Finite-state Markov Channel Model Research Articles

Second-Order Statistics for IRS-Assisted Multiuser Vehicular Network With Co-Channel Interference

In this paper, we consider a downlink multi-user vehicular communication network empowered by an intelligent reflecting surface (IRS) with the base station (BS) employing the orthogonal frequency division multiple access (OFDMA) scheme. The performance of the considered system is evaluated in terms of second-order statistics (SOS) in the presence of multiple co-channel interferers with varying powers and speeds. In particular, we develop the analytical expressions of level crossing rate (LCR) and average outage duration (AOD) of the received signal envelope at a desired vehicle by considering a generalized fading model as - distribution. The impact of various system and channel parameters such as number of reflecting elements, amplitude of reflection coefficients, number of interferers, Doppler frequencies of desired and interfering vehicles, and different channel fading environments has been shown on the LCR and AOD performances. Moreover, we derive an expression for asymptotic LCR under high transmit power conditions. It has been shown that the asymptotic LCR is independent of the threshold and transmit power. In addition, we consider a stop and wait automatic repeat request (SW-ARQ) protocol for reliable data packet transmission and derive the expressions for packet error rate (PER) and throughput by utilizing the finite-state Markov channel (FSMC) model. The optimal packet length is also determined which maximizes the throughput under SW-ARQ protocol. The impact of various system parameters is also shown on the PER and throughput performances.

Improving throughput in SWIPT-based wireless multirelay networks with relay selection and rateless codes

This paper studies a dual-hop Simultaneous Wireless Information and Power Transfer (SWIPT)-based multi-relay network with a direct link. To achieve high throughput in the network, a novel protocol is first developed, in which the network can switch between a direct transmission mode and a Single-Relay-Selection-based Cooperative Transmission (SRS-CT) mode that employs dynamic decode-and-forward relaying accomplished with Rateless Codes (RCs). Then, under this protocol, an optimization problem is formulated to jointly optimize the network operation mode and the resource allocation in the SRS-CT mode. The formulated problem is difficult to solve because not only does the noncausal Channel State Information (CSI) cause the problem to be stochastic, but also the energy state evolution at each relay is complicated by network operation mode decision and resource allocation. Assuming that noncausal CSI is available, the stochastic optimization issue is first to be addressed by solving an involved deterministic optimization problem via dynamic programming, where the complicated energy state evolution issue is addressed by a layered optimization method. Then, based on a finite-state Markov channel model and assuming that CSI statistical properties are known, the stochastic optimization problem is solved by extending the result derived for the noncausal CSI case to the causal CSI case. Finally, a myopic strategy is proposed to achieve a tradeoff between complexity and performance without the knowledge of CSI statistical properties. The simulation results verify that our proposed SRS-and-RC-based design can achieve a maximum of approximately 40% throughput gain over a simple SRS-and-RC-based baseline scheme in SWIPT-based multi-relay networks.

Multisensor Scheduling for Remote State Estimation Over a Temporally Correlated Channel

This article studies multisensor scheduling for remote state estimation in cyber-physical systems. We consider that each sensor monitors a dynamic process and sends its data to the remote end. This article focuses on minimizing remote estimation errors over a temporally correlated communication channel. The problem is formulated as the Markov decision process (MDP) with finite-horizon cost criterion. The optimal structured policies are derived for both Markov packet dropout and finite-state Markov channel models, which can reduce computation overhead. For the infinite-horizon case, we design algorithms to address the issues of unknown channel statistics and the curse of dimensionality in the MDP, respectively. Particularly, a heuristic algorithm with linear complexity is proposed to schedule multisensor in a decentralized manner. Simulation examples are provided to verify the theoretical results.

Effect of Strong Time-Varying Transmission Distance on LEO Satellite-Terrestrial Deliveries

In this paper, we investigate the effect of the strong time-varying transmission distance on the performance of the low-earth orbit (LEO) satellite-terrestrial transmission (STT) system. We propose a new analytical framework using finite-state Markov channel (FSMC) model and time discretization method. Moreover, to demonstrate the applications of the proposed framework, the performances of two adaptive transmissions, rate-adaptive transmission (RAT) and power-adaptive transmission (PAT) schemes, are evaluated for the cases when the transmit power or the transmission rate at the LEO satellite is fixed. Closed-form expressions for the throughput, energy efficiency (EE), and delay outage rate (DOR) of the considered systems are derived and verified, which are capable of addressing the capacity, energy efficiency, and outage rate performance of the considered LEO STT scenarios with the proposed analytical framework.

