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In this article, we propose new formulations of max-sum and max-min dispersion problems that enable solutions via the Grover adaptive search (GAS) quantum algorithm, offering quadratic speedup. Dispersion problems are combinatorial optimization problems classified as NP-hard, which appear often in coding theory and wireless communications applications involving optimal codebook design. In turn, GAS is a quantum exhaustive search algorithm that can be used to implement full-fledged maximum-likelihood optimal solutions. In conventional naive formulations, however, it is typical to rely on a binary vector spaces, resulting in search space sizes prohibitive even for GAS. To circumvent this challenge, we instead formulate the search of optimal dispersion problem over Dicke states, an equal superposition of binary vectors with equal Hamming weights, which significantly reduces the search space leading to a simplification of the quantum circuit via the elimination of penalty terms. In addition, we propose a method to replace distance coefficients with their ranks, contributing to the reduction of the number of qubits. Our analysis demonstrates that as a result of the proposed techniques, a reduction in query complexity compared to the conventional GAS using the Hadamard transform is achieved, enhancing the feasibility of the quantum-based solution of the dispersion problem.

Maximum-likelihood multiuser detection incurs a large computational complexity, and its low-complexity detection scheme suffers from a performance loss, where this tradeoff is inevitable and inherent in a classical computer. In this paper, we use the Grover adaptive search (GAS) to break the tradeoff, which is a quantum exhaustive search algorithm guaranteed to obtain the optimal solution, achieving a quadratic speedup. Specifically, we design two specific parameters of GAS to achieve the optimal performance with a reduced complexity: the initial threshold and the number of Grover rotations. The initial threshold of GAS can be optimized using a solution of semi-definite programming, and it is possible to calculate the distribution of the number of solutions smaller than the initial threshold in advance, which depends on instantaneous channel coefficients. In addition, we analyze the number of quantum gates required for GAS and show that the gate count can be reduced by bypassing the higher-order terms in the objective function, leading to a reduced circuit runtime. Our analysis and simulation results demonstrate that the proposed approach achieves the same performance as the optimal maximum-likelihood detection while reducing the query complexity of GAS, implying that the large constant overhead of quadratic speedup can be further reduced.

Grover adaptive search (GAS) is a quantum exhaustive search algorithm designed to solve binary optimization problems. In this article, we propose higher order binary formulations that can simultaneously reduce the numbers of qubits and gates required for GAS. Specifically, we consider two novel strategies: one that reduces the number of gates through polynomial factorization, and the other that halves the order of the objective function, subsequently decreasing circuit runtime and implementation cost. Our analysis demonstrates that the proposed higher order formulations improve the convergence performance of GAS by reducing both the search space size and the number of quantum gates. Our strategies are also beneficial for general combinatorial optimization problems using one-hot encoding.

Facial skin blood flow (SkBF) has attracted attention as an autonomic indicator because it influences facial colour, which informs others of emotional states, and facial temperature related to social anxiety. Previous studies have examined the facial SkBF in people experiencing emotions; however, facial SkBF changes in the observers of emotions are poorly understood. Our study clarified facial SkBF changes related to observing others’ emotions by comparing the changes with other physiological indices. Thirty healthy participants (24 females; mean age: 22.17) observed six types of facial expressions (neutral, angry, and embarrassed expressions with and without facial blushing) and rated the emotional intensity of the other person. We measured their facial SkBF, finger SkBF, and cardiac RR interval as they made their observations. Facial SkBF generally decreased in relation to observing emotional faces (angry and embarrassed faces) and significantly decreased for angry expressions with blushing. None of the participants noticed blushing of facial stimuli. For the RR interval and finger SkBF, there was no variation depending on the observed facial expressions, although there was a general increase related to observation. These results indicated that facial SkBF is sensitive and reactive to emotional faces—especially angry faces with blushing— compared with other autonomic indices. The facial SkBF changes were not related to either RR interval changes or the intensity rating, suggesting that facial SkBF changes may be caused by vasoconstriction and have potential functions for our emotions. The decrease in facial SkBF may have a role in calming observers by preventing them from adopting the same emotional state as a person with intense anger. These findings clarify daily facial SkBF fluctuations and their relationship with our emotional processing in interpersonal situations.

In this article, we propose a novel method of formulating an NP-hard wireless channel assignment problem as a higher-order unconstrained binary optimization (HUBO), where the Grover adaptive search (GAS) is used to provide a quadratic speedup for solving the problem. The conventional method relies on a one-hot encoding of the channel indices, resulting in a quadratic formulation. By contrast, we conceive ascending and descending binary encodings of the channel indices, construct a specific quantum circuit, and derive the exact numbers of qubits and gates required by GAS. Our analysis clarifies that the proposed HUBO formulation significantly reduces the number of qubits and the query complexity compared with the conventional quadratic formulation. This advantage is achieved at the cost of an increased number of quantum gates, which we demonstrate can be reduced by our proposed descending binary encoding.

