Current Channel State Research Articles

Performance evaluation of pre-equalized UVLC links over outdated lognormal turbulence channels

Underwater visible light communication (UVLC) is important for various underwater applications, including diver-to-diver information sharing, oil field exploration, port security, underwater surveillance systems, and environmental monitoring. However, it should be remembered that UVLC links are strongly affected by underwater optical turbulence (UOT). This may necessitate frequent adjustments in transmit power based on current channel state information (CSI) to mitigate fading effects. In some applications, such as diver-to-diver links, the quasi-static variations in the channel coefficient between transmission frames—attributable to the semi-fixed positions of the transmitting and/or receiving nodes—lead to practical implementations of the transmit power selection that may rely on outdated CSI. In this paper, we investigate the degradation in error rate performance caused by the use of outdated channel information in setting transmit power. Especially, we derive a closed-form expression for the bit error rate (BER) for a pre-equalized UVLC link over outdated lognormal turbulence channels. We verify the derived expression using Monte Carlo simulations.

Adaptive-Duplex Relay Selection for Cooperative Non-Orthogonal Multiple Access Networks

To further improve the spectral efficiency of the cooperative non-orthogonal multiple access (NOMA) network with half-duplex (HD) relaying and avoid the zero diversity gain due to the imperfect self interference estimation at high signa-to-noise ratio (SNR) value with full-duplex (FD) relaying, this paper developed a novel two-stage relay selection for a two-user multi-relay cooperative NOMA systems with adaptive-duplex (AD) relaying which can switch opportunistically between HD and FD relaying modes. An adaptive duplex based on fixed power allocation factor relay selection (AD-FPA-RS) scheme for cooperative NOMA networks is proposed in this paper, the relay can switch the work mode to the FD or HD state adaptively according to the current channel state information and the result of decode at the relay. Futhermore, the exact outage probability for the proposed AD-FPA-RS scheme is obtained in closed-form expression. The simulation and numerical results show that the proposed AD-FPA-RS scheme not only outperforms the existing HD-FPARS and FD-FPA-RS schemes in terms of outage behavior significantly, but also can obtain full diversity gain without the floor outage performance at high SNR region.

Adaptive-Duplex Relay Selection for Cooperative Non-Orthogonal Multiple Access Networks

To further improve the spectral efficiency of the cooperative non-orthogonalmultiple access (NOMA) network with half-duplex (HD) relaying and avoid the zero diversity gain due to the imperfect self interference estimation at high signa-to-noise ratio (SNR) value with full-duplex (FD) relaying, this paper developed a novel two-stage relay selection for a two-user multi-relay cooperative NOMA systems with adaptive-duplex (AD) relaying which can switch opportunistically between HD and FD relaying modes. An adaptive duplexbased on a fixed power allocation factor relay selection (AD-FPA-RS) scheme for cooperative NOMA networks is proposed in this paper, the relay can switch the work mode to the FD or HD state adaptively according to the current channel state information and the result of decode at the relay. Furthermore, the exact outage probability for the proposed AD-FPA-RS scheme is obtained in a closed-form expression. The simulation and numerical results show that the proposed AD-FPA-RS scheme not only outperforms the existing HD-FPARS and FD-FPA-RS schemes in terms of outage behavior significantly but also can obtain full diversity gain without the floor outage performance at high SNR region.

CNN and Attention-Based Joint Source Channel Coding for Semantic Communications in WSNs

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.

Consensus Control of Discrete-Time Multiagent Systems Over Correlated Fading Channels: A Compressed Coding Scheme

This article focuses on the mean-square consensus control problem for a class of discrete-time multiagent systems (DT-MASs) over time-correlated multistate Markovian fading channels, where the packet loss probability is time-varying and depends on the current channel state. In order to save limited network bandwidth, a compressed coding scheme is developed by preprocessing the measurement output. With the aid of a stochastic Lyapunov–Krasovskii functional, a sufficient condition is first obtained under which the consensus error system is mean-square stable for DT-MASs over identical fading channels. Then, the consensus gain is formulated as the feasible solution to a set of linear matrix inequalities (LMIs) whose dimensions are independent of the number of agents. Furthermore, for the case that agents communicate over nonidentical fading channels, the mean-square consensus problem is transformed into an analyzable edge agreement issue in the mean-square sense by means of properties of the edge Laplacian combined with a mapping technique. Next, a sufficient condition is derived to ensure the mean-square consensus performance, based on which the existence of the controller can be guaranteed by the feasibility of a set of LMIs. Finally, the validity and feasibility of the developed design scheme are shown by two illustrative examples.

Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services

Aiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth allocation method based on user services. This method comprehensively considers factors, such as user service QoS, channel state information, and resource periodicity, to adaptively allocate bandwidth for users using different services. Firstly, based on the service priority, the user priority is dynamically adjusted according to the current channel state information and the continuous periodicity of the allocation, and the user is scheduled.; Secondly, the dynamic priority is combined with the minimum guaranteed time slot to establish the objective function of adaptive bandwidth allocation. Finally, chaos theory, Levy flight, and reverse learning are integrated to improve the bald eagle optimization algorithm. The improved bald eagle algorithm is used to solve the problem, and the optimal solution to bandwidth allocation is obtained. The simulation shows that compared with the traditional bandwidth allocation method based on user service quality perception, the bandwidth allocation algorithm based on the minimum rate requirement, and the ant colony-based allocation algorithm, the bandwidth allocation method proposed in this paper improves the system utility value, bandwidth utilization rate, throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, and better meets the business needs of users.

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

Through offloading computing tasks to visible satellites for execution, the satellite edge computing architecture effectively issues the high-delay problem in remote grids (e.g., mountain and desert) when tasks are offloaded to the urban terrestrial cloud (TC). However, existing works are usually limited to offloading tasks in pure satellite networks and make offloading decisions based on the predefined models. Additionally, runtime consumption for offloading decisions is rather high. Furthermore, privacy information may be maliciously sniffed since computing tasks are transmitted via vulnerable satellite networks. In this paper, we study the task-offloading problem in satellite–terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. A privacy leakage scenario is described, and we consider preserving privacy by sending extra random dummy tasks to confuse adversaries. Then, the offloading cost with privacy protection consideration is modeled, and the offloading decision that minimizes the offloading cost is formulated as a mixed-integer programming (MIP) problem. To speed up solving the MIP problem, we propose a deep reinforcement learning-based task-offloading (DRTO) algorithm. In this case, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that the offloading overhead is reduced by 17.5% and 23.6% compared with pure TC computing and pure SatEC computing, while the runtime consumption of DRTO is reduced by at least 42.6%. The dummy tasks are exhibited to effectively mitigate privacy leakage during offloading.

A Belief-Based Task Offloading Algorithm in Vehicular Edge Computing

In vehicular edge computing (VEC), where vehicles offload their tasks to nearby edge clouds, it is not a trivial issue to design an optimal task offloading policy due to the dynamic nature of VEC environment and limited information on computing and communication resources. In this paper, we propose a belief-based task offloading algorithm (BTOA) where a vehicle selects target edge clouds (for computing) and subchannels (for communications) based on its belief, and observe their current resource and channel conditions. Based on the observed information, the vehicle finally determines the most appropriate edge cloud and subchannel. Evaluation results under a realistic traffic scenario demonstrate that BTOA can reduce the total latency of the task offloading over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$42\%$</tex-math> </inline-formula> compared to a conventional offloading algorithm where the target edge clouds and subchannels are determined without any real observations.

A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET

Vehicular ad hoc networks (VANETs) are crucial components of intelligent transportation systems (ITS) aimed at enhancing road safety and providing additional services to vehicles and their users. To achieve reliable delivery of periodic status information, referred to as basic safety messages (BSMs) and event-driven alerts, vehicles need to manage the conflicting requirements of situational awareness and congestion control in a dynamic environment. To address this challenge, this paper focuses on controlling the message transmission rate through a Markov decision process (MDP) and solves it using a novel reinforcement learning (RL) algorithm. The proposed RL approach selects the most suitable transmission rate based on the current channel conditions, resulting in a balanced performance in terms of packet delivery and channel congestion, as shown by simulation results for different traffic scenarios. Additionally, the proposed approach offers increased flexibility for adaptive congestion control through the design of an appropriate reward function.

Asymmetric Adaptive LDPC-Based Information Reconciliation for Industrial Quantum Key Distribution.