Fast Simulation of Vehicular Channels Using Finite-State Markov Models

Designing an accurate vehicular channel simulator which generates samples at the high speed is a key issue to the vehicular communications transmission scheme and protocol design. Although the finite-state Markov channel (FSMC) models have been extensively investigated to describe fading channels, FSMC models cannot show the combined effect of shadowing and small-scale fading on the temporal statistical characteristic of the vehicular channel. In this letter, a vehicular channel simulator based on the FSMC is designed, which incorporates the impacts of shadowing with small-scale fading and movement speed on the temporal statistical characteristic. The steady-state probabilities and the closed-form expressions of state transition probabilities between channel states are derived based on the approximation of Lognormal–Nakagami distribution. To validate the accuracy of the proposed FSMC channel simulator, the derived model parameters and second-order statistical parameters of the simulated channel are compared with the real field measurements of high-speed vehicular channels.

K–µ fading channels: a finite state Markov modelling approach

Finite state Markov channel (FSMC) is the automatic choice for accurate modelling of slow fading channels with memory. FSMC model for a k–µ fading channel is investigated in this paper. Small-scale variations of the fading signal under Line-Of-Sight conditions are represented by k–µ fading distributions. Here, FSMC is constructed by partitioning the fading amplitude into several regions. Each region corresponds to a particular state in the Markov model. The transition among these channel states characterizes the physical fading process. Performance measures such as steady-state probability, state-transition probability, level crossing rate and state-time duration corresponding to the k–µ fading channel are derived, plotted and analysed. Finally, numerical results prove that FSMC modelling provides an effective way to reflect the nature of the k–µ fading channel.

Novel Expressions and Applications for the Level Crossing Rate of Maximal Ratio Combining in the Presence of Cochannel Interferers

The level crossing rate (LCR) is an important second-order statistical quantity that characterizes the rate of occurrence of fading time intervals. In this paper, we investigate the LCR of multiantenna flat-fading channels in the presence of additive white Gaussian noise and cochannel interferers with unequal received powers and Doppler shifts. We first present a unified approach to derive the exact LCR of the signal-to-interference-noise ratio at a receiver, where maximum ratio combining is deployed over spatially correlated or uncorrelated systems. Through the exact LCR derivation, we identify an accurate approach to obtain a simplified approximate LCR expression in the spatially uncorrelated system case. Benefits of the LCR expressions derived in the paper are demonstrated in two important applications: 1) the packet error rate is evaluated through the finite-state Markov channel model; and 2) the optimum packet length to maximize the throughput of the system with the stop-and-wait automatic repeat request protocol is derived. The analytical results are validated by simulations.

Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks

Both caching and interference alignment (IA) are promising techniques for next-generation wireless networks. Nevertheless, most of the existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, in this paper, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses a deep $Q$ network to approximate the $Q$ value-action function. We use Google TensorFlow to implement deep reinforcement learning in this paper to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network's sum rate and energy efficiency can be significantly improved by using the proposed approach.

Joint Routing and Resource Management in Energy Harvesting Aided Wireless Mesh Backhaul Networks

The energy-efficient and spectrum-efficient end-to-end transmission of different delay-sensitive data is crucial for the successful deployment of energy harvesting aided wireless mesh backhaul network. In this paper, considering the scenario with time-varying channel state, stochastic harvested energy arrival process and unknown traffic arrival process under grid power constraints, we propose a unified framework for utilizing both physical-layer and network-layer techniques to achieve the maximum data delivery ratio, bandwidth efficiency and harvested energy utilization. We assume the channel gains follow the finite-state Markov channel model, and model the traffic and the harvested energy arrival as Markov processes. We formulate the optimal problem of route selection and resource management as an ant colony optimization. Simulation results show that the proposed strategy achieves an improved performance in terms of average transmission delay, packet delivery ratio, bandwidth efficiency and harvested energy utilization under time-varying network environment.

Delay–Rate–Distortion Optimized Rate Control for End-to-End Video Communication Over Wireless Channels

In addition to the rate-distortion (R-D) behavior, in a real-time wireless video communication system, the end-to-end delay would also significantly affect the overall video reception quality. To analyze, control, and optimize the R-D behavior under the end-to-end delay constraint, in this paper we extend the traditional R-D optimization (RDO) for the wireless video communication system and formulate a novel delay-RDO-based rate control problem, by investigating the allocation of end-to-end delay to different delay components. It aims at minimizing the average total end-to-end distortion under the transmission rate and end-to-end delay constraints, by a joint selection of both the source coding and the channel coding parameters. The wireless channel is represented by a finite-state Markov channel model characterizing the time-varying process and predicting the future channel condition. As applicable solutions, a practical algorithm based on the Lagrange multiplier approaches, Karush-Kuhn-Tucker conditions, and sequential quadratic programming methods is developed. The experimental results demonstrate the superiority of the proposed algorithm over the existing schemes.

