State Markov Channel Research Articles

Alphabet Size Matching Techniques Based on Non-Binary Gilbert-Varshamov Bounded Limits for Synchronization Finite State Markov Channel

The Gilbert-Varshamov (GV) lower bound is used to provide indications and prescriptions for the outer code coding parameters for a memory synchronisation model that focuses solely on the internal resynchronisation process. The binary and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -ary GV bounds are utilised in this analysis to indicate parameters to remove the remaining substitution errors and provide a complete framework. Procedures and examples are provided to determine optimal outer code parameters for given inner-entropies and residual substitution errors produced during resynchronisation. In particular, using the non-binary GV bounds allows us to match the best alphabet size for given parameters. For the cases explored, a 16-ary GV bound provides the best results, with an ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> ) code of (120, 57, 37) being a possible outer code when the inner entropy is 0.1. Using GV bounds for outer code parameter considerations frees the system from using stringent codes and instead allows any outer code to be utilised to meet the required error correction needs.

Adaptive Bitrate Streaming in Wireless Networks With Transcoding at Network Edge Using Deep Reinforcement Learning

Adaptive bitrate (ABR) streaming has been used in wireless networks to deal with the time-varying wireless channels. Traditionally, wireless video is fetched from remote Internet server. However, wireless video streaming from Internet server faces challenges such as congestion and long latency. ABR streaming and video transcoding at the radio access network (RAN) edge have shown the potential to overcome such problem and provided better video streaming service. In the paper, we consider joint computation and communication for ABR streaming based on mobile edge computing (MEC) under time-varying wireless channels. We propose a joint video transcoding and quality adaptation framework for ABR streaming by enabling RAN with computing capability. By modeling the wireless channel as a finite state Markov channel, we formulate the optimization problem as a stochastic optimization problem of joint computational resource assignment and video quality adaptation for maximizing the average reward, which is defined as the tradeoff between user perceived quality of experience and the cost of performing transcoding at edge server. By using deep reinforcement learning (DRL) algorithm, we develop an automatic algorithm to perform the computational resource assignment and video quality adaptation without any prior knowledge of channel statistics. Simulation results using Tensorflow show the effectiveness of the designed MEC-enabled ABR streaming system and DRL algorithm.

Log Likelihood Ratio Based Relay Selection Scheme for Amplify and Forward Relaying with Three State Markov Channel

This paper presents log likelihood ratio (LLR) based relay selection scheme for a cooperative amplify and forward relaying system. To evaluate the performance of the aforementioned system model, a three state Markov chain based fading environment has been presented to toggle among Rayleigh, Rician, and Nakagami-m fading environment. A simulation is carried out while assuming that there is no possibility of direct transmission from the source and destination terminal. Simulation results on the basis of Bit Error Rate (BER), Instantaneous Channel Capacity, and Outage probability have been presented and compared for different cases. In each case, the best performance of the proposed algorithm is obtained with a Binary Phase Shift Keying (BPSK) modulation scheme.

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.

Detection in neuronal communications with finite channel state

Nano-networks are the key factors in developing future nano-machines. The use of molecules to transmit information in such networks is the subject of molecular communications. The future of the nano-networks depends on the development of the molecular communications. Neuro-synaptic communication, which models the data transmission in the body nervous system, is an important example of molecular communication. In this paper, we introduce a comprehensive channel model for the neuro-synaptic communication channel. Our model incorporates the effect of different channel states and spike rates that exist in the body nervous system. We propose a finite state Markov channel scheme for the communication system. This scheme has a two dimensional state space according to the neural firing rate and the number of available carrier resources. Next, we suggest an M-ary signaling scheme for the synaptic area that models the synaptic multi-site activity. In this activity, several neuronal endpoints participate in the neurotransmitter release process. Moreover, we obtain a closed-form solution for the detector at the destination neuron. Finally, we evaluate the decision rules in the detector with simulations.

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.

HMM modeling for OFDM–BER performance

OFDM based systems widely adopted in several wired and wireless standards can be analyzed efficiently with reduced cost, time and computation by characterizing the realistic physical layer with a Finite State Markov Channel. In this paper, accurate HMMs have been derived using BWA for a wide range of channel SNR conditions and different OFDM system parameters such as number of subcarriers, FFT size and number of symbols. OFDM with pilot based channel estimation errors have also been accurately parameterized for the symbol error rate. All estimated error statistics have been validated by comparison in terms of log likelihood, average error probability, autocorrelation function and the distributions of error or error free interval. Sequences from higher order modulation in OFDM systems can be reproduced with relatively fewer HMM states. The reliability of the model is confirmed by an excellent match to those of the underlying OFDM based physical layer. Accuracy of the ACF comparisons with number of HMM states has also been quantified.

