User Signal Research Articles

Framed Fidelity MAC: Losslessly packing multi-user transmissions in a virtual point-to-point framework

As the number of subscribers in wireless communication systems grows rapidly, it has become important to design efficient medium access control (MAC) protocols to realize resources sharing among multiple users. When designing a MAC, effective management of co-channel interference (CCI) among multiple concurrent transmissions is critical to enhancing system’s spectral efficiency (SE). Although MACs based on existing signal processing can suppress/mitigate CCI among multiple concurrent transmissions, they either consume communication resources or attenuate the desired signals’ transmission, hence incurring loss of transmission performance. To remedy this deficiency, we propose a Framed Fidelity MAC (F2MAC) to losslessly pack multi-user data transmissions into a virtual point-to-point (p2p) framework. Under F2MAC, the common receiver (Rx) selects a virtual framed-channel (a.k.a. framework) and broadcasts it to all mobile terminals. The latter calculate their precoding vectors based on the received virtual framework and their respective communication channels with respect to (w.r.t.) the Rx. Then, the pre-processed signals of multiple users are sent to the Rx via different eigenmodes of the framed-channel. By applying a receive filter to the received mixed signal, Rx can recover users’ desired data without interference. Since F2MAC can decouple the channel quality information (CQI) from channel direction information (CDI) related to the user’s signal transmission, — i.e., CQI is exclusively determined by the user’s communication channel while CDI depends on the unified framework, it can avoid the transmission performance loss incurred by existing signal processing based MAC which adjusts both CQI and CDI simultaneously. Moreover, we propose a Flexible Framed Fidelity MAC (F3MAC) to further improve the system’s SE. By dynamically re-constructing the virtual framed-channel at each mobile user, the user’s transmission can be realized with the principal eigenmode’s gain of his/her communication channel. Our in-depth analysis and simulation results have shown the proposed MAC methods to significantly improve the SE of multi-user communication systems.

SEMG-Based Hand Posture Recognition and Visual Feedback Training for the Forearm Amputee.

sEMG-based gesture recognition is useful for human–computer interactions, especially for technology supporting rehabilitation training and the control of electric prostheses. However, high variability in the sEMG signals of untrained users degrades the performance of gesture recognition algorithms. In this study, the hand posture recognition algorithm and radar plot-based visual feedback training were developed using multichannel sEMG sensors. Ten healthy adults and one bilateral forearm amputee participated by repeating twelve hand postures ten times. The visual feedback training was performed for two days and five days in healthy adults and a forearm amputee, respectively. Artificial neural network classifiers were trained with two types of feature vectors: a single feature vector and a combination of feature vectors. The classification accuracy of the forearm amputee increased significantly after three days of hand posture training. These results indicate that the visual feedback training efficiently improved the performance of sEMG-based hand posture recognition by reducing variability in the sEMG signal. Furthermore, a bilateral forearm amputee was able to participate in the rehabilitation training by using a radar plot, and the radar plot-based visual feedback training would help the amputees to control various electric prostheses.

Efficient User Grouping for MU-MIMO Visible Light Communications Based on Block Diagonalization

This paper investigates efficient user grouping methods for multi-user multi-input multi-output (MU-MIMO) visible light communication (VLC) systems. Block diagonalization (BD) precoding is considered for interference avoidance. In addition, time division multiplexing (TDM) is applied to perform user grouping when the number of users exceeds the limit of BD precoding based on the number of light emitting diode (LED) transmitters and the total number of users' photodiodes (PDs). User grouping methods are proposed based on pairwise interference considerations among users in the same group. The proposed methods can be implemented through integer linear programming (ILP), which requires less computation than exhaustive search. The numerical results on the average minimum user throughputs over random scenarios indicate that the proposed hybrid method can significantly outperform random user grouping and performs reasonably well compared to exhaustive search. Finally, this study demonstrates that, when BD precoding greatly attenuates the desired user signals, user grouping can help improve minimum user throughputs even though BD precoding can support all users as a single group.

