Multiple-input And Single-output Research Articles

Energy Efficient Beamforming for Small Cell Systems: A distributed Learning and Multicell Coordination Approach

The integration of small cell architecture and edge intelligence is expected to make high-grade mobile connectivity accessible and thus provide smart and efficient services for various aspects of urban life. It is well known that small cell architecture will cause high inter-cell interference since the adjacent cells share the same frequency band. One of the most promising techniques to mitigate inter-cell interference is beamforming, however, how to coordinate the beamformers in a multicell dynamic network to reach a global optimum is an extremely challenging problem. In this paper, we consider analog beamforming with low-resolution phase shifters, and propose a distributed learning and multicell coordination based energy efficient beamforming approach for multiple-input and single-output (MISO) small cell system. The goal is to maximize the energy efficiency (EE) of the whole system by jointly optimizing the beamformer and transmit power. We perform extensive simulations in both static and dynamic scenarios, and validate the performance of the proposed approach by comparing with baseline and existing schemes. The simulation results demonstrate that the proposed approach outperforms the baseline and existing schemes with an significant improvement in terms of EE for both static and dynamic network settings.

Uplink and Downlink NOMA Based on a Novel Interference Coefficient Estimation Strategy for Next-Generation Optical Wireless Networks

Non-orthogonal multiple access (NOMA) has been widely recognized as a promising technology to improve the transmission capacity of wireless optical communication systems. NOMA considers the principle of successive interference cancellation (SIC) to separate a user’s signal at the receiver side. To improve the ability of optical signal detection, we developed a quantum dot (QD) fluorescent concentrator incorporated with multiple-input and single-output (MISO) to realize an uplink NOMA-based optical wireless system. However, inaccurate interference assessment of multiple users using the SIC detection algorithm at the receiver side may lead to more prominent error propagation problems and affect the bit error rate (BER) performance of the system. This research aims to propose a novel recurrent neural network-based guided frequency interference coefficient estimation algorithm in a NOMA visible light communication (VLC) system. This algorithm can improve the accuracy of interference estimation compared with the traditional SIC detection algorithm by introducing interference coefficients. It provides a more accurate reconstruction possibility for level-by-level interference cancellation and weakens the influence of error propagation. In addition, we designed uplink and downlink NOMA-VLC communication systems for experimental validation. When the power allocation ratio was in the range of 0.8 to 0.97, the experimental results of the downlink validated that the BER performance of both users satisfied the forward error correction (FEC) limit with the least squares (LS)-SIC and the long short-term memory recurrent neural networks (LSTM)-SIC detection strategy. Moreover, the BER performance of the LSTM-SIC algorithm was better than that of the LS-SIC algorithm for all users when the power allocation ratio was in the range of 0.92 to 0.93. In particular, our proposed system offered a large detection area of 2 cm2 and corresponding aggregate data rate up to 40 Mbps over 1.5 m of free space by using QDs, and we successfully achieved a mean bit error rate (BER) of 2.3 × 10−3 for the two users.

A Novel Energy Efficient Harvesting Technique for SDWSN using RF Transmitters with MISO Beamforming

Software Defined Wireless Sensor Networks (SDWSN) is emerged to overcome the additional energy consumption in WSN. Even then the sensor nodes in the SDWSN suffer from scarce battery resources. Generally, the Radio Frequency (RF) transmitters are deployed around the base station in the SDWSN to overcome the high energy consumption problem. To enhance harvesting energy and coverage of nodes in the network, a new energy harvesting technique using RF transmitters with Multiple Input and Single Output (MISO) beamforming is proposed. In this method, multiple antenna RF transmitters and single antenna sensor nodes are deployed. The optimization problem subject to Signal to Noise Ratio (SNR) and energy harvesting constraints is formulated for hybrid beamforming design to reduce the transmit power in the network. The optimization problem based on convex Second Order Cone Programming (SOCP) is derived to get the optimal solution for hybrid beamforming design. The beamforming technique is used to steer the beam in the desired direction and null to the other direction improves the energy harvesting. The simulation results show that the proposed technique provides better average harvesting energy, average transmit power, average residual energy and throughput than the existing RF transmitter based energy harvesting methods.

