Fast Fading Channels Research Articles

Reordered Amplitude Phase Shift Keying Aided Differential Spatial Modulation: DFDD-Based Low-Complexity Detector and Performance Analysis Over Fading Channels

Differential spatial modulation (DSM) is a multiple input multiple output (MIMO) wireless transmission technique that achieves additional data bit transmission by utilizing antenna activation indexes. Combined with amplitude phase shift keying (APSK) constellations, the spectrum efficiency of DSM system can be further improved. However, the theoretically optimal maximum likelihood (ML) detection suffers from a high complexity that grows exponentially with the number of transmit antennas (TAs). Besides, the imperfect channel state information in fast fading channels leads to a severe deterioration of the detection performance. To address these problems, we propose a novel transmission scheme named reordered amplitude phase shift keying aided differential spatial modulation (RAPSK-DSM) and its low-complexity decision-feedback differential detection (DFDD) based detection algorithm. The proposed RAPSK-DSM scheme provides a better solution for addressing the error propagation problem and its detector utilizes the preceding channel gain vectors from previous detection results to mitigate the performance loss caused by time-selective channel fading. Furthermore, by decomposing and pre-computing the detection process, the complexity of proposed detector is effectively reduced due to the avoidance of redundant computations. Numerical analyses and simulation results show that the proposed RAPSK-DSM system is able to achieve substantial bit error rate (BER) performance improvement under fast fading channel conditions at the expense of low complexity.

Joint Task Offloading and Resource Allocation for IoT Edge Computing With Sequential Task Dependency

Incorporating mobile-edge computing (MEC) in the Internet of Things (IoT) enables resource-limited IoT devices to offload their computation tasks to a nearby edge server. In this article, we investigate an IoT system assisted by the MEC technique with its computation task subjected to sequential task dependency, which is critical for video stream processing and other intelligent applications. To minimize energy consumption per IoT device while limiting task processing delay, task offloading strategy, communication resource, and computation resource are optimized jointly under both slow and fast-fading channels. In slow fading channels, an optimization problem is formulated, which is nonconvex and involves one integer variable. To solve this challenging problem, we decompose it as a 1-D search of task offloading decision problem and a nonconvex optimization problem with task offloading decision given. Through mathematical manipulations, the nonconvex problem is transformed to be a convex one, which is shown to be solvable only with the simple Golden search method. In fast-fading channels, optimal online policies depending on the instant channel state are derived even though they are entangled. In addition, it is proved that the derived policy will converge to the offline policy when the channel coherence time is low, which can help save extra computation complexity. Numerical results verify the correctness of our analysis and the effectiveness of our proposed strategies over the existing methods.

Reliable and Secure Deep Learning-Based OFDM-DCSK Transceiver Design Without Delivery of Reference Chaotic Sequences

Chaos-based communications can be applied to high-speed vehicular information transmissions thanks to the anti-jamming and anti-interference capabilities of chaotic transmissions. In traditional practical chaotic systems, reference chaotic signals are required to be delivered to remove complex chaotic synchronization circuits. However, the direct transmission of reference signals will degrade the security performances, while interferences and noises imposed on the reference signals due to imperfect channel conditions will deteriorate the reliability performances. In order to enhance the reliability and security performances over vehicular channels such as the railway channel and the channels undergoing fast fadings, in this paper, we propose a deep learning (DL) aided intelligent OFDM-DCSK transceiver. In this design, no reference chaotic signals are delivered, and we propose to utilize the time-delay neural network (TDNN) to learn the chaotic maps, followed by the long short-term memory (LSTM) units to extract and exploit the correlations between chaotic modulated signals, and multiple fully connected layers (FCLs) to estimate the user bit data. With the aid of the constructed deep neural network (DNN), after the offline neural network training, the receiver can recover the transmitted information with lower bit error rate (BER) and enhance security performances. Theoretical performance is then analyzed for the proposed intelligent transceiver. Simulation results validate the proposed design, and demonstrate that the intelligent DL-based OFDM-DCSK system can achieve better BER and security performances over fast fading and railway channels compared with the benchmark systems.

Constellation Design for Non-Coherent Fast-Forward Relays to Mitigate Full-Duplex Jamming Attacks

With potential applications to short-packet communication, we address communication of low-latency messages in fast-fading channels under the presence of a reactive jammer. Unlike a traditional jammer, we assume a full-duplex (FD) jammer capable of detecting pre-existing countermeasures and subsequently changing the target frequency band. To facilitate reliable communication amidst a strong adversary, we propose non-coherent fast-forward full-duplex relaying scheme wherein the victim uses a helper in its vicinity to fast-forward its messages to the base station, in addition to ensuring that the countermeasures are undetected by the FD adversary. Towards designing the constellations for the proposed scheme, we identify that existing non-coherent constellation for fast-fading channels are not applicable owing to the cooperative nature of the fast-forward scheme. As a result, we formulate an optimization problem of designing the non-coherent constellations at the victim and the helper such that the symbol-error-probability at the base station is minimized. We theoretically analyze the optimization problem and propose several strategies to compute near-optimal constellations based on the helper’s data-rate and fast-forwarding abilities. We show that the proposed constellations provide near-optimal error performance and help the victim evade jamming. Finally, we also prove the scheme’s efficacy in deceiving the countermeasure detectors at the jammer.

