Complexity Trade-off Research Articles

Intrinsic interference suppressed FBMC QAM for MU-MIMO systems in computing and communications

PurposeIn order to optimize BER and to substantiate performance measures, initially, the filter bank multicarrier (FBMC) quadrature amplitude modulation (QAM) performance metrics are evaluated with the cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) system. The efficiency of CP-OFDM, as well as FBMC/QAM that is transmitting over specific fading channels, is evaluated in terms of quality trade-off metrics over bit error rate (BER) as well as modulation order. When compared with the traditional FBMC systems, the proposed FBMC QAM system shows better performance. The performance metrics of FBMC/QAM with the inclusion of multiuser multiple-input-multiple-output (MUMIMO) is validated with worst case channel environment. The performance penalty gap that exists in CP- OFDM is compared with improved FBMC QAM in terms of both BER and OOB radiation measures. The BER trade off comparison between ML and MMSE optimally determine the prominent signal detection model for high performance FBMC QAM system.Design/methodology/approachThe main objective of this research work is to provide perceptions about performance, co-channel interference avoidance as well as about the techniques that are used for minimizing the complexity of the system that is related to FBMC QAM structure for reducing intrinsic interference with higher spectral features as well as maximal likelihood (ML) detector systems.FindingsThis research work also looks at the efficiency of multiuser multiple-input-multiple-output (MU-MIMO) FBMC/QAM over nonlinear channels. Furthermore, when compared with OFDM, it also significantly reduces the penalty gap efficiency, thereby enabling the accessibility of the proposed FBMC QAM system from BER as well as implementation point of view. Finally, the signal detection is facilitated by the sub-detector and is achieved on the downlink side by making use of threshold-driven statistical measures that accurately minimize the complexity trade-off measures of the ML detector over modulation order. The computation of the proposed FBMC method’s BER performance measures was carried out through MATLAB simulation environments, as well as efficiency of the suggested work was demonstrated through detailed analyses.Originality/valueThis research work intend to combine the efficient MU-MIMO based transmission scheme with optimal FBMC/QAM for improved QoS over highly nonlinear channels which includes both delay spread and Doppler effects. And optimal signal detection model is facilitated at the downlink side by making use of threshold-driven statistical measures that accurately minimize the complexity trade-off measures of the ML detector over modulation order. The computation of the proposed FBMC method’s BER performance measures was carried out through MATLAB simulation environments, as well as efficiency of the suggested work was demonstrated through detailed analyses.

BLAST PERFORMANCE IN MIMO WIRELESS COMMUNICATION SYSTEMS

Recently, a new detection algorithm for the Vertical Bell Laboratories Layered Space Time (V-BLAST) system, which we call Enhanced-V-BLAST, or simply E-V-BLAST, was proposed. V-BLAST has demonstrated very high spectral efficiency, there is a wide gap between the original V-BLAST algorithm and the Maximum Likelihood (ML) algorithm. Recently, some algorithms have been presented to make narrower this gap. In a new detection algorithm (Enhanced (E) V BLAST) for V-BLAST system was presented which showed significantly better performance and flexibility than the original V-BLAST detection. The E-V-BLAST algorithm gets closer to the optimum detection Maximum Likelihood (ML) algorithm by making decisions based on multiple symbols. The Enhanced (E)-V-BLAST has two parameters which can be adjusted to achieve a desired performance and complexity trade-off. In the present paper, we analyze the performance of EV-BLAST as a function of the two inherent adjustable parameters of E-V-BLAST. On the basis of this analysis, we obtain some useful characteristics of the E-V-BLAST algorithm which allow one to achieve the desired performance and complexity trade-off.

MSLM-RF: A Spatial Feature Enhanced Random Forest for On-Board Hyperspectral Image Classification

Hyperspectral imaging (HSI) greatly improves the capacity to identify and monitor ground objects due to the high spectral resolution. As the real-time remote sensing monitoring and warning tasks are getting more attention, new algorithms for low-power on-board classification are required to reduce the transmission time of satellite downlink. In this paper, we propose the Multi-Scale Local Maximum Random Forest (MSLM-RF) to significantly reduce the energy consumption while retaining high classification accuracy. The proposed MSLM-RF uses multi-scale maximum filters for spatial feature extraction and Random Forest for classification after spectral and spatial features fusion. The spatial features are efficiently extracted with low computational complexity by regarding the maximum light intensity values in different ranges of pixels as anchor points. MSLM-RF only consists of integer comparisons and a few additions, thereby eliminating the energy-hungry operations such as multiplication and exponentiation. According to experimental results on the HSI benchmark datasets, MSLM-RF delivers a better trade-off in accuracy and computational complexity than the state-of-the-art classification algorithms. Besides, MSLM-RF gets higher average classification accuracy and lower energy consumption than the previous on-board algorithms. The obtained results show the suitability of the proposed algorithm to accomplish practical real-time classification tasks on-board with low energy consumption.

