Time-varying Wireless Channel Research Articles

Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods.

Optimization of Single-user Task Migration based on Improved DDPG

Aiming at the problems of slow convergence and unstable convergence of traditional reinforcement learning algorithms in minimizing computational cost on edge servers with random task arrivals and time-varying wireless channels, an improved DDPG algorithm (IDDPG) was proposed. The Critic network structure of DDPG was replaced by the Dueling structure, which converged faster by splitting the state value function into an advantage function and a value function. The update frequency of the Critic network was adjusted to be higher than that of the Actor-network to make the overall training more stable. The Ornstein- Uhlenbeck noise was added to the actions selected through the Actor-network to improve the algorithm exploration ability, and the action noise size was set in segments to ensure the stability of convergence. Experimental results show that, compared with other algorithms, the IDDPG algorithm can better minimize the computational cost and has a certain improvement in the convergence speed and convergence stability.

Multiobservation-Multichannel-Attribute-Based Multiuser Authentication for Industrial Wireless Edge Networks

In order to truly promote the further development of the Industrial Internet of Things (IIoT), terminal authentication of the IIoT is essential. Physical-Layer Authentication (PLA) has recently attracted much attention for its high security and lightweight. Nevertheless, most existing PLA schemes in conjunction only the observation of a single receiver will lead to low-reliability and low-robustness of authentication, especially in hostile time-varying wireless channels. To tackle this issue, we developed a multi-observation-multi-channel-attribute (MOMCA)-based multiuser authentication architecture, which considers both the observations of multi-receivers and multiple channel attributes of each observation to enhance wireless security. Specifically, the proposed architecture can provide additional spatial recognition characteristics for multi-users. To better fit the channel features of multi-observations, we proposed two Gradient Boosting Optimization (GBO)-based schemes. One uses Taylor expansion to approximate objective functions and adds the regularization term to avoid over-fitting issues. The other can obtain higher authentication performance by sampling the signal data with small gradient characteristics. The simulations on real industrial indoor and outdoor datasets verify the superiority of the proposed schemes in authentication accuracy over six baseline authentication schemes

Joint MCS Adaptation and RB Allocation in Cellular Networks Based on Deep Reinforcement Learning With Stable Matching

Joint modulation-coding scheme (MCS) adaptation and resource block (RB) allocation is an effective approach to guarantee different quality of service (QoS) requirements of all UEs under dynamic network environments. In this paper, we consider a fifth generation (5 G) cellular network with time-varying wireless channels, in which the BS serves multiple user equipments (UEs) under limited available RBs. We aim to minimize the total RB consumption subject to the rigorous constraints of each UE's QoS requirement. To attain this objective, this paper puts forth an online learning technique, referred to as integrated Deep Reinforcement learning and stable Matching (DeepRM), in the sense that the MCS adaptation and RB allocation decisions are conducted without acquiring the real-time channel quality indicator (CQI) feedback. DeepRM is a closed-loop framework, where the output of deep reinforcement learning (DRL) is imported into the stable matching to guide optimal RB allocation whilst the output of stable matching is fed into the DRL framework to assist efficient MCS decision-making. Specifically, in DeepRM, we first develop a powerful DRL algorithm, termed as Action-and-Reward Branching Deep Q-network (ARBDQ), by incorporating the action branch architecture into conventional DRL and modifying the traditional deep neural network training mechanism, to perform judicious MCS decisions on different links in parallel. Then, a new many-to-one stable matching algorithm, called adaptive deferred acceptance, is exploited to dynamically adjust the RB quota of each UE in a computationally efficient fashion. Simulation results demonstrate that compared with ACO-HM, OLLA-ADA, and ARBDQ-Random algorithms, DeepRM induces much less RB consumption while guaranteeing the QoS requirements of all UEs in various network scenarios. Furthermore, under miscellaneous QoS requirement, number of UEs, and CQI reporting period setups, DeepRM is more robust than other baselines.