Analysis of Quality of Services in LTE and Mobile WiMAX

Third Generation Partnership Project Long Term Evolution (3GPP LTE) and Worldwide Interoperability for Microwave Access (WiMAX) face challenges in providing Quality of Services (QoS). In this Paper, the Quality of Services of Mobile WiMAX and 3GPP LTE are analyzed and compared. We analyze the LTE and WiMAX performances in downlink employing an analytical approach for estimating the Quality of Services based on its parameters including delay and bit error rate (BER). Our proposed approach consists of two steps. The physical layer is simulated using the finite state Markov channel (FSMC) model to calculate the bit error rate. Then, the data link layer is simulated employing the effective bandwidth model for both constant and variable rate traffics. Our analysis demonstrates that WiMAX provides better QoS in low average signal to noise ratio (SNR). However, by increasing the average signal to noise ratio, both technologies provide almost the same QoS.

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.

Delay statistics of cooperative automatic repeat request over wireless networks with link adaptation

SUMMARYThis paper proposes a method to analyze the delay distribution at the transmitter side for cooperative diversity wireless networks with link adaptation, which uses decode‐and‐forward as a relay strategy. The equivalent end‐to‐end channel model is captured by a finite state Markov channel model representing the multiple states of the Nakagami fading channel, and the delay statistics are obtained by using matrix geometry method. Cooperative diversity wireless network has a better delay performance compared with noncooperation wireless network. Copyright © 2012 John Wiley &amp; Sons, Ltd.

A New Approach of Channel Modeling in HAPS Based Networks and Their System Performance Analysis

This paper concerns channel modeling for High Altitude Platform Systems (HAPS) and the performance evaluation of Hybrid_ARQ in the WiMAX network provided by HAPS. The stratospheric platform or HAPS is currently a new proposal for WiMAX technology. Firstly we study the HAPS channel behavior as a Finite State Markov Channel (FSMC). In this way, the range of magnitude of the fading channel gain is partitioned into a finite number of states; then based on level crossing rates, the state transition probabilities are derived. The validity of the proposed model is evaluated by theoretical and link level simulation results. Next, we use the derived state transition probabilities as channel model parameters in OPNET simulator for HAPS-WiMAX to calculate HARQ system level measures. The paper compares the performance obtained using two different models in fading effects, i.e. AWGN and our HAPS channel model. In addition, the influence of parameters is analyzed through comparison between our model and the Stanford University Interim (SUI) channel models, in terms of the Bit Error Rate (BER).

Performance analysis of WLAN physical layers using Markov channel model

Orthogonal Frequency Division Multiplexing (OFDM) and multiple antennas are being used in various standards of Wireless Local Area Network (WLAN) to enhance data rates and to achieve higher reliability in fading channel conditions. Channel modeling plays a crucial role, especially in wireless environment, in evaluating the performance of physical layer in fading environment. The paper presents a Finite State Markov (FSM) channel model, for computation of fading channel performance measures. Frequency and duration analysis is proposed to evaluate the performance of WLAN physical layers for OFDM, OFDM with Selection Combining (SC) and OFDM with Maximum Ratio Combining (MRC) diversity techniques. Level Crossing Rate (LCR) and Average Fade Duration (AFD) are computed with the analysis of FSM model. Bit Error Rate (BER) is then calculated with the help of LCR and AFD. LCR, AFD and BER performances are compared for three physical layers with varying fade depths and for different values of Signal to Noise Ratio (SNR). Results show that OFDM with MRC gives minimum value of BER for fade depths more than 0dB, while BER is minimum for OFDM with SC for fade depths below 0dB. OFDM with diversity always outperforms than OFDM without diversity for all values of fade depths.

Quality-of-service performance bounds in wireless multi-hop relaying networks

The theoretical analysis on quality-of-service (QoS) performances is required to provide the guides for the developments of the next-generation wireless networks. As a good analysis tool, the probabilistic network calculus with moment generating functions (MGFs) recently can be used for delay and backlog performance measures in wireless networks. Different from the existed studies which mostly focused on the single-hop networks with single-user under a two state Markov channel model, this study develops an analytical framework for wireless multi-hop relaying networks under the finite-state Markov channel by using probabilistic network calculus with MGFs. By using the concatenation character of network calculus, the authors regard a two-hop wireless relaying channel as a single server equivalently, which consisting of two dynamic servers in series. When the single-user model is straightforwardly extended and applied in multi-user scenarios, the state space of service process is increased exponentially with the number of users, which is only applicable in case of very small user number. Then, in order to avoid the limitation of user number, the authors propose to reflect the multi-user effects by using the equivalent data rate of the modified service process, whose transition and stationary probabilities are kept unchanged with those in single-user scenarios. Next, delay and backlog bounds of multi-hop wireless relaying networks are derived with the proposed analytical framework. Simulation results show that analytical bounds match simulation results, whose accuracy depends on the required violation probability. The effectiveness of the relaying techniques in improving the performances is also demonstrated.

