Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering 1

Overview REU Cognitive Virginia Tech2 Software Defined Radio Mixer Fundamentals Project Description Simulation Results – Graphical – Quantitative Conclusions

REU Cognitive Virginia Tech 3 Concept of Radio What comes to mind when you here the word “Radio”? Wireless Communications

REU Cognitive Virginia Tech4 Software-Defined Radio (SDR) What is SDR? – Ability to control RF signals via software as opposed to custom hardware Why SDR? – Flexibility – Adaptability – Low Cost – Lower Power Consumption

REU Cognitive Virginia Tech5 Software-Defined Radio (SDR) How does SDR work? – Ideal Case : Software Radio Technology limitations of A/D prevent above implementation – Tx/Rx frequencies up to Giga Hz range Input waveform changing up to few billion times per second Signal too fast to sufficiently convert to digital Analog-to-Digital Conversion

REU Cognitive Virginia Tech6 Software-Defined Radio (SDR) How do we combat A/D limitations? – Provide RF front end between Antenna and A/D Important Functional Unit: Mixer – In Radio Receiver: mixer down converts input signal to lower frequency (slower signal) sufficient for A/D conversion

REU Cognitive Virginia Tech7 Mixer: Frequency Translation 600 M Hz 125 M Hz 475 M Hz Frequency Domain IFLORF Time Domain

REU Cognitive Virginia Tech8 Project Description Design, simulate, and analyze a passive direct sampling mixer using 0.18µm RF CMOS technology Goal of Research: – Increase commonality of the mixer over various wireless communication standards while maintaining high degree of re-configurability

REU Cognitive Virginia Tech9 What does 0.18µm RF CMOS mean? Diameter of Penny = 19,050 µm Substrate Level DiagramSchematic Symbol Metal-Oxide Semi-Conductor Field Effect Transistor (MOSFET) Channel Length = 0.18µm!!!

REU Cognitive Virginia Tech10 DSM Circuit Diagram

REU Cognitive Virginia Tech11 Important Measurable Metrics Conversion Gain – The change in output power with respect to the input power (RF IF) Noise Figure – How many random signals does our system generate as a result of the circuit elements 1 dB Compression Point – At what input power level (RF Signal) does the mixer functionality become undesirable (i.e. Output non-linear) Third-order Intermodulation Intercept Point (IIP3) – How well the system receives the desired information signal with other potential RF signals in close frequency proximity

REU Cognitive Virginia Tech 12 Time Domain Frequency Domain RF=600 MHz IF=125 MHz LO=475 MHz Mixer Simulation Results

REU Cognitive Virginia Tech13 Direct Sampling Mixer Simulations Results MetricSimulationDesired Conversion Gain20.5 dB15 dB 1-dB Compression Point dBm-12 dBm IIP dBm-2 dBm Noise Figure15 dB10 dB Power Consumption3.66 mW3 mW Simulation Results

REU Cognitive Virginia Tech14 Conclusions A Passive Direct Sampling Mixer using 0.18µm RF CMOS technology was designed, simulated and analyzed Acceptable Metrics: – Conversion Gain – IIP3 – Power Consumption Areas to improve: – 1dB Compression point – Noise Figure

REU Cognitive Virginia Tech15 Acknowledgements This research was sponsored by the National Science Foundation (NSF). The authors would like to thank: Dr. Kwang-Jin Koh for the opportunity to be a part of his research efforts; Dr. Carl Dietrich, Dr. Leslie Pendleton, and Dr. Roofia Galeshi for the oversight and mentoring services provided throughout the duration of the program; A special thanks to PhD student Hedieh Elyasi for her patience, as well as her abundant time and effort spent aiding in the learning/research process.

REU Cognitive Virginia Tech16 References H. Shiozaki, T. Nasu and K. Araki, “Design and Measurement of Harmonic Rejection Direct Sampling Mixer,” Proc. APMC, pp , Dec A. Mirzaei, H. Darabi, J. C. Leete, X. Chen, K. Juan, and A. Yazdi,“Analysis and optimization of current-driven passive mixers in narrowbanddirect-conversion receivers,” IEEE J. Solid- State Circuits, vol. 44, no. 10,pp. 2678–2688, Oct R. Bagheri, A. Mirzaei, M. E. Heidari, S. Chehrazi, M. Lee, M. Mikhemar, W. K. Tang, and A. A. Abidi, “Software-defined radio receiver: Dream to reality,” IEEE Commun. Mag., vol. 44, no. 8, pp.111–118, Aug

REU Cognitive Virginia Tech 17 1 dB Compression Graph

REU Cognitive Virginia Tech 18