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Beamforming technique can effectively improve the spectrum utilization in the multi‐antenna systems, while the dirty‐paper coding (DPC) technique can reduce the inter‐user interference. In this letter, it is aimed to maximize the weighted sum‐rate under the total power constraint in the multiple‐input‐single‐output (MISO) system with the DPC technique. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, the beamforming neural network (BFNNet) is devised by utilizing the deep learning technique and the uplink‐downlink duality and exploring the optimal solution structure, which includes the deep neural network module and the signal processing module. Simulation results show that the BFNNet can achieve near‐optimal solutions and significantly reduce computational complexity. [ABSTRACT FROM AUTHOR]

Imperfect Channel State Information at the Transmitter (CSIT) is inevitable in modern wireless communication networks, and results in severe multi-user interference in multi-antenna Broadcast Channel (BC). While the capacity of multi-antenna (Gaussian) BC with perfect CSIT is known and achieved by Dirty Paper Coding (DPC), the capacity and the capacity-achieving strategy of multi-antenna BC with imperfect CSIT remain unknown. Conventional approaches therefore rely on applying communication strategies designed for perfect CSIT to the imperfect CSIT setting. In this work, we break this conventional routine and make two major contributions. First, we show that linearly precoded Rate-Splitting (RS), relying on the split of messages into common and private parts and linear precoding at the transmitter, and successive interference cancellation at the receivers, can achieve larger rate region than DPC in multi-antenna BC with partial CSIT. Second, we propose a novel scheme, denoted as Dirty Paper Coded Rate-Splitting (DPCRS), that relies on RS to split the user messages into common and private parts, and DPC to encode the private parts. We show that the rate region of DPCRS in Multiple-Input Single-Output (MISO) BC with partial CSIT is enlarged beyond that of conventional DPC and that of linearly precoded RS. Gaining benefits from the capability of RS to partially decode the interference and partially treat interference as noise, DPCRS is less sensitive to CSIT inaccuracies, networks loads and user deployments compared with DPC and other existing transmission strategies. [ABSTRACT FROM AUTHOR]

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This review paper explores the history, cultural significance, and functional characteristics of soybean paste (doenjang) in Korean cuisine. In particular, it covers its origins, cultural importance, production methods, functionalities, and associated risks. This review delves into the nutritional and functional aspects of fermented soybean products, focusing on compounds in soybean paste linked to health benefits, such as anticancer, anti-obesity, anti-diabetic, and antithrombotic properties. The key bioactive compounds identified in soybean paste include total phenols, total flavonoids, γ-Aminobutyric acid, vitamin B group, and vitamin E. These compounds are primarily produced through microbial fermentation, which enhances their biological activity. Additionally, the review addresses potential risks such as biogenic amines, aflatoxins, Bacillus cereus contamination, and high sodium levels, and suggests methods for mitigating these risks. By synthesizing current research, this paper aims to provide a comprehensive understanding of the physiological compounds in soybean paste and their health benefits. It also highlights the need for strategies to reduce associated risks. [ABSTRACT FROM AUTHOR]

This paper aims to show how the switching frequency influences the properties of the digitally controlled voltage source inverter (VSI). The measurements of the Bode plots of the inverter are shown and discussed to present the existing signal delays and power conversion efficiency, depending on the switching/sampling frequency. Two types of controllers are presented, Single-Input–Single-Output (SISO) and Multi-Input–Single-Output (MISO), and adequate prediction units (the Smith Predictor for SISO—Coefficient Diagram Method and the full-state Luenberger Observer for MISO—Passivity Based Control) were used to compensate for the delays. It will be shown by comparing the THD of the VSI output voltage that prediction is useful with low VSI switching frequency (about 10 kHz) but is not important for the middle switching frequencies (about 25 kHz) or the high switching frequency (>50 kHz). This paper shows that increasing the switching frequency simplifies digital control without reasonably decreasing efficiency. The theoretical considerations, the Matlab/Simulink 2021b simulations, the final experimental laboratory verification are presented. [ABSTRACT FROM AUTHOR]

In this paper, we consider precoder designs for multiuser multiple-input-single-output broadcasting channels. Instead of using a traditional linear zero-forcing (ZF) precoder, we propose a generalized ZF (GZF) precoder in conjunction with successive dirty-paper coding (DPC) for data transmissions, namely, the GZF-DP precoder, where the suffix “DP” stands for “dirty-paper.” The GZF-DP precoder is designed to generate a band-shaped and lower triangular effective channel F , such that only the entries along the main diagonal and the $\nu $ first lower-diagonals can take non-zero values. Utilizing the successive DPC, the known non-causal inter-user interferences from the other (up to) $\nu $ users are canceled through successive encoding. We analyze optimal GZF-DP precoder designs both for sum-rate and minimum user-rate maximizations. Utilizing Lagrange multipliers, the optimal precoders for both cases are solved in closed-forms in relation to optimal power allocations. For the sum-rate maximization, the optimal power allocation can be found through water filling, but with modified water levels depending on the parameter $\nu $ , while for the minimum user-rate maximization that measures the quality of the service, the optimal power allocation is directly solved in closed-form, which also depends on $\nu $ . Moreover, we propose two low-complexity user-ordering algorithms for the GZF-DP precoder designs for both maximizations, respectively. We show, through numerical results, that the proposed GZF-DP precoder with a small $\nu $ value (≤ 3) renders significant rate increments compared with the previous precoder designs, such as the linear ZF and the user-grouping-based DPC precoders. [ABSTRACT FROM PUBLISHER]

