Channel Delay Research Articles

A method for delay estimation between channels of Analog to Information Converters

Analog to Information Converters (AIC) implements signal acquisition in Compressed Sensing. AIC performance is affected by noise and hardware imperfections. In parallel Random Modulator Pre-Integrator (RMPI) architectures, delay among channels reduces the performance; thus, delay estimation and compensation methods are necessary. In previous works, delay estimation compares a reference signal with a reconstructed signal from the AIC output; however, such methods produce different results according to the recovery method. To avoid this problem, we present a method based on a controlled delay signal to estimate channel delay without the necessity of signal recovery. The obtained results show the technique's feasibility and the possibility of its use as a built-in calibration process for AIC.

Energy-Efficient Power and Subcarrier Allocation in Downlink OFDMA Systems with Channel Estimation Errors

In this paper, we investigate the joint optimization of power and subcarrier allocation for maximizing the energy efficiency (EE) in downlink OFDMA systems. The problem of EE maximization is formulated as a stochastic optimization to determine the optimal power and subcarrier allocation, in which many practical factors including channel estimation errors, delay requirements, and time-varying channel are considered. We first propose a dynamic power and subcarrier allocation (DPSA) algorithm to solve the formulated problem. Then, we derive the two bounds of EE and delay and reveal the tradeoff between them. The theoretical analysis and simulation results demonstrate the variations of EE and delay with channel estimation errors.

Performance of WLAN in Downlink MU-MIMO Channel with the Least Cost in Terms of Increased Delay

To improve the performance of IEEE 802.11 wireless local area (WLAN) networks, different frame-aggregation algorithms are proposed by IEEE 802.11n/ac standards to improve the throughput performance of WLANs. However, this improvement will also have a related cost in terms of increasing delay. The traffic load generated by mixed types of applications in current modern networks demands different network performance requirements in terms of maintaining some form of an optimal trade-off between maximizing throughput and minimizing delay. However, the majority of existing researchers have only attempted to optimize either one (to maximize throughput or minimize the delay). Both the performance of throughput and delay can be affected by several factors such as a heterogeneous traffic pattern, target aggregate frame size, channel condition, competing stations, etc. However, under the effect of uncertain conditions of heterogeneous traffic patterns and channel conditions in a network, determining the optimal target aggregate frame size is a significant approach that can be controlled to manage both throughput and delay. The main contribution of this study was to propose an adaptive aggregation algorithm that allows an adaptive optimal trade-off between maximizing system throughput and minimizing system delay in the WLAN downlink MU-MIMO channel. The proposed approach adopted different aggregation policies to adaptively select the optimal aggregation policy that allowed for achieving maximum system throughput by minimizing delay. Both queue delay and transmission delay, which have a significant impact when frame-aggregation algorithms are adopted, were considered. Different test case scenarios were considered such as channel error, traffic pattern, and number of competing stations. Through system-level simulation, the performance of the proposed approach was validated over the FIFO aggregation algorithm and earlier adaptive aggregation approaches, which only focused on achieving maximum throughput at the expense of delay. The performance of the proposed approach was evaluated under the effects of heterogenous traffic patterns for VoIP and video traffic applications, channel conditions, and number of STAs for WLAN downlink MU-MIMO channels.

Teleoperation of ground-based mobile robotic systems with time delays in data transmission channels

The paper is devoted to the issues of teleoperation of ground mobile robots. Problems of moving a robot in an un-structured environment by commands of a human operator are considered. A significant problem that reduces a quality of control and often leads to loss of stability is time delays that occur in information channels of the complex. To partially compensate for the negative impact of the time delays, an approach based on the prediction of the local goal of movement, the real position of the robot at the time of commands formation and the model of an operator is proposed. To test the approach, a computer simulation of the robot control process was performed on the basis of a training complex based on the Unity engine. The task was consisted in controlling the movement of the robot along the reference trajectory displayed on the screen. The task execution time and the similarity of the recorded trajectory with the reference one were evaluated. The experimental results confirmed the positive effect of the proposed compensation method on the efficiency of the control system.

A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data

High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model’s discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation.

A secure relay selection scheme based on cooperative jamming for Underwater Acoustic Sensor Networks

Physical layer security (PLS) is one of the important methods to solve the security problems of Underwater Acoustic Sensor Networks (UASNs). In this paper, combining the characteristics of cooperative communication and cooperative jamming in PLS, we propose a Secure Relay Selection Scheme based on Cooperative Jamming for UASNs. On the one hand, according to the link quality and residual energy, one or several nodes are selected to forward information as relays. On the other hand, the scheme takes advantage of long propagation delay of the underwater acoustic channel and employs two different jamming ways: Self-Protection Jamming and Assistance Jamming, both of which can interfere with eavesdroppers without affecting the reception of legal users. The proposed scheme can increase the channel capacity of the legal channel by deploying relay nodes to forward the information and reduce the channel capacity of the eavesdropping channel by Self-Protection Jamming and Assistance Jamming. Simulation and field experiment results show that the proposed scheme can improve the PLS of the network.

