Modern wireless systems are expected to support both communication and sensing, forming the foundation of Integrated Sensing and Communication (ISAC). Although many approaches have been proposed, practical and efficient solutions remain an open challenge. This work addresses this issue by constraining transmitted MIMO radar waveforms to finite-alphabet signals, which are better suited for power amplifiers and signal processing hardware. Under this setting, we investigate methods to jointly enhance communication and sensing performance. First, by exploiting the beampattern-invariance property, we reduce the computational complexity of an existing finite-alphabet waveform generation method and address its limitations to further improve performance. Furthermore, we propose beampattern-preserving schemes that embed communication data into radar waveforms without altering the beampattern, enabling practical ISAC implementation. Specifically, two information-encoding methods and a new precoding technique are introduced, together with an extension to multi-user scenarios based on time and code division. Simulation results demonstrate the trade-off between radar and communication performance.

This paper, addressing the integration requirements of radar imaging and communication for High-Altitude Platform Stations (HAPs) platforms, designs a waveform based on linear frequency modulated (LFM) frequency-hopping signals that combines synthetic aperture radar (SAR) and communication functionalities. Specifically, each pulse of an LFM signal is segmented into multiple parts, forming a sequence of sub-pulses. Each sub-pulse can adopt a different carrier frequency, leading to frequency hops between sub-pulses. This design is termed frequency index modulation (FIM), enabling the embedding of communication information into different carrier frequencies for transmission. To further enhance the data transmission rate at the communication end, this paper incorporates quadrature amplitude modulation (QAM) into waveform design. The paper derives the ambiguity function of the proposed waveform and analyzes its Doppler and range resolution, establishing upper and lower bounds for the range resolution. In processing SAR signals, the receiver first removes QAM symbols, and to address phase discontinuities between sub-pulses, a phase compensation algorithm is proposed to achieve coherent processing. For the communication receiver, the user first performs de-chirp processing and then demodulates QAM symbols and FIM index symbols using a two-step maximum likelihood (ML) algorithm. Numerical simulations further confirm the theoretical validity of the proposed approach.

The Dual-function radar communication (DFRC) design aims to exploit the tractability and reusability of both radar and communication systems’ components, parameters, and spectrum to achieve an integrated system. In this work, we propose a novel DFRC signal design by leveraging the phenomenon of the MIMO beampattern invariance, which was comprehensively explored in our previous work. It is demonstrated that the flexibility afforded by this beampattern invariance can be used to construct an integrated waveform while maintaining the required beam. A beampattern-preserving unitary transformation of the MIMO weight matrix is proposed to realize a desired information-bearing symbol at the communication receiver. The proposed method has the following promising features: 1) low-complexity, closed-form solution for the transmitted signals; 2) flexibility of modulation scheme; 3) independence from the type of orthonormal MIMO waveforms; 4) independence from the beampattern design; 5) adaptability for multi-target and multi-user scenarios. Using this generic signal design method, a number of modulation schemes are implemented. Furthermore, the impact of this operation on the estimation accuracy of the target parameters is analyzed by computing their respective Cramer-Rao Lower Bounds (CRLBs). Simulation results show that modulation schemes yielding a range of symbol error rate (SER) performances can be implemented while keeping the radar parameter estimation unaffected.

The conventional method of designing the desired beampattern in MIMO systems involves the computation of the waveform covariance and/or weight matrices. In this work, through some simple derivations, it is demonstrated that there are infinitely many covariance matrices that yield the same beampattern. Furthermore, when beam is created using weighted sums of orthogonal waveforms, there are infinitely many weight matrices that also generate the same beampattern. Thus, the conditions for the beampattern invariance are formulated with respect to the covariance and the weight matrices. This theoretical foundation allows the transmitted waveform to be altered, without changing the beampattern and the orthogonal radar waveforms. Methods for the computation of beampattern-invariant covariance and weight matrices are proposed. Consequently, it is demonstrated that there is additional degrees of freedom in the design space of many applications, such as the dual-function radar communication (DFRC), in which information can be embedded in radar transmissions while keeping the radar function intact. Other potential applications are peak-to-average power ratio (PAPR) reduction at the radar transmitter and deceptive jamming avoidance.