Optimal scheduling in high-speed downlink packet access networks

We present an analytic model and a methodology to determine the optimal packet scheduling policy in a High-Speed Downlink Packet Access (HSDPA) system. The optimal policy is the one that maximizes cell throughput while maintaining a level of fairness between the users in the cell. A discrete stochastic dynamic programming model for the HSDPA downlink scheduler is presented. Value iteration is then used to solve for the optimal scheduling policy. We use a FSMC (Finite State Markov Channel) to model the HSDPA downlink channel. A near-optimal heuristic scheduling policy is developed. Simulation is used to study the performance of the resulting heuristic policy and compare it to the computed optimal policy. The results show that the performance of the heuristic policy is very close to that of the optimal policy. The heuristic policy has much less computational complexity, which makes it easy to deploy, with only slight reduction in performance compared to the optimal policy.

Performance analysis and modelling of single-step power control in finite state Markov channel under different feedback channels

We investigate a commonly used power control scheme called single-step power control (SSPC) in finite state Markov channel (FSMC). The performance of SSPC is highly affected by two major factors, the power control error (PCE) and the imperfect feedback of the SSPC commands. In the first part of this work, we aim at modelling the behavior of PCE in FSMC. Based on the proposed PCE model, BER and PER performance are evaluated. Simulation results have shown the validity of the proposed PCE model, derived PER and BER in this work. In the second part of this work, SSPC models under different scenarios are established in order to understand the impact of the imperfect feedback on SSPC commands and to obtain the interactions between SSPC and FSMC. We consider four scenarios, perfect feedback, command error, command loss and command delay. The associated transitional probabilities of each scenario are given. Extensive simulation has shown the accuracy of the proposed SSPC models in these four scenarios.

Energy optimal transmission scheduling in wireless sensor networks

One of the main issues in the design of sensor networks is energy efficient communication of time-critical data. Energy wastage can be caused by failed packet transmission attempts at each node due to channel dynamics and interference. Therefore transmission control techniques that are unaware of the channel dynamics can lead to suboptimal channel use patterns. In this paper we propose a transmission controller that utilizes different "grades" of channel side information to schedule packet transmissions in an optimal way, while meeting a deadline constraint for all packets waiting in the transmission queue. The wireless channel is modeled as a finite-state Markov channel (FSMC). We are specifically interested in the case where the transmitter has low-grade channel side information that can be obtained based solely on the ACK/NAK sequence for the previous transmissions. Our scheduler is readily implementable and it is based on the dynamic programming solution to the finite-horizon transmission control problem. We also calculate the information theoretic capacity of the finite state Markov channel with feedback containing different grades of channel side information including that, obtained through the ACK/NAK sequence. We illustrate that our scheduler achieves a given throughput at a power level that is fairly close to the fundamental limit achievable over the channel.

Impact of Doppler spread and adaptive modulation on TCP throughput in Rayleigh fading channels

A number of wireless systems have recently adopted adaptive modulation (AM) schemes to improve its efficiency. In this letter, our aim is to study the impact Doppler spread and adaptive modulation has on transmission control protocol (TCP) throughput in Rayleigh fading channels. We consider a finite state Markov channel (FSMC) model, which is a useful model for analyzing radio channel with nonindependent fading. Furthermore, we use a Markov model for TCP evolution and evaluate the TCP performance by computer simulations. In our simulations we have compared the TCP Reno scheme with TCP Tahoe scheme. The results indicate that a large Doppler spread leads to lower TCP throughput due to more frequent transitions of channel states and modulation schemes which make it difficult for the TCP congestion control mechanism to accommodate the dynamic link characteristics.

Modeling End-to-End Wireless Lossy Channels: A Finite-State Markov Approach

In this paper, we present a model for wireless losses in packet transmission data networks. The model provides information about the wireless channel status that can be used in congestion control schemes. A Finite State Markov Channel (FSMC) approach is implemented to model the wireless slow fading for different modulation schemes. The arrival process statistics of the packet traces determine the channel state transition probabilities, where the statistics of both error-free and erroneous bursts are captured. Later, we establish SNR partitioning scheme that uses the transition probabilities as a basis for the state margins. The crossover probability associated with each state is calculated accordingly. We also propose an end-to-end approach to loss discrimination based on the channel state estimation at the receiver. Finally, we present a scheme for finding the channel optimal number of states as a function of the SNR. The presented FSMC approach does not restrict the state transitions to the adjacent states, nor does impose constant state duration as compared to some literature studies. We validate our model by experimental packet traces. Our simulation results show the feasibility of building a fading channel model for better wireless-loss awareness.

Adaptive Transmission of Multi-Layered Video over Wireless Fading Channels

In this paper, adaptive transmission of scalable multi-layered video with quality of service (QoS) assurance over wireless channels is studied. By properly formulating the channel fading as a finite state Markov channel (FSMC) model, three rate adaptation schemes, namely, assured-rate-allocation, neighbor- interleaving, and swing-loaded schemes, are proposed to exploit the inherent multiplexing gain. An analytical model for QoS performance evaluation of video transmission over time-varying erroneous channels is derived. The accuracy of the analytical model is validated by simulations. Analytical and simulation results demonstrate that the proposed rate adaptation schemes can effectively improve the channel utilization and the system throughput.