Training Socially Engaging Robots: Modeling Backchannel Behaviors with Batch Reinforcement Learning

A key aspect of social human-robot interaction is natural non-verbal communication. In this work, we train an agent with batch reinforcement learning to generate nods and smiles as backchannels in order to increase the naturalness of the interaction and to engage humans. We introduce the Sequential Random Deep Q-Network (SRDQN) method to learn a policy for backchannel generation, that explicitly maximizes user engagement. The proposed SRDQN method outperforms the existing vanilla Q-learning methods when evaluated using off-policy policy evaluation techniques. Furthermore, to verify the effectiveness of SRDQN, a human-robot experiment has been designed and conducted with an expressive 3d robot head. The experiment is based on a story-shaping game designed to create an interactive social activity with the robot. The engagement of the participants during the interaction is computed from user's social signals like backchannels, mutual gaze and adjacency pair. The subjective feedback from participants and the engagement values strongly indicate that our framework is a step forward towards the autonomous learning of a socially acceptable backchanneling behavior.

Blind beamforming-based strong interference suppression in underwater acoustic direct-sequence code-division multiple-access systems.

In an underwater acoustic direct-sequence code-division multiple-access (DS-CDMA) system, the performance of the conventional receiver is severely limited by the strong multiple-access interferences, which is due to the near-far effect. In this paper, a time-frequency-time with eigendecomposition-based blind beamformer (TFT-EBB) method is proposed for a distant user to suppress strong interference from a nearby user in DS-CDMA underwater acoustic communications. When the interference level is moderately higher than that of the desired signal, the TFT-EBB method can estimate the composite steering vector of all multipath components of the weak desired user signal via the eigendecomposition of the array covariance matrix at each frequency bin. Then the proposed energy detection-based RAKE receiver is cascaded to achieve multipath diversity, in which it requires less prior information compared with the conventional RAKE receiver. The proposed method is evaluated by using both simulated and field experimental data, which verifies its effectiveness.

Enhanced detection methods for compressive spectrum sensing based on antieigenvalues

Spectrum sensing is a crucial process in devices that dynamically and opportunistically attempt to access the spectrum based on its availability, including secondary users in a cognitive radio network and internet-of-things (IoT) devices with cognitive radio capabilities. Eigenvalue-based detection methods have recently gained considerable attention over classical detection techniques due to their efficiency. In this paper, we propose two new detection methods based on antieigenvalues, which have proven to be more efficient than existing eigenvalue-based methods. The first method uses the smallest antieigenvalue of the incoming signals’ covariance matrix as a test statistic, and the second method uses the slope of the antieigenvalues, whose behavior and probabilistic distribution is studied and presented in this paper. The two methods are also applied in a compressive sensing framework, where the samples are acquired at rates below the Nyquist rate. Both these methods have also proven to be more efficient than the only other existing method based on antieigenvalues, which uses the sum of the smallest P antieigenvalues — where P is the number of primary user signals — as a test statistic.

Index Modulation Recognition Based on Projection Residual Analysis

Index modulation recognition (IMR) at secondary user (SU) receiver is a challenging topic for MIMO-OFDM cognitive radio network (MIMO-OFDM-CRN) with index modulation scheme, in order to make SU preferably adapt to the communication environment by adjusting own parameters. For index modulated signals, this paper proposes an effective IMR algorithm based on projection residual analysis (PRA). The proposed algorithm is suitable for various types of modulation such as spatial index modulation (SIM), frequency index modulation (FIM) and space-frequency index modulation (SFIM). Firstly the sparse structure of primary user (PU) signal is detected through removing the joint sparsity of signal matrix. Secondly, according to the detected sparse structure, the problem of whether the signal is index modulation (IM) or unindexed modulation (UIM) is addressed by projection residual analysis with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula> -test. The hypothesis test judges whether the projection residual power of the received signal is significant compared with that of the UIM case, where the projection residual is obtained through projecting the subcarrier signals in the current index modulation symbol into the subspace of those in the previous symbol. The distribution of the test statistic is derived theoretically under UIM case. Thirdly, combining the detected sparse structure and the results of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula> -test, the index modulation mode of PU signal is identified. Simulation results verify the performance of the proposed algorithm in terms of bit error rate (BER) and recognition rate, respectively.

