Multiple-input Multiple-output Strategy Research Articles

A novel massive MIMO strategy for optimal antenna selection via hybrid algorithm

ABSTRACT One of the capable technologies in networking systems is the Multiple-Input Multiple-Output (MIMO) technology as it provides better system capacity and a high Bit Error Rate (BER). Still, the transmit antenna selection remains as the greatest issue. More transmit antennas are necessary to achieve maximum capacity, which leads to increased power consumption. Therefore, for solving these issues in the M-MIMO system, this paper introduces a novel Wolf Adapted Herding algorithm (WA-HA) for selecting the optimal transmit antennas using multi-objective constraints that raise the capacity and Energy Efficiency (EE). The proposed algorithm combines the concept of Elephant Herding Optimization (EHO) and Grey Wolf Optimization (GWO) algorithm and it is referred to as the Wolf Adapted Herding Algorithm (WA-HA). This WA-HA algorithm optimises the number of transmit antennas and hence decides which antenna has to be chosen. Finally, the performance of the adopted method is evaluated over other traditional methods like MV-GSA, SLnO, I-SLnO, EHO and GWO in terms of various metrics like EE and capacity. In particular, the capacity of the proposed WA-HA method for ZF under set-up 1 is 51.02%, 30.61%, 22.44%, 8.163% and 55.10% better than the existing models. Furthermore, the computational time of our model is 10.96%, 26.13%, 14.08%, 13.48% and 10.26% superior to that of the existing models, respectively.

Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction

Epidemics of influenza are major public health concerns. Since influenza prediction always relies on the weekly clinical or laboratory surveillance data, typically the weekly Influenza-like illness (ILI) rate series, accurate multi-step-ahead influenza predictions using ILI series is of great importance, especially, to the potential coming influenza outbreaks. This study proposes Comprehensive Learning Particle Swarm Optimization based Machine Learning (CLPSO-ML) framework incorporating support vector regression (SVR) and multilayer perceptron (MLP) for multi-step-ahead influenza prediction. A comprehensive examination and comparison of the performance and potential of three commonly used multi-step-ahead prediction modeling strategies, including iterated strategy, direct strategy and multiple-input multiple-output (MIMO) strategy, was conducted using the weekly ILI rate series from both the Southern and Northern China. The results show that: (1) The MIMO strategy achieves the best multi-step-ahead prediction, and is potentially more adaptive for longer horizon; (2) The iterated strategy demonstrates special potentials for deriving the least time difference between the occurrence of the predicted peak value and the true peak value of an influenza outbreak; (3) For ILI in the Northern China, SVR model implemented with MIMO strategy performs best, and SVR with iterated strategy also shows remarkable performance especially during outbreak periods; while for ILI in the Southern China, both SVR and MLP models with MIMO strategy have competitive prediction performance

Coded OFDM Waveforms for MIMO Radars

Emerging digital radar concepts such as orthogonal frequency-division multiplexing (OFDM) allow flexible signal generation. This opens up new opportunities in waveform design in a multiple-input multiple-output (MIMO) system such as introducing coding for signal multiplexing. In this article, coded MIMO OFDM waveforms are proposed and investigated that allow continuous and simultaneous wideband transmission for all transmitters of a multiple transmit and receive antenna array for spatial radar environment perception. Challenges for coded MIMO OFDM radar operation are derived, and three coded MIMO strategies are introduced and analyzed. Their potential is validated and compared to the standard subcarrier interleaving OFDM approach using simulations and measurements of an experimental 4 × <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ 4$</tex-math></inline-formula> MIMO OFDM radar at 77GHz.

Performance comparison of MU-MIMO schemes for indoor visible light communication systems

The correlation of the multiple-input multiple-output (MIMO) channels in indoor visible light communication (VLC) systems are always very high, hence the traditional multi-user multi-stream (MUMS) based schemes do not always work very well. In order to cope with this problem, we introduce a new MIMO strategy for indoor VLC systems in which multiple users are scheduled simultaneously while individual users are allocated only one data stream. Two multi-user multiple-input multiple-output (MU-MIMO) schemes under this strategy are proposed, and compared with the traditional MUMS based scheme. The contrast results indicated that one of our proposed schemes has the minimum computational complexity, and the other proposed scheme has the best performance, which can be used as important references for the design of indoor VLC systems under different application scenarios.