A Network-Assisted Game-Theoretic Design to Power Control in Autoregressive Fading Channels

Several previous works have proposed game-theoretic approaches to controlling the pilot and data power levels in the uplink of both single and multi-cell multi-user multiple input multiple output (MU-MIMO) systems. Unfortunately, the vast majority of existing works design these power control schemes under the assumption that the wireless channels between the mobile terminals and the serving base station are block fading. Meanwhile, several letters have shown that modeling fast fading channels as autoregressive (AR) processes with known or estimated state transition matrices give much more accurate results than those suggested by block fading models. Thus, this letter proposes a game-theoretic approach to controlling the uplink pilot and data power levels in a MU-MIMO system, in which the wireless channels are AR processes with mobile terminal-specific state transition matrices. We find that the proposed approach outperforms a classical cellular path-loss compensating fractional power control scheme and a game-theoretic power control scheme designed for block fading channels.

Free-Space Continuous-Variable Quantum Key Distribution with Imperfect Detector against Uniform Fast-Fading Channels

Free-space continuous-variable quantum key distribution based on atmospheric laser communications is expected to play an important role in the global continuous-variable quantum key distribution network. The practical homodyne detector model is applied in free-space continuous-variable quantum key distribution which models the imperfect characteristics including the detection efficiency and the electronic noise. In the conventional model, we must calibrate them simultaneously. In the modified model, only one of the imperfections needs to be calibrated to simplify the calibration process of the practical experiments, also known as one-time calibration. The feasibility of the modified detector model against the fast-fading channel is proved. The results of the symmetry operations are considered when presenting detailed security analysis. Some remarkable features of the uniform fast-fading channel were found from the simulation results. The performances of the conventional model and the modified model are similar but the modified model has the advantage of achieving one-time calibration.

Generalized Receiver Designs for OFDM Systems with Alamouti Decoding in Fast Fading Channels

In the present paper, the generalized receiver designs for orthogonal frequency-division multiplex-ing (OFDM) systems that exploit the Alamouti transmit diversity technique are addressed. In Alamouti space-time coded OFDM systems, the simple Alamouti decoding at the generalized receiver relies on the assumption that the channels do not change over an Alamouti codeword period (two consecutive OFDM symbol periods). Unfortunately, when the channel is fast fading, the assumption is not met, resulting in severe performance deg-radation. In the present paper, a sequential decision feedback sequence estimation (SDFSE) scheme based on the generalized receiver with an adaptive threshold, a traditionally single-carrier equalization technique, is used to mitigate the performance degradation. A new method to set the threshold value is proposed. For small signal constellations like binary phase-shift keying (BPSK) and quadrature phase shift-keying (QPSK), the SDFSE generalized receiver with the adaptive threshold requires much lower complexity than a previous minimum me-an square error (MMSE) approach based on the generalized receiver at the cost of small performance degradati-on. Furthermore, we show that the performance difference becomes smaller when the channel estimation error is included. Adaptive effort sequence estimation (AESE) scheme based on the generalized receiver is also pro-posed to further reduce the average complexity of the SDFSE generalized receiver scheme with the adaptive threshold. The AESE generalized receiver scheme is based on the observation that a high Doppler frequency does not necessarily mean significant instantaneous channel variations. Simulations demonstrate the efficacy of the proposed SDFSE generalized receiver with the adaptive threshold and AESE generalized receiver schemes.

Enabling Efficient Blockage-Aware Handover in RIS-Assisted mmWave Cellular Networks

Recently, networks operate at frequencies over 28 GHz (mmWave) have emerged as a viable solution for 5G mobile networks to provide Gbps data rate. Due to the high directivity and attenuation of mmWave signals, mmWave communication links are highly vulnerable to the frequent mmWave channel blockages, which can trigger excessive handovers. Thanks to its ability to enrich the scattering environment and create reflective signal multipaths, Reconfigurable Intelligent Surface (RIS) has great potential to counter the blockage effect and thus greatly reduce the number of unnecessary handovers. However, this potential has not been well explored. In this paper, we propose a RIS-assisted handover scheme by leveraging deep reinforcement learning (DRL). Under various channel blockage conditions, the DRL agent manages to reduce the cumulative handover overhead by jointly adjusting beamformers and RIS phase shifts. Compared with the existing schemes without considering RIS, the RIS-assisted handover scheme significantly reduces the number of handovers and achieves higher spectrum efficiency. Besides, to alleviate the impact from the limited observations of the fast fading channels, we propose a lightweight algorithm to sense the blockage status and such sensing results can be utilized to improve the performance of model training. Numerical results show that DRL agent is able to further improve the performance when integrated with the blockage status sensing algorithm.