PLC Physical Layer Link Identification with Imperfect Channel State Information

This paper proposes an accurate physical layer technique to uniquely identify the links of a power line communication network. First, the power line communications (PLC) multipath channel characterization is presented and detailed. Then, a multipath channel delay detection technique is introduced to provide an accurate physical layer identification (PL ID) for the considered PLC links. The accuracy and efficiency are tested by evaluating the successful path detection probability (SPDP) in a simulated scenario under both perfect and imperfect channel state information conditions. The results confirm the advantages of the proposed scheme. Indeed, for a common PLC noise power around 90 dBuV, the provided accuracy reaches ≈90%, while for a noise power below 80 dBuV, the accuracy plateaus at 100%. Overall, the low complexity of the proposed approach and its staggering performance results pave the way for further possible applications in both the PLC and the security domain.

Codiagnosability of Networked Discrete Event Systems With Timing Structure

We address, in this article, the problem of codiagnosability of networked discrete event systems with timing structure (NDESWTS) subject to delays and loss of observations of events between the measurement sites (MS) and local diagnosers (LD). To this end, we first introduce a new timed model that represents the dynamic behavior of the plant based on the, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> , knowledge of the minimal activation time for each transition of the plant and on the maximal delays in the communication channels that connect MS and LD. We then convert this timed model into an equivalent untimed one, and add possible intermittent packet loss in the communication network. Based on this untimed model, we present necessary and sufficient conditions for NDESWTS codiagnosability and propose two tests for its verification: one that deploys diagnosers and another one that uses verifiers.

A Novel fuzzy based user pairing and cuckoo search optimization approach for small cell deployment in NOMA-HetNet

Recently, Heterogeneous Networks (HetNet) are concentrated more in order to improve transmission coverage and spectrum efficiency. When compared to HetNet, Non-Orthogonal Multiple Access (NOMA) is used to allow multiple users to share the same frequency band resource. Moreover, cross-tier links are improved in realistic HetNets due to channel delay and random perturbation. In this research, a fuzzy logic-based user association and pairing was proposed to improve the system robustness and energy utilization. Here, fuzzy logic overcomes the limitations of traditional channel-based and gain-based pairing schemes. The proposed fuzzy logic-based system evaluates distance, channel gain, and reference signal power for mode selection and enhances pairing features. Nature-inspired cuckoo search optimization is also used to improve coverage probability, feasible rate, and energy efficiency by optimizing the path loss, transmit power, and distance between user and base station. The proposed model analysis is carried out through intense simulation to demonstrate improved performance when compared to conventional state-of-the-art techniques.

Joint CFO and highly frequency selective channel estimation in FBMC/OQAM systems

Filter Bank-based Multicarrier with Offset Quadrature Amplitude Modulation (FBMC/OQAM) has been considered as an alternative waveform to the conventional Cyclic Prefix (CP)-based Orthogonal Frequency Division Multiplexing (OFDM). Despite the adoption of variants of the latter in 5G NR, FBMC/OQAM is still at the top of the promising multicarrier modulation waveforms for future generation communication systems. Although it enjoys a number of unique advantages over CP-OFDM, most importantly maximum spectral efficiency and good time-frequency localization, it is also sensitive to Carrier Frequency Offset (CFO), which can cause inter-carrier interference that degrades system performance if left uncompensated. Besides, the presence of CFO prevents accurate channel estimation. In this paper, our goal is to propose joint estimators for the channel impulse response (CIR) and CFO via an approximate Maximum Likelihood (ML) approach. No simplifying assumption is made about the channel delay spread so our results also apply to highly frequency selective channels. One of the novelties is that the estimation procedure is aided by so choosing the pilots as to result in zero intrinsic interference in the absence of impairments. Moreover, to reduce the complexity of CFO estimation, a lighter implementation, based on the truncation of the Taylor series expansion of the exponential term containing the CFO, is proposed. Simulation results demonstrate the superiority of the proposed estimators over existing methods, in terms of both mean squared estimation errors and bit error rate.

Generalized demand-side resource hierarchical control strategy based on multi-agent consensus of historical data online transfer

To achieve large-scale generalized demand-side resources to participate in the “peak-shaving and valley-filling” of the power system, a distributed cooperative control strategy for discrete-time linear multi-agents considering historical data is proposed, which comprehensive consider the changes in load commands, topology, multi-objective function coefficients, communication delays and input delays. This paper combines the distributed multi-agent system with the hierarchical architecture to solve the problems of extensive data calculations in centralized control, which may easily cause communication channel blockage and delay. Considering the cost of changing the generalized demand-side resources and the user’s comfort, the pre-learning historical data storage strategy based on K-means clustering is adopted before the actual task, and the idea of transfer learning is introduced into the consensus algorithm. Finally, through experimental statistics, the number of iteration steps of the consensus algorithm is reduced and the effectiveness of the proposed algorithm is proved.