Over-the-Air Federated Learning Under Time-Varying Wireless Channels Using OTFS

Over-the-Air Federated Learning Under Time-Varying Wireless Channels Using OTFS

Adaptive Automatic Repeat Request (AdARQ) Protocol to Improve the Throughput Characteristic of the Time-Varying Wireless Channel

Adaptive Automatic Repeat Request (AdARQ) Protocol to Improve the Throughput Characteristic of the Time-Varying Wireless Channel

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.

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.

Performance Analysis Model and Deterministic Routing Decision Algorithm for Broadband Real-Time Services in Wireless Multi-Hop Networks

Wireless multi-hop networks (WMN) are considered as one of promising solutions for broadband real-time service transmission in the Internet of Everything. Due to the timevarying wireless channels, the deterministic communication of services becomes extremely challenging for WMN. In order to comprehensively evaluate the service provisioning capability of WMN, we first propose an end-to-end performance analysis model for reliable transmission of broadband real-time services in WMN based on stochastic network calculus (SNC). We define a general expression for the effective service process that a changing channel provides. The end-to-end delay violation bound and hop-delay relationship are obtained by applying the effective service process and the cascade theorem. Then based on this analysis model, we propose a deterministic routing search algorithm, where the initial path search sets are reduced through the hopdelay relationship. And the routes are obtained from the reduced path sets. The deterministic routing decision algorithm can ensure the bounded end-to-end delay of services with a certain probability. Theoretical and simulation results demonstrate the correctness of the proposed performance analysis model and the effectiveness of the routing algorithm.

Federated Learning With Over-the-Air Aggregation Over Time-Varying Channels

We study federated learning (FL) with over-the-air aggregation over time-varying wireless channels. Independent workers compute local gradients based on their local datasets and send them to a parameter server (PS) through a time-varying multipath fading multiple access channel via orthogonal frequency-division multiplexing (OFDM). We assume that the workers do not have channel state information, hence the PS employs multiple antennas to alleviate the fading effects. Wireless channel variations result in inter-carrier interference, which has a detrimental effect on the performance of OFDM systems, especially when the channel is rapidly varying. We examine the effects of the channel time variations on the convergence of the FL with over-the-air aggregation, and show that the resulting undesired interference terms have only limited destructive effects, which do not prevent the convergence of the learning algorithm. We also validate our results via extensive simulations, which corroborate the theoretical expectations.

Optimizing task offloading and resource allocation in edge-cloud networks: a DRL approach

Edge-cloud computing is an emerging approach in which tasks are offloaded from mobile devices to edge or cloud servers. However, Task offloading may result in increased energy consumption and delays, and the decision to offload the task is dependent on various factors such as time-varying radio channels, available computation resources, and the location of devices. As edge-cloud computing is a dynamic and resource-constrained environment, making optimal offloading decisions is a challenging task. This paper aims to optimize offloading and resource allocation to minimize delay and meet computation and communication needs in edge-cloud computing. The problem of optimizing task offloading in the edge-cloud computing environment is a multi-objective problem, for which we employ deep reinforcement learning to find the optimal solution. To accomplish this, we formulate the problem as a Markov decision process and use a Double Deep Q-Network (DDQN) algorithm. Our DDQN-edge-cloud (DDQNEC) scheme dynamically makes offloading decisions by analyzing resource utilization, task constraints, and the current status of the edge-cloud network. Simulation results demonstrate that DDQNEC outperforms heuristic approaches in terms of resource utilization, task offloading, and task rejection.

Wireless Channel Adaptive DNN Split Inference for Resource-Constrained Edge Devices

Split inference facilitates deep neural network (DNN) inference tasks at resource-constrained edge devices. However, a pre-determined split configuration of a DNN limits the inference performance in time-varying wireless channels. To accelerate the inference, we propose a two-stage wireless channel adaptive split inference method by considering memory and energy constraints on the edge device. The proposed scheme is able to offer the privacy of the edge device and improves inference performance in time-varying wireless channels by leveraging a U-shaped DNN splitting framework and adaptively determining the splitting points of a DNN in real-time according to time-varying wireless channel gains.