Conventional radars repeat the transmission of the same waveform after a predefined interval of time called pulse-repetition-interval (PRI). This technique helps estimate the range and Doppler shift of the targets and suppress clutter. However, in dual-function radar communication (DFRC), a different symbol waveform is transmitted after each PRI. Depending on the number of targets, radar receiver output yields several peaks representing different targets’ ranges. Each peak comes with its side-lobes called range-side-lobes (RSL). In DFRC, due to different symbol waveform transmission, peaks and RSLs do not remain coherent, making Doppler shift estimation and clutter suppression challenging tasks. In most of the available literature, iterative receive filters have been designed for DFRC to minimize RSLs and achieve coherent output for different waveforms. However, the proposed receive filter does not guarantee coherent output for more than two waveforms. In contrast, we proposed two novel closed-form algorithms to design receive filters for DFRC that guarantee coherent output response for several waveforms and suppress RSLs. Simulation results demonstrate that the proposed receivers achieve full coherency, and the RSLs are significantly lower than the conventional method. Furthermore, the advantage of achieving coherent output response is shown in target detection and bit-error-rate improvement.

By exploiting fractional-Fourier-transform (FrFT), a novel technique for the QRS complex detection is proposed. The application of the FrFT rotates the Electrocardiograph (ECG) signal in the time-frequency plane. We claim this rotation can give simple and effective QRS complex detection even in the presence of versatile artifacts, such as left-bundle-branch-block, right-bundle-branch-block, and negative polarization. In this work, in the first step, the noise and baseline drifts are removed by applying a wavelet transform on the given ECG signal. While, in the next step, the clean ECG signal is passed through the proposed algorithm, which rotates the ECG signal in the time-frequency plane and detects the QRS complex very easily. The proposed algorithm validated over the 48 signals of the MIT-BIH arrhythmia database, and it yielded 26 false-positive and only five false-negatives compared to the 80 and 42, the best result reported so far.

In contrast to phased-array radar, frequency-diverse-array (FDA) radar transmits signals of linearly increasing frequencies across the array. As a consequence, the beampattern of an FDA radar becomes range, angle, and time dependent, which is different from only angle dependent beampattern of phased-array radar. The main limitation of FDA is its shorter dwell time. In this work, a novel algorithm with low complexity is proposed to focus the transmitted power of an FDA radar in the desired region-of-interest for longer dwell time. The proposed algorithm exploits the discrete-Fourier-transform and provides closed-form solution to find the weights of individual antenna elements of the array. The proposed algorithm is validated through simulation on both continuous-wave (CW) and pulsed FDA radars. Moreover, in contrast to the "S" shaped beampattern of the conventional CW-FDA radar, which is more difficult to deal with at the receiver, the beampattern of our proposed CW-FDA radar changes linearly with respect to the range.

MOTIVATION: Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types. Promoters may have both intra- and interclass variation and similarity in terms of consensus sequences. Accurate classification of various types of sigma promoters still remains a challenge. RESULTS: We present iPromoter-BnCNN for identification and accurate classification of six types of promoters-σ24,σ28,σ32,σ38,σ54,σ70. It is a CNN-based classifier which combines local features related to monomer nucleotide sequence, trimer nucleotide sequence, dimer structural properties and trimer structural properties through the use of parallel branching. We conducted experiments on a benchmark dataset and compared with six state-of-the-art tools to show our supremacy on 5-fold cross-validation. Moreover, we tested our classifier on an independent test dataset. AVAILABILITY AND IMPLEMENTATION: Our proposed tool iPromoter-BnCNN web server is freely available at http://103.109.52.8/iPromoter-BnCNN. The runnable source code can be found https://colab.research.google.com/drive/1yWWh7BXhsm8U4PODgPqlQRy23QGjF2DZ. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we propose studying the disease spread mechanism in the atmosphere as an engineering problem. Aerosol transmission is the most significant mode among the viral transmission mechanisms that do not include physical contact, where airflows carry virus-laden droplets over long distances. Throughout this work, we study the transport of these droplets as a molecular communication problem, where one has no control over the transmission source, but a robust receiver can be designed using bio-sensors. To this end, we present a complete system model and derive an end-to-end mathematical model for the transmission channel under certain constraints and boundary conditions. We derive the system response for both continuous sources such as breathing and jet or impulsive sources such as coughing and sneezing. In addition to transmitter and channel, we assumed a receiver architecture composed of air sampler and Silicon Nanowire field-effect transistor. Then, we formulate a detection problem to maximize the likelihood decision rule and minimize the corresponding missed detection probability. Finally, we present several numerical results to observe the impact of parameters that affect the performance and justify the feasibility of the proposed setup in related applications.

