Showing papers on "Upper and lower bounds published in 2023"

On the Complexity of String Matching for Graphs

Abstract: Exact string matching in labeled graphs is the problem of searching paths of a graph G=(V, E) such that the concatenation of their node labels is equal to a given pattern string P[1.m]. This basic problem can be found at the heart of more complex operations on variation graphs in computational biology, of query operations in graph databases, and of analysis operations in heterogeneous networks. We prove a conditional lower bound stating that, for any constant ε > 0, an O(|E|1 - ε m) time, or an O(|E| m1 - ε)time algorithm for exact string matching in graphs, with node labels and pattern drawn from a binary alphabet, cannot be achieved unless the Strong Exponential Time Hypothesis (SETH) is false. This holds even if restricted to undirected graphs with maximum node degree 2—that is, to zig-zag matching in bidirectional strings, or to deterministic directed acyclic graphs whose nodes have maximum sum of indegree and outdegree 3. These restricted cases make the lower bound stricter than what can be directly derived from related bounds on regular expression matching (Backurs and Indyk, FOCS’16). In fact, our bounds are tight in the sense that lowering the degree or the alphabet size yields linear time solvable problems. An interesting corollary is that exact and approximate matching are equally hard (i.e., quadratic time) in graphs under SETH. In comparison, the same problems restricted to strings have linear time vs quadratic time solutions, respectively (approximate pattern matching having also a matching SETH lower bound (Backurs and Indyk, STOC’15)).

Fixed-Time and Prescribed-Time Consensus Control of Multiagent Systems and Its Applications: A Survey of Recent Trends and Methodologies

Abstract: Fixed-time and prescribed-time consensus control can bring an explicit estimate of the settling time without dependence on initial conditions, which is important in providing control engineers a priori system information. This article aims at presenting a survey of recent trends and methodologies of fixed-time and prescribed-time consensus control in multiagent systems. First, some typical fixed-time consensus results are reviewed. Despite the advantage in deriving a fixed settling time bound, fixed-time consensus controllers usually result in a conservative estimate of the bound and a large magnitude of initial control input, which in turn show the necessity of designing prescribed-time consensus controllers. Second, characteristics and controller design of (practical, respectively) prescribed-time consensus are provided in detail. Particularly, representative time-varying function-based controllers are presented, by which (practical, respectively) consensus can be achieved in prescribed time. Third, applications of fixed-time and prescribed-time consensus control in mobile robots and smart grids are illustrated in case studies. Finally, several challenging issues in prescribed-time consensus control are discussed for future research.

SMC for Discrete-Time Nonlinear Semi-Markovian Switching Systems With Partly Unknown Semi-Markov Kernel

Abstract: This article is devoted to the discrete-time sliding mode control (DSMC) for nonlinear semi-Markovian switching systems (S-MSSs). Motivated by the fact that the complete information of the semi-Markov Kernel is difficult to be obtained in practical applications, it is recognized to be partly unknown as the most common mean. By utilizing the prior information of the sojourn-time upper bound for each switching mode, sufficient conditions under the equivalent DSMC law are proposed for the mean square stability. Moreover, the designed DSMC law realizes the finite-time reachability of the sliding region, and makes the sliding dynamics converge to the predesignated sliding region in a finite time. In the end, a numerical example and an electronic throttle model are given to validate the proposed control strategy.

Influence-Based Community Partition With Sandwich Method for Social Networks

Abstract: Community partition is an important problem in many areas, such as biology networks and social networks. The objective of this problem is to analyze the relationships among data via the network topology. In this article, we consider the community partition problem under the independent cascade (IC) model in social networks. We formulate the problem as a combinatorial optimization problem that aims at partitioning a given social network into disjoint $m$ communities. The objective is to maximize the sum of influence propagation of a social network through maximizing it within each community. The existing work shows that the influence maximization for community partition problem (IMCPP) is NP-hard. We first prove that the objective function of IMCPP under the IC model is neither submodular nor supermodular. Then, both supermodular upper bound and submodular lower bound are constructed and proved so that the sandwich framework can be applied. A continuous greedy algorithm and a discrete implementation are devised for upper and lower bound problems. The algorithm for both of the two problems gets a $1-1/e$ approximation ratio. We also present a simple greedy algorithm to solve the original objective function and apply the sandwich approximation framework to it to guarantee a data-dependent approximation factor. Finally, our algorithms are evaluated on three real datasets, which clearly verifies the effectiveness of our method in the community partition problem, as well as the advantage of our method against the other methods.