Stage Spectrum Sensing Technique for Cognitive Radio Network Using Energy and Entropy Detection

The radio spectrum is one of the world’s most highly regulated and limited natural resources. The number of wireless devices has increased dramatically in recent years, resulting in a scarcity of available radio spectrum due to static spectrum allocation. However, many studies on static allocation show that the licensed spectrum bands are underutilized. Cognitive radio has been considered as a viable solution to the issues of spectrum scarcity and underutilization. Spectrum sensing is an important part in cognitive radio for detecting spectrum holes. To detect the availability or unavailability of primary user signals, many spectrum sensing techniques such as matched filter detection, cyclostationary feature detection, and energy detection have been developed. Energy detection has gained significant attention from researchers because of its ease of implementation, fast sensing time, and low computational complexity. Conventional detectors’ performance degrades rapidly at low SNR due to their sensitivity to the uncertainty of noise. To mitigate noise uncertainty, Shannon, Tsallis, Kapur, and Renyi entropy-based detection has been used in this study, and their performances are compared to choose the best performer. According to the comparison results, the Renyi entropy outperforms other entropy methods. In this study, two-stage spectrum sensing is proposed using energy detection as the coarse stage and Renyi entropy-based detection as the fine stage to improve the performance of single-stage detection techniques. Furthermore, the performance comparison among conventional energy detection, entropy-based detection, and the proposed two-stage techniques over AWGN channel are performed. The parameters such as probability of detection, false alarm probability, miss-detection probability, and receiver operating characteristics curve are used to evaluate the performance of spectrum sensing techniques. It has been shown that the proposed two-stage sensing technique outperforms single-stage energy detection and Renyi entropy-based detection by 11 dB and 1 dB, respectively.

The Risks Associated with the Use of Brain-Computer Interfaces: A Systematic Review

Brain-computer interfaces (BCI) are an emerging technology that can read the brain signals of users, derive behavioral intentions, and manifest them in the control of electronic technologies. While there are potentially great benefits of this technology, there may also be risks associated with their use. However, it is not clear if these risks are being considered in the early BCI literature. This systematic review aimed to identify the scope and types of BCI risks discussed in the literature and the methods used to identify these risks. Following the PRISMA protocol, 1184 articles were initially identified with the final selection of 58 published articles following systematic exclusion. Analysis of the included articles derived 20 different risks, which were categorized into seven risk themes spanning physical health risks through to legal and societal concerns. Only one study in the review used a method of risk assessment, with most articles identifying risks through discussion and opinion pieces. The findings highlight a lack of an empirical and comprehensive understanding of the risks that BCI technology could pose. It is concluded that further work is necessary to proactively identify BCI risks using formal risk assessment methods to inform early users and to direct risk control measures for BCI developers and regulators.

Skewness and access kurtosis as denoised mixed features-based K-Medoids for cooperative spectrum sensing

A traditional cooperative spectrum sensing system based on the energy features of random matrix theory does not function well when the signal-to-noise ratio is low in the Gaussian noise channel. In this paper, the poor performance of this system is improved. A novel blind cooperative spectrum sensing scheme based on new denoised mixed-features exploiting the K-Medoids algorithm has been developed. In the first place, a new mathematical formula is proposed to extract denoised mixed-features and decrease the amount of noise in the received signals of each secondary user. This formula is expressed as the square root of the sum of the squares of the third and fourth-highest order moments of skewness (Sk) and access kurtosis (Ku-3). In the fusion center, two new denoised mixed-features methods are proposed. The first method is based on calculating the Average values of Sk and Ku-3 of the denoised mixed features vectors, we named it the ASKU3 method. The second method is based on calculating the Maximum values of Sk and Ku-3 of the denoised mixed features vectors, we named it the MSKU3 method. In comparison to the existing energy feature-based methods, the denoised mixed-features-based on highest-order moments avoid the computational complexity associated with threshold estimation. Furthermore, they mitigate the effect of noise uncertainty and improve sensing performance by reducing the overlap between clusters of the K-Medoids algorithm. Then, the K-Medoids method makes use of the newly formed methods in order to train the classifier effectively depending on whether the primary user is present or not. Finally, several energy-feature-based methods classified by the clustering algorithm are compared in this study. Moreover, the theoretical and practical performances of the denoised mixed-features methods, ASKU3 and MSKU3, are very well matched. The simulation findings show that the proposed methods have improved the effectiveness of sensing at low signal-to-noise ratio in terms of the probability of detection.