We consider the beamformer design for multiple-input multiple-output (MISO) broadcast channels (MISO BCs) using zero-forcing dirty paper coding (ZF-DPC). Assuming a sum power constraint (SPC), most previously proposed beamformer designs are based on the QR decomposition (QRD), which is a natural choice to satisfy the ZF constraints. However, the optimality of the QRD-based design for ZF-DPC has remained unknown. In this paper, first, we analytically establish that the QRD-based design is indeed optimal for any performance measure under a SPC. Then, we propose an optimal beamformer design method for ZF-DPC with per-antenna power constraints (PAPCs), using a convex optimization framework. The beamformer design is first formulated as a rank-1-constrained optimization problem. Exploiting the special structure of the ZF-DPC scheme, we prove that the rank constraint can be relaxed and still provide the same solution. In addition, we propose a fast converging algorithm to the beamformer design problem, under the duality framework between the BCs and multiple access channels (MACs). More specifically, we show that a BC with ZF-DPC has the dual MAC with ZF-based successive interference cancellation (ZF-SIC). In this way, the beamformer design for ZF-DPC is transformed into a power allocation problem for ZF-SIC, which can be solved more efficiently. [ABSTRACT FROM PUBLISHER]

This paper investigates a backscatter communication (BackCom) based non‐orthogonal multiple access (NOMA) system in a multiple‐input and single‐output (MISO) scenario, where two decoding methods are deployed, including the sum‐capacity approach and QR decomposition. The goal is to maximize energy efficiency (EE) through the optimization of the beamforming matrix and the reflection coefficient of the BackCom devices. Two algorithms, Dinkelbach based on penalty semidefinite relaxation (SDR) and successive convex approximation (SCA), are proposed as high‐performance and low‐complexity solutions, respectively. Simulation results indicate that the combination of the sum‐capacity approach and Dinkelbach yields the best performance, though at the highest complexity, while the amalgamation of QR decomposition and SCA offers the lowest performance but with minimal complexity. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication networks and Internet of Things technology (IoT), higher requirements have been put forward for spectrum resource utilization and system performance. In order to further improve the utilization of spectrum resources and system performance, this paper proposes an intelligent reflecting surface (IRS)-assisted fair energy allocation algorithm for cognitive multiple-input single-output (MISO) wireless-powered networks. The goal of this paper is to maximize the minimum energy receiving power in the energy receiver, which is constrained by the signal-to-interference-plus-noise ratio (SINR) threshold of the information receiver in the secondary network, the maximum transmission power at the cognitive base station (CBS), and the interference power threshold of the secondary network on the main network. Due to the coupling between variables, this paper uses iterative optimization algorithms to optimize and solve different variables. That is, when solving the active beamforming variables, the passive beamforming variables are fixed; then, the obtained active beamforming variables are fixed, and the passive beamforming variables are solved. Through continuous iterative optimization, the system converges. The simulation results have verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has recently been considered as a potential technology for realizing ultra‐reliable and low‐latency (URLLC) in wireless networks. This paper proposes a resource optimization scheme to maximize the sum‐rate for an IRS‐assisted downlink multiuser multi‐input single‐output (MISO) URLLC system. For the perfect CSI scenario, we jointly optimize each user's block‐length and packet‐error probability, the precoding vectors at the base station (BS), and the passive beamforming with discrete phase shifts at the IRS. Given the problem's complexity, we design a computationally efficient iterative algorithm using successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to obtain a locally optimal solution. Specifically, for the imperfect CSI scenario, we construct a robust resource optimization problem model and incorporate the S‐procedure to address the impact of channel uncertainty, proposing an iterative algorithm based on the alternating optimization (AO) method to achieve a locally optimal solution. Simulation results demonstrate that: 1) An IRS equipped with a 2‐bit quantized resolution phase shifter is sufficient to achieve a system sum‐rate comparable to that of an ideal phase shifter; 2) Compared to other Baseline schemes, Algorithm 2 exhibits better robustness and superior performance gains under imperfect CSI. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation forecasting is an important research topic, aiming to mitigate variability caused by weather conditions and improve power generation planning. Climate factors, including solar irradiance, temperature, and cloud cover, influence the energy conversion achieved by PV systems. Long-term weather forecasting improves PV power generation planning, while short-term forecasting enhances control methods, such as managing ramp rates. The stochastic nature of weather variables poses a challenge for linear regression methods. Consequently, advanced, state-of-the-art machine learning (ML) approaches capable of handling non-linear data, such as long short-term memory (LSTM), have emerged. This paper introduces the implementation of a multivariate machine learning model to forecast PV power generation, considering multiple weather variables. A deep learning solution was implemented to analyze weather variables in a short time horizon. Utilizing a hidden Markov model for data preprocessing, an LSTM model was trained using the Alice Spring dataset provided by DKA Solar Center. The proposed workflow demonstrated superior performance compared to the results obtained by state-of-the-art methods, including support vector machine, radiation classification coordinate with LSTM (RCC-LSTM), and ESNCNN specifically concerning the proposed multi-input single-output LSTM model. This improvement is attributed to incorporating input features such as active power, temperature, humidity, horizontal and diffuse irradiance, and wind direction, with active power serving as the output variable. The proposed workflow achieved a mean square error (MSE) of 2.17 × 10 − 7 , a root mean square error (RMSE) of 4.65 × 10 − 4 , and a mean absolute error (MAE) of 4.04 × 10 − 4 . [ABSTRACT FROM AUTHOR]

The metaverse is emerging as a next-generation internet paradigm that will enhance human interaction and connectivity. Digital twinning, a fundamental strategy used in the metaverse, allows for the virtualization of real-world items, people, actions, and settings. A virtual world called the metaverse is built on a variety of technologies. Wireless communication is an important part of these technologies. In particular, wireless 6G communication can be essential for the growth of the metaverse. In line with the goal of achieving higher rates in the next-generation wireless network for the metaverse, in this paper, a novel conceptualization of intelligent reflecting surface (IRS)-assisted multiple-input single output-based wireless communication in physical world environments is proposed. More specifically, this paper proposes that in the physical world, the IRS-assisted communication between a communication network and users can be reflected in the metaverse through the virtual world (such as digital avatars and the virtual environment). In the simulations, the bit-error rate and spectral efficiency of the receiver terminal were performed and calculated in the metaverse engine for future consideration. [ABSTRACT FROM AUTHOR]