Spectral Efficiency Bounds for Interference-Limited SVD-MIMO Cellular Communication Systems

The ergodic spectral efficiency (SE) in interference-limited multiple-input multiple-output (MIMO) downlink cellular systems is characterized based on stochastic geometry. A single user is served by using singular value decomposition precoding and combining. By approximating the expectations of the channel eigenvalues, we derive upper and lower bounds on the ergodic SE. The obtained upper bound is the best possible system-level performance of any MIMO strategy in non-cooperative cellular networks. We validate our analytical results through simulation. We also conjecture that there exists the optimal number of streams being proportional to the pathloss exponent.

An advanced wind speed multi-step ahead forecasting approach with characteristic component analysis

Accurate wind speed prediction provides essential information for system operation and management with wind power integration. Most existing prediction methods chose to model original data directly without considering the inherent characteristics of wind speed time series. However, the properties of characteristic components can offer better information for prediction. This paper proposes to extract the characteristic components of original data using the ensemble empirical mode decomposition and sample entropy techniques. For the multi-step ahead forecasting of characteristic components, a multiple-input multiple-output (MIMO) based extreme learning machine model is constructed. The effectiveness of the proposed approach is illustrated by applying it to two real wind farm datasets. The extracted characteristic components are shown to be of much lower complexity and the MIMO strategy is found to be better than the common iterated strategy and direct strategy. Moreover, compared to the existing neural networks based methods, the proposed approach has been demonstrated to be a more effective method in both prediction accuracy and computational cost.

Multi-step-ahead time series prediction using multiple-output support vector regression

Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterated strategy or direct strategy. This study proposes a novel multiple-step-ahead time series prediction approach which employs multiple-output support vector regression (M-SVR) with multiple-input multiple-output (MIMO) prediction strategy. In addition, the rank of three leading prediction strategies with SVR is comparatively examined, providing practical implications on the selection of the prediction strategy for multi-step-ahead forecasting while taking SVR as modeling technique. The proposed approach is validated with the simulated and real datasets. The quantitative and comprehensive assessments are performed on the basis of the prediction accuracy and computational cost. The results indicate that (1) the M-SVR using MIMO strategy achieves the best accurate forecasts with accredited computational load, (2) the standard SVR using direct strategy achieves the second best accurate forecasts, but with the most expensive computational cost, and (3) the standard SVR using iterated strategy is the worst in terms of prediction accuracy, but with the least computational cost.

Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices

An accurate prediction of crude oil prices over long future horizons is challenging and of great interest to governments, enterprises, and investors. This paper proposes a revised hybrid model built upon empirical mode decomposition (EMD) based on the feed-forward neural network (FNN) modeling framework incorporating the slope-based method (SBM), which is capable of capturing the complex dynamic of crude oil prices. Three commonly used multi-step-ahead prediction strategies proposed in the literature, including iterated strategy, direct strategy, and MIMO (multiple-input multiple-output) strategy, are examined and compared, and practical considerations for the selection of a prediction strategy for multi-step-ahead forecasting relating to crude oil prices are identified. The weekly data from the WTI (West Texas Intermediate) crude oil spot price are used to compare the performance of the alternative models under the EMD–SBM–FNN modeling framework with selected counterparts. The quantitative and comprehensive assessments are performed on the basis of prediction accuracy and computational cost. The results obtained in this study indicate that the proposed EMD–SBM–FNN model using the MIMO strategy is the best in terms of prediction accuracy with accredited computational load.

Advanced Modulation and Multiple-Input Multiple-Output for Multimode Fiber Links

We evaluate the performance of multiple-input multiple-output (MIMO) concepts and their applicability in increasing data rates achievable in multimode fibers. We draw on several MIMO techniques used commonly in wireless standards, and experimentally evaluate their performance benefits to a 3-km multimode fiber link. Using two transmitters and two detectors in concert, with orthogonal frequency-division multiplexing (OFDM), we achieved a data rate of 12 Gb/s, exceeding the fiber's characteristic bandwidth length product by over 20-fold. It is expected that MIMO strategies can provide significant benefits to local and metropolitan area networks in terms of network cost and complexity, power budget, and data rate.

Time Reversal in Multiple-Input Multiple-Output Radar

Time reversal explores the rich scattering in a multipath environment to achieve high target detectability. Multiple-input multiple-output (MIMO) radar is an emerging active sensing technology that uses diverse waveforms transmitted from widely spaced antennas to achieve increased target sensitivity when compared to standard phased arrays. In this paper, we combine MIMO radar with time reversal to automatically match waveforms to a scattering channel and further improve the performance of radar detection. We establish a radar target model in multipath rich environments and develop likelihood ratio tests for the proposed time-reversal MIMO radar (TR-MIMO). Numerical simulations demonstrate improved target detectability compared with the commonly used statistical MIMO strategy.