Efficient Magnetic Resonance SIMO WPT Insensitive to Load Impedance at Short Distances

In general, it is known that magnetic induction (MI) wireless power transfer (WPT) is used at a short distance (~several millimeters) and magnetic resonance (MR) WPT is applied at a longer distance (more than several tens of centimeters). In this letter, it is shown that the MR coil can be robustly designed to have higher efficiency, even when the load impedance changes significantly at short distances. The simulated power transfer efficiencies of MI and MR coil show that the MR coil was more insensitive to load impedance than the MI coil. A 1:4 single input and multiple output (SIMO) MR coil wasdesigned and fabricated. Its performance with the variation of load impedance was verified through measurement.

Performance Analysis Antenna Diversity Technique with Wavelet Transform Using Array Gain for Millimeter Wave Communication System

Utilizing antenna diversity techniques has become a well-known approach to improve the performance of wireless communication systems. Multiple antenna arrays with half-length spacing, such as a uniform linear array (ULA), have been taken into consideration. Since 60 GHz is an unlicensed frequency band and ideal for local propagation, it is where the technology is being used. The transmitter and receiver both accomplish QAM modulation and demodulation. The performance in terms of bit error rate (BER) was tested in MATLAB simulation software for all antenna diversity scenarios: the single input and single output (SISO) DWT, multiple input and single output (MISO) DWT, single input and multiple output (SIMO) DWT, and multiple input and multiple output (MIMO) DWT. The MIMO DWT was shown to be the best of them. The performance of MIMO OFDM using various wavelets was also simulated, and the performance of the Haar wavelet transform was 2 dB better than that of the other wavelet transform. Compared to simulation results, the analytical results showed good agreement with little discrepancy.

Proportional Fair Scheduling for Downlink mmWave Multi-User MISO-NOMA Systems

In this paper, we study non-orthogonal multiple access (NOMA) user scheduling and resource allocation problem for a generic downlink single-cell multiple input and single output (MISO) millimeter wave (mmWave) system. The larger number of packed antennas and the highly directional property of mmWave communications enable directional beamforming to achieve spatial diversity. Toward this end, we consider two different hybrid precoding schemes which are based on orthogonal matching pursuit (OMP). Users are assigned into different clusters and the base station (BS) transmits superposed signals that share the same precoding vector. Moreover, both fixed number of users per cluster and dynamic number of users per cluster are investigated. We aim to jointly optimize the user clustering, service scheduling, and power allocation strategy, in maximizing the proportional fairness (PF) among the users and exploring the multiuser diversity and multiplexing gain. Since the formulated joint user clustering, scheduling and power allocation problem is a mixed integer non-convex optimization problem, we propose a two-fold methodology. First, we apply a hybrid precoding and user clustering scheme, where the hybrid precoder is constructed by singular vector division (SVD) or minimum mean square error (MMSE). Then, with the obtained result, we approximate the proportional fairness power allocation problem by a sequence of Geometric Programming (GP) problems which are solved iteratively. The proposed scheme strikes a balance between the spectral efficiency and service fairness. Results show that the proposed MISO-NOMA scheme which is based on MMSE hybrid precoder and the proposed user scheduling and power allocation strategy under proportional fairness metric can outperform various conventional MISO schemes. Furthermore, our proposed dynamic number of users per cluster scheme outperforms the fixed scheme and can be considered as an upper bound in several aspects, including spectral efficiency and fairness.

Prognostic Model for the Risk of Coronavirus Disease (COVID-19) Using Fuzzy Logic Modeling

This study formulated a model for assessing the risk of coronavirus disease (COVID-19) based on variables associated with the spread of COVID-19 infections. The study used the Mamdani fuzzy logic model based on a multiple input and single output (MISO) scheme which required 12 inputs and 1 output variable. Each of the input variables was identified using binary values, namely: No and Yes while the spread of COVID-19 was assessed using four nominal linguistic values. Two triangular membership functions were used to formulate each associated variable and four triangular membership functions to formulate the spread of COVID-19 using specific crisp intervals. The results of the study showed that 4096 rules were inferred from the possible combination of the binary linguistic values of the associated variables for the assessment of the spread of COVID-19. The study concluded that knowledge about variables associated with the spread of COVID-19 infection can be adopted for supporting decision-making which affects the assessment of the spread of COVID-19 by stakeholders.

