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This paper investigates an intelligent reflecting surfaces (IRS) aided anti-jamming communication strategy in the integrated terrestrial-satellite network (ITSN), where the IRS is exploited to mitigate jamming interference and enhance the integrated system communication performance. In such a network, the terrestrial network and satellite network are co-existing with a spectrum-sharing scheme in the presence of a multi-antenna jammer. We aim at maximizing the weighted sum rate (WSR) of all users by jointly optimizing the terrestrial beamformers and IRS phase shifts while considering the signal-to-interference-plus-noise ratio (SINR) requirements of legitimate users. Different from the non-convex optimization techniques utilized in the IRS-related problem, a novel optimization-driven deep reinforcement learning (DRL) algorithm is proposed, which leverages both the robustness of model-free learning approaches and the efficiency of model-based optimization methods. In the optimization module of the proposed algorithm, we analyze the smart jammer under the unknown jamming model and derive a lower bound of the anti-jamming uncertainty, such that the IRS-aided anti-jamming problem can be solved by alteration method with second-order cone programming (SOCP) algorithm and semidefinite relaxation (SDR) technique. Simulation results demonstrate that the IRS can enhance the anti-jamming performance efficiently, and the proposed optimization-driven DRL algorithm can improve both the learning rate and the system performance compared with existing solutions.

There is an increasing demand of massive machine-type communication (mMTC) to provide scalable access for a large number of devices, which has prompted extensive investigation on grant-free massive random access (RA) in 5G and beyond wireless networks. Although many efficient signal processing algorithms have been developed, the limited radio resource for pilot transmission in grant-free massive RA systems makes accurate user activity detection and channel estimation challenging, which thereby compromises the communication reliability. In this paper, we adopt retransmission as a means to improve the quality of service (QoS) for grant-free massive RA. Specifically, by jointly leveraging the user activity correlation between adjacent transmission blocks and the historical channel estimation results, we first develop an activity-correlation-aware receiver for grant-free massive RA systems with retransmission based on the correlated approximate message passing (AMP) algorithm. Then, we analyze the performance of the proposed receiver, including the user activity detection, channel estimation, and data error, by resorting to the state evolution of the correlated AMP algorithm and the random matrix theory (RMT). Our analysis admits a tight closed-form approximation for frame error rate (FER) evaluation. Simulation results corroborate our theoretical analysis and demonstrate the effectiveness of the proposed receiver for grant-free massive RA with retransmission, compared with a conventional design that disregards the critical user activity correlation. IEEE

This paper studies an integrated sensing and communication (ISAC) system within a centralized cell-free massive MIMO (multiple-input multiple-output) network for target detection. ISAC transmit access points serve the user equipments in the downlink and optionally steer a beam toward the target in a multi-static sensing framework. A maximum a posteriori ratio test detector is developed for target detection in the presence of clutter, so-called target-free signals. Additionally, sensing spectral efficiency (SE) is introduced as a key metric, capturing the impact of resource utilization in ISAC. A power allocation algorithm is proposed to maximize the sensing signal-to-interference-plus-noise ratio while ensuring minimum communication requirements. Two ISAC configurations are studied: utilizing existing communication beams for sensing and using additional sensing beams. The proposed algorithm;#x2019;s efficiency is investigated in realistic and idealistic scenarios, corresponding to the presence and absence of the target-free channels, respectively. Despite performance degradation in the presence of target-free channels, the proposed algorithm outperforms the interference-unaware benchmark, leveraging clutter statistics. Comparisons with a fully communication-centric algorithm reveal superior performance in both cluttered and clutter-free environments. The incorporation of an extra sensing beam enhances detection performance for lower radar cross-section variances. Moreover, the results demonstrate the effectiveness of the integrated operation of sensing and communication compared to an orthogonal resource-sharing approach. Authors