Wireless-based sensing of physical environments has garnered tremendous attention recently, and its applications range from intruder detection to environmental occupancy monitoring. Wi-Fi is positioned as a particularly advantageous sensing medium, due to the ubiquity of Wi-Fi-enabled devices in a more connected world. Although Wi-Fi-based sensing using Channel State Information (CSI) has shown promise, existing sensing systems commonly configure dedicated transmitters to generate packets for sensing. These dedicated transmitters substantially increase the energy requirements of Wi-Fi sensing systems, and hence there is a need for understanding how ambient transmissions from nearby Wi-Fi devices can be leveraged instead. This paper explores the potential of Wi-Fi-based sensing using CSI derived from ambient transmissions of Wi-Fi devices. We demonstrate that CSI sensing accuracy is dependent on the underlying traffic type and the Wi-Fi transceiver architecture, and that control packets yield more robust CSI than payload packets. We also show that traffic containing upload data is more suitable for human occupancy counting, using the Probability Mass Function (PMF) of CSI. We further demonstrate that multiple spatially diverse streams of Wi-Fi CSI can be combined for sensing to an accuracy of 99 % . The experimental study highlights the importance of training Wi-Fi sensing systems for multiple transmission sources to improve accuracy. This research has significant implications for the development of energy-efficient Wi-Fi sensing solutions for a range of applications. [ABSTRACT FROM AUTHOR]

In recent years, the voltage differencing differential difference amplifier (VDDDA) has been used in various analog signal processing circuit designs. A second-order multifunction filter with multiple-inputs and single-output (MISO) voltage mode using VDDDA as active elements is proposed in this paper. The structure of the proposed filter comprises two VDDDAs, two grounded capacitors, and two resisters. The proposed filter has a cascadability feature in a voltage-mode system, producing voltage input and voltage output at high and low impedance ports, respectively. It can offer responses for all-pass (AP), band-reject (BR), band-pass (BP), low-pass (LP), and high-pass (HP) filters without additional inverting and double gain amplifiers, as well as the matching conditions. Choosing the appropriate input signals provides these five filter responses in the same circuit topology. With two VDDDAs, the bias currents can be utilized to electronically tune the natural frequency (!0) independently from the quality factor (Q). Experimental results using available commercial ICs have supported the theoretical expectations and confirmed the practical operation of the proposed multifunction biquad filter. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a novel approach is introduced for the development of a power-efficient voltage differencing buffered amplifier (VDBA) by incorporating an adaptive biasing circuit. The proposed design is specifically tailored to exhibit low power consumption. The VDBA proposed in this study includes a conventional transconductance unit (TU) with an adaptive biasing and a positive feedback circuit. Incorporating adaptive bias cells and cross-coupled positive feedback circuit, the VDBA structure allows for boosted tunable gain based on the input differential voltage. The proposed VDBA shows improved transconductance gain, transient characteristics, linearity for a wide range of inputs, and reduced standby power dissipation. A mathematical formulation has been presented to define the characteristics of the proposed VDBA. To validate the proposed VDBA structure, SPICE simulations were conducted using 180 nm CMOS technology. The input linear range of the proposed VDBA is ± 250 mV. The 3 dB bandwidth of the proposed structure is 200 MHz. The THD has been found low. The Monte Carlo simulations have also been carried out and found the proposed structure is less sensitive to the different parameter variations. The slew rate of the proposed VDBA is found 26.2 V/µS. As an application example, a voltage mode universal MISO biquad filter is also proposed using the proposed structure VDBA. [ABSTRACT FROM AUTHOR]

In this paper, the problem of identifying Multiple-Input-Single-Output (MISO) systems with fractional models from noisy input-output available data is studied. The proposed idea is to use Higher-Order-Statistics (HOS), like fourth-order cumulants (foc), instead of noisy measurements. Thus, a fractional fourth-order cumulants based-simplified and refined instrumental variable algorithm (frac-foc-sriv) is first developed. Assuming that all differentiation orders are known a priori, it consists in estimating the linear coefficients of all Single-Input-Single-Output (SISO) sub-models composing the MISO model. Then, the frac-foc-sriv algorithm is combined with a nonlinear optimization technique to estimate all the parameters: coefficients and orders. The performances of the developed algorithms are analyzed using numerical examples. Thanks to fourth-order cumulants, which are insensitive to Gaussian noise, and the iterative strategy of the instrumental variable algorithm, the parameters estimation is consistent. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces a second‐order voltage‐mode (VM) multiple‐input single‐output (MISO) universal filter and a second‐order current‐mode (CM) single‐input multiple‐output (SIMO) multifunction filter. The introduced VM MISO universal filter includes a single current feedback operational amplifier (CFOA) and a single voltage follower as active elements, while the introduced CM SIMO multifunction filter contains two CFOAs. Both introduced circuits are orthogonally controllable. In other words, the quality factors can be controlled through a single resistor without affecting the resonance frequencies. Both circuits have low sensitivities and no passive element‐matching conditions. The introduced VM MISO universal filter has a low output impedance. Moreover, to extract the output currents of the proposed CM SIMO from high impedance terminals, second‐generation current conveyor‐based version of the circuit is introduced. Simulations are made through the PSPICE program, where 0.13‐μm IBM CMOS technology parameters are utilized. DC supply voltages are selected as ±0.9 V. In the experiments, AD844s are used, and DC supply voltages are chosen as ±7.5 V. [ABSTRACT FROM AUTHOR]

