Time-varying Channel Characteristics Research Articles

A metasurface-based full-color circular auto-focusing Airy beam transmitter for stable high-speed underwater wireless optical communications

Due to its unique intensity distribution, self-acceleration, and beam self-healing properties, Airy beam holds great potential for optical wireless communications in challenging channels, such as underwater environments. As a vital part of 6G wireless network, the Internet of Underwater Things requires high-stability, low-latency, and high-capacity underwater wireless optical communication (UWOC). Currently, the primary challenge of UWOC lies in the prevalent time-varying and complex channel characteristics. Conventional blue Gaussian beam-based systems face difficulties in underwater randomly perturbed links. In this work, we report a full-color circular auto-focusing Airy beams metasurface transmitter for reliable, large-capacity and long-distance UWOC links. The metasurface is designed to exhibits high polarization conversion efficiency over a wide band (440-640 nm), enabling an increased data transmission rate of 91% and reliable 4 K video transmission in wavelength division multiplexing (WDM) based UWOC data link. The successful application of this metasurface in challenging UWOC links establishes a foundation for underwater interconnection scenarios in 6G communication.

Ratio Shift Keying Modulation for Time-Varying Molecular Communication Channels.

Molecular Communications (MC) is a bio-inspired communication technique that uses molecules to encode and transfer information. Many efforts have been devoted to developing novel modulation techniques for MC based on various distinguishable characteristics of molecules, such as their concentrations or types. In this paper, we investigate a particular modulation scheme called Ratio Shift Keying (RSK), where the information is encoded in the concentration ratio of two different types of molecules. RSK modulation is hypothesized to enable accurate information transfer in dynamic MC scenarios where the time-varying channel characteristics affect both types of molecules equally. To validate this hypothesis, we first conduct an information-theoretical analysis of RSK modulation and derive the capacity of the end-to-end MC channel where the receiver estimates concentration ratio based on ligand-receptor binding statistics in an optimal or suboptimal manner. We then analyze the error performance of RSK modulation in a practical time-varying MC scenario, that is mobile MC, in which both the transmitter and the receiver undergo diffusion-based propagation. Our numerical and analytical results, obtained for varying levels of similarity between the ligand types used for ratio-encoding, and varying number of receptors, show that RSK can significantly outperform the most commonly considered MC modulation technique, concentration shift keying (CSK), in dynamic MC scenarios.

Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks.

Traditional low earth orbit (LEO) satellite networks are typically independent of terrestrial networks, which develop relatively slowly due to the on-board capacity limitation. By integrating emerging mobile edge computing (MEC) with LEO satellite networks to form the business-oriented "end-edge-cloud" multi-level computing architecture, some computing-sensitive tasks can be offloaded by ground terminals to satellites, thereby satisfying more tasks in the network. How to make computation offloading and resource allocation decisions in LEO satellite edge networks, nevertheless, indeed poses challenges in tracking network dynamics and handling sophisticated actions. For the discrete-continuous hybrid action space and time-varying networks, this work aims to use the parameterized deep Q-network (P-DQN) for the joint computation offloading and resource allocation. First, the characteristics of time-varying channels are modeled, and then both communication and computation models under three different offloading decisions are constructed. Second, the constraints on task offloading decisions, on remaining available computing resources, and on the power control of LEO satellites as well as the cloud server are formulated, followed by the maximization problem of satisfied task number over the long run. Third, using the parameterized action Markov decision process (PAMDP) and P-DQN, the joint computing offloading, resource allocation, and power control are made in real time, to accommodate dynamics in LEO satellite edge networks and dispose of the discrete-continuous hybrid action space. Simulation results show that the proposed P-DQN method could approach the optimal control, and outperforms other reinforcement learning (RL) methods for merely either discrete or continuous action space, in terms of the long-term rate of satisfied tasks.

