Hybrid Precoding Design Based on Dual-Layer Deep-Unfolding Neural Network

The new radio (NR) system is recently proposed to deploy alongside the long term evolution (LTE) system to improve the wireless communication capacity. The Vandermonde-subspace frequency division multiplexing (VFDM) is an efficient technique for such a two tier network, whereby the NR transmitter can reuse the frequency spectrum of LTE system meanwhile guaranteeing interference free to LTE. In this paper, we first investigate the robust rate-maximization problem in the VFDM system. In the condition of imperfect channel state information (CSI), the precoder is optimized to satisfy both non-interference to LTE system and rate-maximization in the NR system. To tackle the challenge in solving such a non-convex problem, an iterative algorithm is proposed based on the block coordinate descent (BCD) and penalty dual decomposition (PDD) methods. Finally, simulation results demonstrate the superiority of the proposed algorithm in terms of robustness and achievable rate over the conventional SVD-based VFDM technique.

A real-time operating system (RTOS) provides a platform for the design and implementation of a wide range of applications in real-time systems, embedded systems, and mission-critical systems. This paper presents a formal design model for a general RTOS known as RTOS+ that enables a specific target RTOS to be rigorously and efficiently derived in real-world applications. The methodology of a denotational mathematics, Real-Time Process Algebra (RTPA), is described for formally modeling and refining architectures, static behaviors, and dynamic behaviors of RTOS+. The conceptual model of the RTOS+ system is introduced as the initial requirements for the system. The architectural model of RTOS+ is created using RTPA architectural modeling methodologies and refined by a set of Unified Data Models (UDMs). The static behaviors of RTOS+ are specified and refined by a set of Unified Process Models (UPMs). The dynamic behaviors of the RTOS+ system are specified and refined by the real-time process scheduler and system dispatcher. This work is presented in two papers; the conceptual and architectural models of RTOS+ is described in this paper, while the static and dynamic behavioral models of RTOS+ will be elaborated in a forthcoming paper.

Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has arisen as a promising technology for enhancing wireless communication systems by enabling flexible and intelligent wave manipulation. This is achieved through the interconnections among the ports of the impedance network, enabling wave reconfiguration when they flow through the surface. Thus, the output wave at one port depends on waves impinging on neighboring ports, allowing non-local control of both phase and magnitude. Non-reciprocal (NR)-BD-RIS further enhances this capability by breaking circuit reciprocity and, consequently, channel reciprocity. In contrast to conventional reciprocal (R)-BD-RIS and diagonal (D)-RIS that are constrained by circuit and channel reciprocity such that they only allow bidirectional communications, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i>, UE<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ⇄ UE<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>, NR-BD-RIS can additionally enable uni-directional communications, that is, UE<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> → UE<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> → UE<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub>, hence effectively enabling a wireless circulator. Specifically, this paper introduces a novel application of NR-BD-RIS in full-duplex (FD) wireless circulators, where multiple FD devices communicate via an NR-BD-RIS. This system is particularly beneficial for secure transmission, as it enforces one-way communication among FD devices, suppresses signal from all other users (UE), and thus prevents eavesdropping. In addition, a physics-compliant system model is considered by incorporating structural scattering, also known as specular reflection. By accounting for this effect, the advantages of NR-BD-RIS are further validated. Specifically, we formulate an sum-rate maximization problem and propose an iterative optimization algorithm that employs block coordinate descent (BCD) and penalty dual decomposition (PDD) methods. Numerical evaluations illustrate that NR-BD-RIS outperforms conventional R-BD-RIS and D-RIS in terms of sum-rate and secrecy rate.

Unmanned aerial vehicles (UAVs) are promising and powerful aerial platforms that can execute a variety of complex tasks. However, the increasing complexity of tasks and number of UAV nodes pose significant challenges for UAV sensing networks, such as limiting the spectral resources and increasing device complexity. A potential solution is to implement full-duplex (FD) technology in UAV sensor network transceivers. Although appropriate self-interference (SI) cancellation techniques have been employed in the digital domain, the amplitude of the signal of interest (SoI) is relatively small and can be obscured by SI, especially over longer distances. Moreover, the introduction of phase offsets when filtering measurement signals can lead to signal distortion, resulting in estimation errors in the measurement results. To address these issues, this paper presents a joint transmit (TX) and receive (RX) beamforming algorithm based on the penalty dual decomposition (PDD) algorithm, which considers the constraints of transmission power, reception power, and residual SI power. The simulation analyses demonstrate that with a limited number of antennas, the proposed joint TX-RX beamforming algorithm can effectively suppress SI by up to 140 dB, yielding high-precision measurements in UAV sensor networks without compromising the accuracy of the control signals. Compared with that of the traditional frequency-division duplex (FDD) mode, the measurement accuracy is not decreased; compared with those of the time-division duplex (TDD) mode, the distance and speed measurement accuracies of the UAVs are increased by 10 m and 1.5 m/s, respectively, in the FD mode because there is no interruption of the tracking loop and no continuous retracking in the FD mode.