Convex hull pricing has been introduced recently to increase transparency and reduce uplift payments for the U.S. wholesale markets. A convex primal formulation approach is one of the most efficient methods to obtain a high-quality convex hull price. Even though significant progress has been made, the co-optimization of energy and ancillary services is much more challenging due to the discrete nature in formulating ancillary services, for example, the regulating reserve commitment variables, which are binary introduced by some Independent System Operators (ISOs), such as Midcontinent ISO (MISO), to capture the special characteristics of certain generators, such as combined-cycle units. In this paper, we propose convex primal formulations for convex hull pricing considering regulating, spinning, and online/offline-supplemental reserves, in which the regulating reserve commitment variables are considered. Accordingly, we can solve a linear program, instead of a mixed-integer program, which is much harder to solve, to derive the minimum uplift payments and exact convex hull price for the case without general ramping constraints. For the case with general ramping constraints, an upper bound of the minimum uplift payment can also be derived. The final computational experiments on MISO cases verify the effectiveness of our approach in solution quality and computational time. [ABSTRACT FROM AUTHOR]

Massive Internet of Things (IoT) systems are one of the main drivers of new wireless standards. It's necessary that IoT devices cost as little as possible, and to minimize energy consumption. In this paper, new solutions for an extended battery life of IoT devices are proposed. The new signal processing schemes, for massive IoT uplink communication, combine spatial modulation (SM), relaying, and virtual multiple-input multiple-output (MIMO) techniques to improve energy efficiency, in the sense that more bits can be transmitted per same unit of time/energy/spectral resources. SM is particularly suitable because it allows indirect transmission of bits, i.e., it isn't necessary to transmit them from antennas. The larger number of transmitting antennas at IoT device, of which only one is active, provides that more bits can be used for selecting active antenna. In this paper, the new uplink transmission schemes, that enable virtual multiple-input single-output (MISO)/space–time block codes (STBC) channels between IoT devices and radio access point (AP), are proposed. The simulation results show that it's possible to find a compromise solution between bit error probability (BER) and the achieved energy savings, i.e., a network management in terms of energy and reliability can be implemented. [ABSTRACT FROM AUTHOR]

Midcontinent Independent System Operator (MISO) plans to introduce 30-minute short-term reserve (STR) to address 30 minutes system flexibility needs. Transmission bottlenecks in the power system may inhibit the deliverability of STR. Insufficient deliverable reserve may require additional operator manual adjustments, which may be uneconomic and distort the market price signal. To improve reserve deliverability, event-based zonal STR clearing model and nodal STR clearing model are proposed to distribute reserve across the grid. This paper compares reserve deliverability and market clearing prices between the zonal STR model and the nodal STR model. A new design on penalty function is proposed in this paper to reflect the true value of security constraints for reserve deployment with the consideration of multiple events. Analysis of MISO test cases shows that the zonal approach is imprecise and may not guarantee the actual post-event flows within the physical limits. Nodal STR model can improve the reserve deliverability, market efficiency, and price signal. [ABSTRACT FROM AUTHOR]

In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the same performance as computationally complex dirty paper coding, where the quasi-degradation condition is satisfied, conditioned on the users’ channels fall in the quasi-degradation region. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access. [ABSTRACT FROM AUTHOR]

Fuzzy inference engine, as one of the most important components of fuzzy systems, can obtain some meaningful outputs from fuzzy sets on input space and fuzzy rule base using fuzzy logic inference methods. In multi-input–single-output (MISO) fuzzy systems, in order to enhance the computational efficiency of fuzzy inference engine, this paper aims mainly to investigate three MISO fuzzy hierarchical inference engines employed fuzzy implications satisfying the law of importation with aggregation functions (LIA). We firstly find some aggregation functions for well-known fuzzy implications such that they satisfy LIA. The fuzzy implication satisfying LIA with respect to a given aggregation function is then characterized. Finally, three fuzzy hierarchical inference engines in MISO fuzzy systems are constructed according to aforementioned theoretical developments. Three examples are also provided to illustrate our theoretical arguments. [ABSTRACT FROM AUTHOR]

In this paper, an IRS-aided integrated sensing and communications (ISAC) system operating in the terahertz (THz) band is proposed to maximize the system capacity. Transmit beamforming and phase-shift design are transformed into a universal optimization problem with ergodic constraints. Then the joint optimization of transmit beamforming and phase-shift design is achieved by gradient-based, primal-dual proximal policy optimization (PPO) in the multi-user multiple-input single-output (MISO) scenario. Specifically, the actor part generates continuous transmit beamforming and the critic part takes charge of discrete phase shift design. Based on the MISO scenario, we investigate a distributed PPO (DPPO) framework with the concept of multi-threading learning in the multi-user multiple-input multiple-output (MIMO) scenario. Simulation results demonstrate the effectiveness of the primal-dual PPO algorithm and its multi-threading version in terms of transmit beamforming and phase-shift design. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) assisted non-orthogonal multiple access (NOMA) transmission can effectively improve the energy/spectrum efficiency in wireless networks. This paper designs a low-complexity scheme to achieve the balanced tradeoff between the sum-rate and power consumption in a RIS-assisted NOMA system, which can be measured by energy efficiency. To solve the formulated problem effectively, the original non-convex problem is first decomposed into two subproblems, i.e., beamforming optimization and phase shift optimization. Alternating optimization is proposed to solve these two subproblems iteratively. In particular, successive convex approximation (SCA) is utilized to convert the non-convex constraints to convex ones. The provided simulation results demonstrate that the proposed scheme can achieve superior performance on energy efficiency compared to the random phase shifts and orthogonal multiple access (OMA) schemes. [ABSTRACT FROM AUTHOR]