Deep reinforcement learning-based adaptive modulation for OFDM underwater acoustic communication system

Due to the time-varying and space-varying characteristics of the underwater acoustic channel, the communication process may be seriously disturbed. Thus, the underwater acoustic communication system is facing the challenges of alleviating interference and improving communication quality and communication efficiency through adaptive modulation. In order to select the optimal modulation mode adaptively and maximize the system throughput ensuring that the bit error rate (BER) meets the transmission requirements, this paper introduces deep reinforcement learning (DRL) into orthogonal frequency division multiplexing acoustic communication system. The adaptive modulation is mapped into a Markov decision process with unknown state transition probability. Thereby, the underwater communication channel environment is regarded as the state of DRL, and the modulation mode is regarded as action. The system returns channel state information (CSI) and signal–noise ratio in every time slot through the feedback link. Because the Deep Q-Network optimizes in the changing state space of each time slot, it is suitable for a variety of different CSI. Finally, simulations in different underwater environments (SWellEx-96) show that the proposed adaptive modulation scheme can obtain lower BER and improve the system throughput effectively.

Impact of Rotary-Wing UAV Wobbling on Millimeter-Wave Air-to-Ground Wireless Channel

Millimeter-wave rotary-wing (RW) unmanned aerial vehicle (UAV) air-to-ground (A2G) links face unpredictable Doppler effect arising from the inevitable wobbling of RW UAV. Moreover, the time-varying channel characteristics during transmission lead to inaccurate channel estimation, which in turn results in the deteriorated bit error probability performance of the UAV A2G link. This paper studies the impact of mechanical wobbling on the Doppler effect of the millimeter-wave wireless channel between a hovering RW UAV and a ground node. Our contributions of this paper lie in: i) modeling the wobbling process of a hovering RW UAV; ii) developing an analytical model to derive the temporal autocorrelation function (ACF) for the millimeter-wave RW UAV A2G link in a closed-form expression; and iii) investigating how RW UAV wobbling impacts the Doppler effect on the millimeter-wave RW UAV A2G link. Numerical results show that different RW UAV wobbling patterns impact the amplitude and the frequency of ACF oscillation in the millimeter-wave RW UAV A2G link. For UAV wobbling, the temporal ACF decreases quickly and the impact of the Doppler effect is significant on the millimeter-wave A2G link.

Non-stationary time-varying vehicular channel characteristics for different roadside scattering environments

With the deep integration of wireless communication technology and automobile industry, vehicular communication has become one of the key technologies supporting the development of Internet-of-vehicle. Due to the high-speed mobility of vehicles and the rapid change of the propagation environments, vehicle-to-vehicle (V2V) wireless communication channels are generally non-stationary. Meanwhile, the variability of V2V channel characteristics is obvious in different scattering environments. Focusing on these research points, this paper presents the analysis and comparison of V2V channel characteristics for different scattering scenarios based on a series of 5.9 GHz channel measurements. The measurement data are collected from the iron bridge, the soundproof wall, and the road lamp scenarios. The stationary time and frequency are investigated on the basis of method of local scattering functions. The classical channel characteristics, including power delay profile, Ricean K-factor, root means square (RMS) delay spread and RMS Doppler spread are extracted following the propagation principle. Furthermore, considering the source and birth-death process of multi-path components (MPCs) in different scattering propagation environments, cluster identification and statistical results are presented and compared. The different values of the channel parameters and the different performance of the channel under different scattering environments can help us understand the V2V channel deeply. The research results can be used for the design and optimization of vehicular communication systems in different scattering environments.

Two-Timescale Uplink Channel Estimation for Dual-Hop MIMO Relay Multi-User Systems

Multiple-input multiple-output (MIMO) relay systems have received great attention for the development of future wireless networks, since they can be invoked for improving system capacity and extending the coverage area. Considering the fixed base station (BS) and relay station (RS) and the highly mobile users (UEs), the dual-hop MIMO relay channel possesses the two-timescale characteristics, i.e., the UEs-RS channel varies in a short-timescale manner whereas the RS-BS channel varies in a long-timescale manner. In order to acquire the knowledge of all individual channels at the BS directly, we propose two-timescale channel estimation algorithms with one-stage training (OST) scheme based on the above time-varying channel characteristics. Specifically, the two-timescale channel estimation problem is firstly formulated as an approximate maximum likelihood (AML) problem. Then, we design batch algorithm to solve the resultant optimization problem. Moreover, to overcome the drawbacks of batch algorithm, a low-complexity online algorithm is also proposed based on the two-stage stochastic optimization framework. Finally, simulation results are provided to verify the effectiveness of the proposed two-timescale channel estimation algorithms.

