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The system considered in this paper operates with a feedback that is characterized by a gain and a desired final state (DFS), which is the main parameter of interest in the present study. The system is, however, subjected intermittently to stochastic inputs according to a Markov process. Since the system operates in two modes | under the feedback to the DFS and under a stochastic input|the Interacting Multiple Model (IMM) estimator is used. Two approaches are considered: (i) the DFS is a discrete-valued random variable | one of a finite number of possible states | with an a priori probability mass function (pmf), and (ii) the DFS is a continuous-valued random variable with an a priori probability density function (pdf). For Approach (i), we use a multiple IMM estimator (MIMM) that features one IMM for each one of the possible DFS. The a posteriori probability of the model for each IMM, i.e., of each DFS, will be computed based on the likelihood function (LF) of the corresponding IMM. For Approach (ii), we design a single IMM to handle the unknown DFS to be estimated (mode M1), and the random inputs (mode M2). Simulation results explore several scenarios and investigate the degree of observability of this stochastic problem.

We consider the optimal control of input-queued switches under a cost-weighted variant of MaxWeight scheduling, for which we establish theoretical properties that include showing the algorithm exhibits optimal heavy-traffic queue-length scaling. Our results are expected to be of theoretical interest more broadly than input-queued switches.

In the mobile edge computing environments, Quality of Service (QoS) prediction plays a crucial role in web service recommendation. Because of distinct features of mobile edge computing, i.e., the mobility of users and incomplete historical QoS data, traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments. In this paper, we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices. By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition (SVD) with the classical ARIMA model, we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently. Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.

Existing Orthogonal Frequency-Division Multiplexing (OFDM) variants based on cyclic prefix (CP) allow for efficient time synchronization, but suffer from lower power efficiency compared to zero-padded (ZP)-OFDM. Because of its power efficiency, ZP-OFDM is considered as an appealing solution for the emerging low-power wireless systems. However, in the absence of CP, time synchronization in ZP-OFDM is a challenging task. In this paper, the non-data-aided (NDA) maximum-likelihood (ML) time synchronization for ZP-OFDM is analytically derived. We show that the optimal NDA-ML synchronization algorithm offers a high lock-in probability and can be efficiently implemented using Monte Carlo sampling (MCS) technique in combination with golden-section search method in one dimension. To obtain the optimal NDA-ML time synchronization algorithm, we first derive a closed-form expression for the joint probability density function (PDF) of the received ZP-OFDM samples in frequency-selective fading channels. The derived expression is valid for doubly-selective fading channels with mobile users as well. The performance of the proposed synchronization algorithm is evaluated under various practical settings through simulation experiments. It is shown that the proposed optimal NDA-ML synchronization algorithm and its MCS implementation substantially outperforms existing NDA time synchronization algorithms in terms of lock-in probability and mean-squared error (MSE).

The multi-connectivity is a key enabler for ultra-reliable low-latency communications. To evaluate its performance and suitability to various use casessystem-level studies are essential, where the physical layer (PHY) plays an important role. Therefore, PHY modeling is required which is time-intensive and requires highly complex computations. For this purpose, the PHY performance is usually abstracted in terms of signal-to-interference-plus-noise ratio (SINR), also known as physical layer abstraction (PLA). However, due to fading, the symbols inside a packet could have different SINRs which require effective SINR mapping to compute an equivalent SINR. In the context of multi-connectivity, the received SINR depends on all connected links where each link experiences independent fading and on the used link combining technique. As a result, the computation of effective SINR also depends on the combining technique and fading experienced by individual links. To model PHY performance by considering all these effects, we develop PLA for multi-connectivity communications. This includes the computation of received symbols SINR for various link combining techniques and mapping them to effective SINR using enhanced exponential effective SINR mapping (eEESM). Furthermore, a new optimization method is introduced for eEESM to reduce its optimization complexity. Simulation results show that the proposed PLA accurately estimate the performance of different order of multi-connectivity communications under various fading conditions.

We propose a novel privacy-preserving QoS forecasting approach - Edge-Laplace QoS (QoS forecasting with Laplace noise in mobile Edge environments). Edge-Laplace QoS is able to accurately and efficiently forecast Quality of Service (QoS) of various Web Services, while effectively protecting user privacy in mobile edge environments. We employ an improved differential privacy method to add dynamic disguises to the original QoS data in the edge environment to protect user data privacy. A collaborative filtering method is adopted to retrieve similar users' accessing records based on geographic locations of their accessed servers for QoS forecasting. We conduct a set of experiments using several public network data sets. The results show that the efficiency of Edge-Laplace QoS is superior to traditional forecasting approaches. Edge-Laplace QoS is also validated to be more suitable for edge environments than traditional privacy-preserving approaches.

