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Complex block floating-point format with box encoding in communication systems
This research project entails an efficient numeric digital representation in communication systems design. A complex block floating-point format with box encoding is proposed to encode an array of complex numbers that has better numeric resolution than its IEEE-754 counterpart when the same number of bits are allocated to the dominant value in the array. It is estimated that at least 10% of bit savings could be achieved by the new complex block representation on a quad-precision IEEE-754 format. A further bits savings of up to 18% could potentially be achieved for complex blocks at half-precision and single-precision IEEE-754 representation. The implementation cost of the proposed block floating-point format is evaluated in terms of memory usage, design of arithmetic units, and memory input/output rates for communications system modeling and block diagrams. Further analysis is performed on the limitation and quantization effects of this complex block format relative to complex IEEE-754 format. The coverage of the arithmetic units design include complex block adder and complex block multiplier. The appropriate systems that would be required to perform algorithms such as the fast Fourier transform (forward and inverse) are designed using the proposed complex block format in multi-stages complex block multiply-adder. The proposed block floating-point format is simulated as a new numeric class defined and implemented in MATLAB simulation environment. The MATLAB simulation is divided into two major parts. The first part of MATLAB simulation targets the simulation of complex block addition and complex block multiplication units for arbitrary size of complex samples per input block. The reference output values of complex block arithmetic are those computed with similar precision in IEEE-754 format. The second part of MATLAB simulation is performed on the system model of the single-carrier modulation-based and multi-carrier modulation-based communication systems. The quadrature amplitude modulation (QAM) is the baseband modulation type targeted in this work. The specification identified in the system model is relevant to those specified in the Long-Term Evolution (LTE) Standards for Base Station, Release 12.Electrical and Computer Engineerin
Complex Block Floating-Point Format with Box Encoding For Wordlength Reduction in Communication Systems
We propose a new complex block floating-point format to reduce implementation
complexity. The new format achieves wordlength reduction by sharing an exponent
across the block of samples, and uses box encoding for the shared exponent to
reduce quantization error. Arithmetic operations are performed on blocks of
samples at time, which can also reduce implementation complexity. For a case
study of a baseband quadrature amplitude modulation (QAM) transmitter and
receiver, we quantify the tradeoffs in signal quality vs. implementation
complexity using the new approach to represent IQ samples. Signal quality is
measured using error vector magnitude (EVM) in the receiver, and implementation
complexity is measured in terms of arithmetic complexity as well as memory
allocation and memory input/output rates. The primary contributions of this
paper are (1) a complex block floating-point format with box encoding of the
shared exponent to reduce quantization error, (2) arithmetic operations using
the new complex block floating-point format, and (3) a QAM transceiver case
study to quantify signal quality vs. implementation complexity tradeoffs using
the new format and arithmetic operations.Comment: 6 pages, 9 figures, submitted to Asilomar Conference on Signals,
Systems, and Computers 201