Channel Estimation Based on Deep Learning in Vehicle-to-Everything Environments

Channel estimation in vehicle-to-everything (V2X) communications is a challenging issue due to the fast time-varying and non-stationary characteristics of wireless channel. To grasp the complicated variations of channel with limited number of pilots in the IEEE 802.11p systems, data pilot-aided (DPA) channel estimation has been widely studied. However, the error propagation in the DPA procedure, caused by the noise and the channel variation within adjacent symbols, limits the performance seriously. In this letter, we propose a deep learning based channel estimation scheme, which exploits a long short-term memory network followed by a multilayer perceptron network to solve the error propagation issue. Simulation results show that the proposed scheme outperforms currently widely-used DPA schemes for the IEEE 802.11p-based V2X communications.

Signal Detection in Uplink Time-Varying OFDM Systems Using RNN With Bidirectional LSTM

In this letter, we propose a deep learning-assisted approach for signal detection in uplink orthogonal frequency-division multiplexing (OFDM) systems over time-varying channels. In particular, we utilize a recurrent neural network (RNN) with bidirectional long short-term memory (LSTM) architecture to achieve signal detection. In addition, with the help of convolutional neural network (CNN) and batch normalization (BN), a new network structure CNN-BN-RNN Network (CBR-Net) is proposed to obtain better performance. The sequence feature information of the OFDM received signal is extracted from big data to successfully train a RNN-based signal detection model, which simplifies the architecture of OFDM systems and can adapt to the change of channel paths. Simulation results also demonstrate that the trained RNN model has the ability to recall the characteristics of wireless time-varying channels and provide accurate and robust signal recovery performance.

Physical-layer authentication based on adaptive Kalman filter for V2X communication

Security and authentication are an important guarantee for vehicle to everything (V2X). At present, V2X security schemes in most countries are based on public key infrastructure (PKI). These schemes require additional digital signatures when sending basic safety messages (BSMs), which means security requires additional overhead. Consequently, V2X communication efficiency will be affected in the case of traffic congestion. To solve this problem, we propose a physical-layer authentication model based on channel characteristics in the V2X environment. The model uses the Sage-Husa adaptive Kalman filter, which can dynamically adjust statistical properties of the noises according to observation. Furthermore, we select CSI and RSSI to design a physical-layer authentication scheme based on this model, which can replace the digital signature. Finally, we analyze the impact of the filter algorithm and the factors affecting the authentication threshold, and compare our work with similar schemes. As a result, the proposed scheme makes minor changes to the PKI-based V2X security and effectively reduces the overhead of security. Due to the time-varying channel characteristics, this scheme can prevent impersonation by malicious attackers, thereby improving the security of communication.

Joint Resource Allocation and Computation Offloading With Time-Varying Fading Channel in Vehicular Edge Computing

Vehicular edge computing (VEC) is considered as a novel paradigm to enhance the safety of automated vehicles and intelligent transportation systems (ITS). The computation offloading strategies are the key point of VEC, and the effect of time-varying channels cannot be ignored during the task transmission period. This paper investigates the utility maximization problem with task delay requirement constraints, in which the influence of time-varying channel on the task offloading strategies during the task offloading period is considered. The time-varying fading channel leads to the time-varying spectrum efficiency (SE), so the previous offloading strategies are questionable when the additional uncertain allocated bandwidth is taken into account. To deal with it, we first propose a linearization based Branch and Bound (LBB) algorithm to solve the fixed SE problem without considering the time-varying channel characteristics. Considering the complexity of the LBB algorithm, a closest rounding integer (CRI) algorithm is proposed to solve the fixed SE problem. Then, based on the resource allocation strategies of the fixed SE problem, we propose the LBB based computation offloading (LBBCO) algorithm and the CRI based computation offloading (CRICO) algorithm to solve the original problem for both the static tasks and dynamic tasks. The proposed LBBCO/CRICO algorithms are also applicable to multi-vehicle and multi-task scenarios. Furthermore, we analyze the effect of small-scale fading on the proposed offloading strategies. The simulation results show that the average utilities of LBBCO and CRICO algorithms have a small gap by 3.93% and 6.13% only to the upper bound, respectively. Meanwhile, the proposed LBBCO and CRICO algorithms can outperform the previous state-of-the-art solution by 4.52% and 2.38%, respectively.