Throughput of Coded DS CDMA/Unslotted ALOHA Networks with Variable Length Data Traffic and Two User Classes in Rayleigh Fading FSMC Model

Previous papers analyzed the throughput performance of the CDMA ALOHA system in Rayleigh fading channel, but they assume that the channel coefficient of Rayleigh fading was the same in the whole packet, which is not realistic. We recently proposed the finite-state Markov channel (FSMC) model to the throughput analysis of DS uncoded CDMA/unslotted ALOHA networks for fixed length data traffic in the mobile environment. We now propose the FSMC model to the throughput analysis of coded DS CDMA/unslotted ALOHA networks with variable length data traffic and one or two user classes in the mobile environment. The proposed DS CDMA/unslotted ALOHA wireless networks for two user classes with access control can maintain maximum throughput for the high priority user class under high message arrival per packet duration.

Wireless channel model using stochastic high‐level Petri nets for cross‐layer performance analysis in orthogonal frequency‐division multiplexing system

In this study, the authors form a wireless channel model for orthogonal frequency-division multiplexing (OFDM) systems with stochastic high-level Petri net (SHLPN) formalism in order to simplify the cross-layer performance analysis of modern wireless systems. Compared with existing finite state Markov channel model whose state space grows exponentially with the number of OFDM subchannels, the author's proposed SHLPN model uses state aggregation technique to deal with this problem. Closed-form expressions to calculate the transition probabilities among the compound markings of the SHLPN model are provided. When applied to derive the performance measures for OFDM system in terms of the average throughput, average delay and packet dropping probability, the SHLPN model can accurately capture the correlated time-varying nature of wireless channels. Simulation is performed to show that the numerical results offered by the proposed model are more accurate compared with other simplified channel models for avoiding state space complexity.

Joint detection scheme for spectrum sensing over time‐variant flat fading channels

As the application scope of cognitive radio grows continuously, time-variant flat fading (TVFF) channels become common in practical spectrum sensing scenarios. Unfortunately, most existing spectrum sensing methods which are designed for time-invariant propagation channels could hardly obtain good performance when they operate in realistic TVFF channels. To combat this difficulty, in this investigation the authors design a promising spectrum sensing method. Firstly, a novel dynamic state-space model is proposed in which a two-state Markov chain is employed to abstract the evolution of primary user states and a finite-state Markov channel model is utilised to characterise the TVFF channel. Secondly, based on the maximum a posteriori probability criteria and the particle filtering mechanic, a joint estimation algorithm of the time-dependent fading channel gain and the state of primary user is presented. Experimental simulations verify the performance superiority of the authors presented joint detection scheme, which could be properly applied to spectrum sensing in realistic TVFF channels.

Finite-State Markov Modeling for Wireless Channels in Tunnel Communication-Based Train Control Systems

Communication-based train control (CBTC) is being rapidly adopted in urban rail transit systems, as it can significantly enhance railway network efficiency, safety, and capacity. Since CBTC systems are mostly deployed in underground tunnels and trains move at high speeds, building a train–ground wireless communication system for CBTC is a challenging task. Modeling the tunnel channels is very important in designing the wireless networks and evaluating the performance of CBTC systems. Most existing works on channel modeling do not consider the unique characteristics of CBTC systems, such as high mobility speed, deterministic moving direction, and accurate train-location information. In this paper, we develop a finite-state Markov channel (FSMC) model for tunnel channels in CBTC systems. The proposed FSMC model is based on real field CBTC channel measurements obtained from a business-operating subway line. Unlike most existing channel models, which are not related to specific locations, the proposed FSMC channel model takes train locations into account to have a more accurate channel model. The distance between the transmitter and the receiver is divided into intervals and an FSMC model is applied in each interval. The accuracy of the proposed FSMC model is illustrated by the simulation results generated from the model and the real field measurement results.

Two-dimensional POMDP-based opportunistic spectrum access in time-varying environment with fading channels

In this research, we study the problem of opportunistic spectrum access (OSA) in a time-varying environment with fading channels, where the channel state is characterized by both channel quality and the occupancy of primary users (PUs). First, a finite-state Markov channel model is introduced to represent a fading channel. Second, by probing channel quality and exploring the activities of PUs jointly, a two-dimensional partially observable Markov decision process framework is proposed for OSA. In addition, a greedy strategy is designed, where a secondary user selects a channel that has the best-expected data transmission rate to maximize the instantaneous reward in the current slot. Compared with the optimal strategy that considers future reward, the greedy strategy brings low complexity and relatively ideal performance. Meanwhile, the spectrum sensing error that causes the collision between a PU and a secondary user (SU) is also discussed. Furthermore, we analyze the multiuser situation in which the proposed single-user strategy is adopted by every SU compared with the previous one. By observing the simulation results, the proposed strategy attains a larger throughput than the previous works under various parameter configurations.