Abstract In the IEEE 802.11 specifications, a wireless fidelity (Wi‐Fi) access point (AP) broadcasts a beacon frame every 0.1024 s, and the received signal strength indicator (RSSI) is measured at a user terminal. RSSI is used as a metric for AP selection, but it does unlikely correlate with the actual quality of service, as has been pointed out in previous literature. In this letter, a simple metric is proposed that correlates with the effective throughput offered by a public Wi‐Fi AP. Specifically, the number of beacon frames within a time window is counted instead of using RSSI. The proposed metric can be easily calculated as it does not rely on advanced techniques such as machine learning. In a realistic IEEE 802.11a/n/ac network, the correlation coefficient between the proposed metric and the effective total throughput is 0.874 in the time window of 1 s, which is higher than a conventional metric, indicating the potential for low‐latency identification of whether a public Wi‐Fi AP can provide good throughput.

This paper presents a quantum-assisted index modulation for next-generation IoT wireless networks. The NP-hard index selection problem is first formulated by a quadratic unconstrained binary optimization (QUBO) problem consisting of constraints of feasible solutions. To minimize the number of qubits required for a quantum circuit, this formulation is then simplified by a dictionary-based approach that partially exploits a classical computer. For both formulations, the numbers of required qubits and non-zero elements in QUBO matrices are analyzed algebraically, and found to be in close agreement with the actual measurement. It is observed that the Grover adaptive search can provide the quantum speedup for the index selection problem. This promising result implies that the on-off structure of index modulation is suitable for quantum computation, and future fault-tolerant quantum computers may be useful for obtaining high-performance index activation patterns.

In this paper, we propose a differential multiple-input multiple-output (MIMO) scheme based on the novel concept of chaos-based time-varying unitary matrices to demonstrate—for the first time in the literature—the ability of differential encoding in achieving practical physical layer security even without the need for using channel estimation. In the proposed scheme, an erroneous secret key, which is extracted from the wireless nature, is used to initialize a chaos sequence that is responsible for generating artificially time-varying unitary matrices capable of obfuscating the transmitted data symbols from illegitimate eavesdroppers. Contrary to conventional studies, the key agreement ratio in this study is assumed to be imperfect, which is often true and very realistic in high-mobility scenarios. Following this, we conceive a new calibration algorithm for reconciling the chaotic sequence generated at the legitimate parties, thus making this calibration algorithm a unique, novel solution to the key sharing problem of conventional chaos-based communication techniques, which has been overlooked over the past few decades. It is found out that differential encoding obviates additional complexity and insecurity in dealing with channel estimation, whereas an eavesdropper must tackle the complicated differentially encoded patterns, which have an exponentially increasing complexity order. In addition, the obtained simulation results demonstrate that the proposed scheme can outperform conventional chaos-based MIMO schemes that assume perfect channel knowledge.

Sixty years of coherent versus non-coherent tradeoff as well as the twenty years of coherent versus non-coherent tradeoff in Multiple-Input Multiple-Output (MIMO) systems are surveyed. Furthermore, the advantages of adaptivity are discussed. More explicitly, in order to support the diverse communication requirements of different applications in a unified platform, the 5G New Radio (NR) offers unprecendented adaptivity, abeit at the cost of a substantial amount of signalling overhead that consumes both power and the valuable spectral resources. Striking a beneficial coherent versus non-coherent tradeoff is capable of reducing the pilot overheads of channel estimation, whilst relying on low-complexity detectors, especially in high-mobility scenarios. Furthermore, since energy-efficiency is of salient importance both in the operational and future networks, following the powerful Index Modulation (IM) pholosophy, we conceive a holistic adaptive pholosophy striking the most appropriate coherent/non-coherent, single-/multiple-antenna and diversity/multiplexing tradeoffs, where the number of RF chains, the Peak-to-Average Power Ratio (PAPR) of signal transmission and the maximum amount of interference tolerated by signal detection are all taken into account. We demonstrate that this intelligent tripple-fold adaptivity offers significant benefits in next-generation applications of mmWave and Terahertz solutions, in space-air-ground integrated networks, in full-duplex techniques and in other sophisticated channel coding assisted system designs, where powerful machine learning algorithms are expected to make autonomous decisions concerning the best mode of operation with minimal human intervention.

In this paper, we propose, implement, and evaluate a simple yet powerful estimation method for the quality of service of a large-scale Wi-Fi network, which is achieved using our passive and low-complexity measurements. Specifically, our proposed method estimates a time-varying frame error rate at an access point (AP) by counting null function data frames, which are emitted periodically by stationary user equipments (UEs), such as mobile phones and tablets. We implemented our method as a user-level script and a production-level firmware. Our real-world measurements in an uncontrolled 1500-seat hall demonstrated that our method exhibited high scalability against the increase in the numbers of APs and UEs with the aid of its simple structure and was capable of detecting the quality-of-service loss induced by an unexpected dynamic frequency selection.