13th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2023 -- 23 March 2023 through 25 March 2023 -- 188327, Track While Scan (TWS) radars are generally used in military systems, constantly monitoring the positions of the target and generating data about it. They measure a target's coordinates, determine its trajectory, and predict its next location. They can usually track a single target but do so with very high sensitivity. "TWS Radar"is often used as a "Fire Control Radar"in the armed forces. This study addresses the design of a TWS radar with MATLAB programming language. The operation of this radar was simulated, and targets were detected using matched filter in MATLAB. Transmitted and received signal parameters are explained. A MATLAB algorithm which generates the transmitted and the received pulses for single Pulse Repetition Interval (PRI) was written in the first step. Match filtering was implemented for detecting targets. A fixed threshold value is defined, and position of the targets are reported with MATLAB algorithm. A white Gaussian noise was added to the output of the matched filter and targets are reported for the same threshold value. Radar was tested under the different Signal to Noise Ratio (SNR) rates. The scope view of the TWS Radar was plotted for 2 degrees antenna beam width for different SNR values. © 2023 IEEE.

This paper examines how to minimize the energy consumption of a user equipment (UE) when transmitting short data payloads. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS), which requires additional pilot signals to be configured, to improve the channel conditions. The challenge is that the pilot signals increase the energy consumption and must be balanced against energy savings during data transmission. We derive a formula for the energy consumption, including both pilot and data transmission powers and the effects of imperfect channel state information and discrete phase-shifts. To shorten the pilot length, we propose dividing the RIS into subarrays of multiple elements using the same reflection coefficient. The pilot power and subarray size are tuned to the payload length to minimize the energy consumption. Analytical results show that there exists a unique energy-minimizing solution. For small payloads and when the direct path loss between the BS and UE is weak compared to the path loss via the RIS, the solution is using subarrays with many elements and low pilot power and vice versa. The optimal percentage of energy spent on pilot signaling is in the order of 10-40%. IEEE

Passive position determination of the radiating source is widely applied in wireless sensor networks (WSN). Compared with traditional two-step methods, the direct position determination (DPD) methods estimate the position in a single step and exhibit conspicuous performance superiority at a low signal-to-noise ratio (SNR). Unfortunately, the computational load for the exhaustive maximum likelihood (ML) DPD search is unacceptable for real-time processing. Although the conventional gradient-based iterative DPD method has lower computational complexity, it easily converges to the local maxima. In this paper, for a stationary source, we aim to tackle the problem of DPD using Doppler frequency shifts in a computationally attractive way. The waveform of the transmitted signal is a priori knowledge, and the transmitted frequency offset exists as the nuisance parameter. Based on the expectation maximization (EM) concept, we propose an approximately globally-optimal iterative ML DPD algorithm. The proposed method regards the source-receiver vector as the unobserved latent variable, and then by exploiting the Laplace approximation technique and polar coordinate system, the original multi-dimensional grid search is transformed into multiple one-dimensional estimations for polar angle. To guarantee global optimality for the estimation of transmitted frequency offsets, a method different from its counterpart in the iterative cycle is utilized when the local maxima are achieved. Moreover, by correctly simplifying the estimation cost function in the expectation step, both the search of the polar angle and the design of the iteration parameter is avoided, which contributes to prominent computational savings. Simulation results demonstrate that the proposed modified EM-based DPD estimator (MEM-DPD) has high probability of converging to the global maximum. Meanwhile, the estimation performance is very close to the Cramér-Rao lower bound (CRLB) with minor iterations. IEEE

In this letter, we investigate the robust beamforming design for a hybrid active-passive reconfigurable intelligent surface (RIS) assisted integrated sensing and communication (ISAC) system, where the RIS consists of both active and passive reflecting elements. Different from the existing works, we consider the target location estimation error. We aim to maximize the minimum sensing beampattern gain among multiple targets, subject to the signal-to-interference-plus-noise ratio (SINR) constraint for each communication user (CU), the transmit power constraints for the BS and the active elements. To address the non-convexity of the max-min RIS-assisted ISAC design problem, we propose an alternating-optimization based design solution to alternately optimize the BS’s transmit beamforming design and the hybrid RIS’s coefficient matrix in a low-complexity manner. Simulation results demonstrate the benefit of the proposed design scheme over the existing baseline schemes. IEEE

This letter focuses on maximizing the energy efficiency (EE) for secure visible light communication (VLC) network with simultaneous lightwave information and power transfer (SLIPT) by optimizing the beamforming vectors and direct current (DC) bias under the constraints of the minimal rate requirement, the secure transmission requirement, the minimal energy harvesting (EH) requirement, and total power budget at the transmitters. To solve the non-convex optimization problem, we present a dual-layer iterative algorithm based on Dinkelbach’s and successive convex approximation (SCA) methods. An efficient algorithm is also designed to find a good initial point to accelerate the convergence. Simulation results show three typical phenomena: (1) the EE first remains unchanged and then decreases with the increasing EH requirement; (2) compared with the traditional sum-rate maximization design, our proposed approach achieves notable EE performance gain under the secure constraints; (3) the number of the authorized receivers, as well as the number of the unauthorized receivers, imposes significant impact on the EE performance.

