Impact of Channel Estimation Errors on Multiuser Detection via the Replica Method

For practical wireless DS-CDMA systems, channel estimation is imperfect due to noise and interference. In this paper, the impact of channel estimation errors on multiuser detection (MUD) is analyzed under the framework of the replica method. System performance is obtained in the large system limit for optimal MUD, linear MUD and turbo MUD, and is validated by numerical results for finite systems.Comment: To appear in the EURASIP Journal on Wireless Communication and Networking - Special Issue on Advanced Signal Processing Algorithms for Wireless Communication

Ant-Colony-Based Multiuser Detection for MC DS-CDMA Systems

In this contribution we present a novel ant colony optimization (ACO) based multi-user detector (MUD) designed for synchronous multi-carrier direct sequence code division multiple access (MC DSCDMA) systems. The operation of the ACO-based MUD is based on the behaviour of the ant colony in nature. The ACO-based MUD aims for achieving the same bit-error-rate (BER) performance as 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 the system is capable of supporting almost as many users as the number of chips in the spreading sequence, while searching only a small fraction of the entire ML search space. It will also be demonstrated that the number of floating point operations per second is a factor of 108 lower for the proposed ACO-based MUD than that of the ML MUD, when supporting K = 32 users in a MC DS-CDMA system employing 31-chip Gold codes as the T-domain spreading sequence

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

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

Simulated annealing based multiuser detection for synchronous SDMA system

In this treatise, a novel Simulated Annealing (SA) based Multi-User Detection (MUD) is proposed in synchronous Space Division Multiple Access (SDMA) system. SA MUD modifies experiential Cooling Schedule (CS) of traditional SA algorithm according to its use in MUD. Moreover, in order to ensure sufficient diversity acquired in the whole Markov chain and to prevent from being trapped at local optima, Uniform Mutation (UM) based trial vector generation scheme is brought forward. In addition, the optimal solution recording scheme is also invoked in case of being lost during cooling process. Simulation results illustrate that in comparison with Genetic Algorithm (GA) MUD in the same simulation conditions, without turbo processing and soft-information, SA MUD proposed in this paper performs better, approaching the performance of Maximum Likelihood (ML) MUD and imposes lower complexity

Fast Convergence and Reduced Complexity Receiver Design for LDS-OFDM System

Low density signature for OFDM (LDS-OFDM) is able to achieve satisfactory performance in overloaded conditions, but the existing LDS-OFDM has the drawback of slow convergence rate for multiuser detection (MUD) and high receiver complexity. To tackle these problems, we propose a serial schedule for the iterative MUD. By doing so, the convergence rate of MUD is accelerated and the detection iterations can be decreased. Furthermore, in order to exploit the similar sparse structure of LDS-OFDM and LDPC code, we utilize LDPC codes for LDS-OFDM system. Simulations show that compared with existing LDS-OFDM, the LDPC code improves the system performance

Fixed-complexity quantum-assisted multi-user detection for CDMA and SDMA

In a system supporting numerous users the complexity of the optimal Maximum Likelihood Multi-User Detector (ML MUD) becomes excessive. Based on the superimposed constellations of K users, the ML MUD outputs the specific multilevel K-user symbol that minimizes the Euclidean distance with respect to the faded and noise-contaminated received multi-level symbol. Explicitly, the Euclidean distance is considered as the Cost Function (CF). In a system supporting K users employing M-ary modulation, the ML MUD uses MK CF evaluations (CFE) per time slot. In this contribution we propose an Early Stopping-aided Durr-Høyer algorithm-based Quantum-assisted MUD (ES-DHA QMUD) based on two techniques for achieving optimal ML detection at a low complexity. Our solution is also capable of flexibly adjusting the QMUD's performance and complexity trade-off, depending on the computing power available at the base station. We conclude by proposing a general design methodology for the ES-DHA QMUD in the context of both CDMA and SDMA systems

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

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

Space-Time Equalisation Assisted Minimum Bit-Error Ratio Multiuser Detection for SDMA Systems

This contribution investigates a space-time equalisation assisted multiuser detection scheme designed for multiple receiver antenna aided space division multiple access (SDMA) systems. A novel minimum bit error ratio (MBER) design is invoked for the multiuser detector (MUD), which is shown to be capable of improving the attainable performance and enhancing system capacity in comparison to that of the standard minimum mean square error (MMSE) design. The adaptive MUD coefficient adjustment procedure of the MBER space-time MUD is implemented using a stochastic gradient based least bit error rate (LBER) algorithm, which consistently outperforms the classic least mean square (LMS) algorithm, while maintaining a lower computational complexity than the latter