Managing Digital Integration Routines in Engineering Firms: Cases of Disruptive BIM Cloud Collaboration Protocols

The challenge for engineering firms to align with an unprecedented variation of digital integration agendas across projects is a recent phenomenon. The literature has offered only a partial understanding of this challenge due to the episodic focus on the digital delivery of individual projects or the initial adoption of digital practice in firms. Through a longitudinal ethnographic study of an engineering firm, this paper explored how practitioners act upon the surge of custom digital integration agendas across multiple projects. By looking at and beyond a large portfolio of projects that demanded building information modeling (BIM) cloud collaboration, this study found that disparate digital agendas across projects disrupt digital integration routines, and they have knock-on effects that can overreach project boundaries and impact projects portfolios and firm operations. With this evidence, this research spotlights the shifting loci of information integration in practice, wherein custom agendas pressure practitioners to shift the focus of integration from their permanent practice at firms to temporary workflows in projects. By revealing organizational responses to this phenomenon, this paper introduces the concept of genericity–complexity trade-off in digital integration routines to explain how firms generate routines that address integration either with ad hoc responses in projects or with systemized integrative efforts across all constellations of practice.

Single- Versus Multicarrier Terahertz-Band Communications: A Comparative Study

The prospects of utilizing single-carrier (SC) and multi-carrier (MC) waveforms in future terahertz (THz)-band communication systems remain unresolved. On the one hand, the limited multi-path components at high frequencies result in frequency-flat channels that favor low-complexity wideband SC systems. On the other hand, frequency-dependent molecular absorption and transceiver characteristics and the existence of multi-path components in indoor sub-THz systems can still result in frequency-selective channels, favoring off-the-shelf MC schemes such as orthogonal frequency-division multiplexing (OFDM). Variations of SC/MC designs result in different THz spectrum utilization, but spectral efficiency is not the primary concern with substantial available bandwidths; baseband complexity, power efficiency, and hardware impairment constraints are predominant. This paper presents a comprehensive study of SC/MC waveforms for THz communications, utilizing an accurate wideband THz channel model and highlighting the various performance and complexity trade-offs of the candidate schemes. Simulations demonstrate that discrete-Fourier-transform spread orthogonal time-frequency space (DFT-s-OTFS) achieves a lower peak-to-average power ratio (PAPR) than OFDM and OTFS and enhances immunity to THz impairments and Doppler spreads, but at an increased complexity cost. Moreover, DFT-s-OFDM is a promising candidate that increases robustness to THz impairments and phase noise (PHN) at a low PAPR and overall complexity.

Throughput maximization of an IRS-assisted wireless powered network with interference: A deep unsupervised learning approach

We consider an intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) in which a multi antenna power beacon (PB) sends a dedicated energy signal to a wireless powered source. The source first harvests energy and then utilizing this harvested energy, it sends an information signal to destination where an external interference may also be present. For the considered system model, we formulated an analytical problem in which the objective is to maximize the throughput by jointly optimizing the energy harvesting (EH) time and IRS phase shift matrices. The optimization problem is high dimensional non-convex, thus a good quality solution can be obtained by invoking any state-of-the-art algorithm such as Genetic algorithm (GA). It is well-known that the performance of GA is generally remarkable, however it incurs a high computational complexity. To this end, we propose a deep unsupervised learning (DUL) based approach in which a neural network (NN) is trained very efficiently as time-consuming task of labeling a data set is not required. Numerical examples show that our proposed approach achieves a better performance–complexity trade-off as it is not only several times faster but also provides almost same or even higher throughput as compared to the GA.

