Dynamic Channel Conditions Research Articles

A Deep Reinforcement Learning‐Based Physical Layer Security Enhancement Design for RIS‐Aided mmWave Communications With Practical Constraints

ABSTRACTReconfigurable intelligent surfaces (RIS) offer new opportunities for enhancing security in millimeter‐wave (mmWave) communications. However, some significant practical challenges still need to be addressed before their extensive implementation in future wireless networks. This article considers the practical constraints in a secure mmWave system aided by distributed RISs, including imperfect channel state information (CSI) in dynamic channel conditions and high complexity of non‐convex optimization in complex environments. To address these challenges, we propose a robust and efficient physical layer security (PLS) enhancement algorithm based on the deep reinforcement learning (DRL) framework to effectively tackle the issues of limited dynamic adaptation and high computational complexity encountered with conventional optimization methods. This algorithm, utilizing an actor‐critic architecture, can dynamically track channel variations and optimize strategies for improved system secrecy rate. Numerical simulations demonstrate that the proposed DRL‐based PLS enhancement algorithm outperforms non‐convex optimization benchmarks in robustness and efficiency for secure mmWave communication systems aided by distributed RISs and affected by imperfect CSI.

Cost-effective task offloading and trajectory optimization in UAV assisted edge networks with DDPG

PurposeUnmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.Design/methodology/approachTo address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.FindingsThe analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.Originality/valueA multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

Error mitigation in LPWAN systems: A study on the efficacy of Hamming-coded RPW.

Rotating Polarization Wave (RPW) is a novel Low Power Wide Area Networks (LPWAN) technology for robust connectivity and extended coverage area as compared to other LPWAN technologies such as LoRa and Sigfox when no error detection and correction is employed. Since, IoT and Machine-to-Machine (M2M) communication demand high reliability, RPW with error correction can significantly enhance the communication reliability for critical IoT and M2M applications. Therefore, this study investigates the performance of RPW with single bit error detection and correction using Hamming codes to avoid substantial overhead. Hamming (7,4) coded RPW shows a remarkable improvement of more than 40% in error performance compared to uncoded RPW thereby making it a suitable candidate for IoT and M2M applications. Error performance of coded RPW outperforms coded Chirp Spread Spectrum (CSS) modulation used in LoRa under multipath conditions by 51%, demonstrating superior adaptability and robustness under dynamic channel conditions. These findings provide valuable insights into the ongoing developments in wireless communication systems whilst reporting Q-RPW model as a new and effective method to address the needs of developing LPWAN and IoT ecosystems.

EC-MAC: Energy-Aware Cooperative MAC Protocol in Wireless Sensor Network

Nowadays, cooperative communication algorithms have been utilized in Wireless Sensor Network (WSN) to enhance the overall network performance. As is well known, a WSN needs to consider a number of important factors, such as the energy effectiveness and the longevity of the sensor nodes. The energy-aware cooperative medium access control (EC-MAC) protocol is a novel protocol proposed in this paper for usage in WSNs. EC-MAC protocol allows the source nodes to use the intermediate nodes as relays that can be used to transmit the source's data to the access point (AP). This paper demonstrates how the suggested relay selection method can be used by the EC-MAC protocol to choose the best relay node. After channel state information (CSI) has been calculated and acquired, the best relay should have the highest residual energy and the quickest transmission time. Then, by establishing suitable cooperative links, data transmission from a source node to an AP can be carried out. The effectiveness of the EC-MAC protocol in terms of system energy efficiency is examined in this study using the MATLAB simulation tool and compares the outcomes with other cooperative protocols like Modified Cooperative Access MAC Protocol (MCA-MAC) and Throughput and Energy aware Cooperative MAC Protocol (TEC-MAC) and the performance of WSNs employing the suggested EC-MAC protocol is examined in this research for both ideal and dynamic channel conditions. EC-MAC protocol achieved energy efficiency improvements of 20%, and 40% respectively, more than MCA-MAC and TEC-MAC protocols. The results indicated that EC-MAC protocol offers a higher level of energy efficiency for the WSN than other cooperative protocols currently in use.