A moisture advection scheme is an essential module of a numerical weather/climate model representing the horizontal transport of water vapor. The Piecewise Rational Method (PRM) scalar advection scheme in the Global/Regional Assimilation and Prediction System (GRAPES) solves the moisture flux advection equation based on PRM. Computation of the scalar advection involves boundary exchange, and computation of higher bandwidth requirements is complicated and time-consuming in GRAPES. Recently, Graphics Processing Units (GPUs) have been widely used to solve scientific and engineering computing problems owing to advancements in GPU hardware and related programming models such as CUDA/OpenCL and Open Accelerator (OpenACC). Herein, we present an accelerated PRM scalar advection scheme with Message Passing Interface (MPI) and OpenACC to fully exploit GPUs' power over a cluster with multiple Central Processing Units (CPUs) and GPUs, together with optimization of various parameters such as minimizing data transfer, memory coalescing, exposing more parallelism, and overlapping computation with data transfers. Results show that about 3.5 times speedup is obtained for the entire model running at medium resolution with double precision when comparing the scheme's elapsed time on a node with two GPUs (NVIDIA P100) and two 16-core CPUs (Intel Gold 6142). Further, results obtained from experiments of a higher resolution model with multiple GPUs show excellent scalability.

Automatic modulation categorization (AMC) is used in many applications such as cognitive radio, adaptive communication, electronic reconnaissance, and non-cooperative communications. Predicting the modulation class of an unknown radio signal without having any prior information of the signal parameters is challenging. This paper proposes a novel multiscale deep-learning-based approach for the automatic modulation classification using radio signals. The approach considered the fixed boundary range-based Empirical wavelet transform (FBREWT) based multiscale analysis technique to decompose the radio signal into sub-band signals or modes. The sub-band signals computed from the radio signal combined with the deep convolutional neural network (CNN) are used to classify modulation types. The approach is tested using the radio signals of different signal-to-noise ratio (SNR) values and four different channel types such as additive white Gaussian noise (AWGN) combination of Rayleigh fading and AWGN channels, the combination of Rician flat fading and AWGN channels, and combination of Nakagami-m fading and AWGN channels for AMC. The results show that the proposed FBREWT based deep-learning approach achieves an overall classification accuracy of 97% for AMC using the radio signals with 10dB SNR for the AWGN channel. Moreover, the proposed approach has obtained the accuracy’s as 94.56%, 95%, and 97.33% using radio signals with 10 dB SNR values for Rayleigh fading, Rician flat fading, and Nakagami-m fading channels combined through AWGN link. The comparison of the proposed multiscale deep learning-based approach with existing methods is shown for AMC.

Edge infrastructures, including edge computing servers, are increasingly powered by renewable energy and smart grid combined. Two-way energy trading allows the surplus or shortfall of renewable energy to be traded between a server and the smart grid, but is non-trivial due to randomly varying computation demands and renewables. This paper proposes a new online policy, namely, distributed online resource allocation and load management (DORL), which enables such an edge server and its serving devices to minimize their energy cost and energy consumption, respectively, in a fully distributed manner. The key idea is that we employ the stochastic dual-subgradient method to interpret the battery of the server as a virtual queue. Based on the virtual queue and task queues, the CPU frequencies of the devices and the edge server, the offloading transmit rates of the devices (to the server) and the energy trading decisions of the server (with the smart grid) are decoupled over time and among devices, and optimized on an ongoing basis. Furthermore, we prove that the DORL yields a feasible and asymptotically optimal solution with a cost-backlog tradeoff of [\eta, 1\eta]. Simulations show that the DORL reduces the system cost by nearly 50%, as compared to existing benchmarks.

In this brief, a new discrete-time implementation is proposed for the super-twisting control with a semi-implicit Euler method. In contrast to the conventional realization methods, the proposed implementation scheme exploits two signum functions in the super-twisting algorithm (STA) such that the numerical chattering is totally avoided and the control precision is enhanced in terms of smallest control errors in the steady state. It has an advantage that the control accuracy is insensitive to the gain overestimation of STA, which benefits the gain tuning of the STA in practice. These superiorities of the proposed scheme are demonstrated through simulations and comparisons with other state of the art relevant to STA realization methods.

The footprint noise is a type of coherent noise that arises from the acquisition geometry. The footprint noise is commonly seen in 3-D seismic volumes and greatly affects the amplitude-based processing and interpretation steps in the seismic exploration workflow. Thus, removal of the footprint noise is necessary for warranting a reliable interpretation output of seismic data processing. However, because footprint noise is usually weak and also spatially coherent, it is inevitable to cause damages to useful signals during its removal steps. Here, we propose a dictionary learning (DL)-based method to effectively remove the footprint noise. We design an algorithm framework to effectively learn the dictionary atoms of the signal waveforms and separate the features of the footprint noise from the learned atoms. Considering the special features of the footprint noise in the dictionary atoms, we propose a statistics-guided way to separate the dictionary atoms into footprint-affected and footprint-free atoms. Then, the footprint-affected atoms are processed via a 2-D median filtering step. The combination between the untouched footprint-free atoms and filtered footprint-affected atoms result in a better dictionary of the signal waveforms and the footprint atoms. We use residual DL to encode the input data by a linear combination of signal atoms and footprint atoms. Removal of footprint atoms and their corresponding sparse coefficients leads to a successful footprint removal. We use both 3-D synthetic and field data examples to demonstrate the effectiveness of the proposed method.

