Time-varying Channels Research Articles

Joint AP Selection and Task Offloading Based on Deep Reinforcement Learning for Urban-Micro Cell-Free UAV Network

The flexible mobility feature of unmanned aerial vehicles (UAVs) leads to frequent handovers and serious inter-cell interference problems in UAV-assisted cellular networks. Establishing a cell-free UAV (CF-UAV) network without cell boundaries effectively alleviates frequent handovers and interference problems and has been an important topic of 6G research. However, in existing CF-UAV networks, a large amount of backhaul data increases the computational pressure on the central processing unit (CPU), which also increases system delay. Meanwhile, the mobility of UAVs also leads to time-varying channel conditions. Therefore, designing dynamic resource allocation schemes with the help of edge computing can effectively alleviate this problem. Thus, aiming at partial network breakdown in an urban-micro (UMi) environment, an urban-micro CF-UAV (UMCF-UAV) network architecture is proposed in this paper. A delay minimization problem and a dynamic task offloading (DTO) strategy that jointly optimizes access point (AP) selection and task offloading is proposed to reduce system delay in this paper. Considering the coupling of various resources and the non-convex feature of the proposed problem, a dynamic resource cooperative allocation (DRCA) algorithm based on deep reinforcement learning (DRL) to flexibly deploy AP selection and task offloading of UAVs between the edge and locally is proposed to solve the problem. Simulation results show fast convergence behavior of the proposed algorithm compared with classical reinforcement learning. Decreased system delay is obtained by the proposed algorithm compared with other baseline resource allocation schemes, with the maximize improvement being 53%.

Performance analysis of cooperative multi-relay spatial modulation based OTFS system in V2V communication

Owing to the characteristics of spatial modulation (SM) and orthogonal time frequency space (OTFS) modulation, SM based OTFS (SM-OTFS) system has an excellent capability to improve the effectiveness and reliability of vehicle-to-vehicle (V2V) communication. To further combat the severe shadow effect, improve the system performance, and extend the transmission range of V2V communication in the urban scenario with dense buildings, we propose a cooperative multi-relay SM-OTFS system with the optimal relay selection in this paper, where both amplify-and-forward (AF) and decode-and-forward (DF) protocols have been employed and only the optimal relay participates in the signal forwarding. Moreover, the fast time-selective fading is mainly considered, the closed-form outage probability and the system capacity of the cooperative SM-OTFS (CSM-OTFS) system are derived from the viewpoint of signal transmissions in the time domain. Numerical results verify the superiority of the proposed CSM-OTFS system over the non-cooperative SM-OTFS system and illustrate the influence of the main system parameters on the system outage probability and capacity, such as the relaying protocol, the number of relays, the estimation rate of channel state information (CSI), and the time-varying channel characteristic.

A Tutorial on Cross-layer Optimization Wireless Network System Using TOPSIS Method

Each other, leading to issues such as interference, limited bandwidth, and varying channel conditions. These challenges require specialized optimization techniques tailored to the wireless environment. In wireless communication networks is to maximize the overall system throughput while ensuring fairness among users and maintaining quality of service requirements. This objective can be achieved through resource allocation optimization, where the available network resources such as bandwidth, power, and time slots are allocated to users in an optimal manner. Optimization-based approaches in wireless resource allocation typically involve formulating the resource allocation problem as an optimization problem with certain constraints.. These techniques provide practical solutions with reduced computational complexity, although they may not guarantee optimality. In summary, optimization-based approaches have been instrumental in studying resource allocation problems in communication networks, including the wireless domain. While techniques from the Internet setting have influenced the understanding of congestion control and protocol design, specific challenges in wireless networks necessitate tailored optimization techniques that account for interference, limited bandwidth, and varying channel conditions. power allocation problem in wireless ad hoc networks Cross-layer optimization refers to the process of jointly optimizing the allocation of resources across different layers of wireless networks, the interactions between different layers become more complex due to the shared medium and time-varying channel conditions. Nash equilibrium, where no user can unilaterally improve its own performance by changing its strategy. Game theory can capture the distributed nature of wireless networks and provide insights into the behavior of users in resource allocation scenarios Additionally, heuristics and approximation algorithms are often employed in wireless resource allocation due to the complexity of the optimization problems involved. In traditional cellular systems, each user is allocated a fixed time slot for transmission, regardless of their channel conditions. However, in opportunistic scheduling. Alternative parameters for “Data rate Ž kbps, Geographic coverage , Service requirements , cost ” Evaluation parameter for “Circuit-switched cell, CDPD, WLAN, Paging, Satellite.” “the first ranking training is obtained with the lowest quality of compensation.”