Multiplexing or transmission diversity via dual links enables the reduction in age of information (AoI). We address the open issue of selection between the two transmission modes for AoI-oriented short-packet system over fading channels, with a comprehensive analysis. Closed-form expressions for the average AoI and peak AoI (PAoI) are derived based on the discrete-time Markov-chain process. Then, to obtain the explicit region of preference (RoP) and quantitative PAoI gains of multiplexing/diversity over the single-queue case, we derive the signal-to-noise ratio (SNR) threshold for transmission mode selection, which is shown to be a decreasing function of the arrival rate and saturates at high arrival rate. Also, the monotonicity of the PAoI gains by multiplexing and diversity, and their achievable gains, are analyzed in a comprehensive manner. It is shown that diversity is able to achieve a PAoI gain of more than 3 dB over the single-queue case at low SNR, while multiplexing has a larger RoP than diversity and is selected at high SNR and high arrival rate. Furthermore, both throughput and PAoI violation probability are considered alongside the average PAoI for a wide range of tradeoff in system design. IEEE

We investigate the performance of a reconfigurable intelligent surface (RIS) assisted single-input single-output (SISO) communication system where RIS elements are narrowly spaced so that the channels between the RIS elements and receiver are spatially correlated. Adjacent RIS elements are grouped together to reduce the channel estimation overhead. The statistical properties of the optimal received SNR are analytically derived and these are used to characterize the coverage probability of the system. An analytical upper bound for the average achievable rate is also derived. This analysis provides insight into the impact of the grouping strategy and element spatial channel correlation level on the coverage probability and the achievable rate of the system. Numerical results validate the tightness of the closed-form expressions. Our results show that the average achievable rate for a given group size is higher for the spatially correlated RIS elements as compared to the uncorrelated configuration.

Active reconfigurable intelligent surfaces (RISs) have recently been proposed to complement and generalize passive RIS. In this work, the design for an active RIS based on phase-reconfigurable reflection amplifiers is proposed. The proposed active RIS elements’ design consists of a two-layer patch antenna and a phase-reconfigurable reflection amplifier that is realized by a cascade of a phase shifter and a reflection amplifier. Theoretical and numerical analyses are used to quantify key parameters that need to be met in the design of the reflection amplifier and phase shifters. These show that a tradeoff between the reflection amplifier’s gain and the phase shifter’s return and insertion loss is required to be balanced to obtain a stable and effective overall design. In particular, a reflection amplifier with about 13-dB gain and a 2-bit phase shifter with only about 1.9-dB insertion loss and $-$ 25-dB return loss are designed. The final RIS element obtains 8.5-dB gain with four reconfigurable phases ranging from 0 $^{\circ}$ to 360 $^{\circ}$ with small root mean square (rms) gain and phase errors. An active RIS can be constructed by concatenating these elements together, and an efficient analytical method to calculate the scattered pattern by the active RIS is also proposed. Experimental results for a $2$ $\times$ $2$ element active RIS prototype are also provided. These show that the active RIS provides received powers around 8.5 dB higher than that from using an equivalent passive RIS. The results demonstrate the f

Optimum and suboptimum energy detection receivers with diversity reception in Rayleigh fading, that only require limited channel state information based on the channel norm, are introduced. The importance of these channel norm based energy detection (CNBED) receivers is that they achieve the same diversity order as that of a coherent receiver. Furthermore, their performance degradation when compared to a coherent receiver can be made sufficiently low by appropriate choice of parameters. Due to their performance and the low complexity of energy detection, the CNBED techniques have potential applications to internet of things. Analysis of the CNBED receivers is performed for multi-level amplitude-shift keying (ASK). For the optimum receiver, an analytical approximation of the symbol error probability (SEP) as a sum of univariate integrals involving averaging of Gaussian Q-functions is derived. For the suboptimum receiver, closed form expressions for the exact SEP and for the approximate SEP under the condition of high signal-to-noise ratio (SNR) are obtained; in addition, using asymptotic approximations, expressions for the optimal ASK transmit levels with high SNR and large number of receive diversity branches, that minimize the SEP subject to an energy constraint, are found. Numerical results are provided to demonstrate the performance of the receivers. IEEE

