Transmit Antenna Selection, Power and Energy Efficient Algorithm for Massive MIMO Applications

Multiple Input Multiple Output (MIMO) low complexity receiver which utilizes the compressive sensing detection for Spatial Modulation in large scale MIMO system in order to reduce the system complexity. In conventional MIMO system; huge amount of antennas is used at both ends to exploit the multipath propagation. This system maximizes the throughput performance and data rates are increased but only at the cost of high hardware complexity and increased power-consumption. Spatial Modulation Matching Pursuit (SMMP) is the proposed enhanced CS technique used for the improvement of detection performance. Hence, this paper reviews recent research findings concerning normalized Compressive Sensing (CS) detection algorithm, used for Spatial Modulation (SM) in massive MIMO, to lowers the signal processing complexity, which in result improves the energy efficiency of system against that of conventional MIMO system.This strategy is achieved by involving additional structures and sparsity in which a single transmitter antenna or a subset of it is turned on at each case to transmit a certain data. The subset of antenna which is turned on for transmission depends on approaching data bits. Therefore, the total increase in the spectral efficiency of the system is given as base-two logarithm of whole antennas at the transmitter. It reduces the signal processing load at base station and doesn’t depend upon any synchronization between transmitters. The Spatial Modulation Matching Pursuit used prevents the Inter Channel Interference (ICI) of the system which in result improves the Bit Error Rate (BER) performance than the typical MIMO system.

Massive Multiple Input Multiple Output (MIMO) systems can significantly improve the system performance and capacity by using a large number of antenna elements at the base station (BS). To reduce the system complexity and hardware cost, low complexity antenna selection techniques can be used to choose the best antenna subset while keeping the system performance at a certain required level. In this paper, Tabu Search (TS) and three bio-inspired optimization algorithms were used for antenna selection in Massive MIMO systems. The three bio-inspired algorithms were: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Bee Colony (ABC). Simulations showed promising results for the TS by achieving higher capacity with GA than PSO and ABC, and much shorter CPU time than any of the bio-inspired techniques.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

In the present research, four meta-heuristic algorithms named Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) have been used for optimal design of cascade spillway to minimize the construction cost.Usually, the traditional design methods such as Vittal and Porey (VP) method or experimental modeling are used to solve this problem leading to infeasible or near optimal solution.The main novelty of this paper is to use effective methods to solve this complex highly constrained problem.Therefore, due to unique features of meta-heuristic algorithms, these algorithms are used here to minimize the construction cost of cascade spillway as the energy dissipater structure.As the case study, cascade spillway of Tehri dam in India had been chosen.The algorithms results have been compared together and with the VP results.Comparison of the results show the effectiveness and affectivity of these algorithms to solve this optimization problem.In other words, when three-stepped spillway are considered, the results are improved with 16.16%, 16.4%, 17.73% and 17.63% respectively using GA, GSA, PSO and ABC algorithms and in the same manner, for four-stepped spillway, the results are improved 14.5%, 16.1%, 16.45% and 16.05% respectively using GA, GSA, PSO and ABC algorithms in comparison with the VP results.

The Transmit antenna selection (TAS) technique is essential to increase the efficiency of spatial modulation (SM) systems. This TAS is an effective technique for reducing the Multiple Input Multiple Output (MIMO) systems, which can increase the computational difficulty and Bit error rate (BER) by various TAS algorithms. But these selection methods cannot provide code gain, so it is essential to join the external code in TAS to obtain code gain advantages in BER. In some existing work, the improved BER has been perceived by joining Forward Error Correction Code (FEC) and Space Time Block Code (STBC) for MIMO systems provided greater code gain. A multiple TAS-OSTBC technique with new integration of Bose–Chaudhuri–Hocquenghem (BCH)-Turbo code (TC) is proposed in this paper. The TAS-OSTBC system connects with the external BCH code in sequence of internal Turbo code. This combination can provide increasing code gain and the effective advantages of the TAS-OSTBC system. To perform the system analysis Rayleigh channel is utilized. In the case with multiple TAS-OSTBC systems, better performance can provide by this new combination of the BCH-Turbo compared to the conventional Turbo code for the Rayleigh fading.