To increase market transparency, independent system operators (ISOs) have been working on minimizing uplift payments based on convex hull pricing theorems. Along this direction, in this paper, based on the analysis of specific generator features in the Midcontinent ISO (MISO) system, besides reviewing integral formulations for several special cases, we develop two integral formulations of a single generator that can capture these features. We then build a compact convex hull pricing formulation based on these integral formulations, which guarantees to obtain an exact convex hull price by solving a large-scale linear program. Meanwhile, to address the computational challenges caused by the large-scale MISO system, we propose innovative iterative algorithms with convergence properties, plus a complementary algorithm, to obtain an approximated convex hull price. The case studies on MISO instances with and without transmission constraints indicate that our algorithms lead to an exact convex hull price for all numerical studies and, the solutions can be obtained within 20 minutes. [ABSTRACT FROM AUTHOR]

This paper describes the design and evaluation of a controller for multi-input single-output (MISO) propofol-remifentanil anesthesia, guided by feedback from a measure of depth-of-hypnosis (DOH). DOH monitors are commonly used in clinical practice to guide anesthetic dosing, however, there is currently no widely accepted nociception monitor to guide remifentanil (analgesic) infusion. Variability in the DOH measure has been associated with insufficient analgesia, and feasibility of closed-loop control of both propofol and remifentanil infusion using DOH feedback has been demonstrated. However, DOH variability does not provide a measure of analgesia in the absence of stimulation. Consequently, control of the opioid-hypnotic balance is lost in control systems relying on DOH feedback alone. The proposed design overcomes this limitation by introducing a second, indirect control objective. This paper defines clinical design specifications to achieve adequate anesthesia in a wide range of clinical cases, proposes a modification of the habituating control framework, and presents methods to translate the clinical objectives into control objectives within this framework. The developed design methodology provides a controller that: 1) meets the clinical objectives; 2) is robust to interpatient variability, both in single-input single-output and MISO operation; 3) is robust to nonlinear drug interactions; 4) gives the user control of the opioid-hypnotic balance in the absence of stimulation and in the presence of input saturation; and 5) improves disturbance rejection following nociceptive stimulation. The MISO system performed as designed in 80 clinical cases. [ABSTRACT FROM AUTHOR]

This paper considers the downlink channel of multicell multiuser multiple-input single-output systems with arbitrary architecture. We aim to maximize the minimum weighted signal-to-interference-plus-noise ratio (SINR) through user-base station (BS) association, coordinated beamforming among BSs, and power control subject to per BS power constraints. The problem is known to be NP-hard. In the high-SNR regime, where the performance is interference-limited, we globally optimize the upper bound, which is achieved asymptotically, of the balanced SINR. In the low-SNR regime, where the performance is restricted by the tightest per BS power constraint, we balance the transmit power using relaxed integer programming and group sparse optimization techniques. The two goals can be integrated to achieve a universally better performance. From the perspective of optimization methods, the corresponding algorithms are based on Lagrangian relaxation and are guaranteed to converge. Simulation results show that the proposed algorithms outperform the existing algorithms designed for the downlink single-input single-output systems. [ABSTRACT FROM PUBLISHER]

A zero forcing (ZF) assisted single layer beamforming is developed for spatial modulation (SM) and space shift keying (SSK) modulation multiple input multiple output (MIMO) systems, termed as SZF-SM/SZF-SSK, to achieve both transmit beamforming and receive diversity gain in this paper. Specifically, the proposed SZF-SM/SZF-SSK maps part of the bits to spatial domain, simultaneously forming a ZF precoding assisted single layer data both in multiple input single output (MISO) and MIMO systems, that differs greatly from conventional ZF beamforming assisted MIMO systems. With MISO configuration, the ZF precoder of SZF-SM/SZF-SSK converges to the maximum ratio transmission (MRT), achieving only the transmit beamforming gain. With MIMO configuration, SZF-SM/SZF-SSK tranceiver could be further modified along with the MIMO channel to achieve single layer transmission and joint detection, attaining both the transmit beamforming and receive diversity gain in SM-MIMO system. The theoretical analysis and the simulation results of the proposed SZF-SM/SZF-SSK with MISO and MIMO configurations are presented in this paper respectively, which show an attractive performance gain over the conventional SM/SSK. Compared with the conventional ZF beamforming assisted MIMO systems and some other CSI assisted SM-MIMO schemes, the proposed scheme exhibits better performance, especially under highly correlated MIMO channels, rendering a new and practical way to achieve low rank MIMO transmission with good performance and high data rate. [ABSTRACT FROM AUTHOR]

In this paper, we investigate joint retrospective interference alignment (RIA) and caching schemes for multiple-input single-output (MISO) broadcast channels (BC) with delayed channel state information at the transmitter (CSIT). By exploiting the cached content, we propose one sophisticated cache-aided RIA scheme to cancel the interference. Importantly, our scheme takes the number of symbols in a packet into account, as different number of symbols in a packet result in different system configuration, which may have significant impact on the design of the transmission scheme and achievable degrees of freedom (DoF). Furthermore, we present two theorems to quantify the achievable sum DoF of the proposed cached-aided RIA scheme for different system configuration. Moreover, the system configuration of the existing cache-aided RIA scheme with mixed CSIT can be treated as a special case in our paper. And also our scheme can simultaneously support more users than the existing cache-aided RIA scheme with mixed CSIT to invoke zero forcing (ZF) precoding, which limits the number of users simultaneously supported. [ABSTRACT FROM AUTHOR]