This paper presents a proposal of an open-source index modulation (IM) toolkit, which facilitates reproducible research and accelerates open innovation in IM studies. The proposed toolkit is implemented based on massively parallel algorithms that are designed for state-of-the-art graphics processing units (GPUs). Since high-performance GPUs are available at low cost, along with the intensive development in deep learning, this toolkit achieves large scale but significantly fast Monte Carlo simulations at low cost. Two large-tensor-based parallel algorithms are introduced for bit error ratio and average mutual information simulations. Additionally, the design of active indices is newly formulated into an integer linear programming problem that guarantees optimality, which is applicable to the generalized spatial modulation and subcarrier-index modulation schemes. Performance comparisons demonstrated that the proposed GPU-aided algorithms were up to 145 times faster than the conventional CPU-aided efficient counterparts. Furthermore, the designed active indices achieved the theoretical optimum performance in contrast to widely used conventional methods. A comprehensive database of these designed active indices is released online and is available to any researcher.

The existing time-division duplex (TDD) and frequency-division duplex (FDD) techniques rely on a guard time and/or a guard band to avoid self-interference (SI) between the uplink and downlink channels, which results in the wastage of precious spectral resources. The full-duplex (FD) schemes of in-band FD (IBFD), as well as multicarrier division duplex (MDD), may overcome this drawback while retaining the key benefits of both TDD and FDD. Moreover, the MDD exhibits the exclusive benefits of the reduced peak-to-average-power ratio (PAPR) for signal transmission as well as the SI-free signal detection. Against this background, in this paper, we propose a novel FD scheme conceived for frequency-selective millimeter-wave (mmWave) channels, which have not been investigated in the open literature. Furthermore, we propose a novel projection-aided iterative eigenvalue decomposition (P-IEVD) algorithm, which performs null-space SI cancellation in the inherent beamforming structure of mmWave communication. Our simulation results confirm that the MDD is capable of outperforming its half-duplex (HD) counterparts of the TDD/FDD, even the IBFD can only achieve a better bandwidth efficiency than the MDD when a sufficiently high SNR is provided.

The achievable performance of subcarrier-index modulation (SIM) is analyzed in terms of its minimum Euclidean distance, constrained and unconstrained average mutual information, as well as its peak-to-average power ratio (PAPR). Our performance investigations identify the beneficial operating region of the SIM scheme over its conventional orthogonal frequency-division multiplexing (OFDM) counterpart, hence providing general design guidelines for the SIM parameters. More specifically, an SIM scheme is shown to be beneficial for the scenario of a relatively low transmission rate below 2 b/s/Hz. In addition, we demonstrate that the PAPR of the SIM scheme is comparable with that of its OFDM counterpart under the idealized simplifying assumption of having Gaussian input symbols.

In this paper, we present a constellation optimization technique that can be invoked for optical spatialmodulation (OSM) multiple-input multiple-output (MIMO) systems within the context of optical wireless (OW) systems that is designed to negate the high channel correlation imposed on conventional OSM schemes. More specifically, the proposed unified OSM architecture relies on power-imbalanced (PI) multiple transmit light sources. Furthermore, we formulate the constrained capacity of the proposed PI-OSM scheme, and then provide the design guidelines of our OSM scheme's PI constellation, in which the parameters have been optimized to ensure the associated constrained capacity is maximized. Our simulation results demonstrate that our proposed scheme is capable of outperforming conventional OSM and OWMIMO schemes over a wide range of signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the in vitro anti-proliferative effect and mechanism of action of Moringa oleifera Lam. leaf extract on human colon carcinoma HCT116 cell line. Methods: M. oleifera leaves were extracted with methanol. It was fractionated by Sephadex LH-20 column chromatography. Several fractions were identified by thin layer chromatography (TLC), proton nuclear magnetic resonance (1H NMR) and mass spectrometry (MS). The growth inhibitory activity and mechanism of action of the extracts in HCT116 colon cancer cells were investigated by 3-(4, 5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide (MTT) assay and Western blotting. Results: Successive fractions from M. oleifera leaf crude extracts by column chromatography were combined into four pooled batches (MOL1 - MOL4) according to their absorbance at 260 nm and TLC pattern. MOL2 and MOL3 contain astragalin and isoquercetin, respectively. The results from MTT assay indicated that cell proliferation was significantly (p < 0.05) inhibited in a concentration-dependent fashion, especially by MOL2, MOL3 and MOL4. MOL2 and MOL3 exhibited a stronger cell growth inhibition than their major ingredients. The anti-proliferative activity of MOL2 - MOL4 in HCT116 colon cancer cells was mediated by downregulation of ERK1/2 phosphorylation. Conclusion: M. oleifera leaf extract has a strong anti-proliferative activity which is exerted by decreasing ERK1/2 phosphorylation. Thus, the extract has a potential for use in cancer chemoprevention. [ABSTRACT FROM AUTHOR]