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Details of Grant 

EPSRC Reference: EP/D020913/1
Title: Advanced Linear Microwave Transmitter Architectures Using Computational Intelligence Techniques
Principal Investigator: Gardner, Professor P
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Eudyna Devices Europe Ltd Semelab Plc Xilinx (UK) Ltd
Department: Electronic, Electrical and Computer Eng
Organisation: University of Birmingham
Scheme: Standard Research (Pre-FEC)
Starts: 01 October 2005 Ends: 31 March 2009 Value (£): 278,356
EPSRC Research Topic Classifications:
Artificial Intelligence Digital Signal Processing
Electronic Devices & Subsys. RF & Microwave Technology
EPSRC Industrial Sector Classifications:
Information Technologies Electronics
Communications
Related Grants:
Panel History:  
Summary on Grant Application Form
The proposed research relates to communications transmitters, both in hand sets and basestations. The aim is to apply adaptive computational intelligence techniques to the problem of maintaining a linear output from the amplifier in a transmitter, even when the number and the type of signals may vary, the environmental conditions fluctuate and the properties of the semiconductor devices in the amplifier gradually change as they get older. A linear output is necessary, because if the amplifier system is non-linear, interference will occur between different users of the communications system. The more linear we can make the transmitters, the more user channels we can fit into the available radio spectrum. This issue will become increasingly important with future generations of mobile systems, as more and more users demand broader band wireless access. The easy way to make an amplifier linear is to use one with a very high output power capability, compared to the actual output power required, but this results in unnecessarily high power consumption, with undesirable environmental consequences. An alternative is to linearise the amplifier response so that it can achieve high linearity and high efficiency simultaneously. This can be done by a range of existing techniques, which involve injecting a correcting signal before or after the amplification. Existing techniques are unlikely to be capable of achieving the high degree of linearity correction needed for forthcoming generations of mobile communications systems. Part of the reason for this is the existence of memory effects in the semiconductor devices used in the amplifiers. Thermal, electromagnetic and quantum memory effects cause the state of an amplifier's output at any instant in time to be a function not just of the inputs at the same instant, but also of the inputs at earlier instants in time. This means that the circuit or processor that generates the correcting signals also needs to have similar memory effects built into it. Computational intelligence techniques, using recurrent loops that feed delayed inputs and delayed feedback from the output into the processor, can provide an excellent way to incorporate these memory effects, both in modelling and correcting the amplifiers. In this project, we will find and investigate ways of generating the required correcting signals by adaptive computational intelligence methods. Adaptive computational intelligence techniques use processes derived from simple models of the operation of a human brain. The techniques include artificial neural networks and adaptive neuro-fuzzy inference systems. These will be be adapted as necessary and applied to provide the new level of sophistication required in highly efficient and linear transmitters for future generations of communications systems.
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Organisation Website: http://www.bham.ac.uk