In this paper, we present a comprehensive performance analysis of multiple-input multiple-output (MIMO) systems with transmit antenna selection (TAS) and stochastic power allocation (SPA) for the spatially correlated fading channels. Two best transmit antennas that maximize the instantaneous received signal-to-noise (SNR) are selected to transmit the Alamouti scheme and maximal-ratio combining (MRC) is applied at the receiver. With correlation matrices available to the transmitter, SPA is applied on these selected antennas. Two different methods are given to derive the explicit upper bounds on the bit-error rate (BER) performance. Finally we present numerical results to verify our analysis. It is shown that the TAS/SPA scheme can achieve high performance in spatially correlated channels.

The fifth generation cellular communication is all about high speed internet access with devices consuming less amount of power. Millimeter wave Multiple Input Multiple Output (MIMO) systems is a promising technology that offers high data rates due to a large bandwidth. These systems are compact with minimal antenna heights which allows us to deploy these systems more widely. The mm-Wave Massive MIMO systems enhances the data rates to a large extent. But due to the increasing number of antennas to support high data rates, the power consumption of the system goes high. In this paper, a limited channel state information (CSI) based precoding technique has been used to optimize the power consumption and to reduce the computational complexity of the system. The use of Hybrid Precoder architecture with sub array of precoding blocks helps in reducing the computational complexity by many folds. The conditional switching between two stages minimizes the number of computations required. The estimation of Angle of arrival (AoA) enables the precoder to steer the beam at the transmitter for higher throughput. The smart antenna selection algorithm optimizes the power consumption of the system. The two – staged precoding algorithm proposed employs all these techniques to optimize the performance of mm-Wave MIMO systems.

For the past decades, we have witnessed an extraordinary advancement in the field of Wireless Sensor Networks (WSNs) in both academic and industrial settings. Moreover, the network lifetime is regarded as one of the most critical issues in this field, and quite a large number of researchers are increasingly exploring this topic of interest. In this study, the linear-scaling method is initially implemented onto the Gravitational Search Algorithm (GSA) for the mass calculation. This is due to the fact that the exploitation and exploration abilities of the algorithm can be fully controlled using this approach. The results obtained from the simulation revealed that this novel GSA achieves the same level of performance as that of conventional GSA and significantly outperforms the state-of-the-art metaheuristic search algorithms. Additionally, this improved GSA can be readily utilized to solve the K-of-N lifetime problem in two-tiered WSN architecture. In our proposed method, the novel GSA was employed in order to find the optimum location of the base station to enhance network lifetime. Furthermore, the simulation results indicated that despite the simplicity in implementation, our proposed method has a higher level of performance compared to other approaches used to address K-of-N lifetime problem in two-tiered WSN architecture.

Everyone in the world always wants to use a better wireless network which provide high capacity and high data rates. High data rates and capacity can be produce by Multiple Input Multiple Output(MIMO) systems. This paper mainly focused on MIMO systems because the MIMO systems have multiple number of antennas in transmitting and receiving end which can produce more data rates and capacity compared to SISO, SIMO, MISO systems. The multiple antennas allow MIMO systems to produce multiplexing gain and diversity gain. The capacity of MIMO systems will be increased by increased the number of antennas in transmitter and receiver end.