On autoregressive and neural methods for massive-MIMO channel de-noising

In modern wireless communication systems, the Multiple-Input Multiple-Output (MIMO) technology allows to greatly increase power efficiency, the serving area, and the overall cell throughput through the use of the antenna array beamforming. Nevertheless, the MIMO systems require accurate channel state knowledge to apply correct precoding. In 5G Time Division Duplex (TDD) systems, the Channel State Information (CSI) is obtained via Sounding Reference Signals (SRS) transmitted by the User Equipment (UE). UEs have limited power capabilities and thus cannot achieve high Uplink (UL) Signal-to-Noise Ratio (SNR) on gNodeB (gNB) in large bandwidth. There are multiple techniques that can be applied to improve the accuracy of Channel Estimation (CE) in noisy conditions. In this paper, we describe a classical method, namely the Vector Autoregression (VAR) with adaptive model order estimation, as well as a modern Deep Neural Network (DNN) approach for the massive-MIMO channel estimation de-noising problem. The developed methods and signal pre and postprocessing steps are described, followed by their performance evaluation in a set of realistic simulations. The designed algorithms provide results outperforming the baseline spatio-temporal windowing approaches by approximately equal to 2dB effective Downlink (DL) Signal-to-Interference-plus-Noise Ratio (SINR) metric in single and multi-user MIMO scenarios. Extensive simulation results demonstrate the robustness of the developed methods to the dynamic channel conditions.

Performance enhancement of RIS-assisted MRR-UOWC systems using the spectral-power-efficient [formula omitted]QAM-MPPM

Reconfigurable Intelligent Surfaces (RISs), proposed as innovative units, possess the potential to establish reliable connections in Underwater Optical Wireless Communication (UOWC) systems, where direct transmission (DT) experiences significant weakening due to dynamic channel conditions. In this paper, we examine the performance of a UOWC system consisting of an RIS and a modulating retroreflector (MRR), referred to as the RIS-assisted MRR-UOWC system. The channel is affected by a combination of path loss, gamma–gamma (G–G) turbulence, and pointing error (PE). The considered system employs the spectral-power-efficient L-ary Quadrature Amplitude Modulation Multi-Pulse Pulse-Position Modulation (LQAM-MPPM) technique. For our system performance measures, we present upper-bound (UB) and approximate UB expressions for the average bit-error rate (BER) of LQAM-MPPM signals. These expressions are used to analyze the system’s performance. Performance metrics are established by deriving the Probability Density Function (PDF), Cumulative Distribution Function (CDF), and Moment-Generating Function (MGF) for the end-to-end signal-to-noise ratio (SNR), resulting from the sum of products of independent but not identically distributed (i.ni.d) channel coefficients. The results of our study indicate that the LQAM-MPPM scheme surpasses traditional schemes.

Adaptive Combined Channel Network Coding for Cognitive Radio Networks with Cooperative Relay

Cognitive radio is one of the new technologies for 4G/5G applications, so cooperative relay communication and network coding are considered as certain methods to help improve their re-spective applications. A primary broadcast system for multimedia video streaming applications that broad¬casts data to primary users and an auxiliary cooperative relay secondary cognitive ra-dio system are considered. The secondary cooperative overlay system can use multiple error control coding methods for point-to-point data retransmission, such as channel coding, network coding, and combi¬ned coding methods to improve system performance under variable link con-ditions. A new technique of adaptive coding of AC2NC combined channel network for data re-transmission is proposed. The new AC2NC first analyzes the channel feedback information and then selects the best retransmission coding technique based on the target bandwidth or trans-mission time optimization. This is instead of using a single static channel or a network coding technique with dynamic channel conditions. The proposed AC2NC improves system through-put, reduces retransmission time, and provides more spectrum access opportunities for the sec-ondary sys¬tem's own data transmissions. AC2NC's relative throughput and time saving capabil-ities for cognitive radio users have been shown to exceed ninety percent, under certain channel conditions, compared to some static coding methods.

TCP and MP-TCP in mmWave 5G Networks

Future 5G networks will likely include mmWave radio access communication links, because of their potential multi-gigabit-per-second capacity. However, these frequencies are characterized by very dynamic channel conditions which lead to wide fluctuations in the received signal quality. This article explains how the end-to-end user experience in mobile mmWave networks could be affected by a sub-optimal interaction between the most widely used transport protocol, TCP, and mmWave links. It also provides insights on the throughput-latency trade-off when Multipath TCP (MP-TCP) is used judiciously across various links (e.g., LTE and mmWave).

Joint Task Offloading and Resource Allocation for Intelligent Reflecting Surface-Aided Integrated Sensing and Communication Systems Using Deep Reinforcement Learning Algorithm.