DESIGN OF ADVANCED PSEUDO RANDOM DISCRETE TIME EVEN SYSTEM FOR FAULT DETECTION

: In this paper, the design of advanced pseudo random discrete time even system for fault detection is implemented to increase the speed of the system. Here sufficient conditions are given to implement the estimator. Pseudo random multiple input signals are given to DES. The main intent of this pseudo random discrete time even system is to increase the speed and produce efficient result in terms of delay and area. The discrete time even system will take multiple signals and perform its operation. The adaptive MISO (multiple input and single output) controller will controls the speed of the system. Self-tuning regulator will tune the multiple inputs and give the single output. Hence, from pseudo random single output block, the output is obtained. Compared to the existed system, the pseudo random discrete time even system gives effective output.

Hybrid Active and Passive Antenna Selection for Backscatter-Assisted MISO Systems

In this article, we propose and investigate hybrid active and passive antenna selection for backscatter-assisted multiple input and single output (MISO) systems. Specifically, each transmit antenna is allowed to work on either the active radio (AR) mode or the passive radio (PR) mode, which are respectively powered by the battery as in traditional communication and the external power source as in the backscatter communication (BackCom). The advantage is that the link reliability can be enhanced, and the power consumption can be opportunistically reduced by switching between the AR and the PR modes. Adaptive antenna selection is investigated and analyzed, where the antenna is selected over all combinations of antennas and their working modes. However, the antenna with the best channel quality will always be chosen. In order to circumvent this problem, two alternative schemes, referred to as reactive antenna selection and proactive antenna selection, are considered, where all antennas take turns to transmit/backscatter the signal. Specifically, in the reactive antenna selection scheme, the working mode for each antenna is determined by choosing the maximum value of the transmit power in the AR mode and the backscatter power in the PR mode. The working mode for the proactive antenna selection scheme is, however, chosen beforehand. Since the exact closed-form expression for the average symbol error rate (SER) is not attainable, approximations and their asymptotic performance are derived for proposed schemes. The power consumption is also analyzed. Simulation results demonstrate the correctness and the tightness of our analysis, and the effectiveness of proposed schemes.

Noncoherent Energy-Modulated Massive SIMO in Multipath Channels: A Machine Learning Approach

This article considers the design of the transmitter and receiver in a noncoherent massive single-input and multiple-output (SIMO) system over a multipath channel, representing a typical Internet-of-Things (IoT) scenario that consists of multiple single-antenna transmitters and one receiver with a large number of antennas. In particular, the autoencoders, which consist of multiple independent neural networks (NNs), are adopted at the transmitters and the receiver and are trained jointly, while working separately. To avoid the delicate design for mitigating the intersymbol interference (ISI) caused by multipath channels, the modulation schemes at the transmitters and the demodulation rule at the receiver are learned by the NNs over a limited number of channel samples. Moreover, the relationship between the number of channel samples and the performance of the trained transceiver is analyzed. The simulation results show that the proposed method achieves a lower error probability in comparison with the conventional optimization-based methods under typical channel conditions.

Gbit/s ultraviolet-C diffuse-line-of-sight communication based on probabilistically shaped DMT and diversity reception.

We demonstrated a high-speed 1×2 single-input and multiple-output (SIMO) diffuse-line-of-sight (diffuse-LOS) ultraviolet-C (UVC) solar-blind communication link over a distance of 5 meters. To approach the Shannon limit and improve the spectral efficiency, we implemented probabilistically shaped discrete multitone modulation. As compared to a single-input and single-output (SISO) counterpart, we observed significant improvement in the SIMO link in terms of the angle of view of the receiver and the immunity to emulated weather condition. A wide angle of view of ± 9° is achieved in the SIMO system, with up to a 1.09-Gbit/s achievable information rate (AIR) and a minimum value of 0.24 Gbit/s. Moreover, the bit error rate of the SIMO link in emulated foggy conditions is lowered significantly when compared to that of the SISO link. This work highlights the practicality of UVC communication over realistic distances and in turbulent environments to fill the research gap in high-speed, solar-blind communication.