Knowledge-Aided Federated Learning for Energy-Limited Wireless Networks

The conventional model aggregation-based federated learning (FL) approach requires all local models to have the same architecture, which fails to support practical scenarios with heterogeneous local models. Moreover, the frequent model exchange is costly for resource-limited wireless networks since modern deep neural networks usually have over a million parameters. To tackle these challenges, we first propose a novel knowledge-aided FL (KFL) framework, which aggregates light high-level data features, namely knowledge, in the per-round learning process. This framework allows devices to design their machine-learning models independently and reduces the communication overhead in the training process. We then theoretically analyze the convergence bound of the proposed framework under a non-convex loss function setting, revealing that scheduling more data volume in each round helps to improve the learning performance. In addition, large data volume should be scheduled in early rounds if the total scheduled data volume during the entire learning course is fixed. Inspired by this, we define a new objective function, i.e., the weighted scheduled data sample volume, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, bandwidth allocation, and power control. To deal with unknown time-varying wireless channels, we transform the considered problem into a deterministic problem for each round with the assistance of the Lyapunov optimization framework. Then, we derive the optimal bandwidth allocation and power control solution by convex optimization techniques. We also develop an efficient online device scheduling algorithm to achieve an energy-learning trade-off in the learning process. Experimental results on two typical datasets (i.e., MNIST and CIFAR-10) under highly heterogeneous local data distributions show that the proposed KFL is capable of reducing over 99% communication overhead while achieving better learning performance than the conventional model aggregation-based algorithms. In addition, the proposed device scheduling algorithm converges faster than the benchmark scheduling schemes.

Joint Channel Estimation and Robust Beamforming Design for AF Relaying Using IMM Kalman Filters

This paper addresses the joint problem of recursive channel estimation and robust beamformer design in peer-to-peer communication through a network of relays over time-varying radio channels. Using observed signal samples at the relay and receiver nodes, the Channel State Information (CSI) is estimated centrally by taking advantage of a Markov model for the transmitter-relay and relay-receiver channels, and employing either the Extended Kalman Filter (EKF) or the Cubature Kalman Filter (CKF). Based on the estimated CSI, two robust approaches are conceived for designing the relay beamforming where the aim is to minimize the total transmission power of the relays subject to Signal-to-Interference plus Noise Ratio (SINR) constraints at each of the receiver nodes. Furthermore, the Interacting Multiple Model (IMM) approach for mixing non-stationary and stationary Markov models is employed to extend the time-varying robust beamforming design to non-stationary environments. Through numerical simulations, the recursive CSI estimation methods are shown to be efficient, i.e., unbiased and converging to the Cramer-Rao Lower Bound (CRLB). Furthermore, the results confirm the better performance of the proposed robust relay beamforming design algorithms compared to existing methods in terms of relevant transmission metrics, including relay power consumption and spectral efficiency.

On the Age of Status Updates in Unreliable Multi-Source M/G/1 Queueing Systems

The timeliness of status message delivery in communications networks is subjective to time-varying wireless channel transmissions. In this letter, we investigate the age of information (AoI) of each source in a multi-source M/G/1 queueing update system with active server failures. In particular, we adopt the method of supplementary variables to derive a closed-form expression for the average AoI in terms of system parameters, where the server repair time follows a general distribution and the service time of packets generated by independent sources is a general random variable. Numerical results are provided to validate the effectiveness of the proposed packet serving policy under different parametric settings.

QoE-Maximized Beam Assignment and Rate Control for Dynamic mm-Wave-Based Full-Duplex Small Cell Networks

This study investigates the resource management problem for millimeter-wave-based switched beam (SWB) full-duplex small cell networks with the consideration of user equipment’s (UE’s) quality of experience (QoE) requirement and time-varying wireless channels. An optimization problem is formulated to maximize the long-term QoE by implementing beam assignment (BA) and rate control (RC) under short-term beam and long-term energy efficiency constraints. By leveraging the Lyapunov optimization technique, the original problem is converted into a series of BA and RC problems in each time slot. To solve the converted problem with affordable complexity, novel closed-form solutions for BA and RC are first derived by considering beam constraints in SWB systems. A decomposition-based BA and RC (DBR) algorithm with only polynomial computational complexity is then proposed based on the derived closed-form solutions. The simulation results demonstrate that the proposed DBR method can effectively[‘effectively’ appears to be a more suitable word in this case.] balance the performance and complexity because the DBR scheme outperforms the benchmark scheme and achieves nearly optimal performance in terms of system delay and QoE.