This paper investigates an intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework to cope with the problem of spectrum scarcity and poor wireless environment. The main goal of the proposed framework in this work is to optimize the overall performance of the system, including sensing, communication, and computational offloading. We aim to achieve the trade-off between system performance and overhead by optimizing spectrum and computing resource allocation. On the one hand, the joint design of transmit beamforming and phase shift matrices can enhance the radar sensing quality and increase the communication data rate. On the other hand, task offloading and computation resource allocation optimize energy consumption and delay. Due to the coupled and high dimension optimization variables, the optimization problem is non-convex and NP-hard. Meanwhile, given the dynamic wireless channel condition, we formulate the optimization design as a Markov decision process. To tackle this complex optimization problem, we proposed two innovative deep reinforcement learning (DRL)-based schemes. Specifically, a deep deterministic policy gradient (DDPG) method is proposed to address the continuous high-dimensional action space, and the prioritized experience replay is adopted to speed up the convergence process. Then, a twin delayed DDPG algorithm is designed based on this DRL framework. Numerical results confirm the effectiveness of proposed schemes compared with the benchmark methods.

Deep Learning-Powered Beamforming for 5G Massive MIMO Systems

In this study, a ResNeSt-based deep learning approach to beamforming for 5G massive multiple-input multiple-output (MIMO) systems is presented. The ResNeSt-based deep learning method is harnessed to simplify and optimize the beamforming process, consequently improving performance and efficiency of 5G and beyond communication networks. A study of beamforming capabilities has revealed potential to maximize channel capacity while minimizing interference, thus eliminating inherent limitations of the traditional methods. The proposed model shows superior adaptability to dynamic channel conditions and outperforms traditional techniques across various interference scenarios.

Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication

Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobility and frequent signal blockages cause link outages. To ensure connectivity in a relay-aided vehicular network, the relay selection policy should be designed to efficiently find unblocked relays. Inspired by recent advances in beam management in mobile millimeter wave (mmWave) networks, this paper addresses the question: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how can the best relay be selected with minimal overhead from beam management?</i> In this regard, we formulate a sequential decision problem to jointly optimize relay selection and beam management. We propose a joint relay selection and beam management policy based on deep reinforcement learning (DRL) using the Markov property of beam indices and beam measurements. The proposed DRL-based algorithm learns time-varying thresholds that adapt to the dynamic channel conditions and traffic patterns. Numerical experiments demonstrate that the proposed algorithm outperforms baselines without prior channel knowledge. Moreover, the DRL-based algorithm can maintain high spectral efficiency under fast-varying channels.

AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning

This paper investigates the problem of age of information (AoI) aware radio resource management for a platooning system. Multiple autonomous platoons exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the cooperative awareness messages (CAMs) to their followers while ensuring timely delivery of safety-critical messages to the Road-Side Unit (RSU). To lower the computational load at the RSU and cope with the challenges of dynamic channel conditions, we exploit a distributed resource allocation framework based on multi-agent reinforcement learning (MARL), where each platoon leader (PL) acts as an agent and interacts with the environment to learn its optimal policy. Motivated by the existing literature in RL, we propose two novel MARL frameworks based on the multi-agent deep deterministic policy gradient (MADDPG), named Modified MADDPG, and Modified MADDPG with task decomposition. Both algorithms train two critics with the following goals: A global critic which estimates the global expected reward and motivates the agents toward a cooperating behavior and an exclusive local critic for each agent that estimates the local individual reward. Furthermore, based on the tasks each agent has to accomplish, in the second algorithm, the holistic individual reward of each agent is decomposed into multiple sub-reward functions where task-wise value functions are learned separately. Numerical results indicate our proposed algorithms' effectiveness compared with other contemporary RL frameworks, e.g., federated reinforcement learning (FRL) in terms of AoI performance and CAM message transmission probability.

