Time-varying Communication Channels Research Articles

Efficient and choreographed quality-of- service management in dense 6G verticals with high-speed mobility requirements

Future 6G networks are envisioned to support very heterogeneous and extreme applications (known as verticals). Some examples are further-enhanced mobile broadband communications, where bitrates could go above one terabit per second, or extremely reliable and low-latency communications, whose end-to-end delay must be below one hundred microseconds. To achieve that ultra-high Quality-of-Service, 6G networks are commonly provided with redundant resources and intelligent management mechanisms to ensure that all devices get the expected performance. But this approach is not feasible or scalable for all verticals. Specifically, in 6G scenarios, mobile devices are expected to have speeds greater than 500 kilometers per hour, and device density will exceed ten million devices per square kilometer. In those verticals, resources cannot be redundant as, because of such a huge number of devices, Quality-of-Service requirements are pushing the effective performance of technologies at physical level. And, on the other hand, high-speed mobility prevents intelligent mechanisms to be useful, as devices move around and evolve faster than the usual convergence time of those intelligent solutions. New technologies are needed to fill this unexplored gap. Therefore, in this paper we propose a choreographed Quality-of-Service management solution, where 6G base stations predict the evolution of verticals at real-time, and run a lightweight distributed optimization algorithm in advance, so they can manage the resource consumption and ensure all devices get the required Quality-of-Service. Prediction mechanism includes mobility models (Markov, Bayesian, etc.) and models for time-variant communication channels. Besides, a traffic prediction solution is also considered to explore the achieved Quality-of-Service in advance. The optimization algorithm calculates an efficient resource distribution according to the predicted future vertical situation, so devices achieve the expected Quality-of-Service according to the proposed traffic models. An experimental validation based on simulation tools is also provided. Results show that the proposed approach reduces up to 12% of the network resource consumption for a given Quality-of-Service.

Lyapunov Optimization Based Mobile Edge Computing for Internet of Vehicles Systems

Mobile-Edge Computing (MEC) is an emerging paradigm in the Internet of Vehicles (IoV) to meet the ever-increasing computation demands of smart applications. To provide satisfactory computation performance, it is of significant importance to conduct computation offloading in IoV. In this paper, we investigate a multi-vehicle IoV system assisted by MECs with limited computation resources, where vehicles with complex applications can offload their subtasks to MEC servers. Applications are modeled as interdependent subtasks with general random task graphs, different from existing works with independent ones. To maximize the average logarithmic data processing rate (LDPR), the computation offloading problem is formulated as a time-average optimization with long-term constraints, which results from variable vehicle number, various applications and time-varying communication channels. To reduce the cooperation overhead, we propose a multi-agent Proximal Policy Optimization algorithm (Ly-MAPPO) which requires local observation only to solve the subproblems achieved by Lyapunov optimization technique in real time. In addition, to improve the performance of the Ly-MAPPO algorithm, Graph Convolutional Neural Network (GCN) is introduced to extract inter-dependencies between subtasks. Extensive simulations show that the GCN embedded Ly-MAPPO outperforms other baseline algorithms, e.g., greedy algorithm and gene algorithm, etc., for different traffic loads and computation resources in MEC servers.

Learning Based Channel Allocation and Task Offloading in Temporary UAV-Assisted Vehicular Edge Computing Networks

High-level autonomous decision making system is one of the key technologies in intelligent transportation networks, it requires the traffic information within a certain range of vehicles in real time. When the traffic roads become congested or the roadside units (RSUs) are unaccessed beyond the communication range, the unmanned aerial vehicle (UAV)-assisted vehicular edge computing network (VECN) is considered as a potential solution. In this paper, we propose a learning based channel allocation and task offloading strategy in temporary UAV-assisted VECNs from a user perspective, in which the UAV passing temporarily can serve as the relay and edge computing node to support the decision making system. However, the limited available computation resources and time-varying communication channel states make it critical to process the received computing tasks. To address the above mentioned challenges, we design an efficient data transmission strategy combined the long-term evolution vehicle-to-everything (LTE-V2X) and time-division multiple access (TDMA) technologies firstly, then, we propose a multi-option task processing scheme, a service cost minimization problem is proposed where the integral decisions of channel allocation and task processing mode selection are jointly optimized. Under dynamic computing resources and the current data transmission conditions, the UAV selects an optimal task processing service model based on deep reinforcement learning (DRL) algorithm. Simulation results show the proposed strategy greatly improves the data transmission efficiency.