A novel implementation of the self-consistent field (SCF) procedure specifically designed for high-performance execution on multiple graphics processing units (GPUs) is presented. The algorithm offloads to GPUs the three major computational stages of the SCF, namely, the calculation of one-electron integrals, the calculation and digestion of electron repulsion integrals, and the diagonalization of the Fock matrix, including SCF acceleration via DIIS. Performance results for a variety of test molecules and basis sets show remarkable speedups with respect to the state-of-the-art parallel GAMESS CPU code and relative to other widely used GPU codes for both single and multi-GPU execution. The new code outperforms all existing multi-GPU implementations when using eight V100 GPUs, with speedups relative to Terachem ranging from 1.2× to 3.3× and speedups of up to 28× over QUICK on one GPU and 15× using eight GPUs. Strong scaling calculations show nearly ideal scalability up to 8 GPUs while retaining high parallel efficiency for up to 18 GPUs.

Phase Modulation Orthogonal Frequency Division Multiplexing (PM-OFDM) and Code Division Multiple Access (CDMA) have distinctive advantages. On one hand, PM-OFDM is an attractive technique to combat multipath fading and reduce high PAPR in conventional OFDM. On the other hand, CDMA has the ability to serve multiple users at the same time and/or frequency. In this article, a new PM-OFDM-CDMA system that combines PM-OFDM and CDMA is proposed. The idea behind the proposed technique is to combine the advantages of these techniques in order to enhance the performance of the 5G system by serving multiple users simultaneously. The performance of the proposed system is analysed in terms of BER under different channel conditions. A Minimum Mean Square Equaliser (MMSE) scheme is used in order to avoid the effect of multipath effect and noise simultaneously. From the simulation results, we conclude that the proposed system has good performance that can improve 5G wireless communication networks, especially for battery-powered mobile devices. This amounts to the constant envelope of the proposed waveform which generates constant instantaneous power and therefore reduced PAPR.

Communications systems concerted over wireless channels depend on the environment. Communications system can be more reliable and efficient by properly analyzing wireless channels. Today's most important features are a high data rate and reliable performance to exploiting viable networks during this new information age. The channel is not time-invariant in wireless communication, so the received signal exhibits amplitude, phase, and angle variations due to multipath fading. Increasing data rates and reducing bandwidth make Orthogonal Frequency Division Multiplexing (OFDM) an important component of wireless communication systems. The OFDM technique uses many carriers very efficiently. With this scheme, interference is robustly reduced, and fading scenarios are easily accommodated. Analyzing digital modulation schemes requires evaluating link performance with fading channels. The paper compares channel performance over varying fading environments using a variety of modulation schemes. We study the BER and SNR properties of the AWGN, Rician fading and Rayleigh fading channels modulated with BPSK, QPSK, and M-ary QAM.

Orthogonal frequency division multiplexing (OFDM) is a popular multicarrier technique in communication system owing to its robustness against multipath fading and less complexity. Deep learning (DL...

Unslotted ALOHA protocol has been adopted as a channel access mechanism in commercial low-power wide-area networks (LPWANs), such as Sigfox and long-range (LoRa) alliance. This work examines the throughput and random access (RA) delay distribution of unslotted ALOHA systems by considering exponential random backoff (ERB) or uniform random backoff (URB) algorithm. We further characterize the operating region of the systems as unsaturated stable, bistable, and saturated regions in terms of the new packet arrival and retransmission rates. To run the system stably with the maximum throughput, we propose a Bayesian online backoff algorithm that estimates the number of backlogged devices. Its performance is compared with other algorithms, such as particle filter (PF)-based algorithm, binary exponential backoff (BEB) algorithm, and the algorithm of exploiting exact backlog size information. Through extensive simulations, it is demonstrated that the performance of the proposed algorithm is very close to the upper bound and robust to time-varying traffic condition.

To save energy consumption of Ethernet switches, IEEE has standardized a new energy-efficient operation for Ethernet links with a low-power state and transition mechanisms between the high-power state for transporting traffic and the low-power state.In this paper, we propose a queueing model with the Markov Modulated Compound Poisson Process that is able to characterize backbone packet traffic. We derive a closed-form solution for the stationary distribution of the proposed queueing model. We show that our model can capture an entire system where the transition times are constant.

The Internet is nowadays suffering dramatically serious attacks, with the distributed denial of service (DDoS) attacks being the representative and dominant ones. It is seen that, to stabilize the buffer queue length around a given target under DDoS attacks in the relevant routes is vitally important and helpful to mitigate the attacks and to improve the quality of service (QoS) for normal users. In the current paper, a stochastic queue dynamic model with L e ´ vy jump noise, which is affected by the continuous Brownian motion and the discontinuous Poisson process, is worked out to develop a novel and accurate mathematical framework for the stability of a route queue that deals with constant-rate DDoS attacks. This article proposes a security defensive mechanism in the network for solving the network collapse that can possibly be caused by DDoS attacks, otherwise. Particularly, based on the formulation of a stochastic queue dynamic with L e ´ vy jump noise, the mechanism that characterizes the behavior of the queue at routers is presented for stabilizing the queue length under constant-rate DDoS attacks. By applying the stochastic control theory into analyzing the performance of queue dynamic under constant-rate DDoS attacks, some explicit conditions are established under which the instantaneous queue length converges to any given target in a route. Simulation results demonstrate the satisfaction of the proposed defense mechanism with sharp contrast to the state of the art active queue management (AQM) schemes.