Reinforcement-Learning-Based Robust Resource Management for Multi-Radio Systems

The advent of the Internet of Things (IoT) has triggered an increased demand for sensing devices with multiple integrated wireless transceivers. These platforms often support the advantageous use of multiple radio technologies to exploit their differing characteristics. Intelligent radio selection techniques allow these systems to become highly adaptive, ensuring more robust and reliable communications under dynamic channel conditions. In this paper, we focus on the wireless links between devices equipped by deployed operating personnel and intermediary access-point infrastructure. We use multi-radio platforms and wireless devices with multiple and diverse transceiver technologies to produce robust and reliable links through the adaptive control of available transceivers. In this work, the term ‘robust’ refers to communications that can be maintained despite changes in the environmental and radio conditions, i.e., during periods of interference caused by non-cooperative actors or multi-path or fading conditions in the physical environment. In this paper, a multi-objective reinforcement learning (MORL) framework is applied to address a multi-radio selection and power control problem. We propose independent reward functions to manage the trade-off between the conflicting objectives of minimised power consumption and maximised bit rate. We also adopt an adaptive exploration strategy for learning a robust behaviour policy and compare its online performance to conventional methods. An extension to the multi-objective state–action–reward–state–action (SARSA) algorithm is proposed to implement this adaptive exploration strategy. When applying adaptive exploration to the extended multi-objective SARSA algorithm, we achieve a 20% increase in the F1 score in comparison to one with decayed exploration policies.

A Deep MARL-Based Power-Management Strategy for Improving the Fair Reuse of UWSNs

Providing qualified and fair communications for underwater wireless sensor networks (UWSNs) has garnered considerable interest in light of scarce resources and dynamic channel conditions. Fairness is critical in various situations, including emergency communications in resource-constrained underwater networks that balance load and energy amongst nodes to optimize network performance. Existing solutions for fair communications, on the other hand, frequently come at the expense of network capacity. This paper focuses on the power management strategy that jointly optimizes the reuse, fairness, and capacity of UWSNs while also proposing a new metric for fair spatial reuse in networks: the fair reuse index. We observe that the fair reuse index for UWSNs is strongly reliant on the network density, channel conditions, and application requirements. As a result, UWSNs commonly exhibit load imbalance and struggle to meet required network lifetimes. Towards this end, we propose DMPM, a Deep Multi-agent reinforcement learning-based Power Management strategy for increasing the fair reuse of UWSNs. DMPM strives to maximize the network’s fair reuse while allowing for gentle network capacity decrease. In two representative communication scenarios, numerical results demonstrate that DMPM achieves a significantly better trade-off between network capacity and fair reuse than baseline techniques. We also construct three reward functions for DMPM and discuss how different reward functions affect node behaviors. Delivery delays of different models are discussed. We hope that the work provided in this study will prove to be invaluable in the design and optimization of UWSNs.

Slicing-based resource optimization in multi-access edge network using ensemble learning aided DDPG algorithm

Recently, the technological development in edge computing and content caching can provide high-quality services for users in the wireless communication networks. As a promising technology, multi-access edge computing (MEC) can offload tasks to the nearby edge servers, which alleviates the pressure of users. However, various services and dynamic wireless channel conditions make effective resource allocation challenging. In addition, network slicing can create a logical virtual network and allocate resources flexibly among multiple tenants. In this paper, we construct an integrated architecture of communication, computing and caching to solve the joint optimization problem of task scheduling and resource allocation. In order to coordinate network functions and dynamically allocate limited resources, this paper adopts an improved deep reinforcement learning (DRL) method, which fully jointly considers the diversity of user request services and the dynamic wireless channel conditions to obtain the mobile virtual network operator (MVNO) maximal profit function. Considering the slow convergence speed of the DRL algorithm, this paper combines DRL and ensemble learning. The simulation result shows that the resource allocation scheme inspired by DRL is significantly better than the other compared strategies. The output of the result of DRL algorithm combined with ensemble learning is faster and more cost-effective.

A Graph Convolution Network Based Adaptive Cooperative Spectrum Sensing in Cognitive Radio Network

The hidden node problem is one of the most challenging issue in Cooperative Spectrum Sensing (CSS). The system models adopted by the existing Deep Learning-based spectrum sensing methods have not focused on modeling the hidden node scenario in cognitive radio networks. Further, these methods are unable to adapt to the dynamic channel conditions in the wireless environment since they have not considered the effect of fading environment. Motivated from these limitations, we propose GCN-CSS, a novel Graph Convolution Network (GCN) based cooperative spectrum sensing methodology which adapts to the dynamic changes in the Cognitive Radio Network. To the best of the author's knowledge, this is the first work to apply GCN for solving CSS problem. We have considered a practical system model which handles the dynamic channel condition i.e. SUs with multiple antennas experiencing different fading models with different fading severity. We have also catered the scenario of imperfect reporting channel between the SUs and the fusion centre along with the imperfect sensing channel to prove the robustness of the proposed model. With sufficient simulations, the superiority of the proposed methodology is proven in different dynamic scenarios of the wireless environment.