Stability Conditions for Remote State Estimation of Multiple Systems over Multiple Markov Fading Channels

We investigate the stability conditions for remote state estimation of multiple linear time-invariant (LTI) systems over multiple wireless time-varying communication channels. We answer the following open problem: what is the fundamental requirement on the multi-sensor-multi-channel system to guarantee the existence of a sensor scheduling policy that can stabilize the remote estimation system? We propose a novel policy construction and analytical framework and derive the necessary-and-sufficient stability condition in terms of the LTI system parameters and the channel statistics.

Stability Conditions for Remote State Estimation of Multiple Systems Over Semi-Markov Fading Channels

This work studies remote state estimation of multiple linear time-invariant systems over shared wireless time-varying communication channels. We model the channel states by a semi-Markov process which captures both the random holding period of each channel state and the state transitions. The model is sufficiently general to be used in both fast and slow fading scenarios. We derive necessary and sufficient stability conditions of the multi-sensor-multi-channel system in terms of the system parameters. We further investigate how the delay of the channel state information availability and the holding period of channel states affect the stability. In particular, we show that, from a system stability perspective, fast fading channels may be preferable to slow fading ones.

Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach

Non-orthogonal multiple access (NOMA) exploits the potential of the power domain to enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This article develops an intelligent resource allocation scheme for uplink NOMA-IoT communications. To maximise the average performance of sum rates, this work designs an efficient optimization approach based on two reinforcement learning algorithms, namely deep reinforcement learning (DRL) and SARSA-learning. For light traffic, SARSA-learning is used to explore the safest resource allocation policy with low cost. For heavy traffic, DRL is used to handle traffic-introduced huge variables. With the aid of the considered approach, this work addresses two main problems of fair resource allocation in NOMA techniques: 1) allocating users dynamically and 2) balancing resource blocks and network traffic. We analytically demonstrate that the rate of convergence is inversely proportional to network sizes. Numerical results show that: 1) Compared with the optimal benchmark scheme, the proposed DRL and SARSA-learning algorithms have lower complexity with acceptable accuracy and 2) NOMA-enabled IoT networks outperform the conventional orthogonal multiple access based IoT networks in terms of system throughput.

Identification of Fast Time-varying Communication Channels Using the Preestimation Technique

Abstract Accurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state-of-the-art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately chosen basis functions. The paper shows that tracking performance of the resulting local basis function estimation algorithms can be further improved by means of regularization. The method is illustrated by an important recent application - identification of fast time-varying acoustic channels used in underwater communication.

Cooperative Offloading and Resource Management for UAV-Enabled Mobile Edge Computing in Power IoT System

The lack of the computation services in remote areas motivates power Internet of Things (IoT) to apply unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) technology. However, the computation services will be significantly affected by the UAVs' capacities, and distinct power IoT applications. In this paper, we firstly propose a cooperative UAV-enabled MEC network structure in which the UAVs are able to help other UAVs to execute the computation tasks. Then, a cooperative computation offloading scheme is presented while considering the interference mitigation from UAVs to devices. To maximize the long-term utility of the proposed UAV-enabled MEC network, an optimization problem is formulated to obtain the optimal computation offloading decisions, and resource management policies. Considering the random devices' demands and time-varying communication channels, the problem is further formulated as a semi-Markov process, and the deep reinforcement learning based algorithms are proposed in both of the centralized and distributed UAV-enabled MEC networks. Finally, we evaluate the performance of the proposed DRL-based schemes in the UAV-enabled MEC framework by giving numerical results.

