Agilent Measurement Journal - Issue 3 - 2007 - (Page 35)

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 / (Îor + Ioc) where Îor = received power of desired signal Ioc = all other co-channel received power Figure 2 shows the familiar hexagonal cellular pattern and it can be shown that at the boundary of two cells the G factor will on average be no better than –3 dB, and at the boundary of three cells no better than –4.8 dB. The principle remains, however, that the potential capacity of the radio channel varies from excellent at the center and degrades signiﬁcantly at the edges. Older systems such as GSM were designed to work to full speciﬁcation at the cell edge and the better radio conditions further into the cell were used to reduce uplink and downlink transmission power rather than deliver higherrate services. Newer systems take advantage of the variation in radio conditions across the cell to opportunistically deliver higher rate services. For example, GSM requires 9 dB SNR at the cell boundary but full-rate EDGE requires 24 dB SNR and so only performs at its peak rate towards the middle of a cell dimensioned for basic GSM. This phenomenon creates islands of high performance cellular in a sea of average performance, with the positions of the islands being deﬁned by proximity to the cell sites. Examining the distribution of geometry factor Figure 3 shows the distribution of G factor that would be typical for randomly distributed outdoor users in a major metropolitan area. Of note, 20 percent of users experience a G factor below 0 dB, the 50 percentile point is at 5 dB, and only 10 percent of users experience better than 15 dB. For indoor — particularly at frequencies well above 1 GHz where building penetration loss is signiﬁcant — conditions degrade and the distribution would move signiﬁcantly to the left. This is a big concern for QoE given the high proportion of cellular calls that are made indoors. Dedicated indoor networks are the only realistic way round this. 90% of users 10% of users High SNR Low SNR 100% 90% 80% Cumulative distribution 70% 60% 50% 40% 30% 20% Most new high-datarate/MIMO performance targets require geometry factors experienced by < 10% of the cell population Figure 2. Variation of SNR in a single-frequency cellular system It is not straightforward to directly relate the SNR in the theorem with the geometry factor and then conclude the capacity of the channel since there are other factors that need to be taken into account — not least the proportion of the transmitted cell power Ior allocated to the user and the dynamic effects of channel fading due to mobility. 10% 0% -30 -20 -10 0 10 20 30 Geometry factor in dB Figure 3. Outdoor geometry factor distribution in a metropolitan area Agilent Measurement Journal 35

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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