<p>The advanced signal processing systems of today require extreme data throughput and low power consumption. The only way to accomplish this is to use parallel processor architecture.</p><p>The aim of this thesis was to evaluate the use of parallel processor architecture in baseband signal processing. This has been done by implementing three demanding algorithms in LTE on Ambric Am2000 family Massively Parallel Processor Array (MPPA). The Ambric chip is evaluated in terms of computational performance, efficiency of the development tools, algorithm and I/O mapping.</p><p>Implementations of Matrix Multiplication, FFT and Block Interleaver were performed. The implementation of algorithms shows that high level of parallelism can be achieved in MPPA especially on complex algorithms like FFT and Matrix multiplication. Different mappings of the algorithms are compared to see which best fit the architecture.</p>

The advanced signal processing systems of today require extreme data throughput and low power consumption. The only way to accomplish this is to use parallel processor architecture. The aim of this thesis was to evaluate the use of parallel processor architecture in baseband signal processing. This has been done by implementing three demanding algorithms in LTE on Ambric Am2000 family Massively Parallel Processor Array (MPPA). The Ambric chip is evaluated in terms of computational performance, efficiency of the development tools, algorithm and I/O mapping. Implementations of Matrix Multiplication, FFT and Block Interleaver were performed. The implementation of algorithms shows that high level of parallelism can be achieved in MPPA especially on complex algorithms like FFT and Matrix multiplication. Different mappings of the algorithms are compared to see which best fit the architecture.

In this thesis, the design and implementation of a Portable Feedback Active Noise Controller Headphone System, which is based on Texas Instruments TMS320VC5416PGE120 Fixed Point DSP, is described. Problems resulted from fixed-point implementation of LMS algorithm and delays existing in digital ANC implementation are determined. Effective solutions to overcome the aforementioned problems are proposed based on the literature survey. Design of the DSP based control card is explained and crucial points about analog-digital-mixed board design for noise sensitive applications are explained. Filtered input LMS algorithm, filtered input normalized LMS algorithm and filtered input sign-sign LMS algorithm are implemented as adaptation algorithms. The advantages and disadvantages of using modified LMS algorithms are indicated. The selection of the parameters of these algorithms is based on theoretical results and experiments. The real time performances of different adaptation algorithms are compared with each other as well as with a commercial analog ANC headphone under different types of artificial and natural noise signals. Moreover, practical conditions such as put on - put off case and dynamic range overflow case are handled with additional software implementations. It is shown that adaptive ANC systems improve the noise reduction significantly when the noise is within a narrow frequency range and this reduction can be applied to a wider frequency range. It is also shown that the problems of digitally implemented adaptive filters which are based on tracking capability, stability, dynamic range and portability can be fixed to challenge with the analog commercial ANC systems.