Performance of BER with Different Diversity Techniques for Millimeter-Wave Communication System

In a communications system, a diversity technique is used to enhance the reliability of a message signal by using at least two channels with different characteristics. In this paper, all four possible scenarios are considered: Single-input and singleoutput (SISO), single input and multi-output (SIMO), multipleinput and single-output (MISO), Multiple-input and multiple output (MIMO) systems. Antenna arrays will be used to reduce BER and improve the performance of the system using array gain in the line of sight channel for 60 GHz frequency in an indoor scenario. Single input and single output are investigated before analysis of multiple inputs and multiple output channel in the line of sight (LOS) and multipath propagation. MATLAB simulation has been performed using BPSK modulation. The comparative studies show that the performance of the MIMO diversity technique is more reliable in terms of BER to improve the performance and efficiency of the communication system.

Superimposed Training-Based Channel Estimation for MISO Optical-OFDM VLC

In this paper, we investigate a novel channel estimation (CE) method for multiple-input and single-output (MISO) systems in visible light communication (VLC). Direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) is commonly used in VLC where half of the available subcarriers are spent to guarantee a real-valued output after the inverse fast Fourier transform operation. Besides, dedicated subcarriers are typically used for CE, thus, many resources are wasted and the spectral efficiency is degraded. We propose a superimposed training approach for CE in MISO DCO-OFDM VLC scenarios. Analytical expressions of mean squared error (MSE) and spectral efficiency are derived when the least squares estimator is considered. This analysis is valid for outdoor and indoor scenarios. For the CE error, simulation results of MSE show a perfect match with analytical expressions. Moreover, results prove that this technique guarantees a larger spectral efficiency than previous schemes where dedicated pilots were used. Finally, the optimal data power allocation factor is also analytically derived.

Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks

In this paper, by considering multiple slices, a downlink transmission of a sparse code multiple access (SCMA) based cloud-radio access network (C-RAN) is investigated. In this setup, by assuming multiple-input and single-output (MISO) transmission mode, a novel robust radio resource allocation is proposed where considering uncertain channel state information, the worst case approach is applied. We consider a radio resource allocation problem with the objective to maximize the total sum rate of users subject to a minimum required rate of each slice and practical limitations of C-RAN and SCMA. To solve the proposed optimization problem in an efficient manner, an iterative method is deployed where beamforming and joint codebook allocation and user association subproblems are sequentially solved. By introducing auxiliary variables, the joint codebook allocation and user association subproblem is transformed into an integer linear programming, and to solve the beamforming optimization problem, minorization-maximization algorithm is applied. Via numerical results, the performance of the proposed algorithm is investigated versus different uncertainty level for different system parameters.

Secure Users Oriented Downlink MISO NOMA

This paper proposes a secure users oriented multiple-input and single-output (MISO) non-orthogonal multiple access (NOMA) downlink transmission scheme, where multiple legitimate users are categorized as quality of service (QoS)-required users (QU) and the security-required users (SU) overheard by a passive eavesdropper. The basic idea is to exploit zero-forcing beamforming (ZFBF) to cancel interference among SUs, and then several QUs are efficiently scheduled based on the obtained beamforming vectors to divide the legitimate users into several user clusters, in such a way that the QUs could share the concurrent transmissions and help to interfere with the eavesdropper to enhance SU secrecy. The goal is to maximize the achievable minimum secrecy rate (MSR) and sum secrecy rate (SSR) of all SUs, respectively, subject to the secrecy outage probability (SOP) constraint of each SU and the QoS constraint of each QU. To provide a comprehensive investigation we consider two extreme cases that the eavesdropper has perfect multiuser detection ability (lower bound of secrecy) or does not have multiuser detection ability (upper bound of secrecy). In the lower bound case, the Dinkelbach algorithm and the monotonic optimization (MO)-based outer polyblock approximation algorithm are proposed to solve the max-min secrecy rate (MMSR) and max-sum secrecy rate (MSSR) problems, respectively. As for the upper bound case, an alternative optimization (AO)-based algorithm is proposed to solve the two non-convex problems. Finally, the superiority of the proposed cases to the conventional orthogonal multiple access (OMA) one is verified by numerical results.