We develop a new approach for asymmetric LDPC-based information reconciliation in order to adapt to the current channel state and achieve better performance and scalability in practical resource-constrained QKD systems. The new scheme combines the advantages of LDPC codes, a priori error rate estimation, rate-adaptive and blind information reconciliation techniques. We compare the performance of several asymmetric and symmetric error correction schemes using a real industrial QKD setup. The proposed asymmetric algorithm achieves significantly higher throughput, providing a secret key rate that is close to the symmetric one in a wide range of error rates. Thus, our approach is found to be particularly efficient for applications with high key rates, limited classical channel capacity and asymmetric computational resource allocation.

Concurrent Low-power Listening: A New Design Paradigm for Duty-cycling Communication

In this article, we explore a new design paradigm of duty-cycling mechanism that supports low-power devices to fully turn channel contention into transmission opportunities. To achieve this goal, we propose Concurrent Low-power Listening (CLPL) to enable contention-tolerant and concurrent media access control (MAC) for widely deployed low-power devices. The fundamental principle behind CLPL is that frequency modulated receiver can reliably demodulate the strongest signal even if cochannel interference and noise exist. By using CLPL, a sender inserts a series of tailor-made signals (namely, wake-up signal) between adjacent data frames to awaken appointed receiver, making it capable to receive the next data frame. According to system-defined maximum transmission power level, CLPL adopts an adaptive algorithm to adjust the transmission power of wake-up signals so that its signal strength is above receiver sensitivity and will not interfere with the other data frames in transit. By exploiting the spatial-temporal correlation, we further develop a light-weight wake-up signal detection method to enable a waiting sender to accurately identify the current channel condition. Then, it schedules the sender’s data frame transmissions by overlapping with those wake-up signals, without conflicting with existing data frame transmissions. We have implemented the prototype of CLPL and conducted extensive experiments on a real testbed. In comparison with the state-of-the-art low-power MAC schemes, such as ContikiMAC, A-MAC, BoX-MAC, and opportunistic scheme ORW, CLPL can improve the throughput by 2–6 times and halve the end-to-end transmission delay.

Deep reinforcement learning-based beam training with energy and spectral efficiency maximisation for millimetre-wave channels

The millimetre-wave (mmWave) spectrum has been investigated for the fifth generation wireless system to provide greater bandwidths and faster data rates. The use of mmWave signals allows large-scale antenna arrays to concentrate the radiated power into narrow beams for directional transmission. The beam alignment at mmWave frequency bands requires periodic training because mmWave channels are sensitive to user mobility and environmental changes. To benefit from machine learning technologies that will be used to build the sixth generation (6G) communication systems, we propose a new beam training algorithm via deep reinforcement learning. The proposed algorithm can switch between different beam training techniques according to the changes in the wireless channel such that the overall beam training overhead is minimised while achieving good performance of energy efficiency or spectral efficiency. Further, we develop a beam training strategy which can maximise either energy efficiency or spectral efficiency by controlling the number of activated radio frequency chains based on the current channel conditions. Simulation results show that compared to baseline algorithms, the proposed approach can achieve higher energy efficiency or spectral efficiency with lower training overhead.

Exponential bipartite tracking consensus in cooperative-antagonistic nonlinear Multi-Agent Systems with multiple communication time-varying delays

This article deals with the bipartite tracking consensus problem for high-order nonlinear Multi-Agent Systems (MASs) with cooperative–antagonistic agents interacting via a directed non-ideal communication network. For a more realistic interaction scenario, a different time-varying delay is associated to each communication link connecting a couple of agents, whose actual value is determined by the current wireless channel condition. In this context, we propose a novel distributed delayed bipartite tracking consensus control strategy which ensures the exponential stability of the whole networked control system. The theoretical analysis, carried out via Lyapunov theory and Halanay’s Lemma, provides the control gains tuning rule and an estimation of the maximum admissible delay upper bound. Numerical simulations corroborate the theoretical derivations.

