Time-varying Fading Channels Research Articles

A Novel Domain Transformation Based on Iterative Rotation for Linear Time-Variant Fading Channels

A Novel Domain Transformation Based on Iterative Rotation for Linear Time-Variant Fading Channels

Optimization of Vehicular Edge Computing Under Time-Varying Fading Channels With Path Prediction

Optimization of Vehicular Edge Computing Under Time-Varying Fading Channels With Path Prediction

Inter-Numerology Interference in Mixed Numerology OFDM Systems in Time-Varying Fading Channels With Phase Noise

Mixed numerology has been adopted in the orthogonal frequency division multiplexing-based physical layer of 5G new radio (NR) to serve diverse use cases and services. Since the subcarriers of different numerologies have different bandwidths and symbol durations, they interfere with each other despite being centered at different frequencies. We analyze the inter-numerology interference (INI) encountered by these systems in wideband time-varying channels in the presence of phase noise. We derive novel expressions for the fading-averaged INI power at each subcarrier as a function of the channel’s power delay profile. These lead to insightful, tight bounds for the bandwidth-averaged INI power, which bring out the combined impact of Doppler spread and phase noise. Our comprehensive approach applies to the entire family of numerologies of 5G NR, and accounts for partial subcarrier loading, guard bands, and non-line-of-sight and line-of-sight channels. Our results show that INI affects high-rate modulation and coding schemes (MCSs). To mitigate its impact, we propose a novel statistical square root power allocation scheme that exploits the variation in the average INI powers across the subcarriers. It achieves a lower block error rate than uniform power allocation, which is used in 5G NR.

Improved Dynamic Event-Triggered Control for Nonlinear Systems with Fading Channels

This paper is devoted to the protocol-based control design for nonlinear systems with fading channels. Distinguished from the existing event-triggering criteria associated with only two consecutive packets, an improved dynamic event-triggered protocol is formulated, which accounts for some historical available transmitted packets. As a consequence, the number of triggering times can be reduced efficiently while maintaining desired control performance. Meanwhile, the time-varying fading channel is described by a Markov process within finite space, whose mode can be detected via a hidden Markov mode detector. Based on Lyapunov stability theory, sufficient conditions are derived to achieve the stochastic stability of the closed-loop system. In the end, the validity of the derived results is verified through an application study.

Power-Delay-Profile-Based MMSE Channel Estimations for OFDM Systems

This paper deals with the problem of the channel estimation of orthogonal frequency division multiplexing (OFDM) signals transmitted through a time-varying fading channel. We show that an accurate power delay profile (PDP) estimator can be obtained by utilizing the cyclic redundancy induced by a cyclic prefix (CP), which can be applicable to OFDM systems with insufficient pilot or training symbols (IEEE 802.11p/WAVE system), and then a least mean square error (MMSE) channel estimation scheme can be obtained in a manner based on the estimated PDP. The simulation results highlight the benefit of the proposed methods compared with the state-of-the-art standard and three achievable performance bounds.

OTFS-Aided RIS-Assisted SAGIN Systems Outperform Their OFDM Counterparts in Doubly Selective High-Doppler Scenarios

The recently developed reconfigurable intelligent surfaces (RISs) are capable of improving the coverage of space–air–ground integrated networks (SAGINs), where the signals can be reflected in the desired direction without relying on power-thirsty radio-frequency (RF) chains. However, in the face of the substantially increased Doppler frequency, the classic orthogonal frequency-division multiplexing (OFDM) becomes inadequate in supporting RIS for the following reasons. First, the detrimental doubly selective fading leads to intersymbol interference (ISI) and intercarrier interference (ICI), which result in error floors for OFDM operating in the time–frequency (TF) domain. Second, it is far from trivial to configure RIS based on the time-varying fading channels. Third, the interpolation-based TF-domain channel estimation methods become impractical for the high-Doppler and high-dimensional RIS systems. Against this background, in this article, we propose the powerful 2-D orthogonal time–frequency space (OTFS) modulation for RIS-aided SAGINs, which transforms the time-varying fading encountered in the TF-domain to the time-invariant fading in the delay-Doppler (DD) domain. More explicitly, first, for the first time in the literature, we devise the DD-domain channel model of RIS-assisted SAGINs in the face of doubly selective fading. Second, in order to facilitate the RIS configuration in the DD-domain, we propose to create “virtual” Doppler frequencies that guide the phase changes at the RIS, even though the RIS phase rotations do not suffer from Doppler effects. Third, we conceive an attractive DD-domain RIS channel estimation method that can support both OFDM and OTFS, where the TF-domain interpolation is eliminated. Our simulation results demonstrate that the proposed DD-domain RIS configuration and channel estimation methods for both OFDM and OTFS are capable of mitigating the error floors encountered in the TF-domain. Furthermore, our simulation results confirm that OTFS-based RIS-assisted SAGIN systems are capable of outperforming their OFDM counterparts and exhibit excellent performance across a wide range of SAGIN channel parameters including the Ricean K factor, Doppler frequency, delay spread, coverage distance, and carrier frequency.

