplistic measures of spectrum use efficiency and deriving spectrum allocation policy based on technical merit alone. We also compare the benefit of TVWS access against existing approaches, namely current Wi-Fi technology operating in the unlicensed 2.4 and 5 GHz bands. We illustrate the danger of basing policy decisions on overly simplistic engineering metrics and analysis, argue that rational regulatory decisions cannot be based solely on considerations of “spectral efficiency” or “range,” and show that a simplified analysis cannot easily distinguish between competing solutions such as LTE and “super WiFi” [2]. Figure 1 shows the estimated downlink rate versus coverage range for a Wi-Fi-like system of access points (APs) operating using a carrier sense multiple access with collision avoidance (CSMA/CA) MAC protocol in different frequency bands. We compare the performance of a Wi-Fi-like secondary system operating in TVWS (center frequency of 630 MHz), as planned in the draft IEEE 802.11af standard [3], to that of the existing IEEE 802.11g and IEEE 802.11a Wi-Fi standards operating in the unlicensed 2.4 and 5 GHz frequency bands, respectively. We assume each AP in the network transmits at the European industrial, scientific,
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TVWS (no inter-AP interference) 2.4 GHz (no inter-AP interference) 5 GHz (no inter-AP interference) TVWS (with inter-AP interference, 4 x 24-MHz channels available) 2.4 GHz (with inter-AP interference, 3 x 20-MHz channels available) 5 GHz (with inter-AP interference, 15 x 20-MHz channels available)
60
70 Estimated downlink rate (Mb/s)
Estimated downlink rate (Mb/s)
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50 40 30 20 10 0
TVWS (no inter-AP interference) 2.4 GHz (no inter-AP interference) 5 GHz (no inter-AP interference) TVWS (with inter-AP interference, 4 x 24-MHz channels available) 2.4 GHz (with inter-AP interference, 3 x 20-MHz channels available) 5 GHz (with inter-AP interference, 19 x 20-MHz channels available)
60 50 40 30 20 10 0
0
50
100 150 Coverage range (m)
200
250
0
2
4
6
(a)
8 10 12 14 Coverage range (m)
16
18
20
22
(b)
Figure 1. Rate vs. range performance for a network of Wi-Fi-like APs (each transmitting at power of 20 dBm) operating in different frequency bands, for the a) outdoor urban; b) indoor urban deployment scenarios. When considering inter-AP interference, we assume each AP in the network randomly selects one of a number of non-overlapping channels to operate on (thus the estimated AP downlink rates represent the average throughput per channel).
Scenario
Outdoor urban
Indoor urban
Propagation characteristics (log-distance path loss model)
Path loss exponent, k = 3
Path loss exponent, k = 4 and 18 dB wall loss
Density of Poisson point process generating random AP locations
l = 12.5 APs/km2
l = 125 APs/km2
Network study area
(2 km ¥ 2 km) in central Aachen
(500 m ¥ 500 m) in central Aachen
Table 1. Wi-Fi system model parameters for different deployment scenarios.
and medical (ISM) band regulatory limit of 20 dBm and randomly selects one of a number of available channels on which to operate. In the 2.4 GHz ISM band there are three non-overlapping 20 MHz channels, whereas in the unlicensed 5 GHz band there are 19 and 15 20-MHz channels available for indoor and outdoor use, respectively (in Europe) [4]. In the TV bands we assume aggregation of three adjacent 8 MHz channels to enable operation on a channel of comparable width (24 MHz). The amount of TVWS available for secondary operation varies from place to place; in our analysis we use TVWS availability estimates for the German city of Aachen, where four 24 MHz channel chunks are found to exist [5, 6]. Our results were obtained via simulations of a Wi-Fi network under different inter-AP interference conditions and for two application scenarios: outdoor urban hotspot deployment and indoor urban use (Table 1 lists key parameters characterizing each scenario). We assume that the APs employ an autorate function r (b ), which maps minimum received signal-to-interference-plus-noise ratio (SINR) b at the associated user terminal to the