research-article Free Access Share on Embedded systems in real time applications, design & architecture Authors: A. L. Suseela View Profile , V. Lalith Kumar View Profile Authors Info & Claims UbiquityVolume 2005Issue AugustAugust 2005 pp 2https://doi.org/10.1145/1088431.1086450Online:01 August 2005Publication History 0citation170,093DownloadsMetricsTotal Citations0Total Downloads170,093Last 12 Months1,290Last 6 weeks56 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed. HIGHLIGHTS 1. This paper discusses application of ANN in different emerging trends in food process. 2. Application of ANN to develop non-linear multivariate modeling is illustrated. 3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy. 4. ANN resulted in fast response making it suitable for application in real time systems.

A wide application of real time systems is safety critical systems. These systems should be highly reliable. Fault tolerance is one of the approaches to achieve reliability. In this paper, a fault tolerance model for real time systems is proposed. This model incorporates the concept of time stamped fault tolerance. This model is based on distributed computing along with feed forward artificial neural network methodology. The proposed technique is based on execution of design diverse variants on replicated hardware, and assigning weights to the results produced by variants. Thus the proposed method encompasses both the forward and backward recovery mechanism, but main focus is on forward recovery. The main essence of the proposed technique is the inclusion of time for decision mechanism

A large body of human image processing techniques use skin detection as a first primitive for subsequent feature extraction. Well established methods of colour modelling, such as histograms and Gaussian mixture models have enabled the construction of suitably accurate skin detectors. However such techniques are not ideal for use in adaptive real time environments. We describe methods of skin detection using a Self-Organising Map or SOM, and show performance comparable (94% accuracy on facial images) to conventional techniques. We also introduce the AXEON Learning Processor as the basis for a hardware skin detector, and outline the potential benefits of using this system in a demanding environment, such as filtering Internet traffic, to which conventional techniques are not best suited.

Fast object detection in digital grayscale images The problem of specific object detection in digital grayscale images is considered under the following conditions: relatively small image fragments can be analysed (a priori information about the size of objects is available); images contain a varying undefined background (clutter) of larger objects; processing time should be minimised and must be independent from the image contents; proposed methods should provide for efficient implementation in application-specific electronic circuits. The last two conditions reflect the aim to propose approaches suitable for application in real time systems where known sophisticated methods would be inapplicable. The research is motivated by potential applications in the food industry (detection of contaminants in products from their X-ray images), medicine (detection of anomalies in fragments of computer tomography images etc.). Possible objects to be detected may include compact small objects, curved lines in different directions, and small regions of pixels with brightness different from the background. The paper describes proposed image processing approaches to detection of such objects and the results obtained from processing of sample food images.

This paper presents a novel application of a hybrid learning approach to the optimisation of membership and nonmembership functions of a newly developed interval type-2 intuitionistic fuzzy logic system (IT2 IFLS) of a Takagi–Sugeno–Kang (TSK) fuzzy inference system with neural network learning capability. The hybrid algorithms consisting of decoupled extended Kalman filter (DEKF) and gradient descent (GD) are used to tune the parameters of the IT2 IFLS for the first time. The DEKF is used to tune the consequent parameters in the forward pass while the GD method is used to tune the antecedents parts during the backward pass of the hybrid learning. The hybrid algorithm is described and evaluated, prediction and identification results together with the runtime are compared with similar existing studies in the literature. Performance comparison is made among the proposed hybrid learning model of IT2 IFLS, a TSK-type-1 intuitionistic fuzzy logic system (IFLS-TSK), and a TSK-type interval type-2 fuzzy logic system (IT2 FLS-TSK) on two instances of the datasets under investigation. The empirical comparison is made on the designed systems using three artificially generated datasets and three real world datasets. Analysis of results reveal that IT2 IFLS outperforms its type-1 variants, IT2 FLS and most of the existing models in the literature. Moreover, the minimal run time of the proposed hybrid learning model for IT2 IFLS also puts this model forward as a good candidate for application in real time systems.