Joint Antenna and Device Scheduling in Full-Duplex MIMO Wireless-Powered Communication Networks

In this article, we study a joint antenna and Internet of Things device (ID) scheduling problem in the full-duplex (FD) multiple-input–multiple-output (MIMO) wireless-powered communication network (WPCN) over time-varying fading channels. We first formulate an optimization problem to maximize the average sum rate of IDs while satisfying their minimum average data rate requirements by jointly scheduling ID selection for uplink data transmission, antenna switching, and beamforming. To deal with the problem, we propose a scheduling algorithm based on Lagrangian duality and the stochastic optimization theory. The proposed scheduling algorithm necessitates solving per-time-slot problems, each of which aims at maximizing the weighted sum of the selected ID’s uplink data rate and the nonselected IDs’ harvested power from the downlink by jointly optimizing ID selection, antenna switching between uplink and downlink, and beamforming at that time slot. To solve the per-time-slot problem, we develop a joint ID selection, antenna switching, and beamforming (Joint-IAB) algorithm based on the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Through simulation, we demonstrate that our scheduling algorithm with the proposed Joint-IAB algorithm provides better performance than the other scheduling algorithms while well satisfying the given minimum average data rate requirements of IDs.

Online Cognitive Data Sensing and Processing Optimization in Energy-Harvesting Edge Computing Systems

Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with a wireless device (WD) replenishing its battery by means of energy harvesting (EH) and opportunistically accessing the licensed spectrum of an overlaid primary communication link to offload its sensing data to an MEC server (MS) for edge processing. Under time-varying fading channel, random energy arrivals, and stochastic ON-OFF state of the primary link, we aim to design an online algorithm to jointly control the cognitive data sensing rate and processing method (i.e., local and edge processing) without knowing future system information. In particular, we aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MS and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure related to the current energy state, secondary queueing backlogs and primary link activity. Simulation results collaborate with our analysis and demonstrate more than 46.7% data sensing rate improvement of the proposed PLySE over representative benchmark methods.

Deep Channel Prediction: A DNN Framework for Receiver Design in Time-Varying Fading Channels

In time-varying fading channels, channel coefficients are estimated using pilot symbols that are transmitted every coherence interval. For channels with high Doppler spread, the rapid channel variations over time will require considerable bandwidth for pilot transmission, leading to poor throughput. In this paper, we propose a novel receiver architecture using deep recurrent neural networks (RNNs) that learns the channel variations and thereby reduces the number of pilot symbols required for channel estimation. Specifically, we design and train an RNN to learn the correlation in the time-varying channel and predict the channel coefficients into the future with good accuracy over a wide range of Dopplers and signal-to-noise ratios (SNR). The proposed training methodology enables accurate channel prediction through the use of techniques such as teacher-force training, early-stop, and reduction of learning rate on plateau. Also, the robustness of prediction for different Dopplers and SNRs is achieved by adapting the number of predictions into the future based on the Doppler and SNR. Numerical results show that good bit error performance is achieved by the proposed receiver in time-varying fading channels. We also propose a data decision driven receiver architecture using RNNs that further reduces the pilot overhead while maintaining good bit error performance.