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raw bit rate r provided by the AP (e.g., from 6 to 54 Mb/s for IEEE 802.11a/g Wi-Fi); r(b) is a piecewise constant function given by the spectral efficiency and minimum receiver sensitivity specifications of the IEEE 802.11 Wi-Fi standard [4]. We take into account the impact of congestion and interference among co-channel APs via the model adopted in [6], whereby an AP’s downlink throughput is estimated as the raw bit rate r(b) divided by the number of APs sharing its contention domain. Further details of the system model used in our analysis, including comments on rural deployments, are given in [6]. Let us consider the case of outdoor deployment in Fig. 1a. In the simplistic case that ignores the effects of inter-AP interference, the results support the claims by advocates of “super Wi-Fi” and “Wi-Fi on steroids” of a greatly increased range for a given rate when operating in TVWS. However, when we consider more realistic interference conditions, the extended AP range in TVWS leads to many overlapping AP contention domains, resulting in significantly degraded performance due to congestion. Consequently, the gain of operating in the lower
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TVWS frequencies compared to existing Wi-Fi at 2.4 or 5 GHz diminishes significantly. For example, for a downlink rate of around 50 Mb/s, the TVWS coverage range is almost halved from 80 to 47 m when inter-AP interference is taken into account, compared to a negligible reduction of several meters at 2.4 or 5 GHz. Moreover, Fig. 1a reveals that, under realistic interference conditions, the peak and average data rates when operating in TVWS are lower compared to existing Wi-Fi, despite a 20 percent wider channel bandwidth. Thus, the situation is much more complex than stating that we have peak 2.7 b/s/Hz spectral efficiency, as in 802.11a/g, and higher range for TVWS Wi-Fi. These results illustrate the danger of basing policy decisions on overly simplistic engineering metrics and analysis. The early proponents of uncoordinated access to TVWS presented an attractive case for a “plug-and-play” Wi-Fi-like system based on a simplistic calculation of spectral efficiency and range at the lower TV frequencies, backing the FCC’s regulatory decision to open up the UHF frequencies for unlicensed secondary use. The “super Wi-Fi” claim [2] was likely generated by the thought of increased range, without understanding that this also increases the interference radius which lowers efficiency, especially if CSMA/CA type medium access is used. Our more detailed analysis reveals some of the real complexity of the situation; the performance of the proposed technology strongly depends on the characteristics of the deployment scenario; for example, uncoordinated Wi-Fi-like access may be suboptimal for TVWS in areas of high user density. This indicates that the rational regulatory decision cannot be based solely on “spectral efficiency” or “range,” since the application and expected user density play an important role. The indoor deployment scenario in Fig. 1b suggests a more favorable case for Wi-Fi-like operation in TVWS, where the lower TV frequency allows better penetration through walls while coverage remains short-range enough to prevent excessive congestion due to overlapping AP contention domains. However, the achievable rate is simply proportional to channel bandwidth, so in this context, the TVWS spectrum is simply providing a new ISM band with slightly extended range compared to the existing 2.4 GHz band. This raises the question of whether we actually need a new ISM band for indoor use, since the 5 GHz band remains underutilized. Thus, the regulatory decision is no longer about the efficiency of TVWS Wi-Fi per se, but whether we believe there is need for an extra ISM band and somewhat increased range. One should also note that a simple regulatory change to allow more transmission power for 802.11a operating in the underutilized 5 GHz unlicensed band would lead to extended coverage. Let us now turn our attention to a cellular Long Term Evolution (LTE) system concept, and its successor LTE-Advanced (LTE-A), which are natural contenders for lower frequency bands used in either a licensed or opportunistic manner. LTE also provides an excellent example of why choosing representative performance metrics is not easy, even in the case of cellular
IEEE Communications Magazine • December 2012
Antenna configuration
Uplink
Downlink
Average spectrum efficiency (bps/Hz/cell)
Cell edge spectrum efficiency (bps/Hz/cell/user)
Average spectrum efficiency (bps/Hz/cell) by a vendor estimate*
1¥ 2
1.2
0.04
1.39
2¥ 4
2.0
0.07
2.25
2¥ 2
2.4
0.07
2.26
4¥ 2
2.6
0.09
2.68
4¥ 4
3.7
0.12
3.46
* ZTE simulation results,3GPP Case 1 scenario, http://wwwen.zte.com.cn/en/solutions/wireless/lte/fdd_lte/201010/t20101025_ 193824.html
Table 2. Average and cell edge spectrum efficiency for LTE-A for a case study and a representative vendor estimate of the achievable rates (according to 3GPP TR36.913). In this scenario, 10 users are distributed randomly with 10 MHz bandwidth, 20 dB path loss and 3 km/h average user mobility.