Short frame structure and its optimization plays an important role in ultra reliable and low latency communication (URLLC). We investigate and compare the latency and throughput performances of the independent encoding (IE) and joint encoding (JE) frame structures for heterogeneous multi-device URLLC in the finite block length regime. There is a counter-intuitive finding that, despite a longer frame, IE enables a much lower average latency and higher reliability than JE, thanks to lower queuing latency, while JE achieves higher throughput with lower traffic heterogeneity, thanks to less channel dispersion. It is also shown that traffic heterogeneity has less adverse effects on the performance of the IE frame structure, and can even help reduce its average latency with the shortest block length first (SBF) scheduling rule proposed. We also provide an intensive analysis of the trade-off between pilot power and pilot overhead, with near-optimal pilot power and block length derived in closed form. Low-complexity joint pilot power, pilot length and block length optimization algorithms are proposed for IE and JE frame structures. Numerical results verify the effectiveness of the proposed algorithms, and also show that pilot power optimization plays a significant role in enhancing throughput at low to medium SNR. IEEE

The calculation of calorie consumption is of paramount importance for the human diet and health management. Most existing solutions use image-processing techniques to identify the food type and refer to the nutrition table to compute the total calorie, which is quite challenging to differentiate foods that look the same but contain vastly different quantities of calories. To address this issue, we propose to leverage near-infrared spectroscopy (NIRS) to derive the concentration of nutrients based on the unique absorption spectrum of foods. Instead of using the professional NIRS system that is bulky, expensive, and impractical for daily use by non-expert users, we develop a low-cost portable NIRS system using commercial LEDs. As the quality of the signals of the low-power LEDs is relatively poor, we carefully design modulation schemes and interference elimination algorithms to improve the signal quality and remove the interference. Extensive experiments show that outperforms the image-based baseline in achieving more accurate calorie estimation, especially for look-alike foods, and is robust to various environmental factors. IEEE

This paper investigates the channel capacity of coherent reception with antenna diversity in Rayleigh fading using asymmetric constellations, such as nonbinary two-sided amplitude-shift keying (ASK) and quadrature amplitude modulation (QAM) with ≥8 levels. Due to the asymmetries, the capacity achieving input or a priori probability distributions deviate from uniformity. To perform the investigation, an analytical expression for the mutual information, in terms of an integral over a single variable, between the input (transmitted symbol) and the output (received signal vector comprising signals at receive antennas), is derived. For two-sided ASK, 8-QAM with cross constellation, and 16-QAM, analytical closed form expressions for the high and low signal-to-noise (SNR) approximations of the capacity achieving input probability distribution, the capacity, and the relative gain in mutual information (between using the uniform and the capacity achieving input probability distributions) are obtained. Numerical results show that at low SNRs, the use of the capacity achieving nonuniform input probability distribution offers significant advantage over the use of the uniform input probability distribution, while at high SNRs, this advantage is significant only for configurations with one receive antenna. Furthermore, the high and low SNR approximations are shown to be reasonably accurate.