Method of space-frequency block coding with dual-mode index modulation aided OFDM

The technology of combining Multiple Input-Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) for modern Wireless Communication Systems is considered. The areas of priority use of time and frequency variants of Alamouti’s diversity scheme for Wireless Communication Channels with limited frequency and energy resources are determined. The concept of Index Modulation (IM) is analyzed. The method of OFDM subcarriers index modulation is determined as the most promising means of increasing noise immunity and speed of information transmission for real frequency-selective channels of Wireless Communication Systems. The benefits of Dual-Mode OFDM-IM with optimized signal constellations based on BPSK, QPSK and 16-QAM are demonstrated. A novel Signal-Code design combining the Dual-Mode Index Modulation Aided OFDM and the Orthogonal Space-Frequency Block Coding with Alamouti’s core SFBC-DM-OFDM-IM is proposed. Compared to classical SFBC-OFDM the proposed method allows simultaneously to improve spectral and energy efficiency of information transmission systems in frequency and time-varying wireless communication channel. The SFBC-DM-OFDM-IM method is advisable for providing high-quality communications with highly dynamic ground objects, unmanned aerial vehicles and other aircrafts in the conditions of Signal-to-Noise Ratio critical reduction, which takes place under the influence of electronic suppression means. The obtained scientific results can be effectively used for designing new generation Wireless Communication Networks with limited frequency and energy resources.

Learning-Aided Physical Layer Authentication as an Intelligent Process

Performance of the existing physical layer authentication schemes could be severely affected by the imperfect estimates and variations of the communication link attributes used. The commonly adopted static hypothesis testing for physical layer authentication faces significant challenges in time-varying communication channels due to the changing propagation and interference conditions, which are typically unknown at the design stage. To circumvent this impediment, we propose an adaptive physical layer authentication scheme based on machine-learning as an intelligent process to learn and utilize the complex and time-varying environment, and hence to improve the reliability and robustness of physical layer authentication. Explicitly, a physical layer attribute fusion model based on a kernel machine is designed for dealing with multiple attributes without requiring the knowledge of their statistical properties. By modeling the physical layer authentication as a linear system, the proposed technique directly reduces the authentication scope from a combined N-dimensional feature space to a single dimensional (scalar) space, hence leading to reduced authentication complexity. By formulating the learning (training) objective of the physical layer authentication as a convex problem, an adaptive algorithm based on kernel least-mean-square is then proposed as an intelligent process to learn and track the variations of multiple attributes, and therefore to enhance the authentication performance. Both the convergence and the authentication performance of the proposed intelligent authentication process are theoretically analyzed. Our simulations demonstrate that our solution significantly improves the authentication performance in time-varying environments.

Orthogonally Precoded Massive MIMO for High Mobility Scenarios

Massive multiple-input multiple-output (MIMO) systems are of high interest for ultra-reliable low-latency communication (URLLC) links. They provide channel hardening, i.e. reduced channel variations, due to the large number of transmit antennas which exploit spatial diversity by beam-forming. Massive MIMO requires channel state information (CSI) on the base station side. For time-varying vehicular communication channels the CSI acquired during the uplink phase will be outdated for the following downlink phase, leading to reduced spatial channel hardening. We investigate a combination of massive MIMO with general orthogonal precoding (OP) to compensate this effect. OP uses two-dimensional precoding sequences in the time-frequency domain and provides channel hardening by exploiting time- and frequency diversity. We show that the combination of massive MIMO and OP is beneficial for time-varying communication channels. While the spatial channel hardening of massive MIMO decreases, the time-frequency channel hardening of OP increases with larger time-variance of the communication channel. An iterative receiver algorithm for massive MIMO with OP as well as a detailed analysis of the channel hardening effect is presented. We demonstrate a BER reduction by more than one order of magnitude for a velocity of 50 km/h = 16.6 m/s using the orthogonal frequency division multiplexing (OFDM) based 5G new radio (NR) physical layer.