Implementation of Block-Level Double Encryption Based on Machine Learning Techniques for Attack Detection and Prevention

Cloud computing is one of the most important business models of modern information technology. It provides a minimum of various services to the user interaction and low cost (hardware and software). Cloud services are based on the newline architectures on virtualization by using the multitenancy for better resource management and newline strong isolation between several virtual machines (VMs). The spying on a victim VM is challenging, particularly when one wants to use per-core microarchitectural features as a side channel. For example, the cache contains the most potential for damaging side channels, but shared information across different cores affects the cloud information. To overcome this problem, propose the Secure Block-Level Double Encryption (SBLDE) algorithm for user signature verification in the cloud server. It uses identity-based detection techniques to monitor the colocated VMs to identify abnormal cache data and channel behaviors typically during VM data transformation. The identity-based linear classification (IBLC) method is used for classifying the attacker channel when the data is transferred/retrieved from the VM cloud server. This cloud controller finds the channel misbehavior to block the port or channel, changing other available ports’ communication. The service verification provides strong user access permission on the cloud server when the unknown request to the cloud server suddenly executes the key authentication to verify the user permission. This linear classification trains the existing side-channel attack datasets to the classifier and identifies the VM cloud’s attack channel. The study focused on preventing attacks from interrupting the system and serves as an effective means for cross-VM side-channel attacks. This proposed method protects the cloud data and prevents cross-VM channel attack detection efficiently, compared to other existing methods. In this overall proposed method, SBLDE’s performance is to be evaluated and then compared with the existing method.

Uplink-Downlink Duality of Multi-Cell Non-Orthogonal Multiple Access Systems

Towards the sixth generation (6G) wireless communications, multiple access is a potential technology to achieve thousand times of traffic capacity enhancement comparing with 5G. This paper investigates the feasible signal-to-interference-plus-noise-ratio (SINR) region for the multi-cell downlink and uplink non-orthogonal multiple access (NOMA) systems. Within a cell, the signals of different users are multiplexed on a channel with different power levels and successive interference cancellation is applied at the receivers to decode the signals. For the downlink, based on Perron-Frobenius Theory, we first derive a necessary and sufficient condition for an SINR vector to be feasible under a fixed decoding order. Then, a closed-form expression for the feasible SINR region is given by the union of the SINR regions under all possible decoding orders. Following a similar idea, the expression of the feasible SINR region for the uplink is also derived. Furthermore, the duality between the multi-cell downlink and uplink NOMA systems is explored. It is proved that the two systems have same feasible SINR region under dual channels. Finally, we propose an efficient algorithm to approximate the SINR region boundary. Simulation results are provided to validate the efficiency of the algorithm and compare the performance with orthogonal multiple access scheme.