Latency Guarantee for Task Computation in Wireless-Powered Cloud Radio Access Networks

With the massive and rapid deployment of Internet of Things (IoT) devices, the number of IoT devices in the cloud radio access network (C-RAN) has increased dramatically. It is intractable to provide deterministic quality of service (QoS) guarantee for task processing due to the task burstiness and the time-varying channels. In order to guarantee latency requirements of energy-limited IoT devices in C-RAN, this paper proposes a statistical latency guarantee scheme for task computation. With the assistance of densely distributed remote radio heads (RRHs) providing wireless power transfer to the IoT devices, a wireless powered edge computing C-RAN model is constructed. Then, the problem of minimizing task latency violation probability is formulated. With the help of effective capacity theory, the problem is decoupled into multiple sub-problems, which are QoS parameter optimization, task offloading optimization, wireless power transfer and energy allocation. Thereafter, low-complexity schemes are designed to jointly optimize the wireless power transfer, energy allocation and task offloading. The effectiveness of the proposed statistical latency guarantee scheme is finally validated by extensive simulations.

Double Threshold Structure of Sensor Scheduling Policy Over a Finite-State Markov Channel.

In this article, we consider the optimal sensor scheduling for remote state estimation in cyber-physical systems (CPSs). Different from the existing works concerning the time-invariant channel state in the wireless communication network, our work considers the time-varying channel state modeled by a finite-state Markov channel (FSMC). We focus on the problem of how to schedule the transmission of the sensor to minimize the estimation error at the remote side with less communication cost. Using the framework of the Markov decision process (MDP), the optimal scheduling policy is shown to be deterministic stationary (DS). We further derive its double threshold structure with respect to remote estimation errors and channel states. Moreover, a necessary and sufficient condition guaranteeing the mean-square stability of the remote estimator is given based on the structured scheduling policy. Numerical simulations are provided to verify the theoretical results.

A Non-Stationary Cluster-Based Channel Model for Low-Altitude Unmanned-Aerial-Vehicle-to-Vehicle Communications

Under the framework of sixth-generation (6G) wireless communications, the unmanned aerial vehicle (UAV) plays an irreplaceable role in a number of communication systems. In this paper, a novel cluster-based low-altitude UAV-to-vehicle (U2V) non-stationary channel model with uniform planar antenna arrays (UPAs) is proposed. In order to comprehensively model the scattering environment, both single and twin clusters are taken into account. A novel continuous cluster evolution algorithm that integrates time evolution and array evolution is developed to capture channel non-stationarity. In the proposed algorithm, the link between the time evolution of twin clusters and that of single clusters is established to regulate the temporal evolution trend. Moreover, an improved observable radius method is applied to UPAs for the first time to describe array evolution. Based on the combination of cluster evolution and time-variant channel parameters, some vital statistical properties are derived and analyzed, including space–time correlation function (ST-CF), angular power spectrum density (PSD), Doppler PSD, Doppler spread (DS), frequency correlation function (FCF), and delay spread (RS). The non-stationarity in the time, space, and frequency domain is captured. It demonstrates that the airspeed, density of scatterers within clusters, and carrier frequency have an impact on statistical properties. Furthermore, twin clusters have more flexible spatial characteristics with lower power than single clusters. These conclusions can provide assistance and reference for the design and deployment of 6G UAV communication systems.

Ultra-Reliable Deep-Reinforcement-Learning-Based Intelligent Downlink Scheduling for 5G New Radio-Vehicle to Infrastructure Scenarios.