In this work, we leverage the clustered $\beta$ -Ginibre point process (GPP) and the queueing theory to analyse the delay performance in a large-scale repulsive fog radio access network (F-RAN) with computation offloading, which plays a key role on the delay performance. In particular, the average delay defined as the mean value of delay for executing the computing tasks and the task completion probability (TCP) defined as the probability of the delay for executing the computing task being smaller than a target delay, are derived in analytical forms and validated by Monte Carlo simulations. Based on these results, the optimal rate threshold, which affects the task offloading probability and the transmission delay, and the optimal cloud offloading probability to minimize the average delay and maximize the TCP are jointly analysed numerically. Moreover, an approximated result of the average delay is provided to reduce its computational complexity. The results show that the repulsion in the F-RAN significantly decreases the average delay and increases the TCP, but equipped with the optimal rate threshold and the cloud offloading probability, the effects of repulsion on the average delay and the TCP become trivial. Furthermore, with the optimal cloud offloading probability and rate threshold, the average delay and the TCP can be significantly reduced and improved in the hybrid processing mode compared with those in the local processing mode, respectively. The optimal rate thresholds lie in the range of 3 to 13 Mbps under our simulation environment, which can reduce a maximum of 60% of the average delay and increase a minimum of 0.17 of the TCP.

In this paper, we investigate the consensus problem for discrete linear multi-agent systems (MASs) with Markovian packet dropouts. Both identical and nonidentical packet dropouts are studied. For the discrete-time MASs under identical packet dropouts, we present the expectation of the total sojourn time of packet dropouts and successful message transmission, the switching number from packet dropouts to successful message transmission, and the awaken number of packet dropouts and successful message transmission. Based on these expectations, a linear consensus controller is designed through analyzing the transient properties of the Markov process such that the MASs can reach consensus almost surely for any initial distribution of packet dropouts. When it comes to the nonidentical packet dropouts where all the packets are independent and stochastic, a Markovian-lossy-channel based switching model (MLCBS model) is proposed. Based on the MLCBS model, we also propose an easy-to-implement linear consensus controller such that the MASs with nonidentical packet dropouts can achieve consensus almost surely. Finally, the theoretical results are illustrated by simulation examples.

MapReduce includes three phases of map, shuffle, and reduce. Since the map phase is CPU-intensive and the shuffle phase is I/O-intensive, these phases can be conducted in parallel. This paper studies a joint scheduling optimization of overlapping map and shuffle phases to minimize the average job makespan. New concepts of the strong pair and the weak pair are introduced. Two jobs are defined as a strong pair if the shuffle and map workloads of one job equal the map and shuffle workloads of the other job, respectively. Two jobs are defined as a weak pair if their total map workloads equal their total shuffle workloads. We prove that if the entire set of jobs can be decomposed to strong pairs of jobs, then the optimal schedule can pairwisely execute jobs that can form a strong pair. Following the above intuition, several offline and online scheduling policies are proposed. Extensions are made based on weak pairs. Real data-driven experiments validate the efficiency and effectiveness of the proposed policies.

Although queueing models in the mathematical field of queueing theory are mainly studied in the steady-state regime and practical applications are interested mostly in queues in transient situations subject to burst arrivals and congestion, their use is still justified as a first step towards a more complex and thorough analysis. In these practical applications, parameters such as the traffic intensity, which is the ratio between the arrival rate and service rate, are unknown and need to be estimated statistically. In this study, a Markovian arrival and deterministic single-server queueing system, known in Kendall notation as an M ∕ D ∕ 1 queueing model, is considered. This is one of the simplest queueing models with deterministic service time and may be seen as an approximation of a variety of applications in the performance evaluation of production management, telecommunications networks, and other areas. The main goal of this manuscript is to propose a methodology to determine the sample size for an M ∕ D ∕ 1 queueing system under the Bayesian setup by observing the number of customer arrivals during the service time of a customer. To verify the efficiency and efficacy of the proposed approach, an extensive set of numerical results is presented and discussed.

Chained Training Scheme (CTS) and Chained training scheme with Revised Sequence (CRS) were proposed to train artificial neural networks (ANNs) to design doubly reinforced concrete (RC) beams. CRS a...

This paper proposes a novel cooperative spectrum sensing network (CSSN) with improved energy detector (IED) based cognitive radio (CR) users. Every CR user is furnished with multiple antennas (M) and performs itself selection combining (SC) operation. All the CRs sense a primary user (PU) via erroneous sensing channels (S) and send the data to a fusion center (FC) via erroneous reporting channels (R). At FC, decision about PU is evaluated with the assistance of k-out-of-N rule. Detection probability expressions for a CR and FC subject to noise plus Rayleigh/Rician fading are developed. Also both simulation and analytical frameworks for throughput analysis are presented. The analytical performance results are also validated using simulation performance results. Performance comparisons between IED and conventional energy detectors (CED) are presented in terms of throughput and total error rate for several parameter values. Further, overall performance of throughput and total error rate in Rayleigh/Rician fading channels is investigated. The joint effects of diversity and fading on the CSSN throughput is also additionally discussed. Channel error (r) impact on the throughput and total error performances for each proposed and traditional networks is studied. Optimization of several parameters for maximizing the throughput and minimizing the total error is also studied. Throughput overall performance of proposed CSSN is plenty better than the conventional network in each fading channel. For several values of M and r, $$p_{\mathrm{{opt}}}$$ , $$\lambda _{\mathrm{{n, opt}}}$$ and $$N_{\mathrm{{opt}}}$$ values are calculated subject to each fading environment. Both analytical and MATLAB simulation results are matched. Under imperfect channel conditions, performances in terms of throughput and total error are not up to the mark but significant performance improvement has been obtained with diversity at each CR level.