The enormous data distributed at the network edge and ubiquitous connectivity have led to the emergence of the new paradigm of distributed machine learning and large-scale data analytics. Distributed principal component analysis (PCA) concerns finding a low-dimensional subspace that contains the most important information of high-dimensional data distributed over the network edge. The subspace is useful for distributed data compression and feature extraction. This work advocates the application of over-the-air federated learning to efficient implementation of distributed PCA in a wireless network under a data-privacy constraint, termed AirPCA. The design features the exploitation of the waveform-superposition property of a multi-access channel to realize over-the-air aggregation of local subspace updates computed and simultaneously transmitted by devices to a server, thereby reducing the multi-access latency. The original drawback of this class of techniques, namely channel-noise perturbation to uncoded analog modulated signals, is turned into a mechanism for escaping from saddle points during stochastic gradient descent (SGD) in the AirPCA algorithm. As a result, the convergence of the AirPCA algorithm is accelerated. To materialize the idea, descent speeds in different types of descent regions are analyzed mathematically using martingale theory by accounting for wireless propagation and techniques including broadband transmission, over-the-air aggregation, channel fading and noise. The results reveal the accelerating effect of noise in saddle regions and the opposite effect in other types of regions. The insight and results are applied to designing an online scheme for adapting receive signal power to the type of current descent region. Specifically, the scheme amplifies the noise effects in saddle regions by reducing signal power and applies the power savings to suppressing the effects in other regions. From experiments using real datasets, such power control is

Objective: To adopt deep transfer learning combined with Digital Twins (DTs) in Magnetic Resonance Imaging (MRI) medical image enhancement. Methods: MRI image enhancement method based on metamaterial composite technology is proposed by analyzing the application status of DTs in medical direction and the principle of MRI imaging. On the basis of deep transfer learning, MRI super-resolution deep neural network structure is established. Results: the optimal Peak Signal to Noise Ratio (PSNR) of 34.11dB can be obtained by introducing modified linear element and loss function of deep transfer learning neural network structure. The Structural Similarity Coefficient (SSIM) is 85.24%. It indicates that the MRI truthfulness and sharpness obtained by adding composite metasurface are improved greatly. The proposed medical image fusion algorithm has the highest overall score in the subjective evaluation of the six groups of fusion image results. Group III had the highest score in Magnetic Resonance Imaging- Positron Emission Computed Tomography (MRI-PET) image fusion, with a score of 4.67, close to the full score of 5. Conclusion: the proposed algorithm has better performance than other algorithms in preserving spatial details of MRI images and color information direction of SPECT images, and the other five groups have achieved similar results. IEEE, Export Date: 12 May 2022; Article

Intelligent reflecting surfaces (IRSs) are promising enablers for high-capacity wireless communication systems by constructing favorable channels between the transmitter and receiver. However, general, accurate, and tractable outage probability analysis for IRS-aided multiple-input-multiple-output (MIMO) systems is not available in the literature. In this paper, we first characterize the distribution of the mutual information (MI) for IRS-aided MIMO systems by capitalizing on large random matrix theory (RMT). Based on this result, a closed-form approximation for the outage probability is derived and a gradient-based algorithm is proposed to minimize the outage probability with statistical channel state information (CSI). We also investigate the diversity-multiplexing tradeoff (DMT) with the finite signal-to-noise ratio (SNR). Based on these theoretical results, we further study the impact of the IRS size on system performance. In the high SNR regime, we provide closed-form expressions for the ergodic mutual information (EMI) and outage probability as a function of the IRS size, which analytically reveal that the benefit of increasing the IRS size saturates quickly. Simulation results validate the accuracy of the theoretical analysis and confirm the increasing cost to improve system performance by deploying larger IRSs. For example, for an IRS-aided MIMO system with 20 antennas at both the transmitter and receiver, we need to double the size of the IRS to increase the throughput from 90% to 95% of its maximum value. IEEE

Offset spatial modulation (OSM) is an attractive multiple-input–multiple-output (MIMO) transmission scheme, which alleviates the switching frequency limitation of the radio frequency chain in the spatial modulation technology. In this article, we investigate the bit error performance of OSM in the presence of imperfect channel estimation toward a practical transmission scenario. More specifically, with the aid of union bound technique, a general performance analysis framework of the OSM system with channel estimation errors is proposed. Based on the above analysis framework, the upper bounds of bit error rate for the static and dynamic modes of OSM are both derived, respectively. Finally, simulation results demonstrate that the analytical bounds are tight for a wide range of signal-to-noise ratio (SNR) and variable system configurations., QC 20220216