systems. Both LTE and LTE-A are complex standardized 4G systems, and we refer the reader to relevant literature for the details [7]. One of the flexible aspects of the LTE family is that it can be deployed in different frequency bands, and it also supports different bandwidths, which can generally range from 1.4 to 20 MHz for LTE. LTE and LTE-A are based on orthogonal frequency-division multiplexing (OFDM) modulation with orthogonal frequency-division multiple access (OFDMA) as the medium access method. Their peak spectral efficiency depends on the multiple-input multiple-output (MIMO) antenna configuration and a number of other parameters. The theoretical peak performance for the downlink in the high signal-to-noise ratio (SNR) region (single user, short distance) can reach 16.3 b/s/Hz in the case of LTE (4 ¥ 4 MIMO, Release 8), compared to 30.6 b/s/Hz for LTE-A (8 ¥ 8 MIMO, Release 10). The theoretical peak efficiencies for the uplink are 8.4 and 16.8 b/s/Hz, respectively. These numbers sound significantly higher than for the case of Wi-Fi, but one has to remember that these are theoretical peak performance numbers. In the case of IEEE 802.11n Wi-Fi, the theoretical peak performance is similarly about 15 b/s/Hz (using MIMO), and the future IEEE 802.11ac has a target of 5.3 b/s/Hz (without MIMO) and 16.25 b/s/Hz (3 ¥ 3 MIMO). Obviously, the range, power, and real average throughput vary for these systems. In fact, published estimates of spectral efficiency for LTE systems vary significantly. This is partly due to implementation uncertainties, but even more due to differences in scenarios and underlying assumptions. The published results are often very good engineering; the caveat is that many outsiders take away oversimplified lessons from these numbers. A recent analysis by 4G Americas members [8] shows that the average downlink spectral efficiency of LTE is likely to be around 1.4 b/s/Hz (2 ¥ 2 MIMO) and vari-
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Even within the same system, direct comparisons are often not sensible unless we first define the reference scenarios and fix parameters (e.g., the center frequency, bandwidth, and user density). This is one of the potential caveats on why the debate on spectral efficiency between licensed and unlicensed approaches has been raging for so long.
ous improvements, including 4 ¥ 4 MIMO, may scale that toward 2.3 b/s/Hz or higher (see also recent results from Ericsson in [9]). Like the case of Wi-Fi, at the cell edge, where interference from other cells starts to be significant, the spectral efficiency is significantly lower. Table 2 shows the Third Generation Partnership Project (3GPP) target efficiency for LTE-A for one of the standard scenarios. As we see, the expected spectral efficiency is likely to be around 1.2–3.7 b/s/Hz per cell, and the cell edge user efficiency is probably as low as around 0.1 b/s/Hz. This makes evident the fact that licensed cellular systems also cannot be properly characterized without detailed technical arguments and considering a range of different parameters. While the peak data rate or peak spectral efficiency is an important performance measure from a marketing point of view, the user experience is mostly dependent on the equal service guarantees across the cells. This means that average and cell-edge data rates are more relevant than peak data rates. As we can see from the analysis, the unlicensed Wi-Fi and licensed LTE systems provide surprisingly similar efficiency metrics under some constraints, especially as long as the user density is rather low. This should not be a surprise, as the decreasing cost of microelectronics has driven the core PHY layer techniques to be similar and equally close to the Shannon limit in different systems. However, we also emphasize that the metrics are very sensitive to different assumptions that are often not clearly stated, especially in arguments put forward for regulation and releasing spectrum. Nevertheless, from these numbers it is clear that with simplified analysis one cannot easily distinguish LTE and “super Wi-Fi” systems.
HIGH-DIMENSIONAL DESIGN SPACE As illustrated by the technical case studies presented in the previous section, one cannot conclude from simplified technical arguments alone whether unlicensed or licensed approaches are universally better for, say, TV white space spectrum access. Similarly, different conclusions can be reached about the relative efficiency of TVWS Wi-Fi and traditional Wi-Fi. These value judgments are ultimately decisions of social and economic policy; the constraints and values of the political process, including commercial and social justice considerations, will invariably shape the outcome. In this section we first argue that engineers ought to use an N-dimensional parametric space to properly reason about the technical efficiency of candidate wireless technologies. Second, we argue that the debate on the efficiency of spectrum use and regulation must also explicitly acknowledge the non-metric part of the design space, which encompasses policy issues. Lastly, we discuss the resulting challenges for researchers and policy makers.