The core reason for the nutritional richness and special flavour composition of traditional Chinese fermented soybean products lies in the diversity of microbial structures and their metabolites, which include a variety of potential probiotics. However, relatively little mainstream research has been conducted on these microorganisms, especially in regions with unrelated product dietary habits, limiting the high-value utilisation of the characteristic microbial resources. In order to enhance the efficient use and functional exploitation of microbial resources in Chinese traditional fermented soybean products, this paper investigated and reviewed a variety of products such as soybean paste, soy sauce, douchi, sufu, and fermented soybean milk, and carried out a detailed analysis of the sources, flavour characteristics, and potential functionality of microorganisms therein, which elucidated that the microorganisms in Chinese traditional fermented soybean products have the potential to be used as functional probiotics, with a view to contributing to the probiotic resources exploitation, novel food development, and the development of new food products, as well as the development of new foods. [ABSTRACT FROM AUTHOR]

This paper proposes an architecture based on Intelligent Reflecting Surfaces (IRSs) to improve the performance of future cellular networks. Specifically, we investigate the use of IRSs in combination with statistical Channel State Information (CSI) to enhance the coverage of Base Stations (BSs) in Multiple-Input Single-Output (MISO) systems. Furthermore, we exploit IRSs to reduce the complexity of the proposed architecture, and therefore the total cost, by reducing the number of required antennas at the transmitters (i.e., BSs). At first, we consider a Rayleigh fading channel between the transmitter and the receiver, and we assume the existence of a Line of Sight (LoS) between the BS and the IRS, as well as between the IRS and the destination. In the second part, we investigate the case of a Single-User Multiple-Input Single-Output (SU-MISO) system, where we study the benefits of IRSs in terms of coverage of the BS; then, we formulate a problem for a Multi-User Multiple-Input Single-Output (MU-MISO) system where the IRS is considered as a block of resources that can assist a certain number of users. The problem of managing the IRS resources is formulated as a nonlinear integer problem. We solve the optimization problem using an exhaustive search and propose two low-complexity heuristic algorithms. The performance of the system is evaluated with respect to a variable number of users, the position of the IRS, the required SNR, and the size of the cell. Simulation results corroborate the proposed approach and show that the introduction of the IRS in the network architecture enhances the overall performance of the network, extends the coverage area, enhances users' satisfaction, and improves the SNR value, while optimizing the required number of antennas at the BS. [ABSTRACT FROM AUTHOR]

Pumped storage hydro units (PSHU) can provide flexibility to power systems. This becomes particularly valuable in recent years with the increasing shares of intermittent renewable resources. However, due to emphasis on thermal generation in the current market practices, the flexibility from PSHUs has not been fully explored and utilized. This paper proposes a configuration based pumped storage hydro (PSH) model for the day-ahead market, in order to enhance the use of PSH resources in the system. A strategic design of incorporating and fully optimizing PSHUs in the day-ahead market is presented. We show the compactness of the proposed model. Numerical studies are presented in an illustrative test system and the Midcontinent Independent System Operator (MISO) system. [ABSTRACT FROM AUTHOR]

In this paper, the optimal beamforming problem of multi-input single-output (MISO) cognitive radio (CR) downlink networks with simultaneous wireless information and power transfer is studied. Due to the nonconvexity of the objective function, the considered nonconvex optimization problem is firstly transformed to an equivalent subtraction problem and then an approximated convex optimization problem is obtained by using the successive convex approximation (SCA). When the instantaneous channel state information (CSI) of the eavesdropping link is unknown to the legitimate transmitter, another interruption-constrained energy efficiency optimization problem is proposed and the Bernstein-type inequality (BTI) is used to conservatively approximate the probability constraint. The paper proposes a two-level iterative algorithm based on Dinkelbach to find the optimal solution of the EE maximization problem. Numerical results validate the effectiveness and convergence of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an energy efficiency (EE) maximization problem in multiple input single output broadcast channels. The optimization problem in this system model is difficult to solve in general, since it is in non-convex fractional form. Hence, conventional algorithms have addressed the problem in an iterative manner for each channel realization, which leads to high computational complexity. To tackle this complexity issue, we propose a new simple method by utilizing the fact that EE maximization becomes identical to spectral efficiency (SE) maximization for the region of the power below a certain transmit power termed as saturation power. In order to calculate the saturation power, we first introduce upper and lower bounds of the EE performance by adopting a maximal ratio transmission beamforming strategy. Then, we propose an efficient way to compute the saturation power for the EE maximization problem. Once we determine the saturation power in advance, we can transform the EE maximization problem into a simplified sub-optimal EE problem, which can be solved by the SE maximization schemes with low complexity. The derived saturation power is parameterized by employing random matrix theory, which relies only on the second-order channel statistics. Hence, this approach needs much lower computational complexity compared with a conventional scheme, which requires instantaneous channel state information. Numerical results validate that the proposed algorithm achieves near optimal EE performance with significantly reduced complexity. [ABSTRACT FROM PUBLISHER]

In this paper, we address identification of a class of multi-input single-output (MISO) linear time-invariant (LTI) discrete-time systems, where the MISO system is composed of several single-input single-output (SISO) LTI systems and only the sum of the output of each SISO system can be measured. By exciting one SISO system and employing periodic inputs to the remaining SISO systems, we decompose the MISO system into the excited SISO system where its input and output can be calculated from the measured data. As such, the original parameters for each SISO system can be estimated using the well-established identification techniques. The proposed method using periodic inputs is also extended to identification beyond the Nyquist frequency. Both simulation and experimental results on identification of actuators in the dual-stage hard disk drive are carried out to demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper deals with system identification for continuous-time multiple-input single-output (MISO) fractional differentiation models. An output error optimization algorithm is proposed for estimating all parameters, namely the coefficients and the differentiation orders. Given the high number of parameters to be estimated, the output error method can converge to a local minimum. Therefore, an initialization procedure is proposed to help the convergence to the optimum by using three variants of the algorithm. Moreover, a new definition of structured-commensurability (or S-commensurability) has been introduced to cope with the differentiation order estimation. First, a global S-commensurate order is estimated for all subsystems. Then, local S-commensurate orders are estimated (one for each subsystem). Finally the S-commensurability constraint being released, all differentiation orders are further adjusted. Estimating a global S-commensurate order greatly reduces the number of parameters and helps initializing the second variant, where local S-commensurate orders are estimated which, in turn, are used as a good initial hit for the last variant. It is known that such an initialization procedure progressively increases the number of parameters and provides good efficiency of the optimization algorithm. Monte Carlo simulation analysis are provided to evaluate the performances of this algorithm. [ABSTRACT FROM AUTHOR]