Inside Front Cover

The all‐day health monitoring system detects the user's biological signals such as body temperature, movement, heartbeat, and humidity level obtained by a wearable device or sensor attached to or inserted into the body. It subsequently refers to a system that processes and transmits the information to the mobile phone and transmits the status information to the emergency clinician through cloud computing or wireless communication systems to receive appropriate treatment. image

A Stacked Autoencoder and Multilayer Perceptrons for mmWave Beamforming Prediction

The millimeter-wave frequencies planned for 6G systems present challenges for channel modeling. At these frequencies, surface roughness affects wave propagation and causes severe attenuation of millimeter-wave (mmWave) signals. In general, beamforming techniques compensate for this problem. Analog beamforming has some major advantages over its counterpart, digital beamforming, because it uses low-cost phase shifters for massive MIMO systems compared to digital beamforming that provides more accurate and faster results in determining user signals. However, digital beamforming suffers from high complexity and expensive design, making it unsuitable for mmWave systems. The techniques proposed so far for analog beamforming are often challenging in practice. In this work, we have proposed a deep learning model for analog beams training that helps predict the optimal beam vector. Our model uses an available dataset of 18 base stations, over 1 million users, 60 GHz frequency. The training process first applies a stacked autoencoder to extract the features from the training datasets, and then uses a multilayer perceptron (MLP) to train and predict the optimal beams. Then, the results are evaluated by computing the mean squared error between the expected and predicted beams using the test set. The results show high efficiency compared to the benchmark method, which uses only the MLP for the training process.

Machine Learning for Spectrum Information and Routing in Multihop Green Cognitive Radio Networks

Research works on cognitive radio networks (CRNs) together with energy harvesting (EH) promise to address the spectrum scarcity and limited battery power problems on the wireless communication nodes. While radio frequency (RF) signal of primary user (PU) can be used in EH, its absence (non-transmission state) offers an unused spectrum for the availability of the secondary user (SU) data transmission. Spectrum sensing (SS) process that detects the presence or the absence of PU signal, occupies a considerable slot in time-frame and energy consumption in wireless nodes, hence, both SU throughput and battery power get reduced. To address the problems, this work explores a support vector machine (SVM) based PU activity (transmit/non-transmit mode) prediction without accomplishing any SS task in the system operation. Thereafter, a Deep Q-networks (DQN) based energy and spectrum efficient routing strategy is suggested to maximize the CRNs throughput. The proposed joint EH and routing scheme improve the sum throughput, spectrum and energy efficiencies by <inline-formula> <tex-math notation="LaTeX">$\sim 33.48\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sim 34.24\%$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\sim 30.76\%$ </tex-math></inline-formula>, respectively over the conventional dedicated SS based approach.

Spectrum Sensing by Cepstral Covariance Detection

This work presents the cepstral covariance detector for spectrum sensing in cognitive radio. The power cepstral peaks of digitally modulated signals are exploited for spectrum sensing. To enhance the process of signal detection in low signal-to-noise ratio environments and with the knowledge of the data rate of the primary user signal, the cepstral covariance of the received signal is evaluated. The detection test statistic of the proposed detector is evaluated and an expression for the detection threshold is obtained under the null hypothesis. The detection performance of the proposed cepstrum-based detector is compared to covariance-based detectors and the obtained results validate its detection reliability at low signal-to-noise ratio levels with relatively low computational complexity.

Message-Passing Receiver Design for Multiuser Multi-Backscatter-Device Symbiotic Radio Communications

Symbiotic radio (SR) has emerged as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi- backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time-frequency resource block. Due to the presence of inter-user and inter-BD interference, multiuser and multi-BD detection in the receiver design become much more challenging. Concretely, the detection problem involves several key components: direct-link channel estimation, backscatter-link channel estimation, user signal decoding, and BD symbol detection. A conventional way is to realise these components in two separate phases, in which a channel estimation phase is followed by a data decoding phase. However, channel state information (CSI) acquisition is very difficult for the multiuser multi-BD SR communication, as compared to the case of orthogonal multiple access. In addition, the backscatter-link is relatively weak, which further increases the difficulty of CSI acquisition. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Based on the factor graph representation of the joint estimation problem, we design a message-passing receiver for the multiuser multi-BD SR system to iteratively refine the estimation outputs. Extensive simulation results demonstrate the effectiveness of the proposed receiver design.