SPECTRUM EFFICIENCY: N-DIMENSIONAL METRIC SPACE As we have demonstrated, any single metric may be misleading when used to characterize the efficiency of spectrum use. Any metric is a model,
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an abstraction that leaves out a lot of detail in order to provide a useful thumbnail of the system under analysis. For example, the FCC TAC white paper [1] points out that maintaining or improving a quality of service (QoS) measure for a communications system may actually have a negative impact on spectral efficiency metrics, but may be necessary for a particular application. Therefore, we ought to use an N-dimensional parametric metric space to characterize efficiency of spectrum use and systematically spell out different metrics and design trade-offs. Building a sensible metric efficiency space is not trivial. There are a number of fundamental input parameters, such as spectral bandwidth and maximum transmit power, coupled with other important system parameters such as infrastructure costs and accuracy of possible power control. Parameters can also be nonlinearly interdependent, making the space nonlinear. The metric nature of our space requires that input parameters need to be quantifiable so that different functions can be used to compute the output efficiency values (which may require complex simulations), for example, average or maximum throughput, latency, coverage, et cetera. Values can also be compressed into efficiency ratios, such as spectral efficiency in bits per second per Hertz. One has to consider the full N-dimensional design space to conduct a proper analysis. This is a nontrivial task, and there can be a temptation to simplify things to make them easily comprehensible. A wireless system that can span a large volume of N-dimensional space can be deemed to be flexible, and such flexibility can be one of the desirable design goals. One should note that the parameter space might have exclusion zones (i.e., areas that are unreachable or undesirable due to physical, engineering, financial, or regulatory constraints). Although N-dimensional metric space is in some sense a straightforward concept, we believe that its importance has not been emphasized enough in the research literature, perhaps owing to its perceived obviousness. One of the exemplary papers making a case for studying carefully system aspects of LTE with different projections from the N-dimensional efficiency space is [10]. We will use their approach, in part, to give an example of the above arguments. Even within the same system, direct comparisons are often not sensible unless we first define the reference scenarios and fix parameters, e.g. the center frequency, bandwidth, and user density. This is one of the potential caveats on why the debate on spectral efficiency between licensed and unlicensed approaches has been raging for so long. Varying these numbers we can seek answers to questions about relative efficiency; the question of absolute efficiency is a chimera, as only relative merits can be argued in a metric space. In Fig. 2 we sketch different 2dimensional projections out of N-dimensional space, which illustrate typical functional interdependencies of parameters in communications systems (see also [10]). We emphasize that the interpretation of results can change depending on the projection. Figure 2a depicts how the cell size decreases as a function of frequency given a fixed transmis-
IEEE Communications Magazine • December 2012
Cell size (m)
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Frequency (Hz)
Frequency (Hz) (b)
Spectral efficiency (b/s/Hz)
Average spectral efficiency (b/s/Hz)
(a)
Cell size (m)
Cell size (m) (d)
Spectral efficiency (b/s/Hz/m2)
(c)
Frequency (Hz) (e)
Figure 2. Illustrative example 2D sub-space slices out of the whole N-dimensional space analysis of a generic wireless system.
sion power and physical layer techniques (different curves show different technologies or deployment environments, e.g. rural vs. urban). The scaling is, of course, different depending on the environment. This can easily lead to a claim that lower frequencies are “beach-front property.” We show in Fig. 2c a similar situation for spectral efficiency in bits per second per Hertz vs. the cell size. The reader should note that, using another parameter as an efficiency metric, short range operation — and thus higher frequencies — now looks more attractive. The
IEEE Communications Magazine • December 2012
efficiency drops with the distance due to lower average SNR at cell edges. However, the situation can change if we have some a priori information on the number of users and their spatial distribution within the cell. The latter affects the expected average efficiency due to the behavior shown in Fig. 2d. If the users are highly clustered at short distances (close to the base station), the average spectral efficiency can be significantly higher. The same effect will also have a significant impact through the chosen MAC technique.