In the present work, we propose a novel modulation scheme for 5G wireless communication system. Our contribution is to combine PM-OFDM (Phase Modulation Orthogonal Frequency Division Multiplexing) and CDMA (Code Division Multiple Access) to exploit their distinctive advantages. On the one hand, PM-OFDM is an effective technique to combat multipath fading effects. On the other hand, CDMA can serve multiple users who are using the same resources of time/frequency. The aim is to make a combination of PM-OFDM and CDMA techniques. In this paper, the OFDM-CDMA scheme and its PAPR (Peak-to-Average Power Ratio) statistics are reviewed. In this paper, the proposed scheme PM-OFDM-CDMA is described and its performances in terms of PAPR, power spectral density, and BER (Bit Error Rate) are analyzed. Moreover, MMSE (Minimum Mean Square Error) equalizer is used to avoid multipath and noise effects simultaneously. The simulation through AWGN (Additive white Gaussian noise) and Rayleigh channels is performed using MATLAB. From the simulation results, we observed that PM-OFDM-CDMA is an efficient technique in terms of energy consumption (PAPR = 0dB). Besides, CE-OFDM-CDMA offers high spectral efficiency with low BER due to its low PAPR. In CE-OFDM-CDMA method, the shape of the spectrum varies according to the value of the modulation index h. The band occupied by the spectrum increases with the value of h. Therefore, CE-OFDM-CDMA could be considered as a suitable technique for 5G applications.

In opportunistic networks, the nodes connect to each other wirelessly and use the store-carry-forward technique to transmit the data from one node to another node. The nodes in opportunistic networ...

Age of Information (AoI) is a critical metric in status update systems as these systems require the fresh updates. This letter investigates the uplink of an Internet-of-Thing (IoT) network where ${L}$ nodes transmit their information packets to a base station. The effects of the arrival rate of packets at the nodes, the number of nodes in the system, and queue length of each node have been studied by devising a discrete time Markov chain (MC) model. This model helps in predicting the values of AoI and probability of packet drops in such systems. The notion of first-in first-out is used for queuing, which transmits the oldest packet first, resulting in decreasing the overall AoI of the system. The results show that AoI increases with the increase in queue length, number of nodes and arrival rate and we quantify the aforementioned metrics using the MC model. The results found using the MC model are also validated using extensive simulations.

This paper proposes a general analytical approach to derive the outage probability of hybrid automatic repeat request with incremental redundancy (HARQ-IR) over correlated fading channels in closed-form. Unlike prior analyses, the consideration of channel correlation is one of the key reasons making the outage analysis involved. Using conditional Mellin transform, the outage probability of HARQ-IR over correlated Rayleigh fading channels is exactly expressed as a mixture of outage probabilities of HARQ-IR over independent Nakagami fading channels, where the weights are negative multinomial probabilities. Its straightforward application is to conduct asymptotic outage analysis to gain more insights, in which the asymptotic outage probability is obtained in a concise form. The asymptotic outage probability possesses some special properties which ease optimal power allocation and rate selection of HARQ-IR. Finally, numerical results are presented for validations and discussions.

Implementation of the cross-fade audio effect requires shaping the fade profile for a certain audio content that is to be faded out, as well as customizing the audio fade for an additional audio content, which is to be faded in, with the purpose of achieving a smooth transition between the two different audio contents. Similar to the case of applying adjustable fades, the audio cross-fades are usually carried out in the off-line mode, by employing various transcendental functions to shape the audio fades (both fade-out and fade-in effects). To improve the computational capabilities by minimizing the delay between receiving the position within the cross-fade effect and returning both the volume of the faded out section and the volume of the faded in one, we consider that, during the cross-fade effect, the audio volume of each of the two overlapping sections is the output of a rational function i.e. a mapping defined by a rational fraction. A plain HTML5/JavaScript implementation, prepared to be tested in any major browser, is advanced in the paper in order to highlight the suitability of the suggested approach to audio cross-fade customization with real-time computing.

Existing algorithms for identifying clients on a network segment are based on static client binding by MAC address. MAC address generation is based on pseudo-random sequences of 0–256 characters. W...

We study the reachability problem for queue automata and lossy queue automata. Concretely, we consider the set of queue contents which are forwards resp. backwards reachable from a given set of queue contents. Here, we prove the preservation of regularity if the queue automaton loops through some special sets of transformation sequences. This is a generalization of the results by Boigelot et al. and Abdulla et al. regarding queue automata looping through a single sequence of transformations. We also prove that our construction is possible in polynomial time.