Time-Varying Communication Channel High Altitude Platform Station Link Budget and Channel Modeling

Because of the high BER (Bit Error Rate), low time delay and low channel transmission efficiency of HAPS (High Altitude Platform Station) in the near space. The link budget of HAPS and channel model are proposed in this paper. According to the channel characteristic, the channel model is set up, combined with different CNR (Carrier Noise Ratio), elevation angle, coding situations of wireless communication link by using Hamming code, PSK (Pulse Shift Keying) and Golay code respectively, then the situations of link quality and BER are analyzed. The simulation results show that the established model of the link budget and channel are suitable for the theoretical analysis results. The elevation of the HAPS communication link is smaller while the BER is increasing. The case of channel in the coding is better than in the un-coded situation. When every bit power and thermal noise power spectral density is larger, the BER of the HAPS communication link is becoming smaller.

Stability analysis of nonlinear telerobotic systems with time-varying communication channel delays using general integral inequalities

Existing controller synthesis schemes for teleoperation of nonlinear master-slave systems with time-varying communication channel are subject to conservative stability criteria. This paper presents a new stability criteria for bilateral tele-operation systems with proportional-derivative, position-position and position-force control architectures, considering both passive/non-passive human operator. The communication channel pertains unknown asymmetric time-varying delays with given lower and upper-bounds. Using the Lyapunov Krasovskii approach, the stability analysis of the dynamic system is examined and the stability conditions are derived in the form of Linear Matrix Inequalities (LMIs). Advanced techniques have been employed to find less conservative integral inequalities in the process of stability analysis of the closed-loop teleoperator. Illustrative numerical examples and simulation results emphasize the effectiveness of the proposed stability criteria in enlarging the stability bounds and providing efficient control performances.

Efficient System Tracking With Decomposable Graph-Structured Inputs and Application to Adaptive Equalization With Cyclostationary Inputs

This paper introduces the graph-structured recursive least squares (GS-RLS) algorithm, which is a very efficient means to track a linear time-varying system when the inputs to the system have structure that can be modeled using a decomposable Gaussian graphical model. For graphs with small clique sizes, it is shown that GS-RLS can achieve tracking performance very close to that of the conventional RLS algorithm for a fraction of the computational cost. In particular, after proving that the outputs of wide-sense stationary time-varying communication channels have graphical model structure if the inputs are cyclostationary, significant computational gains are realized for adaptive equalization of the time-varying underwater acoustic communication channel using the GS-RLS algorithm. This is verified using field data from the SPACE08 underwater acoustic communication experiment.

Estimation of Time-varying Communication Channels Using Reduced Length Chirp Pilots

Estimation of Time-varying Communication Channels Using Reduced Length Chirp Pilots

Distributed Consensus Optimization in Multiagent Networks With Time-Varying Directed Topologies and Quantized Communication.

This paper considers solving a class of optimization problems which are modeled as the sum of all agents' convex cost functions and each agent is only accessible to its individual function. Communication between agents in multiagent networks is assumed to be limited: each agent can only interact information with its neighbors by using time-varying communication channels with limited capacities. A technique which overcomes the limitation is to implement a quantization process to the interacted information. The quantized information is first encoded as a binary sequence at the side of each agent before sending. After the binary sequence is received by the neighboring agent, corresponding decoding scheme is utilized to resume the original information with a certain degree of error which is caused by the quantization process. With the availability of each agent's encoding states (associated with its out-channels) and decoding states (associated with its in-channels), we devise a set of distributed optimization algorithms that generate two iterative sequences, one of which converges to the optimal solution and the other of which reaches to the optimal value. We prove that if the parameters satisfy some mild conditions, the quantization errors are bounded and the consensus optimization can be achieved. How to minimize the number of quantization level of each connected communication channel in fixed networks is also explored thoroughly. It is found that, by properly choosing system parameters, one bit information exchange suffices to ensure consensus optimization. Finally, we present two numerical simulation experiments to illustrate the efficacy of the algorithms as well as to validate the theoretical findings.