Bandwidth-Agile Image Transmission With Deep Joint Source-Channel Coding

We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels. We consider the scenario in which images are transmitted progressively in layers over time or frequency, and such layers can be aggregated by receivers in order to increase the quality of their reconstructions. We investigate two scenarios, one in which the layers are sent sequentially, and incrementally contribute to the refinement of a reconstruction, and another in which the layers are independent and can be retrieved in any order. Those scenarios correspond to the well known problems of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">successive refinement</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple descriptions</i> , respectively, in the context of joint source-channel coding (JSCC). We propose DeepJSCC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> , an innovative solution that uses convolutional autoencoders, and present three architectures with different complexity trade-offs. To the best of our knowledge, this is the first practical multiple-description JSCC scheme developed and tested for practical information sources and channels. Numerical results show that DeepJSCC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> can learn to transmit the source progressively with negligible losses in the end-to-end performance compared with a single transmission. Moreover, DeepJSCC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional advantage of graceful degradation with channel SNR.

Performance and Security Analysis of Parameter-Obfuscated Analog Circuits

In this article, key-based obfuscation of the transistor dimensions is proposed to mask the biasing conditions of an analog circuit and, therefore, protect the circuit against intellectual property (IP) piracy. Vector- and mesh-based obfuscations are developed that provide different degrees of circuit security with tradeoffs in design complexity and area. An algorithm for the selection of an obfuscation transistor and a satisfiability modulo theory (SMT)-based algorithm that searches the design space to determine the dimensions of the obfuscation transistors are developed to reduce the computational complexity of designing and implementing the proposed parameter obfuscation techniques. The parameter obfuscation techniques, along with the developed algorithms, are implemented on an active inductor-based second-order bandpass filter (BPF) and an operational amplifier (op-amp). The results from the analysis of the obfuscated BPF and op-amp indicate that the critical circuit performances are properly locked with at least 15% variation from the target circuit parameters when setting incorrect transistor sizes. A simulation-based optimization algorithm is proposed to tune the biasing conditions and transistor body voltages, which mitigates the effects of both the parasitic impedance of the circuit and any variation due to the implementation of the obfuscation circuitry. The proposed simulation-based optimization algorithm determines the biasing conditions and body voltages of the BPF in 500 iterations and the op-amp circuit in 70 iterations, which provides a significant reduction in the design time and the number of circuit recycles. Implementing the parameter obfuscation technique with the proposed algorithms provides an efficient means to secure analog circuits while reducing the design time to implement security features.

CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection With Low Complexity

Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several ML-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems. The main motivation behind is that the complexity of Maximum A-Posteriori (MAP) detection grows exponentially with system dimensions. Instead of using DNNs, essentially being a black-box, we take a slightly different approach and introduce a probabilistic Continuous relaxation of disCrete variables to MAP detection. Enabling close approximation and continuous optimization, we derive an iterative detection algorithm: Concrete MAP Detection (CMD). Furthermore, extending CMD by the idea of deep unfolding into CMDNet, we allow for (online) optimization of a small number of parameters to different working points while limiting complexity. In contrast to recent DNN-based approaches, we select the optimization criterion and output of CMDNet based on information theory and are thus able to learn approximate probabilities of the individual optimal detector. This is crucial for soft decoding in today's communication systems. Numerical simulation results in MIMO systems reveal CMDNet to feature a promising accuracy complexity trade-off compared to State of the Art. Notably, we demonstrate CMDNet's soft outputs to be reliable for decoders.

Joint design of content delivery and recommendation in wireless caching networks

Although content caching and recommendation are two complementary approaches to improve the user experience, it is still challenging to provide an integrated paradigm to fully explore their potential, due to the high complexity and complicated tradeoff relationship. To provide an efficient management framework, the joint design of content delivery and recommendation in wireless content caching networks is studied in this paper. First, a joint transmission scheme of content objects and recommendation lists is designed with edge caching, and an optimization problem is formulated to balance the utility and cost of content caching and recommendation, which is an mixed integer nonlinear programming problem. Second, a reinforcement learning based algorithm is proposed to implement real time management of content caching, recommendation and delivery, which can approach the optimal solution without iterations during each decision epoch. Finally, the simulation results are provided to evaluate the performance of our proposed scheme, which show that it can achieve lower cost than the existing content caching and recommendation schemes.

Sequential Decoding of Short Length Binary Codes: Performance Versus Complexity

Sequential decoding of short length binary codes for the additive white Gaussian noise channel is considered. A variant of the variable-bias term (VBT) metric is introduced, producing useful trade-offs between performance and computational complexity. Comparisons are made with tail-biting convolutional codes decoded with a wrap-around Viterbi algorithm (WAVA) and with polar codes under successive-cancellation list (SCL) decoding. It is found that sequential decoding with the improved VBT metric has a better performance&#x2013;complexity tradeoff than tail-biting codes under WAVA decoding (except at low complexities) but a worse performance&#x2013;complexity tradeoff than polar codes under SCL decoding (except at high complexities).