MOTIVATION: Extracting useful feature set which contains significant discriminatory information is a critical step in effectively presenting sequence data to predict structural, functional, interaction and expression of proteins, DNAs and RNAs. Also, being able to filter features with significant information and avoid sparsity in the extracted features require the employment of efficient feature selection techniques. Here we present PyFeat as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs and RNAs. To build PyFeat we mainly focused on extracting features that capture information about the interaction of neighboring residues to be able to provide more local information. We then employ AdaBoost technique to select features with maximum discriminatory information. In this way, we can significantly reduce the number of extracted features and enable PyFeat to represent the combination of effective features from large neighboring residues. As a result, PyFeat is able to extract features from 13 different techniques and represent context free combination of effective features. The source code for PyFeat standalone toolkit and employed benchmarks with a comprehensive user manual explaining its system and workflow in a step by step manner are publicly available. RESULTS: https://github.com/mrzResearchArena/PyFeat/blob/master/RESULTS.md. AVAILABILITY AND IMPLEMENTATION: Toolkit, source code and manual to use PyFeat: https://github.com/mrzResearchArena/PyFeat/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Aerosol Transmission is one of the major spread mechanism for diseases and is responsible for transmission of virus over long distances. The advancement in nanotechnology has resulted in sensors and systems that allow us to deal with nanosized biological entities such as virus and bacteria. In this work, the idea of viewing virus transmission through aerosols and their transport as a molecular communication problem is introduced. In such problems one has little or no control over transmission, however, a robust receiver can be designed using nano-biosensors for information extraction. Thus, the objective of this work is to treat viral aerosol spread as a blind communication problem and present a mathematical model for it. Specifically, we study the virus transmission from an engineering perspective and derive an end-to-end mathematical model for virus transmission in the atmosphere. The receiver architecture composed of air sampler and Silicon Nanowire field effect transistor is also discussed. Furthermore, a detection problem is formulated and simulation results are reported that justify the feasibility of such setups in bio-monitoring applications.

In this paper, we consider a bistatic multiple-input multiple-output (MIMO) radar. We propose a reduced complexity algorithm to estimate the direction-of-arrival (DOA) and direction-of-departure (DOD) for moving target. We show that the calculation of parameter estimation can be expressed in terms of one-dimensional fast-Fourier-transforms which drastically reduces the complexity of the optimization algorithm. The performance of the proposed algorithm is compared with the two-dimension multiple signal classification (2D-MUSIC) and reduced-dimension MUSIC (RD-MUSIC) algorithms. It is shown by simulations, our proposed algorithm has better estimation performance and lower computational complexity compared to the 2D-MUSIC and RD-MUSIC algorithms. Moreover, simulation results also show that the proposed algorithm achieves the Cramer-Rao lower bound.

In multiple-input multiple-output radar systems, it is usually desirable to steer transmitted power in the region-of-interest. To do this, conventional methods optimize the waveform covariance matrix,R, for the desired beampattern, which is then used to generate actual transmitted waveforms. In this paper, we provide a low complexity closed-form solution to design covariance matrix for the given planar beampattern using the planar array, which is then used to derive a novel closedform algorithm to directly design the finite-alphabet constantenvelope waveforms. The proposed algorithm exploits the two-dimensional fast-Fourier-transform. The performance of our proposed algorithm is compared with the existing methods that are based on semi-definite quadratic programming with the advantage of a considerably reduced complexity.

In this paper, a new detector is proposed for an amplify-and-forward (AF) relaying system. The detector is designed to minimize the symbol-error-rate (SER) of the system. The SER surface is non-linear and may have multiple minimas, therefore, designing an SER detector for cooperative communications becomes an optimization problem. Evolutionary based algorithms have the capability to find the global minima, therefore, evolutionary algorithms such as particle swarm optimization (PSO) and differential evolution (DE) are exploited to solve this optimization problem. The performance of proposed detectors is compared with the conventional detectors such as maximum likelihood (ML) and minimum mean square error (MMSE) detector. In the simulation results, it can be observed that the SER performance of the proposed detectors is less than 2 dB away from the ML detector. Significant improvement in SER performance is also observed when comparing with the MMSE detector. The computational complexity of the proposed detector is much less than the ML and MMSE algorithms. Moreover, in contrast to ML and MMSE detectors, the computational complexity of the proposed detectors increases linearly with respect to the number of relays.

