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A Software Definable Architecture for Adaptive Space Diversity at Handsets in MC-CDMA Systems
K. Robert LAI Yuan-Lung CHANG
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2006/05/01
Online ISSN: 1745-1337
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Spread Spectrum Technologies and Applications
Software-Defined Radio, MC-CDMA, adaptive space diversity, receiver diversity, handsets,
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Software-Defined Radio (SDR) represents a major paradigm shift in the design of radios, allowing a large fraction of the functionality to be implemented through programmable signal processing devices, enabling the radio to change its operating parameters to accommodate new air interface, features and capabilities. However, the actual realization of innovative and software-reconfigurable receiver diversity at mobile handsets in intermediate frequency band to provide wide-ranging benefits, including more effective filtered result, less cost of the mixed channel access, improved capacity, better link reliability, and reduced power consumption, has been slowed down largely due to an absence of effective architecture reducing the complexity of adaptive combining algorithms. This paper proposes a novel reconfigurable architecture for adaptive space diversity at handsets in MC-CDMA (multicode code-division multiple-access) systems. The key to which is the development of a valid and effective alternative to the time-consuming multiplication operation and despreading acquisition. A software definable algorithm can become a multiplier-free architecture if it can restrict the weight factors to power-of-two values and repetitive gradient search procedure to contain shift operations and predicate functions. The results of numerical simulation and experimentation confirm the expectation that the constrained approach should perform comparably to, but not better than the traditional diversity algorithm. That is, the feasibility of SDR depends on its trading some performance for reduced computational complexity, improved area efficiency and less power consumption.