BANKING BLUEPRINT FOR THE CRYPTO WORLD

Due to the rapid development of entropy and connectedness technologies, numerous activities in our daily brio someone been merged online and they beautify solon stretched and statesman good. A Brobdingnagian growing in signal of online users has activated realistic language concepts and created a new acting phenomenon which is cryptocurrency to assist the business activities much as purchasing, marketing and trading. The use of realistic acceptance has transform distributed in many incompatible systems in recent age. Realistic money is not full limited and regulated hence most of the countries eff not admitted this nowness in their scheme activities. This report investigates virtually cryptocurrency mouth legality as shaft as tally responded in cost of regulations &amp; legislations towards crypto currencies to instruct a unclouded ikon of its combat on different laws in India in position to adjust it. The cryptocurrency activity has been described as radical due to the unceasing bailiwick phylogenesis and conception that the blockchain bailiwick provides. Guiding some to anticipate that this could be the incoming tread for the nominal taxon, right similar how fiat acceptance replaced gold. Cryptocurrencies were originally created to be a represent of savings or income for the unbanked, concentrate costs and force activity, for a effectuation of accumulation clearness and to remove financial intermediaries. It is undeniable that the cryptocurrency activity has created a change of opinions, as any appear to explore the marketplace advance time others judge the intellection of adopting this innovation bailiwick completely. This meditate focuses on the representation and intention to use cryptocurrencies. Swimming into previous poverty to be explored advance. A valued way was victimized to stitchery accumulation from 102 participants. The findings indicated that action and try prospect as the most authoritative variables for cryptocurrency acceptance, as people assay tendency as what benefits cryptocurrencies can furnish for them when they reason unable of using the innovative engineering. Phytologist may be shy of cryptocurrencies as they consider that trading these assets increases essay and requires lengthy and dear due industriousness. Withal, digital currencies can give umpteen benefits to business institutions and their customers.

A novel method to reduce indoor signal power fluctuation using multiple adaptive terahertz sources

SummaryThe indoor terahertz sources cause power fluctuation at various locations due to obstacles, which can be avoided by deploying multiple sources, but will lead to high energy consumption. In this paper, the reverse ray‐tracing method is used to establish a terahertz indoor channel. The free path loss, atmospheric absorption, and reflection loss are considered. Adaptive algorithms are adopted to solve the large energy consumption of deploying multiple sources. A weight vector coefficient is used based on the location and received user signal to adjust the transmission power of each terahertz source dynamically. The results show that the adaptive power control of multiple terahertz sources substantially reduces the energy consumption and the signal power fluctuations. For static users, the energy consumption can be reduced by 5 and 9 dB, and the maximum data transmission rate can be increased by one to two times using four and nine sources, respectively. Moreover, energy consumption can be reduced by more than 6 dB for several indoor users. For dynamic users with a changing channel environment, the adaptive algorithm can adjust the source power in real time and improve signal efficiency. However, the efficiency improvement of the algorithm is limited by the distribution of the multiple sources. The energy consumption of four sources with the adaptive algorithm operating in the center of the room is greater than that of nine sources without the adaptive algorithm. Nevertheless, the efficiency of the nine sources with the adaptive algorithm is much higher than that of the four sources.

Intelligent Reflecting Surface-Aided Spectrum Sensing for Cognitive Radio

Spectrum sensing is a key enabling technique for cognitive radio (CR), which provides essential information on the spectrum availability. However, due to severe wireless channel fading and path loss, the primary user (PU) signals received at the CR or secondary user (SU) can be practically too weak for reliable detection. To tackle this issue, we consider in this letter a new intelligent reflecting surface (IRS)-aided spectrum sensing scheme for CR, by exploiting the large aperture and passive beamforming gains of IRS to boost the PU signal strength received at the SU to facilitate its spectrum sensing. Specifically, by dynamically changing the IRS reflection over time according to a given codebook, its reflected signal power varies substantially at the SU, which is utilized for opportunistic signal detection. Furthermore, we propose a weighted energy detection method by combining the received signal power values over different IRS reflections, which significantly improves the detection performance. Simulation results validate the performance gain of the proposed IRS-aided spectrum sensing scheme, as compared to different benchmark schemes.