After perceiving the idle channels using spectrum sensing, the cognitive user (CU) accesses these channels using suitable medium access control (MAC) protocol where the decision on optimal spectrum sensing, transmission time, and proper coordination with other CUs are the key characteristics of the MAC protocol. In the conventional MAC protocol, the CUs exploit the common control channel for the spectrum sensing information exchange in order to negotiate the channel access. After the spectrum sensing, each CU contends for the access of idle channel using various spectrum access protocols. The system model and proposed hybrid self‐scheduling multichannel‐MAC (HSMC‐MAC) protocol are illustrated. The chapter consists of the performance analysis of proposed protocol for the perfect and imperfect spectrum sensing scenarios. It presents the simulation results for the perfect and imperfect spectrum sensing scenarios. An improved frame structure for the distributed cognitive radio networks is proposed in order to enhance the throughput by exploiting the spectrum efficiently.

In this paper, we conduct rate-distortion and outage probability analyses for Wyner-Ziv (WZ) systems, where the information sequences from a source and a helper are transmitted to the destination over block Rayleigh fading multiple access channels (MACs). This work has been motivated by decision-making systems, where the aim is to make right decisions based on the observations. Hence, the most critical performance metric is the accuracy of decisions, even allowing distortion in wireless transmission. A sufficient condition for the successful transmissions is that the WZ and MAC regions intersect. For the ease of calculating the outage probability, we propose an approximated, yet accurate, WZ rate region. The outage probabilities of the WZ systems are then evaluated both in orthogonal and MAC transmissions. It is shown that the transmission efficiency of the system with MAC is significantly improved compared to orthogonal transmission. Furthermore, a helper-selection scheme is introduced to further reduce the outage probability of the WZ-MAC systems. The results show that the outage probability decreases, of which decay corresponds to the $(L+1)$ -order diversity with $L$ helpers in small value range of the average signal-to-noise ratio (SNR), while it converges into $L$ as the average SNR becomes large.

This paper considers Preemptive Mobile Computation Offloading when concurrent local execution (CLE) is used to guarantee task execution time constraints. By allowing simultaneous local and remote execution, CLE ensures that job deadlines are always satisfied in the face of unforeseen wireless channel conditions. In the preemptive offloading case, at the start of each time slot, a decision is made to either continue or temporarily interrupt the offload. This mechanism allows the system to adapt when channel conditions change. The paper considers the case for homogeneous Markovian wireless channels. Using Markovian decision process stopping theory, an online energy-optimal computation offloading algorithm is formulated for preemptive offloading, referred to as Optimal Preemptive Offloading (OPO). Since the computational complexity of OPO can be prohibitive, the paper introduces three computationally efficient techniques motivated by OPO: 1) water-filling ; 2) water-filling with scheduling ; and 3) generalized water-filling . For each method, two variations are considered. The first ( Equ ) uses the equilibrium channel state probabilities in offloading decision calculations, and the second ( Exp ) uses Markovian transition matrix exponentiation. This results in six algorithms with a wide variety of energy performance and computational complexity. Performance of the algorithms is compared that shows the tradeoffs between complexity and mobile energy saving performance.

Recent work demonstrates that IEEE 802.11 networks are vulnerable to cascading DoS attacks, wherein a single node can remotely and suddenly congest an entire network. In this paper, we propose, analyze, simulate, and experimentally verify a counter-measure against such attacks. Our main idea is to optimize the duration of packet transmissions in order to weaken coupling effects between neighboring pairs of nodes. Toward that end, we propose a new theoretical model that relates the utilization of neighboring pairs of nodes using a sequence of iterative equations. The model captures important specifications of the IEEE 802.11 MAC layer. Through a fixed point analysis of the sequence, we show how to optimally set the packet duration so that, on one hand, cascading DoS attacks are avoided and, on the other hand, throughput is maximized. We validate the analysis through extensive ns-3 simulations and demonstrate the effectiveness of the mitigation through experiments with real Wi-Fi cards. A key insight is that IEEE 802.11 networks with relatively large MAC overhead are less susceptible to cascading DoS attacks than networks with smaller MAC overhead.

Consider a multihop wireless network serving multiple flows in which wireless interference constraints between links are described by a link-interference graph. The timely-throughput of a flow is defined as the throughput of packets of that flow that reach their destination node within a specified deadline, and the weighted timely throughput of the network is their weighted average over the flows with a given set of positive weights. The problem is particularly challenging, and has generally been open, when there is wireless interference between transmissions. We show that a modified CSMA routing-scheduling policy with an appropriate set of attempt probabilities is nearly optimal for maximizing weighted timely-throughput. This policy has the useful property that the routing-scheduling decision for an individual packet is solely a function of its location and time-to-deadline, and so a wireless node does not require knowledge of the global network state. It is easily implementable in a decentralized fashion by the nodes given the attempt probabilities. A gradient-based adaptive CSMA routing-scheduling policy to determine the optimal attempt probabilities is further provided. It moves along the gradient of the timely throughput and converges to a local maximum.

A novel method of jitter decomposition by 1 Dimension-convolutional neural network (1D-CNN) using jitter histogram points is proposed for decomposing in the time interval error (TIE) of oscilloscope. Unlike the traditional jitter decomposition method based on the dual-Dirac model and Gaussian mixture model (GMM), the proposed jitter decomposition can reduce complexity and improve accuracy. The proposed method consists of a four-layer 1D-convolutional layer and multi-layer perceptron (MLP). Experimental results show that the proposed approach can decompose the total jitter (TJ) into deterministic jitter (DJ) and random jitter (RJ) and is better than the traditional jitter decomposition method, i.e., GMM, Fast Fourier transform and time lag correlation (FFT+TLC). Besides, results show that the proposed method’s performance is better than 2D-PointCNN, PointRNN, CNN, and PointANN.