Robust yaw control of autonomous underwater vehicle based on fractional-order PID controller

Autonomous underwater vehicles (AUVs) have broad applications owing to their small size, low weight, strong ability to operate autonomously, and ability to replace humans in dangerous operations. AUV motion control systems can ensure stable operation in complex ocean environments and have attracted significant research attention in marine science and technology. The main difficulties with AUV motion control include the large uncertainties of dynamic and hydrodynamic characteristics and time delay of the signal transmission channel. In this study, we propose a robust fractional-order proportional–integral–derivative (FOPID) controller design for an AUV yaw control system. First, a three-dimensional stability region analysis method is proposed to achieve fractional orders. Unlike other stability region analysis methods, the proposed method is supported by theory instead of observation. Then, the other parameters are optimized according to the robust design specifications with respect to the parameter uncertainties. Therefore, the controlled system can tolerate different parameter uncertainties and fulfill transient performance specifications while maintaining system stability. The simulation results illustrate the superior robustness and transient performance of the proposed control algorithm.

Clustering Method of Mobile Cloud Computing According to Technical Characteristics of Cloudlets

The rapid increase in the number of mobile phones and IoT devices connected to the network reduces the bandwidth of the Internet communication channel, and as a result, delays occur in the delivery of data processed in remote clouds. Edge computing systems (cloudlet, fog computing, etc.) are used to eliminate resource shortages, energy consumption, and communication channel delays in mobile devices. Edge computing systems place processing devices (computers) close to users. Cloudlet-based mobile cloud computing is widely used to reduce delays in communication channels and energy consumption in mobile devices. Selection of the most suitable cloudlet allowing users to run applications fast in cloud is still a considerable problem. This paper proposes a strategy for the selection of high-performance cloudlets providing fast solutions, considering the complexity of application (file type). It offers a method for cloudlet selection out of large number of cloudlets with different technical capabilities providing faster processing of user application. The timing of user applications in cloudlets with different technical capabilities (operating frequency, number of cores, volume of RAM, etc.) also varies. The proposed method provides faster solution for the user application. User applications are grouped by type of application, and a set of cloudlets are clustered by the number of groups. Clustering is performed first by the parameters corresponding to the operating frequency of the cloudlets, then by the number of cores and the volume of RAM. The proposed method reduces energy consumption of mobile devices by providing faster processing of applications. Thus, the proposed strategy provides an energy consumption reduction on mobile devices, faster processing of results and decrease of network delays.

Underwater acoustic communication using orthogonal signal division multiplexing with windowing

Underwater acoustic (UWA) communication is an essential technology that supports underwater surveys by transmitting images and movies wirelessly. However, UWA communication is still challenging due to the large delay and Doppler spreads in UWA channels. Doppler-resilient orthogonal signal division multiplexing (D-OSDM) under water has been found effective in such channels, but Doppler modeling errors prevent further improvement in communication quality. In this paper, we clarify the effect of Doppler modeling errors on the communication quality of D-OSDM and propose the use of a window function in D-OSDM to address the issues that such errors raise. We also evaluate the communication quality of D-OSDM using the window function through simulations and experiments. The experimental results show a 56% increase in the number of error-free data blocks, indicating that windowing can improve communication quality.

Improved power controller for enhancement of voltage quality in microgrid

In microgrid, non-linear or unbalanced loads will degrade the voltage quality at the output of the inverters, and the voltage of the load will be distorted or unbalanced. On the other hand, the inverters in microgrid are connected in parallel to improve efficiency, increase transmission capacity, and are easy to repair and maintain. When the microgrid is operating in standalone mode, inverters must be controlled to share power according to their rated power; this is to stabilize frequency and voltage in the microgrid. However, since non-linear or unbalanced loads will produce load currents containing harmonics, it will make the voltage of load unbalanced, which will affect the control of power-sharing for the inverters, and it also degrades the voltage quality at the output of the inverters. This paper has proposed an improved power-sharing control method combined with the sliding mode control (SMC) technique to improve the voltage quality and improve the accuracy of power-sharing for the inverters in the microgrid. The proposed method will ensure accurate power-sharing even if the communication is interrupted. If the load changes while the communication is interrupted, the accuracy of power-sharing is reduced but the proposed method is better than the conventional power-sharing method. In addition, the accuracy of the proposed power-sharing method is not affected by the time delay in the communication channel and local loads at the output of the inverters. Simulation and experimental results will prove the effectiveness of the proposed method.