Recognition of ships based on vector sensor and bidirectional long short-term memory networks

The previous methods of target recognition use feature parameters as the system input. Especially in a better acoustic field, a higher recognition rate can be obtained under the condition of obvious target features. But actually, the acoustic field is complex due to the multipath effect, the time-varying channel characteristics, the variety and complexity of environmental background noise and other factors, which leads to a difficulty in feature extraction. In this paper, a method named bi-LSTM is described and firstly used in the field of underwater acoustic to recognize ships without pre-extracting feature based on vector sensor. Then we compared bi-LSTM and LSTM with SVM and the impact on the recognition rate with some parameters. Experimental results of sea trial are given to demonstrate the robust adaptation. The accuracy of both bi-LSTM and LSTM are higher than SVM and the accuracy of bi-LSTM is higher than LSTM with a long training time. The recognition result of testing set can be received in few seconds which proves the real-time of the method.

Performance analysis of bit error rate of data link system under pulse LFM interference in time-varying rayleigh channel

In such mobile platforms as ships and aircraft, the detection and reconnaissance devices are near to the communication facilities. When working at the same time, they will create interference or jamming themselves. Especially, the high-power radar equipment tend to cause a high bit error rate (BER) in the data link system (DLS), with the result that the DLS cannot operate normally. For this reason, this paper makes a study of the BER performance of the DLS under the pulse linear frequency modulation (LFM) interference in the time-varying Rayleigh channel. In view of the the time-varying characteristics of channel in the practical conditions, the interference channel is modeled as a time-varying Rayleigh fading channel and the formula of BER is derived. Through simulation, the theoretical deduction is proved to be correct. This is of much importance to analyzing the variation of BER performance with the pulse width, power and doppler frequency shift under the pulse LFM interference.

Power Line Communications Networking Method Based on Hybrid Ant Colony and Genetic Algorithm

When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.

Deep Learning Based Channel Estimation Algorithm for Fast Time-Varying MIMO-OFDM Systems

Channel estimation is very challenging for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in high mobility environments with non-stationarity channel characteristics. In order to handle this problem, we propose a deep learning (DL)-based MIMO-OFDM channel estimation algorithm. By performing offline training to the learning network, the channel state information (CSI) generated by the training samples can be effectively utilized to adapt the characteristics of fast time-varying channels in the high mobility scenarios. The simulation results show that the proposed DL-based algorithm is more robust for the scenarios of high mobility in MIMO-OFDM systems, compared to the conventional algorithms.

A Geometry-Based Stochastic Channel Model for the Millimeter-Wave Band in a 3GPP High-Speed Train Scenario

This paper presents a geometry-based stochastic channel model (GSCM) for a 3GPP millimeter-wave (mmWave) high-speed train (HST) scenario. An important demand of the mmWave HST channel model is the spatial consistency during movement. In order to meet this requirement, the environment pattern containing the most effective geometry information is proposed, which is useful for generating an integral and accurate channel geometry. Since the measurement of a mmWave channel is deficient for high mobility, the proposed channel model is developed using a ray-tracing (RT) simulator that is validated with the channel measurements performed in the HST scenario at 93.2 GHz. The paper gives a full parameterization for the proposed model and its implementation for channel simulation. The verification is made through a comparison of the proposed model and the measurement-validated RT simulations in terms of the power delay profile, the Ricean $K$ -factor, and the second-order statistics for the delay and angle domains. Finally, for the design of mmWave HST communication systems, the time-varying channel characteristics are evaluated in the delay and Doppler domains.