Application of Deep Learning to Sphere Decoding for Large MIMO Systems

Although the sphere decoder (SD) is a powerful detector for multiple-input multiple-output (MIMO) systems, it has become computationally prohibitive in massive MIMO systems, where a large number of antennas are employed. To overcome this challenge, we propose fast deep learning (DL)-aided SD (FDL-SD) and fast DL-aided $K$-best SD (KSD, FDL-KSD) algorithms. Therein, the major application of DL is to generate a highly reliable initial candidate to accelerate the search in SD and KSD in conjunction with candidate/layer ordering and early rejection. Compared to existing DL-aided SD schemes, our proposed schemes are more advantageous in both offline training and online application phases. Specifically, unlike existing DL-aided SD schemes, they do not require performing the conventional SD in the training phase. For a $24 \times 24$ MIMO system with QPSK, the proposed FDL-SD achieves a complexity reduction of more than $90\%$ without any performance loss compared to conventional SD schemes. For a $32 \times 32$ MIMO system with QPSK, the proposed FDL-KSD only requires $K = 32$ to attain the performance of the conventional KSD with $K=256$, where $K$ is the number of survival paths in KSD. This implies a dramatic improvement in the performance--complexity tradeoff of the proposed FDL-KSD scheme.

Parity Check Aided SC-Flip Decoding Algorithms for Polar Codes

When polar codes are decoded by the successive cancellation (SC) decoding algorithm, erroneously decoded non-frozen bits are caused by either channel noise or error propagation. SC-Flip algorithms aim to improve error performance by first identifying erroneous hard decisions due to channel noise and then flipping them during the decoding process to reduce error propagation. In existing SC-Flip algorithms, cyclic redundancy check (CRC) is used to check the decoded codeword to detect incorrect hard decisions. Differing from this detection approach based on CRC, we propose new SC-Flip decoders that take advantage of both the CRC and distributed parity checks (PCs) to detect, identify and flip erroneously decoded non-frozen bits. The proposed decoders terminate SC decoding early when a distributed PC is not satisfied. In addition, we propose a new metric to help locate the incorrect hard decisions. Finally, simulation results demonstrate that our SC-Flip decoders achieve better performance complexity tradeoffs than prior flipping algorithms, and approach the performance and complexity of the SC-Oracle algorithms.

Mitigating the noisy solution impact of mixed Gibbs sampling detector in high-order modulation large-scale MIMO systems

A neighborhood-restricted mixed Gibbs sampling (MGS)-based approach is proposed for low-complexity high-order modulation large-scale multiple-input multiple-output (LS-MIMO) detection. The proposed LS-MIMO detector applies a neighborhood limitation (NL) on the noisy solution from the MGS at a distance d — thus, named d-simplified MGS (d-sMGS) — in order to mitigate its impact, which can be harmful when a high-order modulation is considered. Numerical simulation results considering 64-QAM demonstrated that the proposed detection method can substantially improve the MGS algorithm convergence, whereas no extra computational complexity per iteration is required. The proposed d-sMGS-based detector suitable for high-order modulation LS-MIMO further exhibits improved performance × complexity tradeoff when the system loading is high, i.e., when frac {K}{N}geq 0.75. Also, with increasing the number of dimensions, i.e., increasing number of antennas and/or modulation order, a smaller restriction of 2-sMGS was shown to be a more interesting choice than 1-sMGS.

Complexity and Simplification in Language Shift

This paper examines the question of linguistic complexity in two shift ecologies in northeastern Russia. It is frequently claimed that language shift results in linguistic simplification across a range of domains in the grammars of shifting speakers (Campbell and Muntzel 1989;Dorian 1989;O’Shannessy 2011). We challenge the breadth of this claim, showing that while there are undoubtedly patterns that can be described as a simplification of some grammatical domain, the overall grammars of these speakers cannot be said to be “simple,” as simplification in one part of the grammar often corresponds to complexification in other parts (“complexity trade-offs”). Furthermore, patterns that are deemed loss or simplification are often presented in such a way because they are being compared to earlier varieties of the shifting languages; however, such patterns are entirely typologically expected, are consistent with other languages of the world, and can be seen as more or less complex depending on one’s locus of measurement. In this paper, we present incipient changes taking place in Chukchi (Chukotko-Kamchatkan, ISO ckt) and Even (Tungusic, ISO eve) stemming from the modern language shift context. We evaluate these changes against different notions of complexity to demonstrate that a more nuanced approach to morphosyntactic change in language obsolescence is warranted. While morphological simplification is expected in these scenarios, other changes in these speakers’ systems (occurring as potential adaptations in light of simplification) provide a more enlightening avenue for research on shifting varieties.