Compact MIMO System Performances in Metallic Enclosures

In this work, we present a 2 × 2 near-field multi-input multiple-output (MIMO) prototype for bit-error-rate (BER) and error vector magnitude (EVM) measurements in a metal enclosure. The near-field MIMO prototype was developed using software-defined-radios (SDRs) for over-the-air transmission of QPSK modulated baseband waveforms. We checked the near-field MIMO BER and EVM measurements in three different scenarios in a highly reflecting metal enclosure environment. In the first scenario, the line-of-sight (LOS) communication link was investigated when the mode stirrer was stationary. In the stationary channel conditions, near-field MIMO BER and EVM measurements are performed. In the second scenario, BER and EVM measurements were performed in dynamic channel conditions when the mode stirrer was set to move continuously. In the third scenario, LOS communication near-field MIMO BER and EVM measurements were performed in stationary channel conditions but now in the presence of MIMO interference. In three different scenarios, near-field MIMO BER and EVM measurements were investigated at different Tx USRP gain values and in the presence of varying levels of MIMO interference.

Federated Deep Reinforcement Learning Based Task Offloading with Power Control in Vehicular Edge Computing.

Vehicular edge computing (VEC) is a promising technology for supporting computation-intensive vehicular applications with low latency at the network edges. Vehicles offload their tasks to VEC servers (VECSs) to improve the quality of service (QoS) of the applications. However, the high density of vehicles and VECSs and the mobility of vehicles increase channel interference and deteriorate the channel condition, resulting in increased power consumption and latency. Therefore, we proposed a task offloading method with the power control considering dynamic channel interference and conditions in a vehicular environment. The objective is to maximize the throughput of a VEC system under the power constraints of a vehicle. We leverage deep reinforcement learning (DRL) to achieve superior performance in complex environments and high-dimensional inputs. However, most conventional methods adopted the multi-agent DRL approach that makes decisions using only local information, which can result in poor performance, while single-agent DRL approaches require excessive data exchanges because data needs to be concentrated in an agent. To address these challenges, we adopt a federated deep reinforcement learning (FL) method that combines centralized and distributed approaches to the deep deterministic policy gradient (DDPG) framework. The experimental results demonstrated the effectiveness and performance of the proposed method in terms of the throughput and queueing delay of vehicles in dynamic vehicular networks.

Dynamic power and bandwidth allocation for DVB‐based LEO satellite systems

A low Earth orbit (LEO) satellite constellation could be used to provide network coverage for the entire globe. This study considers multi-beam frequency reuse in LEO satellite systems. In such a system, the channel is time-varying due to the fast movement of the satellite. This study proposes an efficient power and bandwidth allocation method that employs two linear machine learning algorithms and take channel conditions and traffic demand (TD) as input. With the aid of a simple linear system, the proposed scheme allows for the optimum allocation of resources under dynamic channel and TD conditions. Additionally, efficient projection schemes are added to the proposed method so that the provided capacity is best approximated to TD when TD exceeds the maximum allowable system capacity. The simulation results show that the proposed method outperforms existing methods.

Sequential Detection and Estimation of Multipath Channel Parameters Using Belief Propagation

This paper proposes a belief propagation (BP)-based algorithm for sequential detection and estimation of multipath component (MPC) parameters based on radio signals. Under dynamic channel conditions with moving transmitter/receiver, the number of MPCs, the MPC dispersion parameters, and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for sequential detection and estimation of MPC dispersion parameters, and represent it by a factor graph enabling the use of BP for efficient computation of the marginal posterior distributions. At each time step, a snapshot-based parametric channel estimator provides parameter estimates of a set of MPCs which are used as noisy measurements by the proposed BP-based algorithm. It performs joint probabilistic data association, and estimation of the time-varying MPC parameters and the mean number of false alarm measurements, by means of the sum-product algorithm rules. The algorithm also exploits amplitude information enabling the reliable detection of "weak" MPCs with very low component signal-to-noise ratios (SNRs). The performance of the proposed algorithm compares well to state-of-the-art algorithms for high SNR MPCs, but it significantly outperforms them for medium or low SNR MPCs. Results using real radio measurements demonstrate the excellent performance of the proposed algorithm in realistic and challenging scenarios.