Design of Stealthy Deception Attacks With Partial System Knowledge

Abstract: This article investigates the design of stealthy deception attacks with the aim of destroying the estimate performance without knowing the filter gain. The residual used to detect attacks is generated by the parity space approach and the Kullback–Leibler divergence is adopted as the metric of stealthiness. We first give the necessary and sufficient condition for the inexistence of the strictly stealthy attack, which does not change the residual and can lead to unbounded estimate error. When the strictly stealthy attack does not exist, a lower bound of the secondary moment of the estimate error is then derived via the posterior Cramér–Rao bound. The zero-mean Gaussian attack that maximizes this lower bound is obtained by solving a convex optimization problem. The proposed method can also be applied to design stealthy attacks with the aim of destroying the control performance. Finally, a numerical example of longitudinal flight control system is illustrated to demonstrate the effectiveness of the proposed attack.

Matrix Measure-Based Projective Synchronization on Coupled Neural Networks With Clustering Trees

Abstract: This article mainly studies the projective quasisynchronization for an array of nonlinear heterogeneous-coupled neural networks with mixed time-varying delays and a cluster-tree topology structure. For the sake of the mismatched parameters and the mutual influence among distinct clusters, the exponential and global quasisynchronization within a prescribed error bound instead of complete synchronization for the coupled neural networks with clustering trees is investigated. A kind of pinning impulsive controllers is designed, which will be imposed on the selected neural networks with some largest norms of error states at each impulsive instant in different clusters. By employing the concept of the average impulsive interval, the matrix measure method, and the Lyapunov stability theorem, sufficient conditions for the realization of the cluster projective quasisynchronization are derived. Meanwhile, in terms of the formula of variation of parameters and the comparison principle for the impulsive systems with mixed time-varying delays, the convergence rate and the synchronization error bound are precisely estimated. Furthermore, the synchronization error bound is efficiently optimized based on different functions of the impulsive effects. Finally, a numerical experiment is given to prove the results of theoretical analysis.

Dynamic Self-Triggered Impulsive Synchronization of Complex Networks With Mismatched Parameters and Distributed Delay

Abstract: Synchronization of complex networks with nonlinear couplings and distributed time-varying delays is investigated in this article. Since the mismatched parameters of individual systems, a kind of leader-following quasisynchronization issues is analyzed via impulsive control. To acquire appropriate impulsive intervals, the dynamic self-triggered impulsive controller is devoted to predicting the available instants of impulsive inputs. The proposed controller ensures the control effects while reducing the control costs. In addition, the updating laws of the dynamic parameter is settled in consideration of error bounds to adapt to the quasisynchronization. With the utilization of the Lyapunov stability theorem, comparison method, and the definition of average impulsive interval, sufficient conditions for realizing the synchronization within a specific bound are derived. Moreover, with the definition of average impulsive gain, the parameter variation scheme is extended from the fixed impulsive effects case to the time-varying impulsive effects case. Finally, three numerical examples are given to show the effectiveness and the superiority of proposed mathematical deduction.

Ziv-Zakai Bound for DOAs Estimation

Abstract: Lower bounds on the mean square error (MSE) play an important role in evaluating the direction-of-arrival (DOA) estimation performance. Among numerous bounds for DOA estimation, the local Cramér-Rao bound (CRB) is only tight asymptotically. By contrast, the existing global tight Ziv-Zakai bound (ZZB) is appropriate for evaluating the single source estimation only. In this paper, we derive an explicit ZZB applicable for evaluating hybrid coherent/incoherent multiple sources DOA estimation. It is first shown that, a straightforward generalization of ZZB from single source estimation to multiple sources estimation cannot keep the bound valid in the a priori performance region. To derive a global tight ZZB, we then introduce order statistics to describe the change of the a priori distribution of DOAs caused by ordering process during the MSE calculation. The derived ZZB is for the first time formulated as a function of coherent coefficients between coherent sources, and reveals the relationship between the MSE convergency in the a priori performance region and the number of sources. Moreover, the derived ZZB also provides a unified tight bound for both overdetermined and underdetermined DOA estimation. Simulation results demonstrate the obvious advantages of the derived ZZB over the CRB on evaluating and predicting the estimation performance for multiple sources DOA.