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Regulators should not rely on the comforting but spurious objectivity of any single numerical engineering result that, even if not simplistic, is based on many simplifying assumptions. Engineering analysis alone cannot and should not justify fundamental policy decisions.
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System performance also scales in the usual way with the frequency and environment. We have illustrated this in Fig. 2b. Due to the different propagation environment, and especially if MIMO and spatial diversity are used, the spectral efficiency tends to peak at some reference frequency and then have lower values in other frequencies. Obviously, this is related to the additional axes of bandwidth, range, and transmission power. Thus, one has to be careful when making claims on the efficiency of moving some technology, say Wi-Fi or LTE, to another band (e.g. TVWS); their efficiency metrics will change in a nontrivial way as a function of operational frequency. Finally, we show what would happen if we replaced a bits per second per Hertz vs. frequency metric as in Fig. 2b with a bits per second per Hertz per area vs. frequency, as in Fig. 2e. We get the opposite conclusion from the original Fig. 2a, that is, higher frequencies are more efficient (using this single metric). The reason is that higher frequencies gradually force a system to adopt a short-range femto-cellular network architecture with potentially very high bits per second per Hertz capabilities. Naturally, this may or may not be economically viable in the real-world environment. This example demonstrates how different two-dimensional projections of the N-dimensional efficiency space can lead to widely divergent conclusions. We argue that some of the highly orthogonal claims in the debate on the benefits of different systems from the perspective of efficient use of spectrum are indeed generated by the absence of a full N-dimensional analysis. One can manipulate this case study to show different winners between Wi-Fi and LTE in TVWS just by choosing projections. One should also note that the cost of LTE femto-base stations can be low, so it would not be a deciding factor. We also stress that as PHY-layer techniques in many systems show very similar link efficiencies and properties, a fair analysis requires one to also explicitly include medium access and use case scenarios before making regulatory conclusions. In short, we believe that explicitly considering the N-dimensional design space of any candidate technology greatly improves the transparency of engineering analysis and thus better supports rational decision-making. This presents two distinct challenges: • Designing a suitably comprehensive Ndimensional metric space • Devising a procedure to describe systems and scenarios within such a framework Designing a well-thought-out N-dimensional space and related methodology for describing systems is a research topic in itself and beyond the scope and length of the present article. In general, aside from the aforementioned metrics, it is important to consider parameters related to implementation and business feasibility, like cost, energy consumption, and complexity. However, we emphasize that simply including a large number of parameters is not enough; presenting analysis that considers different projections from the N-dimensional space is equally important. For some additional discussion on this issue, we refer the interested reader to [11].
NON-METRIC DESIGN SPACE AND SPECTRUM POLICY Aside from considering the quantitative parameters that characterize the technical efficiency of wireless systems, there are many considerations that influence the viability of a candidate technology, but are difficult, or nearly impossible, to quantify. An example is the often stated claim that unlicensed technologies foster “innovation.” Such claims are difficult to quantify and meaningfully incorporate into an overall mathematical optimization; thus, assessing their importance and credibility is ultimately a policy judgment. In our opinion, engineers and even economists should not strain to quantify such arguments but must instead accept that they belong to the domain of qualitative debate. Wireless engineers tend to focus on the seven-layer open systems interconnect (OSI) network stack, particularly Layers 1 and 2 (PHY and MAC). However, the socio-technical context for their work extends beyond the radio system alone. It is useful to consider a more holistic framework that includes not just the OSI network layers but also the regulatory and commercial aspects of wireless technology. As we have seen, it is not possible to state objectively, based solely on technical arguments, whether a wireless system is efficient, especially without making comparisons. In fact, what is possible at the PHY and MAC layer is determined by regulatory rules, what we call Layer 0. Examples of engineering parameters that are directly determined by choices at Layer 0 include permitted power level, paired vs. unpaired spectrum allocation (FDD vs. TDD architecture), access control rules, and mode of spectrum allocation (licensed vs. unlicensed). Thus, there is no fundamentally technologyneutral regulation or policy-free technology. Moreover, both the traditional OSI network stack and Layer 0 are mutually influenced by business models and social practices, which we term Layer 8. An example of the influence of Layer 8 factors