Joint maximum likelihood (ML) multiuser detection for quasi-synchronous multicarrier code division multiple access (MC-CDMA) system has an excessive computational complexity, and it increases exponentially as the number of users and bits in each of the modulation symbols increases. Recently, sphere decoding (SD) has been adopted for multiuser detection whose complexity is polynomial in nature with the number of users, and it does not rely on the number of bits in the modulation. In this article, first we provide a construction of large size quasi-complementary sequence sets (QCSSs), and then a low-complexity optimum SD algorithm is proposed to detect the multiuser communication in QCSS-based MC-CDMA (QCSS-MC-CDMA) system. The complexity analysis has shown that the proposed SD-based QCSS-MC-CDMA receiver for multiuser communication provides lower complexity than that of ML-based QCSS-MC-CDMA receiver. We also show that peak to average power ratio of the proposed system is lesser than that of complete complementary code (CCC) based MC-CDMA (CCC-MC-CDMA) system. The proposed system overcomes the fractional delay problem which is noticed for CCC-MC-CDMA receiver. The bit error rate performance of the proposed SD-based QCSS-MC-CDMA system is compared with maximum ratio combining based MC-CDMA, ML-based MC-CDMA, frequency domain equalization based MC-CDMA, and CCC-MC-CDMA receivers over Rayleigh frequency selective fading channels. The work is validated through Monte Carlo simulation results.

Discrete-time queueing models find a large number of applications as they are used in modeling queueing systems arising in digital platforms like telecommunication systems and computer networks. In this paper, we analyze an infinite-buffer queueing model with discrete Markovian arrival process. The units on arrival are served in batches by a single server according to the general bulk-service rule, and the service time follows general distribution with service rate depending on the size of the batch being served. We mathematically formulate the model using the supplementary variable technique and obtain the vector generating function at the departure epoch. The generating function is in turn used to extract the joint distribution of queue and server content in terms of the roots of the characteristic equation. Further, we develop the relationship between the distribution at the departure epoch and the distribution at arbitrary, pre-arrival and outside observer’s epochs, where the first is used to obtain the latter ones. We evaluate some essential performance measures of the system and also discuss the computing process extensively which is demonstrated by some numerical examples.

A system with heterogeneous servers, Poisson stream with Markov modulation, and instant feedback is studied. Primary requests are serviced in a high-speed server, and after servicing, each request, according to the Bernoulli scheme, either leaves the system or requires re-servicing. Repeated requests are serviced in a slow server; at the same time, after servicing, these requests also either leave the system or require repeated servicing in a slow server, according to the Bernoulli scheme. If at the moment of the arrival of the primary request the length of the queue of such requests exceeds a certain threshold value and the slow server is free, then the incoming request, according to the Bernoulli scheme, is either sent to the slow server or joins it when its turn to do so. It is considered that the lengths of the queues in front of each server are infinite. An adequate mathematical model of the system under study is constructed in the form of a three-dimensional Markov chain with an infinite state space. The ergodicity condition for this circuit is obtained, an approximate algorithm for calculating the stationary probabilities of the states is proposed, and its high level of accuracy is shown. The results of the numerical experiments are presented.

Cooperative communication has occurred to be a promising technology for future wireless networks. Characterizing its performance, in terms of symbol error rate, outage probability, data rate, etc. over the generalized fading channels are essential to wholly attain its benefits. In this paper, the closed form expressions for the outage probability of the multi-relay cooperative communication using decode-and-forward protocol over generalized fading channels ( $$\eta-\mu$$ and $$\kappa-\mu$$ ) has been derived in terms of gamma function. Firstly, the outage probability for generalized $$\eta-\mu$$ and $$\kappa-\mu$$ fading channels are computed, followed by the outage probability expression for the overall system. The final expressions are in the form of infinite series which converges very rapidly and hence it is accurately approximated by considering finite number of terms in summation. Analytical results are plotted to demonstrate the the impact of various channel parameters. It is also shown that how the outage performance improves with the number of relays. The obtained outage probability expressions for generalized fading environment can be readily extended for any generalized system model.

Reconfigurable intelligent surfaces (RIS) allow significant throughput enhancement as all reflections have the same phase at the receiver. In this paper, we suggest to optimize packet length for wireless communications using RIS. Two techniques are suggested in order to maximize average or instantaneous throughput. Average throughput maximization requires only the average signal to noise ratio to determine the optimal packet length. The Gradient algorithm is used to maximize the average throughput. Instantaneous throughput maximization requires the instantaneous SNR in order to adapt packet length to channel conditions. We derive in closed form the expression of optimal packet length, maximizing the instantaneous throughput. Our results are valid for any number of reflecting meta-surfaces N of the RIS.