Deep Reinforcement Learning for Time-Energy Tradeoff Online Offloading in MEC-Enabled Industrial Internet of Things

Mobile edge computing (MEC) has recently emerged as a promising technology to boost the integration ability of sensing, transmission and computation in industrial Internet of Things (IIoT). This paper investigates an MEC-enabled IIoT system, where multiple industrial devices may offload computation-intensive tasks to an edge server through wireless communication. We focus on the online offloading problem to optimize the tradeoff of the task accomplishing time and energy consumption. Time-varying wireless channels, random targeted task data sizes and dynamically changing residual energy as well as adaptively adjusted tradeoff weights make this problem highly challenging. Conventional optimization methods may lead to inefficient or even infeasible solutions. To efficiently tackle this problem, we leverage the deep reinforcement learning (DRL) technology to propose a time-energy tradeoff online offloading algorithm called TETO. In TETO, the online offloading decision policies are empirically learned via a well-designed DRL framework. TETO algorithm incorporates a stochastic strategy, the crossover and mutation technology and a novel feasible suboptimal offloading method to expand the offloading action search space with the provable feasibility guarantee. Extensive experimental results based on a real-world dataset show that, our TETO algorithm performs better than existing baseline algorithms, and obtains near-optimal performance with low CPU execution latency.

Dynamic edge computing empowered by reconfigurable intelligent surfaces

In this paper, we propose a novel algorithm for energy-efficient low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end delay. The proposed strategy enables dynamic control of the system, performing a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS-empowered MEC.

Low-Complexity LMMSE Receiver Design for Practical-Pulse-Shaped MIMO-OTFS Systems

Orthogonal time frequency space modulation (OTFS) scheme establishes reliable communication in a rapidly time-varying wireless channel with a high Doppler spread. We design a low-complexity linear minimum mean squared error (LMMSE) receiver for practical-pulse-shaped multiple-input multiple-output (MIMO)-OTFS systems. The proposed receiver exploits the inherent channel sparsity and the channel-agnostic structure of matrices involved in the LMMSE receiver, and has only a log-linear complexity. It provides exactly the same solution, and hence the same bit error rate (BER), as that of the conventional LMMSE receiver with a cubic order of complexity. We also derive, by using the Taylor series expansion and the results from random matrix theory, a tight closed-form approximation for the post-processing signal-to-noise-plus-interference ratio (SINR) expression of the proposed receiver. This expression is derived by assuming imperfect receive channel state information. We show using extensive numerical investigations that the derived SINR expression, when averaged over multiple channel realizations, accurately characterizes the BER of a MIMO-OTFS system.

Study and Analysis of OTFS and OFDM

High mobility wireless communication systems have been proposed to be robust to channel-induced Doppler shift using Orthogonal Time Frequency Space (OTFS) modulation. The OTFS modulation technique is described in this paper. In addition to being in the delay-Doppler (DD) domain, OTFS has a unique and important feature. The OTFS modulation offers maximum diversity over frequency and time when coupled with an equalizer. This technique converts fading, time-varying wireless channels into time-independent, non-fading interactions that reveal their underlying geometry. Even in challenging 5G deployment settings, OTFS achieves Massive MIMO throughput gains by scaling throughput linearly with MIMO order. An analysis of peak to average power ratio (PAPR) of OFTS modulation wave forms is presented in this paper. OTFS with rectangular pulses is characterized analytically using the complementary cumulative distribution function (CCDF). The simulated CCDF for the PAPR of OTFS is compared with that for orthogonal frequency division multiplexing (OFDM) for different pulse shapes. The results show that OTFS has a better PAPR than OFDM.