Cache-enabled small base stations (SBS) are capable of relieving the heavy burden of the backhaul link and reducing the transmission latency. The hit probability depends on the coverage probability and caching placement probabilities. However, the interference in the small-cell networks may significantly degrade the coverage probability. In this paper, for MIMO small-cell networks consisting of SBS and users, where both of them are equipped with multiple antennas, a joint interference alignment (IA) and probabilistic caching (JIA-ProbC) scheme is proposed. Using tools from stochastic geometry, the $K$ -th order Voronoi cells are constructed to form clusters, where $K$ SBSs cooperatively serve users within each of the $K$ -th order Voronoi cells. Then, the IA scheme for MIMO interference channel (IC) is employed to cancel the intra-cluster interference within each $K$ -th order Voronoi cell. By exploiting the advantage of multiples antennas at users, the IA scheme can simultaneously support more users interference-free than both the zero forcing (ZF) based interference cancellation scheme for MISO systems and SISO systems without interference management, as more interference can be canceled. Furthermore, the coverage probability is analytically approximated by the a closed-form expression. Moreover, the optimal caching placement probability is analytically derived. Numerical Simulation results show that the proposed JIA-ProbC can significantly outperform the existing joint ZF and probabilistic caching (JZF-ProbC) scheme for MISO systems and SISO probabilistic caching (SISO-ProbC) scheme as well as the joint IA and most popular caching (JIA-MPC) caching scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate resource allocation for IRS-assisted green multiuser multiple-input single-output (MISO) systems. To minimize the total transmit power, both the beamforming vectors at the access point (AP) and the phase shifts at multiple IRSs are jointly optimized, while taking into account the minimum required quality-of-service (QoS) of multiple users. First, two novel algorithms, namely a penalty-based alternating minimization (AltMin) algorithm and an inner approximation (IA) algorithm, are developed to tackle the non-convexity of the formulated optimization problem when perfect channel state information (CSI) is available. Existing designs employ semidefinite relaxation in AltMin-based algorithms, which, however, cannot ensure convergence. In contrast, the proposed penalty-based AltMin and IA algorithms are guaranteed to converge to a stationary point and a Karush-Kuhn-Tucker (KKT) solution of the design problem, respectively. Second, the impact of imperfect knowledge of the CSI of the channels between the AP and the users is investigated. To this end, a non-convex robust optimization problem is formulated and the penalty-based AltMin algorithm is extended to obtain a stationary solution. Simulation results reveal a key trade-off between the speed of convergence and the achievable total transmit power for the two proposed algorithms. In addition, we show that the proposed algorithms can significantly reduce the total transmit power at the AP compared to various baseline schemes and that the optimal numbers of transmit antennas and IRS reflecting elements, which maximize the system energy efficiency of the considered system, are finite. [ABSTRACT FROM AUTHOR]

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This paper presents a comparison of Isolated (Flyback) and non-Isolated (Buck) multiple input-single output (MISO) DC-DC converters. The analysis of DC-DC converters is based on pulsed voltage source cells (PVSC). The modeling of both converter types is detailed through their mathematical models and electrical simulations using Matlab/Simulink and PSIM. The comparison focuses on the sizing parameters, non-ideal output characteristics and efficiency. Results show that the output voltage of the MISO Buck converter exhibits a linear dependence on the duty cycles control signal and has slightly higher efficiency than the Flyback converter. To validate the operation of both converters, a scenario with two inputs (low-power hydroelectric and photovoltaic voltage sources) is considered. The modeling and control of both source systems are detailed and the MISO converter performance response is evaluated under sources changes and efficiency point of view. [ABSTRACT FROM AUTHOR]

Combined with reconfigurable intelligent surfaces (RISs), visible light communications (VLCs) can increase the communication performance to a great degree. However, the research into RIS-aided VLC systems has mainly focused on mirror array-based RISs deployed on the wall while neglecting liquid crystal (LC)-based RISs in VLC receivers. With the development of advanced materials, the LC RIS has been gradually attracting attention from researchers. Inspired by the current research into the LC RIS, the applications of the LC RIS in multiple-input single-output (MISO) VLC systems are investigated in this paper. We formulate an optimization problem with asymptotic capacity maximization as the objective function and the refractive index of the LC RIS as the independent variable. As for this nonconvex optimization problem, we propose the particle swarm optimization (PSO) algorithm to determine the configuration of parameters for the LC RIS. The simulation results indicate that the employment of the LC RIS in VLC receivers can raise the communication performance of the MISO-VLC systems; meanwhile, the proposed algorithm is an effective way to deal with the optimization problems for LC RIS-based MISO-VLC systems when compared with the exhaustive search method and a baseline scheme. The LC RIS is also expected to solve the dead zone problem in traditional VLC systems. [ABSTRACT FROM AUTHOR]