A 10-state model for an AMC scheme with repetition coding in mobile wireless networks

Abstract In modern broadband wireless access systems such as mobile worldwide interoperability for microwave access (WiMAX) and others, repetition coding is recommended for the lowest modulation level, in addition to the mandatory concatenated Reed-Solomon and convolutional code data coding, to protect vital control information from deep fades. This paper considers repetition coding as a time-diversity technique using maximum ratio combining (MRC) and proposes techniques to define and to calculate the repetition coding gain G r and its effect on bit error rate (BER) under the two fading conditions: correlated lognormal shadowing and composite Rayleigh-lognormal fading also known as Suzuki fading. A variable-rate, variable-power 10-state finite-state Markov channel (FSMC) model is proposed for the implementation of the adaptive modulation and coding (AMC) scheme in mobile WiMAX to maximize its spectral efficiency under constant power constraints in the two fading mechanisms. Apart from the proposed FSMC model, the paper also presents two other significant contributions: one is an innovative technique for accurate matching of moment generating functions, necessary for the estimation of the probability density function of the combiner's output signal-to-noise ratio, and the other is efficient and fast expressions using Gauss-Hermite quadrature approximation for the calculation of BER of QPSK signal using MRC diversity reception.

Finite-State Markov Modeling of Leaky Waveguide Channels in Communication-Based Train Control (CBTC) Systems

Leaky waveguide has been adopted in communication based train control (CBTC) systems, as it can significantly enhance railway network efficiency, safety and capacity. Since CBTC systems have high requirements for the train ground communications, modeling the leaky waveguide channels is very important to design the wireless networks and evaluate the performance of CBTC systems. In the letter, we develop a finite-state Markov channel (FSMC) model for leaky waveguide channels in CBTC systems based on real field channel measurements obtained from a business operating subway line. The proposed FSMC channel model takes train locations into account to have a more accurate channel model. The overall leaky waveguide is divided into intervals, and an FSMC model is applied in each interval. The accuracy of the proposed FSMC model is illustrated by the simulation results generated from the model and the real field measurement results.

Throughput Analysis of DS CDMA/Unslotted ALOHA Wireless Networks with Fixed Packet Length in Rayleigh Fading Finite-State Markov Channel Model

We propose the finite-state Markov channel (FSMC) model to the throughput analysis of DS CDMA/unslotted ALOHA wireless networks in the mobile environment. The FSMC model can characterize the correl...

Genetic Algorithm Based Finite State Markov Channel Modeling

Statistical properties of the error sequences produced by fading channels with memory have a strong influence over the performance of high layer protocols and error control codes. Finite State Markov Channel (FSMC) models can represent the temporal correlations of these sequences efficiently and accurately. This paper proposes a simple genetic algorithm (GA) based search for the optimum state transition matrix for a block diagonal Markov model. The burst error statistics of the GA based FSMC model with respect to Autocorrelation Function and error free interval distribution of the original error sequence are presented to validate the proposed method. The superiority of the GA approach over the semi-hidden Markov model (SHMM) based Fritchman model is exhibited in significant improvement of closeness of match and in the usage of shorter length of error sequences. Another Baum-Welch algorithm (BWA) based GA search method has been proposed and compared with the BWA and SHMM methods for the same error sequence. Again the superiority of GA approaches is recognized, especially for the smaller error lengths.

Exact bit error probabilities and packet error statistics for SVD transmission over time-varying dual-branch MIMO systems obtained by a Markov model

Using a finite state Markov channel model, we develop an analytical method for evaluation of the packet error structure in multiple-input multiple-output (MIMO) systems based on singular value decomposition (SVD). We consider dual-branch MIMO systems, with either two transmit and arbitrary number of receive antennas, or arbitrary number of transmit and two receive antennas. The corresponding Markov model parameters are obtained using a novel closed-form expressions for probability density function and level crossing rate of the signal-to-noise ratio at the output of eigenchannels in a MIMO system, derived for a case of Rayleigh propagation, imperfect channel state information and any fixed power allocation. The exact bit error rate for the transmission of quadrature amplitude modulated (QAM) symbols through the eigenchannels is derived in polynomial closed form. Furthermore, by using the developed Markov model, the packet error statistics in the corresponding eigenchannels are determined, and the closed-form analytical expression for the system throughput is derived when ‘go-back-N’ automatic repeat request procedure is applied in time-varying eigenchannels. The analytical results are validated by using Monte Carlo simulations.