Agilent Measurement Journal - Issue 3 - 2007 - (Page 36)

Comparing average vs. peak spectral efﬁciency Figure 4 plots an approximation of the average spectral efﬁciency for different systems along with the peak efﬁciency required for operation at their maximum rates. There are several interesting points in this ﬁgure. First, it can be seen that the early (low efﬁciency) systems — AMPS and GSM — were deployed operating at their maximum rates. This was because these systems were designed to operate at their maximum rates at the cell edge. Subsequent systems starting with GPRS employed reduced channel coding then higher-order modulation, which increased the peak user data rate but required a better SNR than was available at the cell edge. Taking advantage of the variation in G factor across the cell is one of the reasons these newer systems show an increase in average efﬁciency. 100 802.16e HSDPA 1xEV-DO W-CDMA EDGE GPRS GSM 0.1 AMPS 0.01 1980 HSDPA W-CDMA 1990 1995 2000 2005 2010 2015 1xEV-DO (A) LTE An HSDPA example from 3GPP TS 25.1011 will illustrate the relationship between throughput, probability of coverage (G factor) and users/sector. These throughput ﬁgures require the advanced features of diversity receiver and equalizer and are for a pedestrian (3 km/h) environment. Table 2. HSDPA throughput versus probability of coverage (G factor) and users/sector Power per user –2 dB –2 dB –3 dB –6 dB –3 dB –6 dB Users/ sector 1 1 2 4 2 4 G factor 18 dB 15 dB 10 dB 10 dB 5 dB 5 dB Approx. Throughput coverage (QoE) Mbps 5% 7% 25% 25% 50% 50% 7.638 6.412 2.464 1.619 1.688 0.779 10 1 Adding closed-loop transmit diversity and MIMO may extract slightly more performance but existing receiver diversity has already gained most of the advantage, especially when line-ofsight exists to the base station (a factor that eliminates any MIMO advantage). Switching to a technology such as 3GPP2’s 1xEV-DO or IEEE’s WiMAX will change the details of Table 2 but not the substance. Interference is the great leveler that removes most of the performance differences between competing cellular systems. Contrasting the users/sector from Table 2 with the people/sector in dense areas illustrates the difﬁculty cellular systems face in providing dense high-data-rate networks. 1985 Average efficiency Peak efficiency Figure 4. Average versus peak spectral efﬁciency over time Dimensioning for the average vs. peak Average performance drives system capacity, revenue potential and QoE. HSDPA today achieves 1 to 2 Mbps per sector and new standards such as Mobile WiMAX, HSPA+, UMB and LTE can improve on this by as much as 4x using techniques such as interference cancellation, OFDM, equalizers and smart antennas including MIMO. Peak performance mainly drives headlines and also carries the risk of driving down QoE due to the setting of unrealistic expectations. Perhaps of more signiﬁcance, dimensioning future cellular systems for their peak rates has implications for the cost of terminals and the network. The peak LTE downlink throughput with 2x2 MIMO is 172.8 Mbps.2 LTE terminal classes will be introduced which set lower limits, but the impact of the highest targets may inﬂuence phone infrastructure, leading to tougher design challenges and increased cost. Dimensioning the network to handle very high rates with reasonable QoE would be extremely The other obvious trend is the widening gap between average efﬁciency and the efﬁciency required at a system’s maximum rate. This is due to new systems specifying performance at very high G factors further into the cell. This has a direct consequence on QoE: G factor distribution is independent of the radio system so as rates rise, a smaller percentage of users can experience the system at its maximum rate. The performance for current HSDPA systems is on the order of 1 to 2 Mbps/sector. Given the recent history of mobile wireless this is a remarkable feat — but with this representing an average normalized spectral efﬁciency of 0.4 b/s/Hz, the gap to the 5 b/s/Hz peak is large (Table 1). Users close to the cell center should see better performance (provided the backhaul exists); however, the average efﬁciency — which drives system capacity, QoE and revenue potential — is rising much more slowly over time than the efﬁciency required to operate at the headline rates. 36 Agilent Measurement Journal

Table of Contents Feed for the Digital Edition of Agilent Measurement Journal - Issue 3 - 2007

Applying Ingenuity to the Measurement of Emerging Technologies Contents Emerging Innovations Department Delivering Bigger Benefits by Optimizing Customer Workflows Measuring Material Properties at the Nanoscale Utilizing In Situ Atomic Force Microscopy in Life Science, Pharmaceutical and other Bio-Related Applications WiMAX™: Plotting a New Path to Global Mobility Addressing the Triple Complexity of Triple-Play Networks What Next for Mobile Telephony? Exploring the Inner Workings of Tire-Pressure Monitoring Systems Developing, Assessing and Applying a High-Resolution Thin-Film Magnetic Probe Making Accurate Settling-Time Measurements Using a Vector Network Analyzer Applying Metabolomics Methods to the Study of Bacterial Leaf Blight in Rice Plants Measuring Stream Dynamics with Fiber Optics survey

Agilent Measurement Journal - Issue 3 - 2007

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