Unlicensed Spectrum Forecasting: An Interference Umbrella Based on Channel Analysis and Machine Learning

In this work a novel framework for predicting future interference levels for IEEE 802.11 networks is developed and experimentally evaluated. At the heart of the framework lies a modelling mechanism which is able to estimate and determine in real-time, the over-the-air performance that each network user will receive over an IEEE 802.11 link, when considering and combining multiple wireless metrics, without requiring a network association (Wi-Fi AP-STA) to be performed. On top of the solution, a Machine Learning approach is integrated, in order to project the real-time predictions to long-term predictions in the future (2-hour interval). Additionally, the framework applies a self-correcting mechanism for the predictions, by extracting short-term predictions and accurate throughput calculations, when the current channel conditions largely differ from the long-term predictions. The proposed framework covers comprehensively the cases of interference created by either 802.11 or non 802.11 devices, which may occur at the target or at any overlapping wireless channel. Finally, extensive testbed experimentation proves the framework's proper functionality and accuracy, under the cases of both Indoor (controlled interference) and Outdoor (uncontrolled massive interference) environments.

Deep neural network based UAV deployment and dynamic power control for 6G-Envisioned intelligent warehouse logistics system

The intelligent warehouse logistics system (IWLS) is an essential component in the emerging industry 5.0. To well assist the IWLS, advanced network architecture and control policies with the adaptive capability to the variation of traffic should be specially designed. In this paper, we consider an air-and-ground cooperative wireless network that enables dynamic coverage to support the flexible scheduling of the automatic guided vehicles (AGVs) in the IWLS. Jointly considering the dynamic deployment of unmanned aerial vehicles (UAVs) as well as the adaptive power control of AGVs, we formulate a two time-scale network control problem to minimize the transmission power consumption of all AGVs under their individual rate requirement. On large time scales, we first propose a particle swarm optimization based algorithm (PSOA) to obtain the deployment position of the ABS. Then, using the results of the PSOA as training data, we design a deep neural network (DNN) framework aimed at reducing the computational time of the PSOA. On small time scales, we devise an online power control algorithm (OPCA) by using some of stochastic network optimization methods. With current channel conditions, the OPCA can generate the real-time power control policy and ensure the long-term rate requirement. Numerical simulations indicate that the DNN framework enhances the coverage performance of the network only consuming a few milliseconds of computation time. Incorporated with the OPCA, the total transmission power of the AGVs is significantly reduced.

Remote State Estimation With Smart Sensors Over Markov Fading Channels

We consider a fundamental remote state estimation problem of discrete-time linear time-invariant (LTI) systems. A smart sensor forwards its local state estimate to a remote estimator over a time-correlated multistate Markov fading channel, where the packet drop probability is time-varying and depends on the current fading channel state. We establish a necessary and sufficient condition for mean-square stability of the remote estimation error covariance in terms of the state transition matrix of the LTI system, the packet drop probabilities in different channel states, and the transition probability matrix of the Markov channel states. To derive this result, we propose a novel estimation-cycle based approach and provide new elementwise bounds of matrix powers. The stability condition is verified by numerical results and is shown more effective than existing sufficient conditions in the literature. We observe that the stability region in terms of the packet drop probabilities in different channel states can either be convex or nonconvex depending on the transition probability matrix of the Markov channel states. Our numerical results suggest that the stability conditions for remote estimation may coincide for setups with a smart sensor and with a conventional one (which sends raw measurements to the remote estimator) though the smart sensor setup achieves a better estimation performance.

Efficient Offloading for Minimizing Task Computation Delay of NOMA-Based Multiaccess Edge Computing

Multi-access edge computing (MEC) has been one promising solution to reduce the computation delay of wireless devices. Due to the high spectrum efficiency of non-orthogonal multiple access (NOMA), this paper studies the single-user multi-edge-server MEC system based on downlink NOMA, aiming to minimize task computation delay by jointly optimizing the NOMA-based transmission duration (TD) and workload offloading allocation (WOA) among edge computing servers. This task computation delay minimization (CDM) problem is formulated as a nonconvex optimization problem. To solve the CDM problem efficiently, we decompose it into the sub-problem of determining the optimal WOA with a given TD and the top-problem of optimizing the TD. For the sub-problem, we first derive its some important properties and then design an efficient channel quality ranking based algorithm to obtain the optimal WOA. We solve the top-problem for the static-channel and dynamic-channel scenarios, respectively. For the static-channel scenario, we design an optimal algorithm which only apply once the golden section search method to obtain the optimal TD of first task and directly obtain the optimal offloading solution for any consequently arrived task with different workloads. For the dynamic-channel scenario where the channel qualities from the wireless device to the edge-computing servers are varying, it is critical to quickly determine the current task&#x2019;s offloading solution under the current channel state and task workload, which is very challenging for the traditional optimization methods. In order to conquer this challenge, we propose the deep reinforcement learning (DRL) based algorithm, which can obtain the near-optimal offloading solution instantly after enough learning. Finally, we validate through simulations the advantages of NOMA over frequency division multiple access (FDMA).