Motivated by the fault tolerance for manufacturing, we investigate a renewal input bulk arrival queue with a fault-tolerant server, in which the server can keep working with a low service rate even if the partial failure occurs. Only when there are no customers in the system, the partial failure can be removed. To explore the performance measures of the queue, a more generic and simpler algorithm based on the right shift operator method for solving difference equations is employed to obtain the queue-length distributions at different time epochs. The significant feature of this algorithm lies in that it does not require the derivation of the transition probability matrix for the corresponding embedded Markov chain. Furthermore, we can resort to the queue-length distribution at the pre-arrival epoch to quickly get the expected sojourn time for an arbitrary customer. Finally, with the help of Pad\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\acute{\mathrm{e}}$$\end{document}e´ approximation, several representative numerical examples are illustrated in tables and graphs, under which we show how to verify the correctness of our theoretical results through Little’s law.

Small drones that navigate using cameras may be limited in their speed and agility by low onboard computing power. We evaluate the role of edge computing in 5G for such autonomous navigation. The offloading of image processing tasks to an edge server is studied with a vision-based navigation algorithm. Three computation modes are compared: onboard, fully offloaded to the edge, and partially offloaded. Partial offloading is expected to pose lower demands on the communication network in terms of transfer rate than full offloading but requires some onboard processing. Our results on the computation time help select the most suitable mode for image processing, i.e., whether and what to offload, based on the network conditions.

This work proposes an average-case performance measurement as a meaningful and non-trivial way of evaluating and comparing queueing protocols on a multiple-access channel (MAC). We prove that Little’s Law holds for this way of measuring latency and number of packets in the system, also for adversarial packet arrival (which was not the case for the previously considered worst-case measurement), and analyze performance of popular MAC protocols. Interestingly, some of them have asymptotically the same average and worst case queue sizes, but not packet latencies, while others exhibit entirely opposite behavior. We also propose a consistent methodology for classifying queueing protocols, based on protocol features, such as synchronization, collision detection, queue-size awareness etc.

This paper study and present, power allocation strategy on sub-carriers of multiuser OFDM employed for orthogonal chaotic vector shift keying (MU OFDM-OCVSK) over multipath frequency selective fading channels. firstly, the MU OFDM-OCVSK system is modeled with power allocated on reference and information bearing subcarriers. Then, the computed bit error rate equation of the power allocation MU OFDM-OCVSK system is derived. The optimal power allocation strategy on subcarriers is obtained using convex optimization. Finally, compared with the traditional MU OFDM-DCSK and MU OFDM-OCVSK without power allocation system the proposed system can achieve an excellent BER performance under multipath Rayleigh fading channels. Numerical and Simulation results emphasize the remarkable features of the proposed optimal power allocation strategy.

Shi et al. (2018) proposed a highly parallel polynomial filtering eigensolver for the computation of planetary normal modes. As a challenge at the Student Cluster Competition in The International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), we reproduce the computational efficiency of the polynomial filtering eigensolver on our Intel Xeon machine. We present the weak scalability, scaling of runtime with model size (in a fixed interval) and the strong scalability results in this report.

Objective: In this study we consider a two-state retrial queueing system with feedback having two identical parallel servers. Transient state probabilities for exact number of arrivals and departures from the system will be obtained when both, one or none of the servers is busy. Numerical and graphical solutions will also be obtained. Methods: The difference-differential equations governing the system are solved recursively, Laplace transform is then used to obtain the transient state probabilities for exact number of arrivals and departures from the system. Findings: Time dependent probabilities are obtained when both, one and none of the servers is busy. Numerical and Graphical solutions are also obtained using MATLAB programming. Novelty: In past research, models considered arrivals and departures from the orbit whereas in present model arrivals and departures from the system are studied along with the concept of feedback. Applications: This type of model is implemented in computer systems. Mathematics Subject Classification: 60K25, 90B22, 60M20 Keywords: Arrivals; Departures; Queueing; Retrial; Feedback

In this article, we propose a cross-layer resource management mechanism for an indoor multiuser visible light communication (VLC) access network. The physical layer builds upon DC-biased orthogonal frequency division multiple access. Channel state information at the physical layer serves as a performance indicator for better data rate opportunity while queue state information at medium access control layer provides the information about packet urgency. A packet based traffic arrival model is considered such that incoming packets with random inter arrival times join a separate queue dedicated to each user if the admission control accepts them, otherwise packets are dropped. We formulate and solve a stochastic cross-layer optimization problem to optimize the network resources under the constraints of queue stability and power to maximize the average system throughput. By using the Lyapunov optimization technique, we convert the long term time-average optimization problem into series of single time slot online problem. Then, we decompose the problem into independent sub problems and propose packet admission, resource (access point and subcarrier) allocation and power control solutions. We present simulation results to demonstrate the superiority of proposed scheme over existing solutions in the literature.

A broad class of parallel server systems is considered, for which we prove the steady-state asymptotic independence of server workloads, as the number of servers goes to infinity, while the system load remains sub-critical. Arriving jobs consist of multiple components. There are multiple job classes, and each class may be of one of two types, which determines the rule according to which the job components add workloads to the servers. The model is broad enough to include as special cases some popular queueing models with redundancy, such as cancel-on-start and cancel-on-completion redundancy. Our analysis uses mean-field process representation and the corresponding mean-field limits. In essence, our approach relies almost exclusively on three fundamental properties of the model: (a) monotonicity, (b) work conservation, (c) the property that, on average, "new arriving workload prefers to go to servers with lower workloads.", Comment: 21 pages, 1 figure. Revision