Higher standards for reliability and efficiency apply to the connection between vehicle terminals and infrastructure by the fifth-generation mobile communication technology (5G). A vehicle-to-infrastructure system uses a communication system called NR-V2I (New Radio-Vehicle to Infrastructure), which uses Link Adaptation (LA) technology to communicate in constantly changing V2I to increase the efficacy and reliability of V2I information transmission. This paper proposes a Double Deep Q-learning (DDQL) LA scheduling algorithm for optimizing the modulation and coding scheme (MCS) of autonomous driving vehicles in V2I communication. The problem with the Doppler shift and complex fast time-varying channels reducing the reliability of information transmission in V2I scenarios is that they make it less likely that the information will be transmitted accurately. Schedules for autonomous vehicles using Space Division Multiplexing (SDM) and MCS are used in V2I communications. To address the issue of Deep Q-learning (DQL) overestimation in the Q-Network learning process, the approach integrates Deep Neural Network (DNN) and Double Q-Network (DDQN). The findings of this study demonstrate that the suggested algorithm can adapt to complex channel environments with varying vehicle speeds in V2I scenarios and by choosing the best scheduling scheme for V2I road information transmission using a combination of MCS. SDM not only increases the accuracy of the transmission of road safety information but also helps to foster cooperation and communication between vehicle terminals to realize cooperative driving.

The time invariant beamformer for Doppler spread suppression in high‐mobility transmission

AbstractBeamforming network with the Doppler suppression scheme reducing the channel time‐varying effects has attracted much research interests in high‐mobility massive MIMO communications. However, the current time‐varying beamforming network still suffers from high volatility peak‐to‐average power ratio (PAPR) fluctuations. For this reason, a time invariant compensation optimized beamformer is proposed to mitigate the peak‐to‐average power ratio high volatility and the channel time variation in the high‐mobility uplink transmission. To attain the Doppler spread and implement the Doppler spread suppression scheme, a time‐varying spectrum (TVS) is proposed and its statistical properties of time‐varying spectrum is analyzed. The simulation results are provided to verify the proposed studies.

PHY Security Design for Mobile Crowd Computing in ICV Networks Based on Multi-Agent Reinforcement Learning

In this paper, we propose a multi-roadside unit (RSU) assisted mobile crowd computing framework for intelligently connected vehicle (ICV) networks, where vehicles within RSUs’ coverage act as workers to provide their computation and communication resources for computing resource limited vehicle user equipments (VUEs). Physical (PHY) layer security is used to secure computation task offloading and results feedback in time-varying vehicular channels. Artificial noise (AN) assisted adaptive wiretap coding is adopted to enhance the security of offloading links. With PHY security, the intended receiver can decode secret message while eavesdropper cannot. A modified exhaustive two-dimensional (2D) search algorithm is proposed to optimize transmission rate and secrecy rate in an effective secrecy throughput maximization problem, and a multi-agent twin delayed deep deterministic policy gradient algorithm (MATD3) is utilized to assign VUEs’ tasks without a central controller, where a reward function is defined according to the computing costs, including execution time, energy consumption, and price paid for computing. Finally, simulations verify the effectiveness of the proposed framework.

Characterization and modeling of high-speed underwater acoustic communication channels

High-speed mobile underwater acoustic communication (HSM-UAC) is a relatively unexplored terrain, but its importance is expected to increase with the widespread application of flexible underwater mobile platforms such as AUVs and UUVs. Knowledge of HSM-UAC channels is a prerequisite to achieving reliable communication. This work conducted two HSM-UAC channel measurement experiments with a maximum speed of 20knots. The analysis of channel time-varying characteristics demonstrates the shortcomings of traditional consistent Doppler frequency offset channel model. The experimental findings reveal the presence of two non-stationary modes in the HSM-UAC channel. For short moving distances, the changes in channel geometry result in each path experiencing a different Doppler scale, thus observing the multiscale-multilag (MSML) channels. For long moving distances, the alteration in sound ray propagation tracks entirely reshapes the channel impulse response (CIR) structure, leading to a rapid decline in channel time correlation. In further analysis, the correlation coefficient of the power delay profile (PDP) is used to measure channel stationarity. Subsequently, the stationary time and corresponding stationary distance are calculated as characterization parameters for the time-varying channel model. The quantitative evaluation results are helpful for the communication signal design and determining the update frequency of channel state information.

A Novel Joint Channel Estimation and Symbol Detection Receiver for Orthogonal Time Frequency Space in Vehicular Networks.