In this paper, we propose a channel estimation scheme for spatial modulation (SM) systems. In general, SM systems require each transmit antenna to separately send pilot symbols for channel estimation. This is a lengthy process, which may incur significant channel estimation errors in a time varying channel. Thus, we propose correlation-based channel estimation (CBCE) scheme, which exploits the correlation between transmit antennas to estimate channels of inactive antennas using the pilot-based estimate of the active antenna. The change in the active channel from the last pilot-based estimate is calculated, and a time-proportionate amount of that change is scaled according to the channel correlation coefficients to estimate the channel state of the inactive channels. With pilot slots designed to spread out in a data frame, the estimation process for every channel is carried out $N_t$ times in a data frame with $N_t$ denoting the number of transmit antennas. We observe that in a high signal-to-noise ratio (SNR) regime, our proposed scheme provides about 2 dB and 5 dB gains compared to conventional channel estimation (CCE) method for moderately correlated and highly correlated antennas, respectively. Through Monte Carlo simulations with different correlation $\rho$ and user speeds, we validate our analysis and show that the proposed scheme outperforms CCE when $\rho \geq 0.3$, while it provides comparable performance for small $\rho$. © 1967-2012 IEEE.

In this paper, we present an achievable security scheme for an interference channel with arbitrary number of users. In this model, each receiver should be able to decode its intended message while it should remain ignorant regarding messages intended for other receivers. Our scheme relies ontransmitters to collectively ensure the confidentiality of the transmitted messages using a cooperative jamming technique and lattice alignment. The Asymmetric compute-and-forward framework is used to perform the decoding operation. The proposed scheme is the first asymptotically optimal achievable scheme for this security scenario which scales to arbitrary number of users and works for any finite-valued SNR. Also, our scheme achieves the upper bound sum secure degrees of freedom of 1 without using external helpers and thus the achievable rates lie within constant gap from sum secure capacity., QC 20210927

In this paper, a simplified deep neural network is proposed, which can be used for channel estimation and signal detection in OFDM system and reduce complexity. To be specific, the method of deep learning is introduced to optimize the channel estimation module of OFDM system. By building deep neural networks and training parameters at the signal-to-noise ratio of 10dB and 25dB, respectively, the channel estimation results can be optimized at a wider range of signal-to-noise ratio. In addition, the influence of training model size for channel estimation and signal detection is also researched. Compared with some other artificial intelligence aided OFDM receivers, proposed deep neural networks has shorter training time and simpler architecture. The simulation results show that by using proposed deep neural networks and training method in OFDM channel estimation, smaller mean square error and lower bit error rate can be obtained, especially in the case of clipping distortion and wide range of signal-to-noise ratio., QC 20220307Part of conference proceedings: ISBN 9781728188232

We consider the problem of reconstructing a complex-valued signal from its phase-only measurements. This framework can be considered as a generalization of the well-known one-bit compressed sensing paradigm where the underlying signal is known to be sparse. In contrast, the proposed formalism does not rely on the assumption of sparsity and hence applies to a broader class of signals. The optimization problem for signal reconstruction is formulated by first splitting the linear measurement vector into its phase and magnitude components and subsequently using the non-negativity property of the magnitude component as a constraint. The resulting optimization problem turns out to be a quadratic program (QP) and is solved using two algorithms: (i) alternating directions method of multipliers; and (ii) projected gradient-descent with Nesterov's momentum. Due to the inherent scale ambiguity of the phase-only measurement, the underlying signal can be reconstructed only up to a global scale-factor. We obtain high accuracy for reconstructing 1-D synthetic signals in the absence of noise. We also show an application of the proposed approach in reconstructing images from the phase of their measurement coefficients. The underlying image is recovered up to a peak signal-to-noise ratio exceeding 30 dB in several examples, indicating an accurate reconstruction., QC 20210322

Smart building management systems rely on sensors to optimize the operation of buildings. If an unauthorized user gains access to these sensors, a privacy leak may occur. This paper considers such a potential leak of privacy in a smart residential building, and how it may be mitigated by corrupting the measurements with additive Gaussian noise. This corruption is done in order to hide when the occupancy changes in an apartment. A lower bound on the variance of any estimator that estimates the change time is derived. The bound is then used to analyze how different model parameters affect the variance. It is shown that the signal to noise ratio and the system dynamics are the main factors that affect the bound. These results are then verified on a simulator of the KTH Live-In Lab Testbed, showing good correspondence with theoretical results., QC 20230307

