Search

MULTI-CRITERIA Assembly sequence planning (ASP) is known as large scale, time consuming combinatorial problem. Production scheduling is a complex combined optimization problem and the optimization method of which is not perfect [1]. The product order of assembly is the main focus of ASP to determine, which is subject to precedence constraint matrix (PM) that is to be strictly followed in the assembly line to shorten the assembly time and hence save the assembly cost. Refs. [2, 3] proposed the concept of Assembly Precedence Relations (APRs), which is applied to determine the precedence relations among the liaisons in the product. Cut-set analysis method by which the number of queries can be reduced by 95% [4]. More efficient queries is proposed in ref. [5]. When number of parts increase the problem became more complex. Heuristic methods developed to overcome this complicity. It is more efficient but it may stick in local optima, no guarantee that global optima may be found. Some heuristic methods may use Neural Network (NN), which need system training before start searching. Meta-heuristic method is able to escape the local optima. Simulated Annealing (SA) is used where search is done in sequence basis and to solve optimization problems. Ref. [6] used (SA) approach, which is based on searching via all the feasible sequences. This disadvantage is overcome by an improved cut-set [7, 8]. Generation and evaluation of assembly plans, when the number of parts is large their planer is slow [9]. Genetic Algorithm (GA), where the genes in chromosomes represents the components of the product [10, 11]. An integrated approach such that liaison graph represents the physical connections between two components [15]. An extension to previous work is proposed in [16]. Finding a method to determine global optima or near global optima more reliably and quickly [17]. The definition of genes and evaluation criteria here are based on the connector concept [18]. The complete or partial aut

Energy efficiency (EE) is a critical design when taking into account circuit power consumption (CPC) in fifth-generation cellular networks. These problems arise because of the increasing number of antennas in massive multiple-input multiple-output (MIMO) systems, attributable to inter-cell interference for channel state information. Apart from that, a higher number of radio frequency (RF) chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers. Therefore, antenna selection, user selection, optimal transmission power, and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems. This work aims to investigate joint antenna selection, optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE, with complete knowledge of large-scale fading with maximum ratio transmission. It also accounts for channel estimation and eliminating pilot contamination as antennasM→∞. This formulates the optimization problem of joint optimal antenna selection, transmits power allocation and joint user selection to mitigate inter-cellinterference in downlink multi-cell massiveMIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm (LCA) for Newton’s methods and Lagrange multipliers. To analyze the precise power consumption, a novel power consumption scheme is proposed for each individual antenna, based on the transmit power amplifier and CPC. Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power, in the case the noise power is less than the received power pilot. The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse, in the case the transmit power allocation ρd = 40 dBm, and the optimal EE=71.232 Mb/j.

The full future of the sixth generation will develop a fully data-driven that provide terabit rate per second, and adopt an average of 1000+ massive number of connections per person in 10 years 2030 virtually instantaneously. Data-driven for ultra-reliable and low latency communication is a new service paradigm provided by a new application of future sixth-generation wireless communication and network architecture, involving 100+ Gbps data rates with one millisecond latency. The key constraint is the amount of computing power available to spread massive data and well-designed artificial neural networks. Artificial Intelligence provides a new technique to design wireless networks by apply learning, predicting, and make decisions to manage the stream of big data training individuals, which provides more the capacity to transform that expert learning to develop the performance of wireless networks. We study the developing technologies that will be the driving force are artificial intelligence, communication systems to guarantee low latency. This paper aims to discuss the efficiency of the developing network and alleviate the great challenge for application scenarios and study Holographic radio, enhanced wireless channel coding, enormous Internet of Things integration, and haptic communication for virtual and augmented reality provide new services on the 6G network. Furthermore, improving a multi-level architecture for ultra-reliable and low latency in deep Learning allows for data-driven AI and 6G networks for device intelligence, as well as allowing innovations based on effective learning capabilities. These difficulties must be solved in order to meet the needs of future smart networks. Furthermore, this research categorizes various unexplored research gaps between machine learning and the sixth generation.