A novel local extrema based gravitational search algorithm and its application in face recognition using one training image per class

— In this paper, two optimization algorithms i.e., Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are combined to produce a hybrid population-based algorithm PSOGSA. The approach of HPSOGSA is to use the assessment of PSO using the social thinking capability and the manipulation of GSA using the local search proficiency. To increase the performance of HPSOGSA, some benchmark functions are used. Two standard database ORL and YALE is used to assess the classification performance of the proposed method. Classification accuracy is compared for diverse number of training samples per class. Further lead of proposed method is confirmed by analyzing percentage improvement in classification accuracy. With limited accessibility of training sample, percentage enhancement is very successful for face recognition applications. Keywords: Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (HPSOGSA). Cite this Article Purvaa Saxena, Gargi Mishra, Apoorva Aggarwal, Ayush Priyadarshi, Nitin Kumar, Rahul. Pattern Recognition Using Hybrid Meta-Heuristic. Journal of Artificial Intelligence Research & Advances. 2019; 6(2): 100–108p.

Estimation of transformer parameters from nameplate data by imperialist competitive and gravitational search algorithms

In conventional ways of fast responding and high performing model predictive control using Field Programmable Gate Arrays (FPGA), the approaches developed are applicable only to Single Input Single Output System (SISO). Many real time systems are Multiple Input Multiple Output (MIMO) systems and loop interaction is a major concern and has to be reduced in control of MIMO systems. Also, direct digital control with ability to solve the Quadratic Programming (QP) problem online, which can be applied to complex systems with fast response time and/or embedded applications and with limited computational resource for supervisory control approach, is required in many complex industrial MIMO systems. A novel direct digital control of Model predictive controller with FPGA for a MIMO system which considers and reduces loop interaction and which can also be implemented with very less investment is proposed here. The system was modeled from first principles and then simulated in Labview environment for MIMO system. Implementation of the system and response was verified for a stirred tank system. The proposed system has good performance in terms of reduction in loop interaction which is a major concern in implementation of controller for MIMO systems. Also, direct digital control of MIMO system instead of supervisory control was accomplished.

In conventional cellular network systems, the cell edge users are vulnerable to interference from neighboring cells. In order to deal with this problem, coordinate multipoint transmission/reception(CoMP) has been proposed in LTE-A system. Yet, in recent years, multiuser multiple- input-multiple output (MU-MIMO) has been well developed. The MU-MIMO technique allows the simultaneous transmission of multiple user signals in the same time at the same frequency band, and hence enhances the system performance effectively. Thus, LTE-A tries to integrate MU-MIMO and CoMP hoping for better peak data rate and higher spectrum efficiency. This thesis aims to investigate intra-site MU CoMP joint processing (JP) techniques. We propose a new antenna-selection-based JP method that can better use the system resource and reduce the feedback overhead. Thanks to the antenna selection technique, we can translate a multipoint CoMP problem to a virtual single-point CoMP problem such that the existing single-point MU-MIMO technique can be directly applied. Numerical results show that the proposed method outperforms the conventional approach and at the same time requires less feedback overhead.

This paper examines the impacts of transmit antenna subset (TAS) selection on the biterror-rate (BER) performance of linear precoding-aided spatial modulation (PSM) multiple-input multipleoutput (MIMO) systems. It is analytically shown that decreasing the number of active transmit antennas by TAS selection, which can be used to reduce the number of RF units at the transmitter side, always degrades the BER performance. The expression of a received signal-to-noise ratio (SNR) loss is also derived and its analytical results are shown to be well-matched with simulation results. Furthermore, it is analyzed that when there are NT transmit antennas, NS selected transmit antennas, and NR receive antennas, an achievable diversity order of the zero-forcing (ZF)-based PSM MIMO systems either with optimal TAS selection or without TAS selection can be given as N T -N R +1. To select an optimal TAS based on exhaustive search, an optimization criterion to maximize the received SNR of the selected TAS is employed. In addition, a low-complexity TAS selection scheme is presented using a decremental strategy. It is also observed that the decremental TAS selection algorithm can achieve almost the same BER and achievable rate performance as the optimal one while attaining much lower complexity. Finally, simulation results show that under the identical number of active transmit antennas, the ZF-PSM MIMO system with TAS selection outperforms the conventional ZF-PSM without TAS selection.