In this paper, we propose a visual attention based least significant bit (LSB) data hiding method for 360° videos. In particular, information about the relative frequency of pixel access is used to control the amount of secret data to be hidden at the different latitudes of 360° videos. In this way, the typical behavior of users paying more attention to the equator region compared to the north and south poles when viewing 360° videos on a head-mounted display is taken into account. An analytical expression for capacity offered by this method to hide secret data in 360° cover videos is derived. A performance assessment of the visual attention based LSB data hiding method is conducted in terms of the peak signal-to-noise ratio (PSNR) and weighted-to-spherically-uniform PSNR (WS-PSNR) which both quantify the fidelity of a 360° stego-video with reference to the related 360° cover video. © 2020 IEEE., open access

This paper considers the uplink of a single-cell multi-user massive multiple-input multiple-output (MIMO) system. Each receiver antenna of the base station is assumed to be equipped with a pair of analog-to-digital converters (ADCs) to quantize the real and imaginary part of the received signal. We propose a novel Bussgang-based weighted zero-forcing (B-WZF) receiver, which distinguishes the clipping and granular distortion. Numerical results demonstrate that for sufficiently high SNR and users that do not experience deep large-scale fading, the B-WZF brings significant performance gain over existing linear receivers in the literature, when the training sequence length is higher than the number of users. © 2020 IEEE.

This paper considers the uplink of a single-cell multiuser massive multiple-input multiple-output system. Each receiver antenna of the base station (BS) is assumed to be equipped with a pair of analog-to-digital converters to quantize the real and imaginary part of the received signal. We propose a novel clipping-aware receiver (CA-MMSE), which performs minimum mean square error (MMSE) reconstruction only on the clipped received samples, while the granular samples are left unchanged after the quantization. On this basis, we present an iterative algorithm to implement the CA-MMSE receiver and derive a sufficient condition for its geometrical convergence to a fixed point. We show that as long as the number of BS antennas or the quantization resolution is sufficiently high, then, the performance of the CA-MMSE is as good as the optimal MMSE receiver which reconstructs all quantized received symbols. Additionally, we propose a novel Bussgang-based weighted zero-forcing (B-WZF) receiver which distinguishes the clipping and granular distortion and it is shown that as long as the received training symbols per antenna are correlated, the CA-MMSE brings significant improvements compared to conventional receivers in the literature while for users that do not experience deep large-scale fading the simpler B-WZF is near to the CA-MMSE for sufficiently high signal-to-noise ratio and quantization resolution. © 1972-2012 IEEE.

The spectrum of millimeter waves, when properly exploited, represents an important asset for achieving the high data rates and low latencies required by applications in vehicular communications. However, vehicle scenarios are characterized by high mobility, which results in frequent misalignment of beams. Therefore, in these types of scenarios, beam tracking algorithms are important because they can keep the beams aligned with a low training overhead. Kalman filters are strong candidates for the implementation of such algorithms, because they ensure high channel tracking performance over a wide range of signal-to-noise-ratios (SNR) and are easy to implement. This work evaluates the performance of channel tracking methods based on Kalman filters for high mobility. Our numerical results show that the proposed Kalman filter-based channel tracking method has high performance at low SNR regimes compared to least square-based methods, and improve the robustness when using planar instead of linear arrays. Moreover, the proposed channel tracking method is shown to perform well in multipath fading scenarios, while achieving high performance in the presence of strong line-of-sight components., QC 20200322

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made positive progress, but high-throughput CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables direct communication from ZigBee to WiFi. Without impacting the ongoing WiFi transmissions, ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state-of-the-art., QC 20211020

Exploiting highly directional antenna arrays to compensate for severe propagation loss is one of the defining features in millimeter-wave (mmWave) communications. With efficient beamforming techniques, mmWave transceivers can form steerable narrow beams. Therefore, different from convenient microwave networks, the signal or interference power in mmWave communications is highly directional and closely related to critical parameters such as interference distance and angles of departure/arrival. The high directivity implies that the co-channel interference among simultaneously active mmWave links can be expected to be significantly smaller than that for omnidirectional links and, meanwhile, the security of mmWave communications may also be enhanced. In this context, will the traditional interference mitigation and physical-layer security techniques still be efficient or necessary in mmWave networks? The answer may be negative in certain conditions. However, there is no detailed analysis on the conditions for this issue. In this paper, we jointly analyze the inter-beam interference and secrecy performance of mmWave communications for their close relation to signal-to-interference-plus-noise ratio. We also derive various performance limits (e.g., interference/eavesdropping distance limit, transmission power limit, offset angle limit, and beamwidth limit) of interference and eavesdropping immunity in mmWave networks. The theoretical and numerical results verify our analysis., QC 20190528