Low-Complexity Dynamic Resource Scheduling for Downlink MC-NOMA Over Fading Channels

Author(s): Kim, Do-Yup; Jafarkhani, Hamid; Lee, Jang-Won | Abstract: In this paper, we investigate dynamic resource scheduling (i.e., joint user, subchannel, and power scheduling) for downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems over time-varying fading channels. Specifically, we address the weighted average sum rate maximization problem with quality-of-service (QoS) constraints. In particular, to facilitate fast resource scheduling, we focus on developing a very low-complexity algorithm. To this end, by leveraging Lagrangian duality and the stochastic optimization theory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby the original problem is decomposed into a series of subproblems, one for each time slot. Accordingly, resource scheduling works in an online manner by solving one subproblem per time slot, making it more applicable to practical systems. Then, we further develop a heuristic joint subchannel assignment and power allocation (Joint-SAPA) algorithm with very low computational complexity, called Joint-SAPA-LCC, that solves each subproblem. Finally, through simulation, we show that our Joint-SAPA-LCC algorithm provides good performance comparable to the existing Joint-SAPA algorithms despite requiring much lower computational complexity. We also demonstrate that our opportunistic MC-NOMA scheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works well while satisfying given QoS requirements.

Performance Assessment of OTFS Modulation in High Doppler Airborne Communication Networks

Emerging 5G and future 6G mobile networks are expected to cater for high mobility scenarios ranging from vehicle-to-vehicle communications to unmanned aerial vehicles and airborne platforms. Communications in this type of deployments suffer from severe Doppler shifts which require new modulation waveforms. Orthogonal time frequency space (OTFS) modulation has recently been proposed as a promising technology for coping with high Doppler channels. OTFS converts a time-varying fading channel into a time-independent channel in the two-dimensional delay-Doppler (DD) domain. The transmit symbols are multiplexed into a nearly constant channel with a complex channel gain in the DD domain. In this paper, we consider a high Doppler airborne communication network where relative mobile node speeds can be above 1200 m/s. The considered system represents a mobile ad-hoc network where the airborne mobile nodes can join or leave the network. Furthermore, each node is equipped with an antenna array that supports directed communication among mobile nodes. The Doppler shifts in this airborne communication network are in the order of 52-72 kHz and may potentially be even higher depending on the selected carrier frequency and the relative speed among the airborne platforms. As such, OTFS modulation is used in this work to efficiently compensate for the high Doppler shifts in the DD domain. In particular, a comprehensive performance assessment in terms of bit error rate (BER) is conducted to reveal the potential of OTFS modulation in dealing with such extreme transmission scenarios. The impact of physical layer parameters, number of delay-Doppler bins in the DD domain used for OTFS modulation, directed versus two-ray channels, and the combination of multiple-input multiple-output (MIMO) systems with OTFS modulation on the BER is assessed. It is shown that both OTFS modulation over a two-ray channel as well as MIMO-OTFS modulation provide a reliable airborne communication network with low BER.

Parity-Check Coding Transmit Diversity for Wireless Communications With High Mobility

Highmobility is an important scenario for the 5G wireless communications. However, the high mobility results in serious time-varying fading, bringing challenges to the reliabilities of wireless transmission. Diversity technique is an essential method to overcome the effect of fading. It is noted that an LDPC (low-density parity-check) code is a concatenation of single parity-check codes (SPC) and repetition codes (REP) by a random interleaver, and a REP can be viewed as a typical example for achieving time/frequency diversity. Thus, it is appealing to jointly consider LDPC codes and diversity techniques. This paper presents parity-check coding transmit diversity (PCTD) schemes based on half-rate invertible LDPC codes, which are capable of achieving both diversity and coding gains from the time-varying fading channels. Besides, we investigate two types of PCTD schemes, and take into consideration two transmission modes, which are single-carrier (SC) and orthogonal frequency division multiplexing (OFDM) transmission modes. A chase combining based message passing algorithm (CC-MPA) is proposed for the proposed schemes, which can take full advantages of signal processing and coding gains. Furthermore, the Shannon limits of time-varying fading channels with BPSK signaling are deduced. Simulation results show that the proposed PCTD with CC-MPA algorithm can provide better BER performances than the classical time-domain diversity scheme in time-varying fading channels, which is appealing for the high mobility communications.

