4 research outputs found

    Ant-colony-based multiuser detection for multifunctional-antenna-array-assisted MC DS-CDMA systems

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    A novel Ant Colony Optimization (ACO) based Multi-User Detector (MUD) is designed for the synchronous Multi-Functional Antenna Array (MFAA) assisted Multi-Carrier Direct-Sequence Code-Division Multiple-Access (MC DS-CDMA) uplink (UL), which supports both receiver diversity and receiver beamforming. The ACO-based MUD aims for achieving a bit-error-rate (BER) performance approaching that of the optimum maximum likelihood (ML) MUD, without carrying out an exhaustive search of the entire MC DS-CDMA search space constituted by all possible combinations of the received multi-user vectors. We will demonstrate that regardless of the number of the subcarriers or of the MFAA configuration, the system employing the proposed ACO based MUD is capable of supporting 32 users with the aid of 31-chip Gold codes used as the T-domain spreading sequence without any significant performance degradation compared to the single-user system. As a further benefit, the number of floating point operations per second (FLOPS) imposed by the proposed ACO-based MUD is a factor of 108 lower than that of the ML MUD. We will also show that at a given increase of the complexity, the MFAA will allow the ACO based MUD to achieve a higher SNR gain than the Single-Input Single-Output (SISO) MC DS-CDMA system. Index Terms—Ant Colony Optimization, Multi-User Detector, Multi-Functional Antenna Array, Multi-Carrier Direct-Sequence Code-Division Multiple-Access, Uplink, Near-Maximum Likelihood Detection

    MIMO-aided near-capacity turbo transceivers: taxonomy and performance versus complexity

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    In this treatise, we firstly review the associated Multiple-Input Multiple-Output (MIMO) system theory and review the family of hard-decision and soft-decision based detection algorithms in the context of Spatial Division Multiplexing (SDM) systems. Our discussions culminate in the introduction of a range of powerful novel MIMO detectors, such as for example Markov Chain assisted Minimum Bit-Error Rate (MC-MBER) detectors, which are capable of reliably operating in the challenging high-importance rank-deficient scenarios, where there are more transmitters than receivers and hence the resultant channel-matrix becomes non-invertible. As a result, conventional detectors would exhibit a high residual error floor. We then invoke the Soft-Input Soft-Output (SISO) MIMO detectors for creating turbo-detected two- or three-stage concatenated SDM schemes and investigate their attainable performance in the light of their computational complexity. Finally, we introduce the powerful design tools of EXtrinsic Information Transfer (EXIT)-charts and characterize the achievable performance of the diverse near- capacity SISO detectors with the aid of EXIT charts

    Quantum search algorithms, quantum wireless, and a low-complexity maximum likelihood iterative quantum multi-user detector design

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    The high complexity of numerous optimal classic communication schemes, such as the maximum likelihood (ML) multiuser detector (MUD), often prevents their practical implementation. In this paper, we present an extensive review and tutorial on quantum search algorithms (QSA) and their potential applications, and we employ a QSA that finds the minimum of a function in order to perform optimal hard MUD with a quadratic reduction in the computational complexity when compared to that of the ML MUD. Furthermore, we follow a quantum approach to achieve the same performance as the optimal soft-input soft-output classic detectors by replacing them with a quantum algorithm, which estimates the weighted sum of a function’s evaluations. We propose a soft-input soft-output quantum-assisted MUD (QMUD) scheme, which is the quantum-domain equivalent of the ML MUD. We then demonstrate its application using the design example of a direct-sequence code division multiple access system employing bit-interleaved coded modulation relying on iterative decoding, and compare it with the optimal ML MUD in terms of its performance and complexity. Both our extrinsic information transfer charts and bit error ratio curves show that the performance of the proposed QMUD and that of the optimal classic MUD are equivalent, but the QMUD’s computational complexity is significantly lower
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