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S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. Monte- Carlo simulations show that, after convergence, the performances reached by all near-optimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.

Efficient Near-Optimum Detectors for Large MIMO Systems Under Correlated Channels

Recently, high spectral and energy efficiencies multiple antennas wireless systems under scattered environments have attracted an increasing interest due to their intrinsically benefits. This work focuses on the analysis of MIMO equalizers, improved MIMO detection techniques and their combinations, allowing a good balance between complexity and performance in Rayleigh channel environment. Primarily, the MIMO linear equalizers combined with detection techniques such as ordering (via sorted QR decomposition), successive interference cancellation, list reduction and lattice reduction were investigated. An important aspect invariably present in practical systems taken into account in the analysis has been the channel correlation effect, which under certain realistic conditions could result in a strong negative impact on the MIMO system performance. The goal of this paper consists in construction a framework on sub-optimum MIMO detection techniques, pointing out a MIMO detection architecture able to attain low or moderate complexity, suitable performance and full diversity.

Reduced Cluster Search ML Decoding for QO-STBC Systems

Since the maximum likelihood (ML) decoding results too complex when the modulation order and the number of receive antennas increase, an efficient reduced complexity ML-based decoding scheme applied to a multiple-input-multiple-output (MIMO) antenna systems with quasi-orthogonal space-time block code (QO-STBC) is proposed, and named reduced cluster search ML decoding (RCS-ML). Its performance and complexity aspects are compared to the conventional ML decoding approach. High-order modulation indexes and short low density parity check codes (LDPC) are considered. Numerical results have indicated no degradation in the performance and an increasing reduction in the complexity of RCS-ML decoding with respect to the conventional ML when the modulation order increases.Coarse-Grained Reconfigurable Architecture (CGRAs) are a promising parallel architecture with both high performance and high power-efficiency. Inner loop pipelining and outer loop merging techniques are usually used to improve the execution performance when mapping loops ontoCGRA. However, the number of concurrently executable operators (CEOs) from the kernel still can not make the best of PEs in a PEA as the kernel width is limited by loop body size. In this paper, we evaluate the number of CEOs from kernel and proposed a novel outer loop parallelization scheme to increase the number of CEOs, and further reduce the execution time of loops. Our experimental results using loops from polybench demonstrated that our approach can increase the performance of nested loop by up to 1.37 times compared to the epilog-prolog merging method. Moreover, modest compilation time is needed to generates the final solution.There is an increasing interest for cloud services to be provided in a more energy efficient way. The growing deployment of large-scale, complex workflow applications onto cloud computing hosts is being faced with crucial challenges in reducing the power consumption without violating the service level agreement (SLA). In this paper, we consider cloud hosts which can operate in different power states with different capacities respectively, and propose a novel scheduling heuristic for workflows to reduce energy consumption while still meeting deadline constraint. The proposed heuristic is evaluated using simulation with four different real-world applications. The observed results indicates that our heuristic does significantly outperform the existing approaches.As enterprises shift from using direct-attached storage to network-based storage for housing primary data, flash-based, host-side caching has gained momentum as the primary latency reduction technique. In this paper, we make the case for integration of flash caching algorithms at the file level, as opposed to the conventional block-level integration. In doing so, we will show how our extensions to Loris, a reliable, file-oriented storage stack, transform it into a framework for designing layout-independent, file-level caching systems. Using our Loris prototype, we demonstrate the effectiveness of Loris-based, file-level flash caching systems over their block-level counterparts, and investigate the effect of various write and allocation policies on the overall performance.

Weighting particle swarm, simulation annealing and local search optimization for S/MIMO MC-CDMA systems

This paper analyzes the complexity-performance trade-off of three heuristic approaches applied to synchronous multicarrier multiuser detection (MUD) of single/multiple transmit antennas and multiple receive antennas code division multiple access (S/MIMO MC-CDMA) systems. Weighting particle swarm optimization (WOPSO) and unitary Hamming distance search-based strategies, specifically 1-opt local search (1-LS) and simulation annealing (SA) multiuser detection algorithms, were analyzed in details using a single-objective antenna-diversity-aided optimization approach. Monte-Carlo simulations show that, after convergence, the performances reached by the three heuristic MUD (HEUR-MUD) S/MIMO MC-CDMA algorithms are identical, with computational complexities remarkably smaller than the optimum multiuser detector (OMUD). However, the computational complexities could differ substantially depending on the operation system conditions. The complexities of the HEUR-MUDs were carefully analyzed in order to demonstrate that 1-LS scheme provides the best trade-off between implementation complexity aspects and bit error rate (BER) performance when applied to multiuser detection of S/MIMO MC-CDMA systems with low order modulation.