A vehicular network embodies a specialized variant of wireless network systems, characterized by its capability to facilitate inter-vehicular communication and connectivity with the encompassing infrastructure. With the rapid development of wireless communication technology, high-speed and reliable communication has become increasingly important in vehicular networks. It has been demonstrated that orthogonal time frequency space (OTFS) modulation proves effective in addressing the challenges posed by high-mobility environments, as it transforms the time-varying channels into the delay-Doppler domain. Motivated by this, in this paper, we focus on the theme of integrated sensing and communication (ISAC)-assisted OTFS receiver design, which aims to perform sensing channel estimation and communication symbol detection. Specifically, the estimation of the sensing channel is accomplished through the utilization of a deep residual denoising network (DRDN), while the communication symbol detection is performed by orthogonal approximate message passing (OAMP) processing. The numerical results demonstrate that the proposed ISAC system exhibits superior performance and robustness compared to traditional methods, with a lower complexity as well. The proposed system has great potential for future applications in wireless communication systems, especially in challenging scenarios with high mobility and interference.

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction.

The Underwater Acoustic Sensor Network (UASN) is a large network in which the vicinity of a transmitting node is made up of numerous operational sensor nodes. The communication process may be substantially disrupted due to the underwater acoustic channel's time-varying and space-varying features. As a result, the underwater acoustic communication system faces the problems of reducing interference and enhancing communication effectiveness and quality through adaptive modulation. To overcome this issue, this paper intends to propose a model for optimal path selection and secured data transmission in UASN via Long Short-Term Memory (LSTM) based energy prediction. The proposed model of transmitting the secured data in UASN through the optimal path involves two major phases. Initially, the nodes are selected under the consideration of constraints like energy, distance and link quality in terms of throughput. Moreover, the energy is predicted with the aid of LSTM and the optimal path is selected with the proposed hybrid optimization algorithm termed as Pelican Updated Chimp Optimization Algorithm (PUCOA), which is the combination of two algorithms including the Pelican Optimization Algorithm (POA) and Chimp Optimization Algorithm (COA). Further, the data is transmitted via the optimal path securely by encrypting the data with the proposed improved blowfish algorithm (IBFA). At last, the developed LSTM+PUCOA model is validated with standard benchmark models and it proves that the performance of the proposed LSTM+PUCOA model attains 90.85% of accuracy, 92.78% of precision, 91.78% of specificity, 89.79% of sensitivity, 7.21% of FPR, 89.76% of F1 score, 89.77% of MCC, 10.20% of FNR, 92.45% of NPV, and 10.22% of FDR for Learning percentage 70.

Pilot Chirp-Assisted OCDM Communications Over Time-Varying Channels

Pilot Chirp-Assisted OCDM Communications Over Time-Varying Channels

Deep Reinforcement Learning Empowers Wireless Powered Mobile Edge Computing: Towards Energy-Aware Online Offloading

Deep integration of wireless power transmission and mobile edge computing (MEC) promotes wireless powered MEC to become a new research hotspot in the field of Internet of Things. In this paper, we focus on the joint optimization problem of online offloading decision and charging resource allocation for minimizing task accomplishing time in dynamic time-varying wireless channel scenarios. The optimal solution involves addressing a mixed integer programming problem in real time, which is proved to be NP-hard, and imposes nontrivial challenges to design with conventional optimization methods. To efficiently address this problem, we leverage the deep reinforcement learning (DRL) technology to propose an energy-aware online offloading algorithm called EAOO. EAOO algorithm learns empirically the online offloading decision policies via a well-designed DRL framework, and adopts the feasible solution region analysis method to implement the charging resource allocation. We further propose a novel feasible decision vector generation method, and incorporate the crossover and mutation technology to expand the offloading vector search space with the provable feasibility guarantee. Extensive experimental results show that, our EAOO algorithm outperforms existing baseline algorithms, and achieves near-optimal performance with low CPU execution latency, which well satisfies the practical requirements of real-time and efficiency.

An Enhanced Approach to Virtually Increase Quasi-Stationarity Regions Within Geometric Channel Models for Vehicular Communications

Vehicular communication channels are intrinsically non-stationary, as they present high mobility and abundant dynamic scatterers. Quasi-stationary regions can assess the degree of non-stationarity within a determined scenario and time variant observation of the channel can be extracted. These regions can aid geometrical models as to increase channel sampling intervals or to develop hybrid stochastic-geometric channel models. In this work, a new methodology for the use of virtual quasi-stationary regions within geometric channel models is proposed, in order to leverage the inherent location information to virtually increase their size. Overall, the use of delay-shifted channel responses improves the mean correlation coefficient between consecutive locations, ultimately reducing computation time for time-variant geometric channel models.

Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism

Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism

Channel-Aware Latency Tail Taming in Industrial IoT

In this paper, we propose a novel channel-aware latency taming scheme, called Optimal Transmission Latency Taming (OTLT), to detect hidden channel state and tame the distribution tail of the packet sojourn time in Industrial Internet of Things (IIoT) devices. Specifically, we design a forward algorithm based on a hidden semi-Markov model to detect the hidden channel state, with a particular emphasis on the state sojourn duration, and to calculate the corresponding channel access probability. Then we develop a time-sensitive model to investigate the minimum sojourn time a packet spends in the IIoT device before leaving successfully. With the obtained channel access probability, the first passage probability of the proposed model is explored to find the maximum probability of a packet being successfully transmitted in a given back-off sojourn duration (BSD). The distribution tail of the packet sojourn time can be tamed by minimizing the cumulative summation of each BSD in consideration of the quadratic penalty latency constraints. Simulation results demonstrate that, in the industrial environment, the OTLT scheme can keep the packet’s sojourn duration within a quantifiable limit and variance. It can also obtain considerably efficient control over packet transmission latency in a time-varying wireless propagation channel even with the increasing number of IIoT devices.

Semantic-Driven Efficient Service Network Towards Smart Healthcare System in Intelligent Fabric

The advantages of intelligent fabrics enable health service systems more efficient and safe to perceive and transmit, where massive sensitive data related to health and behavior are collected through the multi-source flexible sensor network. However, due to its complex network structure, health service systems face the challenge of low-power data transmission and processing in intelligent fabric space. Recently, Deep learning-powered semantic communication (Deep-SC) has emerged as a promising communication paradigm. To tackle the above problem, we introduce the concept of semantic cognition into the cyber-physical system of health service and present the Deep-SC based network service framework for the healthcare system, where physical layer blocks are merged into the traditional communication system to jointly optimize the transceiver in communication. Since semantic encoding and decoding require additional knowledge, consider the introduction of the semantic knowledge base in the system to formulate the joint optimization problem of service offloading and bandwidth allocation in the proposed network service framework, with the goals of reliable semantic communication and efficient network transmission. Furthermore, an online algorithm that optimally adapts service offloading and subcarrier allocation decisions to the time-varying channel conditions is required. Finally, we use a reinforcement learning-based decision algorithm to solve combinatorial optimization problems, which further reduces the computing complexity. Experimental results show that the proposed framework achieves significant performance improvements in system energy consumption and reliable transmission, compared with traditional communication strategies.

Joint Gradient Sparsification and Device Scheduling for Federated Learning

Federated learning is an attractive distributed learning framework where edge server coordinates edge devices to collaboratively train an artificial intelligence (AI) model while training data stays in edge devices. However, implementing federated learning in an energy-efficient way still faces some problems in practice: i) uploading high-dimensional parameters of edge devices is energy-costly and causes communication congestion; ii) The heterogeneity of edge devices, i.e. non-i.i.d. data, different software/hardware configurations, and time-varying channel conditions, affects training accuracy and convergence rate of federated learning. To address these issues, in this paper, we aim at minimizing energy use of the heterogeneous edge devices while maintaining the learning performance. Note that the effects of sparsification and the number of participating devices are highly coupled, and they have opposite effects on both learning performance and energy consumption. Therefore, the gradient sparsification and device scheduling should be jointly optimized for federated learning. We consider the case where a fraction of heterogeneous devices are scheduled to upload their sparsified local gradients for global aggregation at the server. First, a tradeoff between the sparsification and the number of participating devices is revealed through the convergence rate analysis. Then, a joint optimization problem is formulated to minimize energy consumption under the convergence guarantee. An efficient algorithm is proposed to find the globally optimal solution of the considered non-convex problem. Simulations verify the efficiency of the proposed scheme.

Time-Varying Channel Estimation and Symbol Detection for Underwater Acoustic FBMC-OQAM Communications

Time-Varying Channel Estimation and Symbol Detection for Underwater Acoustic FBMC-OQAM Communications