This paper investigates joint antenna selection and optimal transmit power in multi cell massive multiple input multiple output systems. The pilot interference and activated transmit antenna selection plays an essential role in maximizing energy efficiency. We derived the closed-form of maximal energy efficiency with complete knowledge of large-scale fading with maximum ratio transmission while accounting for channel estimation and eliminated pilot contamination when the antennas approach infinity. We investigated joint optimal antenna selection and optimal transmit power under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm for Lagrange multiplayer and Newton methods. The two scenarios of achievable high data rate and total transmit power allocation are critical to the performance maximal energy efficiency. We propose new power consumption for each antenna based on the transmit power amplifier and circuit power consumption to analyze exact power consumption. The simulation results show that maximal energy efficiency could be achieved using the iterative low complexity algorithm based on the reasonable maximum transmit power when the noise power was less than the power received pilot. The proposed low complexity iterative algorithm offers maximum energy efficiency by repeating a minimized pilot signal until the optimal antenna selection and transmission power are achieved., Comment: 10Pages

Massive multiple-input, multiple-output (M-MIMO) is an important knowledge for fifth-generation (5G) wireless cellular networks. The pilot contamination (PC) is an issue in massive MIMO due to interference between adjacent cells. We proposed that the number of pilot sequence inside a cell could become smaller than or equal to the number of users (UEs), taking into account the different number of UEs that transmitted the same pilot sequence in the same cell. In addition, the pilot sequence became mutually orthogonal for different cells to prevent PC among cells. In this paper, we analyzed a channel estimation for time division duplex (TDD) and improved the achievable data rate by reducing the PC for limiting user capacity and using channel orthogonality for minimum mean square error (MMSE) precoding. From the simulation results, the proposed scheme provided a data rate for two several situations, with and without interference PC for an increased number of antennas. Consequently, increasing the number of coherence intervals made the channel estimation critical and provided a small data rate due to increased noise and interference at increased transmit pilot sequence.

Massive multiple-input, multiple-output (M-MIMO) is an important knowledge for fifth-generation (5G) wireless cellular networks. The pilot contamination (PC) is an issue in massive MIMO due to interference between adjacent cells. We proposed that the number of pilot sequence inside a cell could become smaller than or equal to the number of users (UEs), taking into account the different number of UEs that transmitted the same pilot sequence in the same cell. In addition, the pilot sequence became mutually orthogonal for different cells to prevent PC among cells. In this paper, we analyzed a channel estimation for time division duplex (TDD) and improved the achievable data rate by reducing the PC for limiting user capacity and using channel orthogonality for minimum mean square error (MMSE) precoding. From the simulation results, the proposed scheme provided a data rate for two several situations, with and without interference PC for an increased number of antennas. Consequently, increasing the number of coherence intervals made the channel estimation critical and provided a small data rate due to increased noise and interference at increased transmit pilot sequence.

Massive multiple- input–multiple- output has become an important fifth-generation (5G) wireless communication system because it improves transmitted spectral efficiency. In this paper, we obtained the maximal spectral efficiency by improving transmission performance in cell edges. This was achieved by using pilot reuse sequences from all available pilots in order to mitigate the pilot contamination and to suppress interference between adjacent cells. In addition, we investigated the impacts of pilot contamination on the received signal-to-interference-noise ratios (SINR) of users and employed different pilot reuse. We propose a new method called cell-edge-aware maximum ratio transmission (MRT), zero forcing (ZF), and return zero forcing (R-ZF). These were the precoders that employed less spatial dimensions and were able to suppress adjacent cells interference of the maximally vulnerable active user. We conclude that the large pilot reuse value between neighboring cells increased the gain, avoided interference between adjacent cells, and gave the maximal spectral efficiency. Consequently, the R-ZF was better than ZF and MRT because it was able to suppress the SINR.

Massive multiple- input–multiple- output has become an important fifth-generation (5G) wireless communication system because it improves transmitted spectral efficiency. In this paper, we obtained the maximal spectral efficiency by improving transmission performance in cell edges. This was achieved by using pilot reuse sequences from all available pilots in order to mitigate the pilot contamination and to suppress interference between adjacent cells. In addition, we investigated the impacts of pilot contamination on the received signal-to-interference-noise ratios (SINR) of users and employed different pilot reuse. We propose a new method called cell-edge-aware maximum ratio transmission (MRT), zero forcing (ZF), and return zero forcing (R-ZF). These were the precoders that employed less spatial dimensions and were able to suppress adjacent cells interference of the maximally vulnerable active user. We conclude that the large pilot reuse value between neighboring cells increased the gain, avoided interference between adjacent cells, and gave the maximal spectral efficiency. Consequently, the R-ZF was better than ZF and MRT because it was able to suppress the SINR.