2E3-3 Underwater acoustic communication performance of space-frequency diversity applying maximum ration combining with maximum likelihood estimation in time varying fading channel by the movement of underwater vehicle

2E3-3 Underwater acoustic communication performance of space-frequency diversity applying maximum ration combining with maximum likelihood estimation in time varying fading channel by the movement of underwater vehicle

EM-EKF fast time-varying channel estimation based on superimposed pilot for high mobility OFDM systems

The scarcity of wireless spectrum resources and high mobility have brought serious challenges to orthogonal frequency division multiplexing (OFDM) communication systems. To improve spectral efficiency and combat fast time-varying channel fading, this paper studies the OFDM system with high mobility based on superimposed pilots, and adopts a basis expansion model (BEM) to describe fast time-varying channels. An iterative channel estimation algorithm combining expectation–maximization (EM) and extended Kalman filtering (EKF) is proposed, which is called EM-EKF in this paper. It uses EM to calculate the parameter matrix, measured values and the covariance of the process noise of the unknown first-order time-varying autoregressive (TVAR) model to obtain the optimal estimation. To further enhance the system’s robustness for fast time-varying channel fading, EKF and smoothing algorithms are jointly used for time-varying channel tracking. The results show that compared with the existing representative channel estimation algorithms, the iterative EM-EKF algorithm proposed in this paper not only solves the problem of unknown prior information, but also significantly outperforms other non-iterative algorithms, and the performance of the proposed algorithm is similar to that of the iterative EKF algorithm, which is more suitable for the actual OFDM communication systems with high mobility.

Self-Interference Channel Training for Full-Duplex Massive MIMO Systems.

Full-duplex (FD) is a promising technology for increasing the spectral efficiency of next-generation wireless communication systems. A major technical challenge in enabling FD in a real network is to remove the self-interference (SI) caused by simultaneous transmission and reception at the transceiver, and the SI cancellation performance depends significantly on the estimation accuracy of the SI channel. In this study, we proposed a novel partial SI channel training method for minimizing the residual SI power for FD massive multiple-input multiple-output (MIMO) systems. Based on an SI channel training framework under a limited training overhead, using the proposed scheme, the BS estimates only a part of the SI channel vectors, while skipping the channel training for the other remaining SI channel vectors by using their last estimates. With this partial training framework, the proposed scheme finds the optimal partial SI channel training strategy for pilot allocation to minimize the expected residual SI power, considering the time-varying Rician fading channel model for the SI channel. Therefore, the proposed scheme can improve the sum-rate performance compared with other simple partial training schemes for FD massive MIMO systems under a limited training overhead. Numerical results confirm the effectiveness of the proposed scheme for FD massive MIMO systems compared with the full training scheme, as well as other partial training schemes.

Performance Simulation of Underwater Acoustic Spread Spectrum Communication Based on LDPC Code

As we all know, the underwater acoustic channel is a time-varying, space-changing, and frequency-varying fading channel with limited bandwidth and severe noise interference. This paper studies and analyzes the main factors restricting the transmission of underwater acoustic channels, and establishes an underwater acoustic multipath time-varying fading channel. The channel coding adopts Quasi-Cyslic Low-Density Parity-Check Codes (QC-LDPC), combined with spread spectrum technology and high-order modulation technology, and utilizes Log-likelihood Ratios Belief Propagation (LLR-BP) is decoded, the simulation results show that LDPC codes have stronger error correction capabilities in underwater acoustic spread spectrum communication systems, ensuring that the system can operate reliably under low SNR conditions.

A Hybrid DQN and Optimization Approach for Strategy and Resource Allocation in MEC Networks

We consider a multi-user multi-server mobile edge computing (MEC) network with time-varying fading channels and formulate an offloading decision and resource allocation problem. To solve this mixed-integer non-convex problem, we propose two hybrid approaches that learn offloading strategy with DQN (opt-DQN) or Q-table (opt-QL) at each user equipment (UE). The communication resources are allocated with an optimization algorithm at each computational access point (CAP). We also propose a pure DQN method that learns both the offloading strategy and resource allocation via Q-learning (QL). We analyze the convergence behavior of the QL-based algorithms from a game-theoretical perspective and demonstrate the performance of the proposed hybrid approaches for different network sizes. The simulation results show that the hybrid approaches reach lower costs than other baseline algorithms and the pure-DQN approach. Moreover, the performance of the pure-DQN approach degrades severely as the network size increases, while opt-DQN still performs the best, followed by opt-QL. These observations demonstrate that the hybrid approach that combines the advantages of both QL and convex optimization is a promising design for a multi-user MEC network, wherein complicated offloading and resource allocation strategies need to be determined in a timely and accurate fashion.