Optimized Puncturing of the Check Matrix for Rate-Compatible LDPC Codes

In the wireless communication system, it is necessary to change the transmitted code word or code rate in real time according to the channel environment, designing and implementing rate compatible codes is of great practical significance. In allusion to the defects of algebraically constructed QC-LDPC codes algorithm and the time-varying characteristics of wireless channel, we propose an algorithm based on check matrix optimization puncturing technique to construct QC-LDPC codes in this paper. The algorithm selects a high rate matrix with excellent performance as the parent matrix, and then uses the backward node puncturing method to puncture the information bits in the code word from the last column of the check matrix by a multiple of the number of rows to obtain a low rate matrix. The simulation results show that the rate-compatible non-binary LDPC codes constructed by this algorithm not only reduces the complexity of hardware implementation, but also improves the error correction performance of the non-binary LDPC codes, realizes the rate-compatible coding at the same time, and improves the average effectiveness and reliability of the communication system.

Queueing Analysis of Unicast IPTV With Adaptive Modulation and Coding in Wireless Cellular Networks

Unicast IPTV services that can support live TV, video-on-demand (VoD), video conferencing, and online gaming applications over broadband wireless cellular networks have been becoming popular in recent years. However, video streaming services significantly impact the performance of wireless cellular networks because they are bandwidth hogs. To maintain the system performance, effective admission control and resource allocation mechanisms based on an accurate mathematical analysis are required. On the other hand, the quality of a wireless link usually changes with time due to the user mobility or time-varying channel characteristics. To counteract such time-varying channels and improve the spectral efficiency, adaptive modulation and coding (AMC) scheme can be adopted in offering unicast IPTV services for mobile users. In this paper, closed-form solutions for the bandwidth usage, blocking rate, and dropping rate of unicast IPTV services over wireless cellular networks were derived based on the novel queueing model that considers both user mobility and AMC. Simulations were also conducted to validate the accuracy of analytical results. Numerical results demonstrate that the presented analytical results are accurate. Based on the accurate closed-form solutions, network providers can implement precise admission control and resource allocation for their networks to enhance the system performance.

Energy efficiency enhancement in heterogeneous networks: a joint resource allocation approach

To support the drastic growth of wireless multimedia services and the requirements of ubiquitous access, numerous wireless infrastructures which consume enormous energy, such as macrocell, small cell, distributed antenna systems and wireless sensor networks, have been deployed. Under the background of environmental protection, improving the energy efficiency (EE) in wireless networks is becoming more and more important. In this paper, an EE enhancement scheme in heterogeneous networks (HetNets) by using a joint resource allocation approach is proposed. The HetNets consists of a mix of macrocell and small cells. Firstly, we model this strategic coexistence as a multi-agent system in which decentralized resource management inspired from Reinforcement Learning are devised. Secondly, a Q-learning based joint resource allocation algorithm is designed. Meanwhile, with the consideration of the time-varying channel characteristics, we take the long-term learning reward into account. At last, simulation results show that the proposed decentralized algorithm can approximate to centralized algorithm with low-complexity and obtain high spectral efficiency (SE) in the meantime.

Measuring the Time-Varying Channel Characteristics of the Plasma Sheath From the Reflected Signal

Due to high attenuation and large path loss, it is usually very difficult to measure the channel characteristics of the plasma sheath from the received signal. This paper proposes a novel method to measure them from the reflected signal. Using the finite-difference time-domain algorithm, electron density variation of the plasma sheath and its impact on the transmitted and reflected signals are investigated numerically. High correlations between these two signals are observed from both amplitude and phase, which enables us to online estimate one signal from the other for channel measurement and channel modeling conveniently. This would be very useful to further develop some channel adaptive communication techniques for alleviating the radio blackout problem.