In this letter, we introduce time domain hybrid quadrature amplitude modulation (TDHQ) for the single sideband discrete multi-tone systems. The experimental results reveal that with a single precoding set and the proposed adaptive loading algorithm, the TDHQ scheme can achieve finer granularity and therefore smoother continuous growth of data rate than that with the conventional quadrature amplitude modulation. Besides, thanks to the frame construction and the tailored mapping rule, the scheme with TDHQ has an obviously better peak to an average power ratio., Export Date: 22 October 2018; Article; CODEN: IPTLE; Correspondence Address: Tang, M.; Wuhan National Laboratory for Optoelectronics, National Engineering Laboratory for Next Generation Internet Access System, School of Optical and Electronic Information, Huazhong University of Science and TechnologyChina; email: tangming@hust.edu.cn; Funding details: VR, Vetenskapsrådet; Funding details: Göran Gustafssons Stiftelse för Naturvetenskaplig och Medicinsk Forskning; Funding details: VINNOVA; Funding details: SSF, Stiftelsen för Strategisk Forskning; Funding details: 61722108, NSFC, National Natural Science Foundation of China; Funding details: 61331010, NSFC, National Natural Science Foundation of China; Funding details: SSF, Sjögren’s Syndrome Foundation; Funding details: visa0524@163.com;, HUST, Huazhong University of Science and Technology; Funding details: WNLO, Wuhan National Laboratory for Optoelectronics; Funding details: NSFC, National Natural Science Foundation of China; Funding text: Manuscript received June 29, 2018; revised August 11, 2018; accepted August 17, 2018. Date of publication August 21, 2018; date of current version September 5, 2018. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61331010 and Grant 61722108, in part by the Swedish Research Council (VR), in part by the Swedish Foundation for Strategic Research (SSF), in part by Göran Gustafsson Foundation, in part by the Swedish SRA ICT-TNG, VINNOVA SENDATE-FICUS, and SENDATE-EXTEND Projects. (Corresponding author: Ming Tang.) X. Chen, J. Cui, L. Gan, T. Jiang, M. Tang, S. Fu, and D. Liu are with the Wuhan National Laboratory for Optoelectronics and the National Engineering Laboratory for Next Generation Internet Access System, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: hust_chenxi@ hust.edu.cn; jingxian@hust.edu.cn; lingan@hust.edu.cn; visa0524@163.com; tangming

In this paper, we employ power-domain non-orthogonal multiple access (NOMA) to simultaneously transmit signals to both a primary user (PU) and a secondary user (SU) of a cognitive cooperative radio network (CCRN). Higher priority is given to the PU over the SU by keeping the power allocation coefficients at the base station (BS) and relay (R) above a certain threshold. In this way, similar as the interference power limit imposed by the PU in a conventional underlay CCRN, the power allocation coefficients at the BS and R of the CCRN can be controlled to maintain a given outage performance. Analytical expressions of the cumulative distribution function of the end-to-end signal-to-interference-plus-noise ratios at the PU and SU are derived and then used to assess the outage probabilities of both users. Numerical results are presented to study the impact of system parameters on outage performance of the CCRN with power-domain NOMA. In addition, it is illustrated that increased downlink performance can be obtained by combining power-domain NOMA with CCRNs. © 2018 IEEE.

This paper addresses the problem of mutual interference between automotive radars. This problem is getting more attention with an increase in the number of radar systems used in traffic. An adaptive digital beamforming technique is presented here which suppresses the interference without the exact knowledge of the interfering signal's Direction of Arrival (DoA). The proposed technique is robust and does not rely on any calibration for the interference cancellation. The adaptive interference suppression method is evaluated using a simulated scenario. Up to about 20-23 dB improvement in the target Signal to Interference and Noise Ratio (SINR) is measured in the simulation and a better detection performance is achieved using the proposed interference suppression technique. © 2018 IEEE.