Predefined-Time Approximation-Free Attitude Constraint Control of Rigid Spacecraft

Abstract: In this article, a predefined-time approximation-free attitude constraint control scheme is proposed for rigid spacecraft with external disturbances. By combining the backstepping technique, an approximation-free controller is systematically developed to maintain the spacecraft attitude within a prescribed small region in predefined time, and the minimum upper bound of the settling time can be exactly given by adjusting a single control parameter. Instead of employing some piecewise continuous functions, the quadratic-fraction functions are constructed in the controllers design to circumvent the possible singularity issue resulted from the differentiation of the virtual controller. With the presented approximation-free control scheme, the computational burden is reduced due to the avoidance of introducing any function approximators. The effectiveness of the proposed scheme is verified by the numerical simulations.

Bootstrapping $$ \mathcal{N} $$ = 4 super-Yang-Mills on the conformal manifold

Abstract: A bstract We combine supersymmetric localization results with numerical bootstrap techniques to compute upper bounds on the low-lying CFT data of $$ \mathcal{N} $$ N = 4 super-Yang-Mills theory as a function of the complexified gauge coupling τ . In particular, from the stress tensor multiplet four-point function, we extract the scaling dimension of the lowest-lying unprotected scalar operator and its OPE coefficient. While our method can be applied in principle to any gauge group G , we focus on G = SU(2) and SU(3) for simplicity. At weak coupling, the upper bounds we find are very close to the corresponding four-loop results. We also give preliminary evidence that these upper bounds become small islands under reasonable assumptions.

Event-Triggered Recursive State Estimation for Stochastic Complex Dynamical Networks Under Hybrid Attacks

Abstract: In this article, the event-based recursive state estimation problem is investigated for a class of stochastic complex dynamical networks under cyberattacks. A hybrid cyberattack model is introduced to take into account both the randomly occurring deception attack and the randomly occurring denial-of-service attack. For the sake of reducing the transmission rate and mitigating the network burden, the event-triggered mechanism is employed under which the measurement output is transmitted to the estimator only when a preset condition is satisfied. An upper bound on the estimation error covariance on each node is first derived through solving two coupled Riccati-like difference equations. Then, the desired estimator gain matrix is recursively acquired that minimizes such an upper bound. Using the stochastic analysis theory, the estimation error is proven to be stochastically bounded with probability 1. Finally, an illustrative example is provided to verify the effectiveness of the developed estimator design method.

Global existence and wave breaking for a stochastic two-component Camassa–Holm system

Abstract: In this paper, we study the stochastic two-component Camassa–Holm shallow water system on [Formula: see text] and [Formula: see text]. We first establish the existence, uniqueness, and blow-up criterion of the pathwise strong solution to the initial value problem with nonlinear noise. Then, we consider the impact of noise on preventing blow-up. In both nonlinear and linear noise cases, we establish global existence. In the nonlinear noise case, the global existence holds true with probability 1 if a Lyapunov-type condition is satisfied. In the linear noise case, we provide a lower bound for the probability that the solution exists globally. Furthermore, in the linear noise and the periodic case, we formulate a precise condition on initial data that leads to blow-up of strong solutions with a positive probability, and the lower bound for this probability is also estimated.

Secure Formation Control via Edge Computing Enabled by Fully Homomorphic Encryption and Mixed Uniform-Logarithmic Quantization

Abstract: Recent developments in communication technologies, such as 5G, together with innovative computing paradigms, such as edge computing, provide further possibilities for the implementation of real-time networked control systems. However, privacy and cyber-security concerns arise when sharing private data between sensors, agents and a third-party computing facility. In this letter, a secure version of the distributed formation control is presented, analyzed and simulated, where gradient-based formation control law is implemented in the edge, with sensor and actuator information being secured by fully homomorphic encryption method based on learning with error (FHE-LWE) combined with a proposed mixed uniform-logarithmic quantizer (MULQ). The novel quantizer is shown to be suitable for realizing secure control systems with FHE-LWE where the critical real-time information can be quantized into a prescribed bounded space of plaintext while satisfying a sector bound condition whose lower and upper-bound can be made sufficiently close to an identity. An absolute stability analysis is presented, that shows the asymptotic stability of the closed-loop secure control system.