Approximate Message Passing Algorithms for Low Complexity OFDM-IM Detection

Low complexity approximate message passing (AMP) orthogonal frequency division multiplexing combined with index modulation (OFDM-IM) detection algorithms are proposed, which exploit the sparse structure of the frequency domain (FD) OFDM-IM symbols. To circumvent the high root mean square error (RMSE) in the conventional AMP algorithm, a minimum mean square error (MMSE) denoiser is proposed based on the classic Bayesian approach and on the state evolution of AMP. Our simulation results demonstrate that it is capable of improving both the RMSE as well as the convergence rate. However, in practice, the channel's diagonal FD matrix may be a non-Gaussian sensing matrix, hence a damping strategy is conceived. In conclusion, the proposed MMSE denoiser based damping-assisted AMP-aided detector strikes a compelling bit error ratio vs. complexity trade-off.

Sparse, Group-Sparse, and Online Bayesian Learning Aided Channel Estimation for Doubly-Selective mmWave Hybrid MIMO OFDM Systems

Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier's quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequency-domain (FD) correlation of the channel's frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis.

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning.

The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.

SDCRKL-GP: Scalable deep convolutional random kernel learning in gaussian process for image recognition

Deep convolutional neural networks have shown great potential in image recognition tasks. However, the fact that the mechanism of deep learning is difficult to explain hinders its development. It involves a large amount of parameter learning, which results in high computational complexity. Moreover, deep convolutional neural networks are often limited by overfitting in regimes in which the number of training samples is limited. Conversely, kernel learning methods have a clear mathematical theory, fewer parameters, and can contend with small sample sizes; however, they are not able to handle high-dimensional data, e.g., images. It is important to achieve a performance and complexity trade-off in complicated tasks. In this paper, we propose a novel scalable deep convolutional random kernel learning in Gaussian process architecture called SDCRKL-GP, which is characterized by excellent performance and low complexity. First, we successfully incorporated the deep convolutional architecture into kernel learning by implementing the random Fourier feature transform for Gaussian processes, which can effectively capture hierarchical and local image-level features. This approach enabled the kernel method to effectively handle image processing problems. Second, we optimized the parameters of deep convolutional filters and Gaussian kernels by stochastic variational inference. Then, we derived the lower variational bound of the marginal likelihood. Finally, we explored the model architecture design space selection method to determine the appropriate network architecture for different datasets. The design space consists of the number of layers, the channels per layer, and so on. Different design space selections improved the scalability of the SDCRKL-GP architecture. We evaluated SDCRKL-GP on the MNIST, FMNIST, CIFAR10, and CALTECH4 benchmark datasets. Taking MNIST as an example, the error rate of classification is 0.60%, and the number of parameters, number of computations and memory access cost of the architecture are 19.088k, 0.984M, and 1.057M, respectively. The experimental results verified that the proposed SDCRKL-GP method outperforms several state-of-the-art algorithms in both accuracy and speed in image recognition tasks. The code is available at https://github.com/w-tingting/deep-rff-pytorch.

Modeling noisy quantum circuits using experimental characterization

Noisy intermediate-scale quantum (NISQ) devices offer unique platforms to test and evaluate the behavior of quantum computing. However, validating circuits on NISQ devices is difficult due to fluctuations in the underlying noise sources and other nonreproducible behaviors that generate computational errors. Here we present a test-driven approach that decomposes a noisy, application-specific circuit into a series of bootstrapped experiments on a NISQ device. By characterizing individual subcircuits, we generate a composite noise model for the original quantum circuit. We demonstrate this approach to model applications of Greenberger-Horne-Zeilinger(GHZ)-state preparation and the Bernstein-Vazirani algorithm on a family of superconducting transmon devices. We measure the model accuracy using the total variation distance between predicted and experimental results, and we demonstrate that the composite model works well across multiple circuit instances. Our approach is shown to be computationally efficient and offers a trade-off in model complexity that can be tailored to the desired predictive accuracy.