Rotating Synthetic Aperture Radar (ROSAR) can generate a 360° image of its surrounding environment using the collected data from a single moving track. Due to its non-linear track, the Back-Projection Algorithm (BPA) is commonly used to generate SAR images in ROSAR. Despite its superior imaging performance, BPA suffers from high computation complexity, restricting its application in real-time systems. In this paper, we propose an efficient imaging method based on robust sparse array synthesis. It first conducts range-dimension matched filtering, followed by azimuth-dimension matched filtering using a selected sparse aperture and filtering weights. The aperture and weights are computed offline in advance to ensure robustness to array manifold errors induced by the imperfect radar rotation. We introduce robust constraints on the main-lobe and sidelobe levels of filter design. The resultant robust sparse array synthesis problem is a non-convex optimization problem with quadratic constraints. An algorithm based on feasible point pursuit and successive convex approximation is devised to solve the optimization problem. Extensive simulation study and experimental evaluations using a real-world hardware platform demonstrate that the proposed algorithm can achieve image quality comparable to that of BPA, but with a substantial reduction in computational time up to 90%.

Applications of real-time systems have grown significantly in both diversity and popularity, and the appetite for real-time software has never been higher. In contrast, the choice of programming languages used to develop such systems has stagnated, mostly limited to decades-old languages, specifically Ada and C/C++, and more recently real-time Java. We posit that the high cost and difficulty of developing new programming languages for real-time systems is the main reason for this mono-culture. To tackle the lack of diversity, we propose the design of a micro virtual machine on which managed programming languages for real-time systems can be developed. Our design facilitates bringing the advantages of correct managed languages to the real-time domain. We build on a previously published micro virtual machine specification, named Mu, and propose a set of modifications to its abstractions over concurrency and memory management to make it suitable for real-time systems. The resulting design is a basis for a new micro virtual machine specification we call RTMu, designed as a reliable and efficient foundation for the development of managed languages for real-time systems.

Priority-based Functional Reactive Programming (P-FRP) is a new variant of FRP to model reactive applications in real-time systems. In P-FRP, when the currently running task is preempted by an arriving higher-priority task, the lower-priority running task is aborted and the higher-priority task will execute. The lower-priority task restarts when the higher-priority one completes. However, unlike the preemptive model, when a task aborts, all the changes made by this task are discarded. That is to say, when an aborted ask restarts, it should execute from the beginning. In order to provide a realistic Worst-Case Response Time (WCRT) of the tasks in P-FRP, it is therefore mandatory to derive a realistic Worst Case Execution Time (WCET) of each task. Previous studies have ignored memory latency in the derivation of the WCRT, making the resulting estimate inaccurate and unrealistic. Furthermore, these studies have also assumed that the WCET of each task is known a priori. In this paper, we introduce a scratchpad memory (SPM)-based platform for executing P-FRP tasks and an approach to determine the WCET of the tasks by considering the memory cost of the aborted tasks. We first compute the WCET of a task in a P-FRP system, and then calculate the memory penalty caused by preemption. In the next step, we derive the WCRT of the task sets in P-FRP by considering memory latency in the proposed platform. Experimental results from the derivations of the WCET and WCRT using task sets from the SNU real-time benchmarks and randomly generated tasks are presented to validate this approach.

In this work, we consider the joint optimization of virtual cell association and the precoder design for a multi-user downlink cloud radio access network (C-RAN). The optimal design of cell associations and precoders are particularly relevant due to the anticipated ultra-dense networks suffering from heavy interference. As the resulting optimization is a non-convex and combinatorial problem, we propose an iterative low complexity algorithm, employing the recently developed penalty dual decomposition (PDD) framework. In contrast to predominantly used sparsity inducing methods, the proposed method incorporates the penalty/re-weighting strategy into the algorithm, which is then solved as a sequence of convex second-order cone programs. The aforementioned feature leads to a high convergence rate and subsequently a low computational cost for the proposed algorithm. Furthermore, the proposed algorithm meets the requirements to converge to a KKT solution. Numerical simulations verify the superior performance of our proposed method in ultra-dense networks, in comparison to commonly used methods, such as the reweighted $\ell _1$ -norm, in terms of system energy efficiency.

In this paper, the applications of the iterative Gauss-Newton (GN) approach in nonlinear image restoration are considered. The convergence properties of a general class of nonlinear iterative algorithm are studied through the Global Convergence Theorem (GCT). The iterative GN algorithm for the solution of the least-squares optimization problem is presented. The computational complexity of this algorithm is enormous, making its implementation very difficult in practical applications. Structural modifications are introduced, which drastically reduce the computational complexity while preserving the convergence rate of the GN algorithm. With the structural modifications, the GN algorithm becomes particularly useful in nonlinear optimization problems. The convergence properties of the algorithms introduced are readily derived, on the basis of the generalized analysis and the GCT. The applications of these algorithms on practical problems, is demonstrated through an example.