Dynamic Min and Max Consensus and Size Estimation of Anonymous Multiagent Networks

Abstract: In this article, we propose two distributed control protocols for discrete-time multiagent systems, which solve the dynamic consensus problem on the max value. In this problem, each agent is fed an exogenous reference signal and has the objective to estimate and track the instantaneous and time-varying value of the maximum among all the signals fed to the network by exploiting only local and anonymous interactions among the agents. The first protocol achieves bounded steady-state and tracking errors which can be tradedoff for convergence time. The second protocol achieves zero steady-state error and requires knowledge of an upper bound to the diameter of the graph representing the network. Modified versions of both protocols are provided to solve the dual dynamic min-consensus problem. These protocols are then exploited to solve a distributed size estimation problem in a network of anonymous agents in a dynamic setting where the size of the network is time-varying during the execution of the estimation algorithm. Numerical simulations are provided in order to corroborate the characterization of the proposed protocols.

Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live Streaming

Abstract: This paper proposes an edge-assisted crowdsourced live video transcoding approach where the transcoding capabilities of the edge transcoders are unknown and dynamic. The resilience and trustworthiness of highly unstable transcoders in decision making are characterized with mean-variance-based measures to avoid making highly risky decisions. The risk level of each device’s situation is assessed and two upper confidence bounds of the variance of transcoding performance are presented. Based on the derived bounds and by leveraging the contextual information of devices, two risk-aware contextual learning schemes are developed to efficiently estimate the transcoding capabilities of the edge devices. Combining context awareness and risk sensitivity, a novel transcoding task assignment and viewer association algorithm is proposed. Simulation results demonstrate that the proposed algorithm achieves robust task offloading with superior network utility performance as compared to the linear upper confidence bound and the risk-aware mean-variance upper confidence bound-based algorithms. In particular, an epoch-based task assignment strategy is designed to reduce the task switching costs incurred in assigning the same transcoding task to different transcoders over time. This strategy also reduces the computational time needed. Numerical results confirm that this strategy achieves up to 86.8% switching costs reduction and 92.3% computational time reduction.

Approximating Knapsack and Partition via Dense Subset Sums

Abstract: Knapsack and Partition are two important additive problems whose fine-grained complexities in the $(1-\varepsilon)$-approximation setting are not yet settled. In this work, we make progress on both problems by giving improved algorithms. - Knapsack can be $(1 - \varepsilon)$-approximated in $\tilde O(n + (1/\varepsilon) ^ {2.2} )$ time, improving the previous $\tilde O(n + (1/\varepsilon) ^ {2.25} )$ by Jin (ICALP'19). There is a known conditional lower bound of $(n+\varepsilon)^{2-o(1)}$ based on $(\min,+)$-convolution hypothesis. - Partition can be $(1 - \varepsilon)$-approximated in $\tilde O(n + (1/\varepsilon) ^ {1.25} )$ time, improving the previous $\tilde O(n + (1/\varepsilon) ^ {1.5} )$ by Bringmann and Nakos (SODA'21). There is a known conditional lower bound of $(1/\varepsilon)^{1-o(1)}$ based on Strong Exponential Time Hypothesis. Both of our new algorithms apply the additive combinatorial results on dense subset sums by Galil and Margalit (SICOMP'91), Bringmann and Wellnitz (SODA'21). Such techniques have not been explored in the context of Knapsack prior to our work. In addition, we design several new methods to speed up the divide-and-conquer steps which naturally arise in solving additive problems.

Jerk-Level Zhang Neurodynamics Equivalency of Bound Constraints, Equation Constraints, and Objective Indices for Cyclic Motion of Robot-Arm Systems

Abstract: Equivalency is a powerful approach that can transform an original problem into another problem that is relatively more ready to be resolved. In recent years, Zhang neurodynamics equivalency (ZNE), in the form of neurodynamics or recurrent neural networks (RNNs), has been investigated, abstracted, and proposed as a process that can equivalently solve equations at different levels. After long-term research, we have noticed that the ZNE can not only work with equations, but also inequations. Thus, the ZNE of inequation type is proposed, proved, and applied in this study. The ZNE of inequation type can transform different-level bound constraints into unified-level bound constraints. Applications of the jerk-level ZNE of bound constraints, equation constraints, and objective indices ultimately build up effective time-varying quadratic-programming schemes for cyclic motion planning and control (CMPC) of single and dual robot-arm systems. In addition, as an effective time-varying quadratic-programming solver, a projection neural network (PNN) is introduced. Experimental results with single and dual robot-arm systems substantiate the correctness and efficacy of ZNE and especially the ZNE of inequation type. Comparisons with conventional methods also exhibit the superiorities of ZNE.