Multirate Multiuser DS/CDMA with Genetic Algorithm Detection in Multipath Channels

This work analyses a heuristic algorithm based on the genetic evolution theory applied to multirate multicode (MC) direct sequence code division multiple access (DS/CDMA) multiuser detection (GA-MC-MuD) in multipath fading channels. Monte-Carlo simulation results, in two multirate conditions, showed that the detection based on GA is a viable option when compared with the optimum MuD (OMuD). Even under hostile channel conditions and severe system operation (loading and errors in the channel coefficients and amplitudes estimates) the GA-MC-MuD performance results are promising. The GA-MC-MuD algorithm complexity is determined and compared with the OMuD based on the required number of computational operations, showing an expressive improvement in terms of complexity-performance trade-off when compared with the Rake receiver

Genetic Algorithm Applied to Multipath Multiuser Channel Estimation in DS/CDMA Systems

This work analyses a heuristic algorithm based on the genetic evolution theory (GA) applied to multipath multiuser channel estimation (MuChE) in direct sequence code division multiple access (DS/CDMA). Using computer simulations we showed that the proposed GA-MuChE is capable of achieving a normalized mean squared error (MSE) in estimations of the order of 8% for slowly varying multipath fading channels (fD=17 Hz) and medium system load (ap50%), with lower complexity than the maximum likelihood multiuser channel estimation (ML-MuChE) and gradient methods (gradient descent)

GA, SA, and TS near-optimum multiuser detectors for s/MIMO MC-CDMA systems

This paper analyzes the performance and complexity of four heuristic approaches applied to a synchronous multicarrier multiuser detection (MuD) of single/multiple transmit antennas and multiple receive antennas code division multiple access (S/MIMO MC-CDMA) system. The genetic algorithm (GA), simulation annealing (SA) and Tabu search (TS) heuristic algorithms (HA) in a single-objective optimization form were considered. Monte-Carlo simulations showed that the performances, after convergence, achieved by the four near-optimum HA-MuD S/MIMO MC-CDMA are identical. However, their computational complexities differ depending on the operation system conditions. Therefore, the HA-MuD complexities were carefully analyzed in order to determine which one has the best trade-off between bit error rate (BER) performance and implementation complexity aspects.

Weighting Particle Swarm Optimization SIMO MC-CDMA Multiuser Detectors

This paper analyzes the performance of two heuristic approaches applied to a synchronous multicarrier multiuser detection (MuD) of multiple receive antennas code division multiple access (SIMO MC-CDMA) system. The particle swarm optimization (PSO) with weighting particle position based on combining multi-fitness functions (woPSO) is proposed and compared with the conventional PSO SIMO MC-CDMA. The woPSO strategy deal with the multi-objective dilemma imposed by the spatial diversity that results in independent likelihood function for each receive antenna. Additionally, the computational complexity of these algorithms was taken into account in order to show which one has the best trade-off in terms of performance and implementation complexity aspects.

Bit-error-rate minimisation in multiuser transmission schemes for multiple-input–multiple-output communication with increasing number of base station antennas

Multiuser transmission (MuT) can be carried out at the base station of a multiple-input–multiple-output system for mitigating the downlink multiuser interference, achieving a near-single-user performance at the mobile terminals. These MuT systems can be designed for optimising different performance metrics, such as the bit-error-rate (BER), which is the focus of this study. In order to mitigate the MuTs complexity, a convex formulation for the minimum BER (MinBER) optimisation problem is proposed, by employing some slight modifications on its original formulation. The authors have shown that the novel convex formulation makes the application of some optimisation algorithms very efficient, leading to a reduced complexity and enabling the system operation in higher dimensions. Indeed, the authors also show that in these conditions the MinBER–MuT system presents the most noticeable gains in performance. The results demonstrate that, among the investigated techniques, the proposed Line Search Quasi-Newton algorithm relying on the penalty function approach results in the best performance versus complexity trade-off in the context of high capacity multiuser systems.

Performance and complexity analysis of sub-optimum MIMO detectors under correlated channel

The interest in multiple antennas systems comes from its high spectral and/or energy efficiency under scattered environments, being an ongoing research topic in wireless communication systems. In this work, different MIMO equalizers, detection techniques (ordering, interference cancellation, lattice reduction, list reduction) and its combination have been analyzed in order to find the best complexity-performance trade-off topology. Also, it has been considered the correlation between antennas, which is invariably occurs in realistic scenarios and deteriorates the performance of MIMO systems. From this perspective, the goal of this paper is to point out a MIMO architecture with reasonable complexity, while holding suitable performance and full diversity under correlated channel scenarios. We have found that combining lattice reduction (LR) technique and Chase List (CL) detection enables slight BER improvements, but at a relatively high cost complexity. Furthermore, ordering has been shown to be ineffective at high correlated and SNR scenarios with large number of antennas, where no ordering is preferable.