in technology development is evident in the contrast between the multiple stakeholders, open standards culture of the Internet Engineering Task Force (IETF) and the top-down, treaty-driven model of the International Telecommunication Union (ITU), which is also favored by governments, or in the preferences of different national regulators for command-and-control or flexible-use allocations. Thus, regulatory determinations serve industry and societal interests, and have direct engineering consequences that cannot be ignored. In order to provide a more holistic framework for telecommunications technology development, we propose that the traditional network stack should be wrapped into a technology circle as shown in Fig. 3. The representation in Fig. 3 emphasizes that the regulatory and commercial setting ought to be, at the very least, explicitly acknowledged at the outset of technical engineering analysis. Of course, it is difficult for engineers to connect social goals to engineering solutions. The ambiguity tends to be highest during the early
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research and advocacy for new regulation, when different interest groups can be tempted to use overly simplified metrics to draw potentially misguided conclusions that serve parochial interests. Spectrum politics is governed by five permanent policy imperatives: protecting public safety, providing consumer protection, protecting cultural values (e.g., speech and content rules), raising government revenue, and fostering economic vitality [12]. Technology provides the means to achieve these ends, sometimes also undermining them. However, the engineering work is inherently far upstream from political outcomes, and their connection is tenuous. As these policy goals are often difficult to quantify and include the N-dimensional parameter space, they pose a challenge for engineering efficiency measures. We believe engineers ought not even to attempt to incorporate such “soft” (and often special interest group) goals into quantitative system performance metrics. However, one should note that often policies can generate exclusion zones in the technical design space.
IMPLICATIONS FOR RESEARCHERS AND POLICY MAKERS Engineering researchers should be conscious of these policy and social value implications when publishing and using engineering metrics. Engineering findings have an important role to play in this calculus, but considerations of social value and political constraints cannot be ignored. The community should be scrupulous in providing clear qualifiers to show how conclusions may be affected by different regulatory and business assumptions. We see this as a grand challenge to develop robust methods for understanding and illuminating how engineering metrics are embedded in the “soft issues” of regulatory politics. The challenge for regulators is to state social and economic goals clearly; only once this has been done can engineers develop technological means to reach these ends with reasonable certainty. However, this is an iterative process, since engineering feasibility influences policy choices. Naturally, the “imaginable” ends depend on available means and plausible development trajectories. Regulators and policy makers should beware of spurious objectivity. For example, as we have discussed, “spectrum efficiency” is typically used to justify policy decisions and cloak the interests of various industry and political interest groups, despite being designed and fit for an altogether different purpose as a technical metric (e.g., in bits per second per Hertz). The very concept of “spectrum efficiency” is not only ill defined, but dangerous, since the challenge of producing socio-economic value is much more complex than delivering more “bits per second per Hertz.” Thus, regulators should also become savvy in using N-dimensional performance metrics for stating their goals to engineers. Regulators should not rely on the comforting but spurious objectivity of any single numerical engineering result that, even if not simplistic, is based on many simplifying assumptions. Engineering analysis alone cannot and should not justify fundamental policy decisions.
IEEE Communications Magazine • December 2012
Network stack (OSI Layers 1-7) 5 4
6
3
7
2
8
1
0
Business models and social practices “Layer 8”
Regulatory framework “Layer 0”
Figure 3. From a network stack to a technology circle: engineers ought to explicitly consider the influence of the regulatory and commercial framework when designing and evaluating telecommunications technology.
CONCLUSIONS We believe that a broader perspective on spectrum and technology efficiency is required and advocate N-dimensional parameter spaces as a means of better representing and exploring the full problem space. We argue that some of the unresolved debates on the relative merits of different systems and regulatory approaches are symptoms of using overly coarse efficiency measures. We also believe that technical engineering work should be connected to policy in a more systematic and transparent way and that policy constraints need to be understood as an input for engineering research, not the other way around. We refer the interested reader to our recent working paper [11] for some more detailed proposals and recommendations. The research community and the IEEE can play a crucial role in defining and developing a parameter space to enable a conscious and effective debate in regulation and policy.