CQI Prediction Through Recurrent Neural Network for UAV Control Information Exchange Under URLLC Regime

Unmanned aerial vehicles (UAVs) control information delivery is a critical communication with stringent requirements in terms of reliability and latency. In this context, link adaptation plays an essential role in the fulfillment of the required performance in terms of decode error probability and delay. Link adaptation is usually based on channel quality indicator (CQI) feedback information from the user equipment that should represent the current state of the channel. However, measurement, scheduling and processing delays introduce a CQI aging effect, that is a mismatch between the current channel state and its CQI representation. Using outdated CQI values may lead to the selection of a wrong modulation and coding scheme, with a detrimental effect on performance. This is particularly relevant in ultra reliable and low latency communications (URLLC), where the control of the reliability can be negatively impacted, and it is more evident when the channel is fast varying as the case of UAVs. This paper analyzes the effects of CQI aging on URLLCs, considering transmissions under the finite blocklength regime, that characterizes such communications type. A deep learning approach is investigated to predict the next CQI from the knowledge of past reports, and performance in terms of decode error probability and throughput is given. The results show the benefit of CQI proposed prediction mechanism also in comparison with previously proposed methods.

Unconditional Authentication Based on Physical Layer Offered Chain Key in Wireless Communication.

Authentication is a critical issue in wireless communication due to the impersonation and substitution attacks from the vulnerable air interface launched by the malicious node. There are currently two kinds of authentication research in wireless communication. One is based on cryptography and relies on computational complexity, the other is based on physical layer fingerprint and can not protect data integrity well. Both of these approaches will become insecure when facing attackers with infinite computing power. In this paper, we develop a wireless unconditional authentication framework based on one-time keys generated from wireless channel. The proposed unconditional authentication framework provides a new perspective to resist infinite computing power attackers. We study the performance of the unconditional authentication framework in this paper. First, a physical layer offered chain key (PHYLOCK) structure is proposed, which can provide one-time keys for unconditional authentication. The physical layer offered chain keys are generated by XORing the physical layer updated keys extracted from the current channel state information (CSI) and the previous chain keys. The security of PHYLOCK is analyzed from the perspective of information theory. Then, the boundary of the deception probability is conducted. It is shown that unconditional authentication can achieve a probability of deception , where is the entropy of the one-time key used for one message. Finally, the conditions for unconditional authentication are listed. Our analysis shows that the length of the key and the authentication code need to be twice the length of the message and the encoding rules of the authentication code need to satisfy the restrictions we listed.

Enhanced Interference Management for 6G in-X Subnetworks

Short-range low-power 6th generation (6G) in-X subnetworks are proposed as a viable radio concept for supporting extreme communication requirements in emerging applications such as wireless control of robotic arms and control of critical on-body devices, e.g. wireless heart pacemaker. For these applications, ultra-high reliability (e.g., above 6 nines) with sub-ms latency must be guaranteed at all spatio-temporal instants. To meet these requirements, radio systems that are robust against fading and interference are crucial. In this paper, we present a comprehensive investigation on technology enablers and techniques for interference mitigation in 6G in-X subnetworks. We present several techniques including blind-repetition with pseudo-random frequency hopping and environment-aware mechanisms for interference management via dynamic channel allocation. We further propose two novel enhancements viz: (1) repetition order adaptation involving real-time selection of the number of repetitions based on current channel conditions; and (2) anticipatory packet duplication in which each subnetwork duplicates its transmission on a secondary channel group whenever it detects the presence of a potentially harmful neighbouring subnetwork. We perform extensive simulations in an industrial factory environment with mobile in-X subnetworks using models and parameters defined by the 3rd Generation Partnership Project (3GPP). Results show that in-X subnetworks requires large bandwidth (≥ 1 GHz), up to 2 packet repetitions and environment-aware interference coordination in order to support packet loss rate below 10-6 with a latency < 100μs. The number of repetitions can however be reduced for systems with survival time greater than the cycle time. The proposed enhancements also result in up to ×104 packet loss rate reduction for systems with survival time above 2 cycle times.