Interactive Streaming of Sequences of High Resolution JPEG2000 Images

The JPEG2000 image coding system was created with the intention of superseding the original JPEG standard, using a novel wavelet-based method. The main advantage of JPEG2000 is the flexibility of its code-stream, which provides new functionality related to the interactive transmission of images. For this task, JPEG2000 uses the JPIP protocol, which enables real-time spatial random access while the retrieved image is progressively displayed (streaming). The standard also foresees the compression and transmission of sequences of images by repeating this approach for each image. In this framework, this paper presents the Continue data-flow control strategy, a JPIP-compliant solution for the interactive streaming of sequences of images that are transmitted over time-varying communication channels. In this context, the random fluctuation of the capacity of the transmission channel over the time forces the clients to prefetch a minimal amount of the code-stream of each image of the beginning of the transmitted sequence before the playback starts, and the server to decide, in real-time, which amount of the code-stream of each compressed image is going to be transmitted . The estimated channel capacity is performed by clients and the rate-control at the server is straightforward, resulting in a highly scalable image retrieval system. The experiments conducted in this study demonstrate that the proposed method keeps a constant playback frame-rate under severe variations of the channel capacity, even when short prefetch times are used.

An Information Theoretic Approach Toward Assessing Perceptual Audio Quality Using EEG

In this paper, we propose a novel information theoretic model to interpret the entire “transmission chain” comprising stimulus generation, brain processing by the human subject, and the electroencephalograph (EEG) response measurements as a nonlinear, time-varying communication channel with memory. We use mutual information (MI) as a measure to assess audio quality perception by directly measuring the brainwave responses of the human subjects using a high resolution EEG. Our focus here is on audio where the quality is impaired by time varying distortions. In particular, we conduct experiments where subjects are presented with audio whose quality varies with time between different possible quality levels. The recorded EEG measurements can be modeled as a multidimensional Gaussian mixture model (GMM). In order to make the computation of the MI feasible, we present a novel low-complexity approximation technique for the differential entropy of the multidimensional GMM. We find the proposed information theoretic approach to be successful in quantifying subjective audio quality perception, with the results being consistent across different music sequences and distortion types.

Channel equalization using quasi type-2 fuzzy strategies

This paper presents a proposal for channel equalization of non-linear time varying communication channels that uses strategies based on quasi type-2 fuzzy sets. Both triangular and trapezoidal quasi type-2 fuzzy equalizers are designed by means of an incremental design approach. A comparative experimental study among several fuzzy equalization architectures is carried out, showing that the equalizer based on trapezoidal quasi type-2 fuzzy sets exhibits the best performance for low and medium levels of uncertainty. The results of this work also suggest that high order fuzzy sets make the equalization process more robust to the variability of the communications channel.

On the design of LMS-based channel estimators using the Doppler spread parameter

An analysis of the optimization of the LMS (Least Mean Square) algorithm for the estimation of time-varying and frequency-selective communication channels is here presented. In contrast to previous works on this subject, in which the step-size optimization is rooted on the assumption of specific channel models, this analysis is much more generic in respect to the modelling of both the Doppler and delay spreadings. Besides, it addresses not only the steady-state performance of channel estimators, but also their transient behaviors. Several useful approximate expressions for designing LMS-based channel estimators are herein derived and validated by comparisons with simulation and with a previous work on this kind of analysis. These expressions are remarkably suitable for practical application, since they depend on a few parameters of the communication system. With regard to the channel variability, the knowledge of the Doppler spread parameter is shown to be enough to optimize the performance of a LMS channel estimator using the analysis here presented.