Agilent Measurement Journal - Issue 3 - 2007 - (Page 34)

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. To understand the signiﬁcance of Table 1, we need to shift our attention from Moore’s law to the Shannon-Hartley capacity theorem. Dating from the 1940s, the theorem is more fundamental than Moore’s law and, like the law of gravity, is not merely an observation or recommendation. The theorem predicts the errorfree capacity C of a radio channel as: C = B x log2 (1+SNR) where C = Channel capacity in bits per second B = Occupied bandwidth in hertz SNR = linear signal-to-noise ratio The capacity of the channel scales linearly with the bandwidth and as a log function of the SNR, indicating an efﬁciency upper limit with diminishing returns at very high SNR. When cellular systems operate at capacity, the SNR is always dominated by co-channel interference (other users) rather than sensitivity. It is essential to point out from Table 1 that the newer radio systems do not themselves deliver ever-higher spectral efﬁciency; rather, they are designed to take advantage of good radio conditions when they occur. An automotive analogy can help here: Cars can be designed for a wide range of top speeds, but it is only when driving conditions (e.g., road quality and trafﬁc) are good enough that high speeds are possible. When the system gets loaded and trafﬁc slows to a crawl, a V-12 roadster is no better than a three-cylinder compact. In such conditions, the residual advantage of the V-12 is perhaps just looking good while you wait! Another key point to make is that the throughput ﬁgures in Table 1 represent the capacity of an entire cell or cell sector and that the peak rates given must be shared among the active users in the cell. This has a substantial impact on QoE when the system becomes loaded. 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) The ﬁrst two factors increase peak data rates by allocating a wider or longer channel so will neither impact system spectral efﬁciency nor affect system capacity. For example, a Long-Term Evolution (LTE) 20 MHz channel consumes 800 times the spectral resources of a 200 kHz single-slot GSM channel. This largely accounts for much of the increase in data rates and predicts much higher delivery costs for high-speed services. There is no escape from the reality that high-rate services consume large amounts of spectrum, and usable cellular spectrum is very limited. The other four factors all represent increases in spectral efﬁciency that can increase system capacity. However, these methods don’t come for free since for them to work there are signiﬁcant implications for the required SNR and radio-channel propagation conditions, which are not a function of the radio system. 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. 34 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

For optimal viewing of this digital publication, please enable JavaScript and then refresh the page. If you would like to try to load the digital publication without using Flash Player detection, please click here.