Massive multiple-input-multiple-output (MIMO) systems are an exciting area of fifth-generation (5G) technology and very important in maximizing energy efficiency (EE) and saving battery technology. Obtaining energy efficiency without sacrificing the quality of service (QoS) has become increasingly important for mobile devices. In this paper, we investigate the maximal EE for downlink massive MIMO systems using zero-forcing beamforming (ZFBF), dependent on the number of antenna elements and the optimal number of users inside the cell to optimize the transmit power. The linear precoding ZFBF is able to mitigate interbeam interference, in addition to noise, due to expanding the reception at low power transmission. The simulation results reveal that the maximal energy efficiency can be obtained dependent on increasing the number of antennas M and choosing the , where the number of antennas is greater than the critical number of antennas  , which minimizes the received interference due to increased transmit power.

Massive multiple-input-multiple-output (MIMO) systems are an exciting area of fifth-generation (5G) technology and very important in maximizing energy efficiency (EE) and saving battery technology. Obtaining energy efficiency without sacrificing the quality of service (QoS) has become increasingly important for mobile devices. In this paper, we investigate the maximal EE for downlink massive MIMO systems using zero-forcing beamforming (ZFBF), dependent on the number of antenna elements and the optimal number of users inside the cell to optimize the transmit power. The linear precoding ZFBF is able to mitigate interbeam interference, in addition to noise, due to expanding the reception at low power transmission. The simulation results reveal that the maximal energy efficiency can be obtained dependent on increasing the number of antennas M and choosing the , where the number of antennas is greater than the critical number of antennas  , which minimizes the received interference due to increased transmit power.

Massive multi-input, multi-output (MIMO) systems are an exciting area of study and an important technique for fifth-generation (5G) wireless networks that support high data rate traffic. An increased number of antenna arrays at the base station (BS) consumes more power due to a higher number of radio frequency (RF) chains, which cannot be neglected and becomes a technical challenge. In this paper, we investigated how to obtain the maximal data rate by deriving the optimal number of RF chains from a large number of available antenna arrays at the BS when there is equal power allocation among users. Meanwhile, to mitigate inter-user-interference and to compute transmit power allocation, we used the precoding scheme zero forcing beamforming (ZFBF). The achievable data rate is increased because the algorithm of ZFBF enables the choosing of the maximum power in relation to the optimal antenna selection. We conclude that the transmit power allocation allows the use of less number of RF chains which provides the maximum achievable data rate depending on the optimal RF chain at the BS.

Massive multi-input–multi-output (MIMO) systems are crucial to maximizing energy efficiency (EE) and battery-saving technology. Achieving EE without sacrificing the quality of service (QoS) is increasingly important for mobile devices. We first derive the data rate through zero forcing (ZF) and three linear precodings: maximum ratio transmission (MRT), zero forcing (ZF), and minimum mean square error (MMSE). Performance EE can be achieved when all available antennas are used and when taking account of the consumption circuit power ignored because of high transmit power. The aim of this work is to demonstrate how to obtain maximum EE while minimizing power consumed, which achieves a high data rate by deriving the optimal number of antennas in the downlink massive MIMO system. This system includes not only the transmitted power but also the fundamental operation circuit power at the transmitter signal. Maximized EE depends on the optimal number of antennas and determines the number of active users that should be scheduled in each cell. We conclude that the linear precoding technique MMSE achieves the maximum EE more than ZF and MRT because the MMSE is able to make the massive MIMO system less sensitive to SNR at an increased number of antennas.

The ultra-wideband (UWB) antenna is developed to cover a broad bandwidth. ‎The UWB radio systems are interfered ‎by the ‎same ‎spectrum ‎that shared with the local bands. In this paper, two F-shaped slots on a hexagonal patch UWB antenna are demonstrated ‎‎ to realize a high band rejection. The symmetrical couple F-slots is ‎notched on the hexagonal UWB ‎ ‎patch antenna to avoid the interference ‎and ‎‎enhance the notching results at C-band. The demonstrated ‎antenna employs a coplanar waveguide ‎(CPW) technique to meet a fractional bandwidth of 126%. The proposed method validates ‎several ‎‎reconfigurations of the F-slot location on the demonstrated design. Six steps ‎parametric study are considered to test the slots location. The results of the proposed antenna with slots are introduced based on analytical, simulation, and ‎measurement. The total design size ‎‎28 mm × 43 mm × 1.6 ‎mm is simulated by ‎using CST Microwave Studio. The two F-slots are achieved the antenna gain of -6 dB, ‎return loss of -1.2 ‎dB, and ‎VSWR of 15.2 at the rejected band of 4 GHz. The ‎measurement results are compared with the simulation results between the three ‎prototypes. The current ‎distribution on the design is discussed at 2.88 GHz and 4 GHz frequencies. The radiation patterns illustrate ‎omnidirectional of H-plane and bidirectional of E-plane. This paper validates the slots locations to enhance the notches performance and reduce the interference.