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Test and Measurement

Emerging 4G Communications Systems:
Making Digital Pre-Distortion Fast and Practical for all Engineers

June 14, 2011
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A transition is now underway in the wireless communications industry as wireless service providers migrate from 3G to 4G technologies like LTE-FDD, LTE-TDD and WiMAX™ to keep pace with emerging devices like smart phones. For the engineer, this transition means a host of new challenges. For example, they must determine exactly how far away their design is from being able to operate in 4G and whether or not it will need to be completely redesigned to do so. The hardware must also meet or exceed standards-based performance requirements such as ACPR, EVM or throughput (e.g., BLER, BER and PER), while meeting product design goals. Because smart phones and other advanced wireless devices rely so heavily on battery power, getting the most efficiency out of a design is critical. The RF power amplifier (PA) plays a key role here since it directly impacts device hardware and its requirements for 4G operation. As a result, one of the biggest challenges today’s engineers face is choosing and designing the right PA to meet design goals at the lowest possible cost.

Power amplifiers are an essential component in the overall performance and throughput of wireless communications systems, and are inherently nonlinear. That nonlinearity generates spectral re-growth, which leads to adjacent channel interference and violations of the out-of-band emis­sions standards mandated by regulatory bodies. It also causes in-band distortion, which degrades the BER and data throughput of the communications system. Operating the PA at a lower power is one way to reduce this nonlinearity. However, this reduces the service area and increases both the capital and operating expenses of the service provider. Linearization enables the PA to be operated in its high power-added-efficiency (PAE) region, near saturation and without significant signal distortion, thus reducing expenses. Digital pre-distortion (DPD) is a cost effective way to accomplish linearization, but often requires a highly specialized skill set for modeling and implementation.

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