Sharper bounds for the numerical radius

Abstract: In this paper, we discuss and present new sharp inequalities for the numerical radii of Hilbert space operators. In particular, if A and B are bounded linear operators on a Hilbert space, we present new upper bounds for ω(A∗B). The main tool to obtain our results is using block matrix techniques. Among many interesting results, and as an application of the new inequalities, we obtain the following bound for the numerical radius of an operator T, ω(T)≤12(‖T‖1/2‖|T|1/2+|T∗|1/2‖),where ω(⋅), ‖⋅‖, and |⋅| denote the numerical radius, the usual operator norm, and the absolute value operator, respectively. Other difference inequalities will be presented too.

Stabilization of Triangular Nonlinear Systems With Multiplicative Stochastic State Sensing Noise

Abstract: We present new state feedback control designs for lower/upper triangular nonlinear systems with multiplicative stochastic sensor uncertainty. For lower triangular nonlinear systems with small sensor noise, we develop a novel control design where the control gains are suitably constructed to simultaneously dominate the nonlinear functions and sensor noise of sufficiently small multiplicative gain. For upper triangular nonlinear systems, we propose a new low-gain domination design, the advantage of which is that it can effectively deal with the sensor noise with arbitrarily large intensities. These two designs can both ensure that the closed-loop system has an almost surely unique global solution; the origin of the closed-loop system is mean-square stable, and the states can be regulated to zero almost surely. Finally, two simulation examples are given to illustrate the designs.

Sampled-Data Extremum Seeking With Constant Delay: A Time-Delay Approach

Abstract: This article proposes a constructive method for sampled-data extremum seeking (ES) with square wave dithers and constant delays, by using two time-delay approaches: one to averaging and the other to sampled-data control. We consider gradient-based ES for static maps which are of quadratic forms. By transforming the ES system to the time-delay system, we have developed a stability analysis via a Lyapunov–Krasovskii method. We derive the practical stability conditions in terms of linear matrix inequalities for the resulting time-delay system. The time-delay approach offers a quantitative calculation on the upper bound of the dither and sampling periods, constant delays that the ES system is able to tolerate, as well as the ultimate bound of the extremum seeking error. This is in the presence of uncertainties of extremum value and extremum point.

Sequence aligners can guarantee accuracy in almost O(m log n) time: a rigorous average-case analysis of the seed-chain-extend heuristic

Abstract: Seed-chain-extend with k-mer seeds is a powerful heuristic technique for sequence alignment employed by modern sequence aligners. While effective in practice for both runtime and accuracy, theoretical guarantees on the resulting alignment do not exist for seed-chain-extend. In this work, we give the first rigorous bounds for the efficacy of seed-chain-extend with k-mers in expectation. Assume we are given a random nucleotide sequence of length ~ n that is indexed (or seeded) and a mutated substring of length ~ m ≤ n with mutation rate θ < 0.206. We prove that we can find a k = Θ(log n) for the k-mer size such that the expected runtime of seed-chain-extend under optimal linear gap cost chaining and quadratic time gap extension is O(mnf(θ) log n) where f(θ) < 2.43 · θ holds as a loose bound. The alignment also turns out to be good; we prove that more than fraction of the homologous bases are recoverable under an optimal chain. We also show that our bounds work when k-mers are sketched, i.e. only a subset of all k-mers is selected, and that sketching reduces chaining time without increasing alignment time or decreasing accuracy too much, justifying the effectiveness of sketching as a practical speedup in sequence alignment. We verify our results in simulation and on real noisy long-read data and show that our theoretical runtimes can predict real runtimes accurately. We conjecture that our bounds can be improved further, and in particular, f(θ) can be further reduced.