A tradeoff between sum rate and fairness for multiple-input single-output (MISO) broadcast communication employing dirty paper coding or zero-forcing dirty paper coding at physical layer is investigated in this paper. The tradeoff is based on a new design objective termed “tri-stage” approach as well as a new $\ell _1$ -based fairness measure that is much more robust than the well-known Jain's index for comparing fairness levels achieved by various design objectives at a much finer resolution in high SNR regime. The newly proposed tri-stage design also introduces a new concept of statistical power allocation that randomly allocates power to users based on an optimal probability distribution derived from the tradeoff between sum rate and fairness. Simulation results show that the proposed approach can simultaneously achieve a larger sum rate and better fairness than the reputable proportional fairness criterion. A performance upper bound is also given in the paper to show the excellent performance of the proposed approach at moderate and high SNR regimes as well as some potential for further improvement in low SNR regime. [ABSTRACT FROM AUTHOR]

Multiple Inputs Multiple Output (MIMO) is a wireless technology combined with Orthogonal Frequency Division Multiplexing (OFDM) to ensure that the signals are transmitted orthogonally with each other. It is the continuous growth and improvement of the communication environment. It provides reliability, the highest throughput, data throughput, reliable high data rate, high spectral efficiency, high data rate, and resource allocation. The main purpose of this paper is to provide an overview of the fundamentals of MIMO-OFDM channel techniques. [ABSTRACT FROM AUTHOR]

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramér-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a max–min fairness optimization scheme for a downlink multiuser multiple-input single-output (MISO) ultra-reliable and low-latency communication (URLLC) system assisted by an intelligence reflecting surface (IRS). In particular, we formulate a max–min fairness problem to jointly optimize the active beamforming vector at the base station (BS) and the passive beamforming vector at the IRS under the perfect channel state information (CSI). This problem is a non-convex optimization problem with highly coupled variables, making it challenging to obtain the global optimal solution. Subsequently, we propose a computationally-efficient iterative algorithm to obtain a suboptimal solution to this problem. In each iteration, we adopt methods such as the successive convex approximation (SCA) method, semi-positive definite relaxation (SDR) technology, and alternating optimization (AO) method to handle the internal optimization problem. Our simulation results reveal the following: (1) the proposed scheme provides a URLLC rate that is quite close to the Shannon rate, while also exhibiting low computational complexity; (2) the proposed scheme outperforms the random IRS scheme with a performance gain of up to 55%; (3) compare with conventional MISO systems without IRS, the proposed scheme offers a significant performance gain. [ABSTRACT FROM AUTHOR]

With the increasing number of Internet of Things (IoT), Industry 4.0 (I4.0), and mobile devices, it can be expected that base stations will have to serve more and more clients with a limited number of antennas. For their broadcast channels, nonorthogonal multiple access (NOMA) and blind interference alignment (BIA) are two efficient and commonly adopted transmission schemes. This paper conducts a comparison study on these techniques on a 3-user 2 × 1 multiple-input single-output (MISO) broadcast channel with a limited number of transmit antennas. Specifically, space-time block coding based NOMA (STBC-NOMA) and NOMA-assisted beamforming (NOMA-BF) are compared with BIA. Both perfect and imperfect successive interference cancellation (SIC) have been considered for NOMA-based schemes, and the theoretical achievable rates of all schemes have been derived. Furthermore, with a given fairness constraint among end users, the power allocation (PA) problems have been solved for cases when accurate channel state information is available at the transmitter (CSIT) as well as when only path loss information is available. Numerical results show the following: (1) none of the schemes under this study can always outperform the others under different SNR regions. (2) With imperfect SIC, NOMA-BF, and STBC-NOMA both suffer from a significant performance loss under a high SNR condition. (3) Fairness PA with only path loss information provides similar performance as that with perfect CSIT, thus partial CSIT is adequate for system or scheme designs in practice. [ABSTRACT FROM AUTHOR]

This paper considers an intelligent reflecting surface (IRS) aided multiple-input single-output communication system, where statistical channel state information (CSI) is exploited for transmit beam-forming and IRS beamforming. A tight upper bound is derived for the ergodic capacity of the system. Based on which, the joint optimization of transmit beam and IRS beam are studied. Depending on whether a line-of-sight path exists between the access point and user, two different cases, namely, Rician fading and Rayleigh fading, are separately treated. Specifically, for the Rician fading case, an iterative algorithm is proposed, which is guaranteed to converge. For the Rayleigh fading case, closed-form designs are obtained for the transmit beam and IRS beam. Simulation results show the proposed beamforming scheme achieves similar performance as the benchmark algorithm requiring instantaneous CSI. [ABSTRACT FROM AUTHOR]

In this paper, we propose an imperfect quantized feedback-based diagonal precoding scheme for generalized orthogonal space-time block codes in an $N \times 1$ multiple-input single-output (MISO) wireless communication system employing $M$ -QAM constellation. It is well known that a feedback-based precoding scheme aids channel adaptive signaling in wireless communication system and capitulates large improvements in almost all performance metrics. However, a practical feedback link is susceptible to error due to wireless channel fading and leads to wrong precoder selection. Therefore, a signal-to-noise ratio (SNR) adaptive error-tolerant weighting scheme is introduced which provides significant gain in comparison with another proposed arbitrarily fixed SNR error-tolerant weighting scheme. At first, a tight approximate closed-form expression of average symbol-error-rate (SER) and an exact analytical expression of outage probability are derived under imperfect feedback information with the help of order statistics. By minimizing the derived average SER, optimized transmit weights for the diagonal precoding matrix (based on feedback bits) are obtained. Later, a closed-form expression of the ergodic capacity with erroneous feedback bits for arbitrary MISO system is also provided by using the moment generating function approach. Further, numerical results show that the considered error-tolerant schemes outperform the uniform power allocation scheme in term of all the investigated performance metrics. [ABSTRACT FROM AUTHOR]