Joint Antenna and User Scheduling in the Massive MIMO System Over Time-Varying Fading Channels

Massive multiple-input multiple-output (MIMO) technology, mainly equipped with dozens or even hundreds of antennas at transmitter and/or receiver, is one of the most important technologies in 5G/6G era due to its capability of achieving high transmission rate. However, since each transmit antenna typically needs a complete radio frequency (RF) chain, a large number of RF chains need to be installed accordingly, resulting in high economic cost, high hardware complexity, and high power consumption. To resolve these problems, architectures where a small number of RF chains are installed have been proposed in recent years, and an antenna selection technique that activates antennas only as many as the number of RF chains has been envisioned as one of solutions. In this paper, we study the joint antenna and user scheduling problem for the downlink massive MIMO system over time-varying fading channels to maximize the weighted average sum rate while ensuring users’ minimum average data rate requirements. To solve the problem, we first develop an opportunistic joint antenna and user scheduling algorithm (OJAUS) using the dual and stochastic subgradient methods, which makes it possible to schedule antennas and users without any underlying distributions of the fading channels. However, it requires solving a joint antenna and user selection (JAUS) problem to maximize the instantaneous weighted sum rate in every time slot. Thus, we additionally develop a simple heuristic JAUS algorithm with low computational complexity, called JAUS-LCC, which is executed in every time slot within OJAUS. Finally, through simulation results, we first show that our JAUS-LCC provides near-optimal performance despite requiring very low computational complexity, and then show that our OJAUS with JAUS-LCC well guarantees given minimum average data rate requirements.

Accumulated Polar Feature-Based Deep Learning for Efficient and Lightweight Automatic Modulation Classification With Channel Compensation Mechanism

In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the modulation types without handshaking. Thus, it plays an important role in future intelligent modems. The emerging deep learning (DL) technique stores intelligence in the network, resulting in superior performance over traditional approaches. However, conventional DL-based approaches suffer from heavy training overhead, memory overhead, and computational complexity, which severely hinder practical applications for resource-limited scenarios, such as Vehicle-to-Everything (V2X) applications. Furthermore, the overhead of online retraining under time-varying fading channels has not been studied in the prior arts. In this work, an accumulated polar feature-based DL with a channel compensation mechanism is proposed to cope with the aforementioned issues. Firstly, the simulation results show that learning features from the polar domain with historical data information can approach near-optimal performance while reducing training overhead by 99.8 times. Secondly, the proposed neural network-based channel estimator (NN-CE) can learn the channel response and compensate for the distorted channel with 13% improvement. Moreover, in applying this lightweight NN-CE in a time-varying fading channel, two efficient mechanisms of online retraining are proposed, which can reduce transmission overhead and retraining overhead by 90% and 76%, respectively. Finally, the performance of the proposed approach is evaluated and compared with prior arts on a public dataset to demonstrate its great efficiency and lightness.

Wireless Powered Mobile Edge Computing: Dynamic Resource Allocation and Throughput Maximization

Wireless powered mobile edge computing (WP-MEC) has been widely studied as a promising technology to liberate wireless terminals from the computation-intensive and energy-consuming tasks. This article considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a time slotted WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For the DTM algorithm, we characterize a throughput-backlog trade-off of [ <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/V)$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(V)$</tex-math></inline-formula> ] to indicate that the system throughput goes up as the queue backlog increases, where <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> is a control parameter between the system throughput and the queue backlog. However, we find that, as <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog (i.e., <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(V)$</tex-math></inline-formula> ). To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of [ <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/V)$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$\mathcal {O}((\log (V))^2)$</tex-math></inline-formula> ] between the system throughput and the queue backlog. The simulation results demonstrate that IDTM retains close system throughput to DTM with only <inline-formula><tex-math notation="LaTeX">$\mathcal {O}((\log (V))^2)$</tex-math></inline-formula> queue backlog.