Bootstrapping the operator norm in high dimensions: Error estimation for covariance matrices and sketching

Abstract: Although the operator (spectral) norm is one of the most widely used metrics for covariance estimation, comparatively little is known about the fluctuations of error in this norm. To be specific, let Σˆ denote the sample covariance matrix of n i.i.d. observations in Rp that arise from a population matrix Σ, and let Tn=n‖Σˆ−Σ‖ op. In the setting where the eigenvalues of Σ have a decay profile of the form λj(Σ)≍j−2β, we analyze how well the bootstrap can approximate the distribution of Tn. Our main result shows that up to factors of log(n), the bootstrap can approximate the distribution of Tn with respect to the Kolmogorov metric at the rate of n−β−1∕2 6β+4, which does not depend on the ambient dimension p. In addition, we offer a supporting result of independent interest that establishes a high-probability upper bound for Tn based on flexible moment assumptions. More generally, we discuss the consequences of our work beyond covariance matrices, and show how the bootstrap can be used to estimate the errors of sketching algorithms in randomized numerical linear algebra (RandNLA). An illustration of these ideas is also provided with a climate data example.

Detraction of decoherence that arises from the acceleration process

Abstract: The possibility of detracting the decoherence due to the acceleration process of the two-qutrit system is investigated, where we examined the behaviour of the relative entropy and the non-local information. For this purpose, the accelerated subsystems are allowed to pass through local or global noisy channels. It is shown that, the detraction potential depends on the type of the used noisy channel, local or global, and the initial settings of the accelerated qutrit systems, whether it is prepared in free or bound entangled intervals. The improving rate that depicted for systems prepared in the free entangled intervals is much better than those prepared in the bound entangled interval. The maximum bounds of the non-local information in the presence of the amplitude damping channels are larger than those passes in the dephasing channel.

Numerical Simulation for COVID-19 Model Using a Multidomain Spectral Relaxation Technique

Abstract: The major objective of this work is to evaluate and study the model of coronavirus illness by providing an efficient numerical solution for this important model. The model under investigation is composed of five differential equations. In this study, the multidomain spectral relaxation method (MSRM) is used to numerically solve the suggested model. The proposed approach is based on the hypothesis that the domain of the problem can be split into a finite number of subintervals, each of which can have a solution. The procedure also converts the proposed model into a system of algebraic equations. Some theoretical studies are provided to discuss the convergence analysis of the suggested scheme and deduce an upper bound of the error. A numerical simulation is used to evaluate the approach’s accuracy and utility, and it is presented in symmetric forms.

Charge, spin, and heat shot noises in the absence of average currents: Conditions on bounds at zero and finite frequencies

Abstract: Nonequilibrium situations where selected currents are suppressed are of interest in fields like thermoelectrics and spintronics, raising the question of how the related noises behave. We study such zero-current charge, spin, and heat noises in a two-terminal mesoscopic conductor. In the presence of voltage, spin, and temperature biases, the nonequilibrium (shot) noises of charge, spin, and heat can be arbitrarily large, even if their average currents vanish. However, as soon as a temperature bias is present, additional equilibrium (thermal-like) noise necessarily occurs. We show that this equilibrium noise sets an upper bound on the zero-current charge and spin shot noises, even if additional voltage or spin biases are present. We demonstrate that these bounds can be overcome for heat transport by breaking the spin and electron-hole symmetries, respectively. By contrast, we show that the bound on the charge noise for strictly two-terminal conductors even extends into the finite-frequency regime.

A Fast Consensus for Permissioned Wireless Blockchains

Abstract: With the wide deployment of Internet-of-Things (IoT), blockchain systems have been playing a crucial role to establish a trusted computing environment among potentially mistrusting agents without depending on a centralized server. Different from previous blockchain consensus protocols adopted in IoT, which rely on efficient and stable transmissions, in this paper, we consider how to reach blockchain consensus in wireless networks without reliable network support. Specifically, a realistic SINR model is adopted to depict the unreliable transmissions in wireless channels. Based on the SINR model, a distributed and randomized consensus algorithm is proposed to reach k-times consensus among n devices within O(k+logn) time steps with high probability. Note that the time complexity of our algorithm is asymptotically optimal since Ω(k+logn) is a lower bound to achieve k-times consensus in a distributed environment. We conduct both rigorous theoretical analysis and extensive simulations to validate our method. It is believed that our work can facilitate the implementation of blockchains in many wireless scenarios in which the reliable and fast transmissions cannot be guaranteed.