A Low-Complexity Channel Estimation Based on a Least-Squares Algorithm in OFDM Systems

As the channel frequency responses (CFRs) at virtual pilot subcarriers are assumed to be zero, the estimated CFRs will have a leakage effect for discrete Fourier transform (DFT)-based channel estimation in OFDM systems. The CFRs at odd pilot subcarriers and even pilot subcarriers are related if the number of maximum channel delay points is smaller than or equal to half the number of pilots (including virtual pilots). According to this correlation, we propose a low-complexity least-squares (LS) method to estimate the CFRs at virtual even and odd pilot subcarriers, respectively. This will solve the problem of the leakage effect in DFT-based channel estimation. The proposed method does not need to know the statistical properties of the channel or insert extra pilots as with some estimation methods. Furthermore, although this method has less computation than the LS method, both have almost the same channel estimation efficiency in simulation. The channel estimation efficiency of our proposed method is still similar to that of the LS method, even if the number of maximum channel delay points is greater than half the number of pilots. Therefore, the proposed low-complexity method is very suitable for equalizer hardware implementation.

Channel Estimation for Wideband mmWave MIMO OFDM System Exploiting Block Sparsity

In this letter, we investigate time-domain channel estimation for wideband millimeter wave (mmWave) MIMO OFDM system. By transmitting frequency-domain pilot symbols as well as different beamforming vectors, we observe that the time-domain mmWave MIMO channels exhibit channel delay sparsity and especially block sparsity among different spatial directions. Then we propose a time-domain channel estimation exploiting block sparsity (TDCEBS) scheme, which always aims at finding the best nonzero block achieving the largest projection of the residue at each iterations. In particular, we evaluate the system performance using the QuaDRiGa which is recommended by 5G New Radio to generate wideband mmWave MIMO channels. The effectiveness of the proposed TDCEBS scheme is verified by the simulation results, as the proposed scheme outperforms the existing schemes.

Delay Estimation for Ranging and Localization Using Multiband Channel State Information

In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cramér-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.

A 4-Channel Ultra-Low Power Front-End Electronics in 65 nm CMOS for ATLAS MDT Detectors

A 4-channel front-end electronics (4 × FEE) system for the muon drift tube in the ATLAS detector in the High-Luminosity LHC is presented. The overall channel architecture is optimized to reduce the power and area of the design. Each channel comprises a charge-sensitive preamplifier (CSP), shaper, discriminator and differential low-voltage signaling drivers. The proposed channel operates with a 5–100 fC input charge and exhibits a linear sensitivity of 8 mV/fC for the entire input charge range. The peaking time delay of the analog channel is 14.6 ns. At the output, the time representation of the input signal is provided in terms of the CMOS level and in scalable low-voltage signal (SLVS). The FEE consumes a current of 10.6 mA per channel from a single 1.2 V supply voltage. The full 4 × FEE design is realized in TSMC 65 nm CMOS technology and its die-area is 2 mm × 2 mm.

Communication Transport Protocol Strategies for Rail Applications

Current technologies for managing rail traffic such as the Global System for Mobile communications for Railway (GSM-R) will be no longer be available within the upcoming years. The European Shift2Rail Joint Undertaking (S2R-JU) proposed the Adaptable Communication System (ACS) to overcome this problem. In this work, we model the ACS by abstracting it at the Internet Protocol (IP) level, using tunnels for datagrams’ transmission as a communication bearer is available along the rail. Then, to evaluate its performance, an ACS emulator has been implemented. The core part of it is a Tunnel Manager which can establish pseudo-virtual circuits through multi-bearer tunnels, forcing datagrams on a service-basis to follow specific paths between gateways (i.e., from on-board to a train to the network-side rail control center and vice versa). The Tunnel Manager can properly select a given tunnel/bearer for sending messages (and duplicating them on redundant paths) of critical rail applications for train traffic management, relying on tunnels based on either connection-oriented protocol (i.e., the Transport Control Protocol, TCP), connectionless protocol (i.e., the User Datagram Protocol, UDP) or a mix of them. In this paper, we investigate the best solutions in terms of transport protocols for implementing tunnels through the bearers. Results are based on two main use cases: i. the position report/movement authority messages for the European Rail Traffic Management System (ERMTS) and ii. the critical file transmission, considering either TCP or UDP as tunnel transport protocol. For the first rail application, one UDP bearer can be selected only if the end-to-end channel delay is lower than 100 ms and the experienced packet loss is lower than 4% in the whole crossed network. Two UDP bearers, one TCP bearer or two mixed UDP/TCP bearers should be selected in case the channel delay is greater than 300 ms and the experienced packet loss is greater than 15%. Considering the critical file transfer in the rail scenario, TCP should be selected with two bearers to have a throughput greater than 50 Mbit/s even for a packet loss of 1%.