REFERENCES [1] FCC Technological Advisory Council Sharing Work Group, “White Paper: Spectrum Efficiency Metrics,” 20 Dec. 2011. [2] FCC, (2010) Statement of Chairman Julius Genachowski Re: Unlicenced Operation in the TV Broadcast Bands, ET Docket no. 04-186. [3] IEEE (2012, Jan.) Status of project P802.11af. available: http://www.ieee802.org/11/Reports/tgaf_update.htm. [4] IEEE 802.11 Standard: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, Std. IEEE Std 802.11-2007 (Revision of IEEE Std 802.111999), 2007. [5] A. Achtzehn, M. Petrova, and P. Mähönen, “Deployment of a Cellular Network in the TVWS: A Case Study in a Challenging Environment,” Proc. ACM CoRoNeT, Las Vegas, 2011. [6] L. Simic’, M. Petrova, and P. Mähönen, “Wi-Fi, But Not On Steroids: Performance Analysis of a Wi-Fi-like Network Operating in TVWS Under Realistic Conditions,” Proc. IEEE ICC, Ottawa, 2012.
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[7] H. Holma and A. Toskala, LTE for UMTS: Evolution to LTE-Advanced, Wiley, 2011. [8] 4G Americas, “Mobile Broadband Explosion: 3GPP Broadband Evolution to IMT-Advanced,” Rysavy Research, 2011. [9] S. Parkvall, A. Furuskär, and E. Dahlman, “Evolution of LTE Toward IMT-Advanced,” IEEE Commun. Mag., vol. 49, no. 2, Feb. 2011, pp. 84–91. [10] M. Krondorf and G. Fettweis, “Carrier Frequency Dependent Downlink Spectral Efficiency of Cellular LTE Deployments,” Proc. IEEE ICC, Dresden, 2009. [11] L. Simic’, P. Mähönen, M. Petrova, and J. P. de Vries, “Illuminating the Road from Engineering and Policy to Radio Regulation,” Proc. TPRC, Arlington, 2012, available: http://ssrn.com/abstract=2031656. [12] J. P. de Vries, “Internet Governance as Forestry: Deriving Policy Principles from Managed Complex Adaptive Systems,” Proc. TPRC, Arlington, 2008, available: http://ssrn.com/abstract=1229482.
BIOGRAPHIES PETRI MÄHÖNEN is currently a full professor and head of the Institute for Networked Systems at RWTH Aachen University. He has been a principal investigator in several international research and development projects for wireless communications. He was a co-TPC chair for IEEE DySPAN 2010, and co-general chair and head of local organization for IEEE DySPAN 2011. He serves as an Associate Editor of IEEE Transactions on Mobile Communications. His current research interests include cognitive radio systems, radio network architectures, self-organized networks, embedded intelligence, and optimization of communications systems. C received her Bachelor of Engineering (with 1st LJILJANA SIMI´ Class Honours) and Doctor of Philosophy degrees in electrical and electronic engineering from The University of Auckland in 2006 and 2011, respectively. Her Ph.D. research
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focused on energy-efficient cooperative communication for distributed wireless networks, and was supported by a Bright Future Top Achiever Doctoral Scholarship from the Tertiary Education Commission of New Zealand. Since February 2011 she is a postdoctoral researcher at the Institute for Networked Systems at RWTH Aachen University, Germany. Her research interests are in the areas of cognitive radio, cooperative communication, self-organizing and distributed networks, and adaptive wireless systems. MARINA PETROVA is an assistant professor in the Faculty of Electrical Engineering and Information Technology at RWTH Aachen University, Germany. Her research interests are focused on cognitive wireless networks, cognitive radios, self-organization, and optimization of wireless systems. She has participated in several international cooperative projects and industry projects in the field of wireless communications and cognitive radios. She holds a degree in engineering and telecommunications from Ss. Cyril and Methodius University, Skopje, Macedonia, and a Ph.D. from RWTH Aachen University. She has served on technical program committees of numerous IEEE conferences and workshops, and was a co-TPC Chair of DySPAN 2011. J. P IERRE DE V RIES is a senior adjunct fellow of the Silicon Flatirons Center at the University of Colorado, Boulder. He is a former chief of incubation and senior director of advanced technology and policy at Microsoft Corporation. He worked for Korda & Co, a London-based seed capital company and consultancy, advising corporate customers, negotiating investments, and serving as a startup board member. He holds a B.Sc. (Honours) in theoretical physics in 1983 from Stellenbosch University, and a Ph.D. in theoretical physics from Oxford in 1987. He is the inventor or coinventor on six U.S. patents. He explores the intersection between information technology and government policy. His current work focuses on new approaches to the definition of radio operating rights.
IEEE Communications Magazine • December 2012