In multiple-input multiple-output (MIMO) radar setting, it is often desirable to design correlated waveforms such that power is transmitted only to a given set of locations, a process known as beampattern design. To design desired beam-pattern, current research uses iterative algorithms, first to synthesize the waveform covariance matrix, R, then to design the actual waveforms to realize R. In contrast to this, we present a closed form method to design R that exploits discrete Fourier transform and Toeplitz matrix. The resulting covariance matrix fulfills the practical constraints and performance is similar to that of iterative methods. Next, we present a radar architecture for the desired beampattern that does not require the synthesis of covariance matrix nor the design of correlated waveforms.

In this work to exploit the benefits of both multiple-input multiple-output (MIMO)-radar and phased-array a waveform covariance matrix is proposed. Our analytical results show that the proposed covariance matrix yields gain in signal-to-interference-plus-noise ratio (SINR) compared to MIMO-radar while the gain in SINR is close to phased-array and recently proposed phased-MIMO scheme. Transmitted waveforms with the proposed covariance matrix, at the receiver, significantly supress the side-lobe levels compared to phased-array, MIMO-radar, and phased-MIMO schemes. Moreover, in contrast to phased-MIMO our proposed scheme allows same power transmission from each antenna. Simulation results validate the analytical results.

Multiple input multiple output (MIMO) radars have many advantages over their phased-array counterparts: improved spatial resolution; better parametric identifiably and greater flexibility to acheive the desired transmit beampattern. The desired transmit beampatterns using MIMO-radar requires the waveforms to have arbitrary auto- and cross-correlations. To design such waveforms, generally a waveform covariance matrix, R, is synthesised first then the actual waveforms are designed. Synthesis of the covariance matrix, R, is a constrained optimisation problem, which requires R to be positive semidefinite and all of its diagonal elements to be equal. To simplify the first constraint the covariance matrix is synthesised indirectly from its square-root matrix U, while for the second constraint the elements of the m-th column of U are parameterised using the coordinates of the m-hypersphere. This implicitly fulfils both of the constraints and enables us to write the cost-function in closed form. Then the cost-function is optimised using a simple particle-swarm-optimisation (PSO) technique, which requires only the cost-function and can optimise any choice of norm cost-function.

A method for generating constant envelope (CE) waveforms to realise a given covariance matrix for a closely spaced MIMO radar system is proposed. In contrast to available algorithms, the technique provides closed form solutions for finding the required waveforms and suggests that waveforms can be chosen from finite alphabets such as binary-phase shift keying (BPSK) and quadrature-phase shift keying (QPSK). Gaussian random-variables (RV's) are mapped onto CE non-Gaussian RV's using memoryless non-linear functions. The relationship between the correlation of Gaussian RV's at the input to the nonlinear functions and non-Gaussian RV's at their output is established. Simulation results are presented to demonstrate the effectiveness of the methodology.

In his work, we propose a relatively low-complexity iterative algorithm for the detection of transmitted symbols at the uplink of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA) system. The algorithm allows distinct frequency-offsets (FO)s beween each user and the base-station (BS). FOs cause inter-carrier-interference (ICI), which degrades the performance of the receiver and increases the computational-complexity o decode the transmitted symbols. In order o decrease the computational-complexity at each receive antenna the proposed algorithm accounts for the interference of subcarrier k onto only ±D nearby subcarriers and ignores the interference on remaining subcarriers. This yields a banded structured ICI matrix, which is exploited o design a low-complexity soft-interference-cancellation minimum mean-squared error (SIC-MMSE) equalizer. The effects of ignoring the subcarriers are compensated for by increasing the number of receive antennas. Simulation results show ha for an uncoded system the bit-error-rate (BER) performance of the proposed algorithm outperforms the MMSE equalization and is very much close to the no FO scenario.

In this work, we propose an iterative algorithm for the detection of transmitted symbols at the uplink of an orthogonal frequency-division multiple access (OFDMA) system. The algorithm allows distinct frequency-offsets (FO)s from each user that cause self- and multiple-access-interference. The proposed algorithm squeezes the interference of subcarrier k into 2D + 1 nearby subcarriers by preprocessing the received signal, which yields a banded structure interference matrix. This banded structure is exploited to realize a low complexity iterative soft-interference-cancellation minimum mean-squared error (SIC-MMSE) equalizer that can be used in Turbo equalization. Simulation results show that the bit-error-rate (BER) performance of the proposed algorithm outperforms existing detection algorithms and is very much close to the zero-FO frequency- domain-equalization (zero-FO-FDE) at low computational cost.