Performance analysis of fuzzy systems based on quintuple implications method

To improve the quality of approximate reasoning in fuzzy system, quintuple implication principle (QIP) to resolve fuzzy modus ponens (FMP) and fuzzy modus tollens (FMT) problems has been proposed by Zhou et al. [32]. The same approximation as Mamdani-type fuzzy inference can be reached by fuzzy reasoning with QIP method for Gödel implication. It therefore is essential to establish some fundamental properties of fuzzy inference system with QIP method. This paper mainly investigates the robustness and universal approximation capability of fuzzy inference system with QIP method. Firstly, we present the QIP solutions of FMP and FMT for R-, S-, QL-, f- and g-implications. And then the robustness of fuzzy inference system with QIP method is discussed. Finally, we study the universal approximation properties of multiple-input and single-output (MISO) fuzzy systems with QIP method for R-, S- and QL-implications. These results reveal that the QIP method possesses better performance in fuzzy rule-based system.

Voiceless Stop Consonant Modelling and Synthesis Framework Based on MISO Dynamic System

AbstractA voiceless stop consonant phoneme modelling and synthesis framework based on a phoneme modelling in low-frequency range and high-frequency range separately is proposed. The phoneme signal is decomposed into the sums of simpler basic components and described as the output of a linear multiple-input and single-output (MISO) system. The impulse response of each channel is a third order quasi-polynomial. Using this framework, the limit between the frequency ranges is determined. A new limit point searching three-step algorithm is given in this paper. Within this framework, the input of the low-frequency component is equal to one, and the impulse response generates the whole component. The high-frequency component appears when the system is excited by semi-periodic impulses. The filter impulse response of this component model is single period and decays after three periods. Application of the proposed modelling framework for the voiceless stop consonant phoneme has shown that the quality of the model is sufficiently good.

Application of full duplex guarantees secure wireless communication

This paper proposes an amplify-and-forward (AF) based scheme for full duplex systems to realize secure wireless communication between two nodes over block fading medium. The paper consider all four possible scenarios: Multiple-input and multipleoutput (MIMO), multiple-input and single-output (MISO), singleinput andmulti-output (SIMO), and single-input and single-output (SISO) systems. We first devise communication protocol of the scheme that does not bring additional communication cost. The core of the protocol is amplify-and-forward process, in which artificial noise sent by the receiver is amplified and retransmitted by the transmitter with information signal back to the receiver. Since the receiver has the knowledge of the noise, the artificial noise only degrades the eavesdropper. Then, we present the form of the artificial noise for the four scenarios respectively. Thereafter, we derive the closed form expressions of secrecy capacity, and reformulate them irrelevant to the channel of the eavesdropper. This makes our scheme valid when the transmitter does not know any information about the eavesdropper. We investigate the solution for the secrecy capacity optimization problem in SISO scenario at first, which can be extended to SIMO, MISO, and MIMO systems. The optimization problems are easy to solve and have analytical solutions. Numerical results are presented to show the validation of our scheme.

Regularized Zero-Forcing Beam Design under Time-varying Channels

In this paper, an efficient beam tracking algorithm for a regularized zero-forcing (RZF) approach in slowly fading multiple-input and single-output (MISO) broadcast channels is considered. By modifying an RZF equation, an RZF beam tracking algorithm is proposed using matrix perturbation theory. The proposed algorithm utilizes both beams from the previous time step and channel difference (between the previous and current time steps) to calculate the RZF beams. The tracking performance of the proposed algorithm is analyzed in terms of the mean square error (MSE) between a tracking approach and an exact recomputing approach, and in terms of the additional MSE caused by the beam tracking error at the receiver. Numerical results show that the proposed algorithm has almost the same performance as the exact recomputing approach in terms of the sum rate.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.