Environmental Signal Processing and Adaptation

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As device bandwidths expand, it becomes increasingly expensive, from a power consumption and reliability perspective, to operate such real-time systems for worst-case static performance requirements. In contrast, it is attractive to design algorithms, architectures and circuits that are power-performance tunable and can adapt dynamically, via self-learning techniques, to the requirements of system-level applications for extended battery usage and device lifetime.

Such future systems will feed application level demands to the underlying algorithm-architecture-circuit design fabric through built-in sense-and-control infrastructure hardware, software. The sense functions assess instantaneous application level demands e.

Adaptation and Learning in Control and Signal Processing 2001

The control functions actuate algorithm-through-circuit level tuning knobs that continuously trade off performance vs. Application to wireless communications systems, digital signal processing and control algorithms is discussed. Article :. The first one is to reduce the computational complexity number of arithmetic operations per time unit of the DSP algorithms. The second way is to use low-precision computations and hardware, which however also means more signal distortion.

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In this project, we work both on developing computationally efficient agile DSP algorithms, that can adapt to meet the system requirements with as low energy consumption as possible, and on developing DSP algorithms that can handle the increased signal distortion from energy-efficient low-precision hardware. The first part of the project is to develop efficient agile DSP algorithms that can adapt to the changes in functionality and quality requirements in communication systems with time-varying operation modes.

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  5. An example is a filter with adaptable bandwidths and center frequencies functionality as well as adaptable attenuations and word-lengths quality requirements. In this way, one can meet different system requirements with minimum computational complexity and energy consumption in the corresponding hardware implementation.

    In addition, it is important that the adaptation is fast to enable real-time operation. One important wireless communication technology, where energy-efficient hardware becomes indispensable, is massive MIMO. In massive MIMO, the base stations have to be equipped with hundreds of individually controllable radio chains, and each chain has to include all necessary hardware to modulate and demodulate radio-frequency signals.

    The overall energy consumption of the hardware would be enormous without energy-efficient solutions. The second part of the project works on developing DSP algorithms for massive MIMO, that will make it possible to use energy-efficient hardware in base stations despite high signal distortion.

    The impact that the signal distortion has on the end communication performance is characterized in this part.