In this work, we propose an iterative algorithm for the detection of transmitted symbols at the uplink of an orthogonal frequency-division multiple access (OFDMA) system. The algorithm allows distinct frequency-offsets (FO)s from each user that cause multiple-access and self interference. The proposed algorithm squeezes the interference of subcarrier k into 2D + 1 nearby subcarriers by preprocessing the received signal and yields a banded structure interference matrix. Here, the value of D depends on the FO and determines the squeezing depth. The proposed algorithm exploits this banded structure and realizes a low complexity iterative soft-interference-cancellation minimum mean-squared error (SIC-MMSE) equalizer that can be used in Turbo equalization. Simulation results show that the bit-error-rate (BER) performance of the proposed algorithm outperforms existing detection algorithms and is very much close to the zero-FO frequency-domain-equalization (zero-FO-FDE).

In recent years, due to the low complexity nature of the turbo processing and excellent bit-error-rate (BER) performance, designing turbo-like receivers for frequency-selective MIMO channels has been of great research interest. The performance gain in turbo decoders is due to an extrinsic information transfer (EXIT) process between the detection and the decoding stages as compare to a traditional system that treats these processes in isolation. However, the challenge faced with these iterative receivers is the understanding of their performance and convergence behaviour. In this paper, we study an iterative maximum a posteriori (MAP) receiver for MIMO orthogonal frequency division multiplexing (OFDM) channels and its convergence behaviour. We analyze the performance of the proposed transceiver system with Gray and anti-Gray mapping using EXIT chart and study the effects of various settings of transmit and receive antenna on the turbo cliff in BER performance.

The application of turbo principle in designing receivers for multiple-input multiple-output (MIMO) wireless systems not only achieves practical complexity receiver systems but also near optimal performances for many of the next generation systems. In particular, our recent research has shown that significant performance can be achieved by using a low complexity iterative soft interference cancellation minimum mean-squared error (SIC-MMSE) equalizer in various coded MIMO wireless channels including frequency-selective MIMO channels (S. Ahmed et al., 2006). This performance gain is due to an extrinsic information exchange process between the equalization and the channel decoding stages compared to a traditional system that treats these processes in isolation. However, the challenge faced with these iterative receivers is the understanding of their convergence behaviour. In this paper, to better understand the convergence behaviour of the proposed iterative receiver, we study the notion of extrinsic information transfer (EXIT) characteristics. Using simulations, we derive the extrinsic information trajectory on the EXIT chart at various Eb/N0ranges to confirm the convergence of the proposed equalizer.

Orthogonal frequency division multiplexing (OFDM) requires an expensive linear amplifier at the transmitter due to its high peak-to-average power ratio (PAPR). Single carrier with cyclic prefix (SC-CP) is a closely related transmission scheme that possesses most of the benefits of OFDM but does not have the PAPR problem. Although in a multipath environment, SC-CP is very robust to frequency-selective fading, it is sensitive to the time-selective fading characteristics of the wireless channel that disturbs the orthogonality of the channel matrix (CM) and increases the computational complexity of the receiver. In this paper, we propose a time-domain low-complexity iterative algorithm to compensate for the effects of time selectivity of the channel that exploits the sparsity present in the channel convolution matrix. Simulation results show the superior performance of the proposed algorithm over the standard linear minimum mean-square error (L-MMSE) equalizer for SC-CP.

We consider estimation of frequency offset (FO) and equalization of a wireless communication channel, within a general framework which allows for different frequency offsets for various multipaths. Such a scenario may arise due to different Doppler shifts associated with various multipaths, or in situations where multiple basestations are used to transmit identical information. For this general framework, we propose an approximative maximum-likelihood estimator exploiting the correlation property of the transmitted pilot signal. We further show that the conventional minimum mean-square error equalizer is computationally cumbersome, as the effective channel-convolution matrix changes deterministically between symbols, due to the multiple FOs. Exploiting the structural property of these variations, we propose a computationally efficient recursive algorithm for the equalizer design. Simulation results show that the proposed estimator is statistically efficient, as the mean-square estimation error attains the Crame/spl acute/r-Rao lower bound. Further, we show via extensive simulations that our proposed scheme significantly outperforms equalizers not employing FO estimation.