Time-Domain Channel Estimation Scheme for OFDM over Fast Fading Channels

In high-mobility scenarios, the time variation of mobile radio channels leads to a loss of orthogonality among subcarriers in orthogonal frequency division multiplexing (OFDM) systems, resulting in intercarrier interference (ICI) and performance deterioration. Conventional channel estimation schemes are usually based on pilot tones, which are distributed in each OFDM symbol to estimate the channel variation. Hence, the channel estimator itself suffers from ICI. In this study, a new estimation scheme, which does not suffer from ICI, is proposed to estimate the channel variation within OFDM symbols. The main idea is to zero-pad (ZP) the OFDM symbol in the time domain. Then, in the middle of the ZP interval, an impulse signal is inserted as a pilot sample, which is used to estimate the channel at the pilot signal in the OFDM symbol. Finally, a linear model is used to estimate the channel variation over an OFDM symbol. Additionally, we derive the mean squared error (MSE) of the proposed estimation technique under the constraint that the channel varies linearly within OFDM symbols. Simulation results show that our scheme can achieve a substantial improvement in the bit error rate (BER) performance of OFDM, in spite of the OFDM symbol length being increased. Moreover, in many cases, the new scheme can achieve the same BER performance as the perfect knowledge of channel state information (CSI). Theoretical analysis and numerical simulations show that our scheme achieves excellent performance with much lower computational complexity.

A DFDD Based Detector for Space-Time Block Coded Differential Spatial Modulation Under Time-Selective Channels

Space-time block coded differential spatial modulation (STBC-DSM) is a recently proposed spectral efficient multiple-input-multiple-output (MIMO) transmission technique. Existing detection algorithms for STBC-DSM are designed for quasi-static channels, which limits the application of STBC-DSM in fast fading channels. In this letter, we propose a novel decision-feedback differential detection (DFDD) based detector for STBC-DSM system under fast-fading channels. The proposed detector mitigates the impact of channel vector changes by using the DFDD metric which contains multiple previous detection results. Simulation results show that the proposed detector is more tolerable in time-selective channels than other existing STBC-DSM detectors.

Stability Conditions for Remote State Estimation of Multiple Systems Over Semi-Markov Fading Channels

This work studies remote state estimation of multiple linear time-invariant systems over shared wireless time-varying communication channels. We model the channel states by a semi-Markov process which captures both the random holding period of each channel state and the state transitions. The model is sufficiently general to be used in both fast and slow fading scenarios. We derive necessary and sufficient stability conditions of the multi-sensor-multi-channel system in terms of the system parameters. We further investigate how the delay of the channel state information availability and the holding period of channel states affect the stability. In particular, we show that, from a system stability perspective, fast fading channels may be preferable to slow fading ones.

Selection Square Combining: A Fully-Distributed Radio Source Detector for Fast Fading Channels Based on Energy Measurements

In this article, we tackle the problem of fully-distributed detection of a radio source emitting a signal in a fast fading scenario. We consider that geographically distributed sensor nodes obtain energy measurements and compute cooperatively and in a distributed fashion a decision metric for detecting if the source is present or absent without the need of a central node. Since we assume that the sensing channels -from the source to the nodes- vary rapidly due to the nodes and/or the source mobility, we derive an algorithm based on the average probability density function (PDF) of the measurements with respect to the channel PDF. The derived model has some unknown parameters (like the source transmitted energy or the channel mean attenuation). Therefore, we follow the philosophy of the Generalized Likelihood Ratio Test (GLRT) theory and obtain two test statistics suitable for practical scenarios: i) the S-GLRT statistic, and ii) the selection square combining (SSC) statistic. We show that SSC is asymptotically equivalent to S-GLRT in the low signal-to-noise ratio regime, and compute its theoretical performance in that scenario. Finally, we compare the algorithms' performance in finite regimes through Monte Carlo simulations. We show that the two proposed algorithms offer similar performance, and are capable of exceeding that of other algorithms which are frequently used in fast fading scenarios.

Adaptive Transmission With Frequency-Domain Precoding and Linear Equalization Over Fast Fading Channels

In this paper, the emerging orthogonal time frequency space (OTFS) modulation is firstly restructured as a precoded orthogonal frequency division multiplexing (OFDM) system, so that the well-established frequency-domain approach can be applied to perform signal in fast fading channels. Then a frequency-domain minimum mean squared error (MMSE) equalizer for OTFS is introduced and its performance is analyzed based on the eigenvalue decomposition of the channel matrix. Inspired by the frequency-domain precoding structure, an adaptive transmission scheme with frequency-domain precoding matrix composed of the eigenvectors of the channel matrix is proposed to improve the system performance under MMSE equalization, and its optimized performance is derived with simple expression. Finally, considering two extreme channel conditions, the lower and upper bounds for the diversity performance of the adaptive transmission scheme are derived. Simulation results show that the proposed adaptive transmission achieves significantly better performance for short signal frames and can work well with imperfect channel state information (CSI). The derived performance bounds can serve as benchmarks for OTFS and other precoded OFDM systems.

Fast Fading Channel Estimation for OFDM Systems with Complexity Reduction

Channel estimation plays a significant role in the Inter-carrier interference (ISI) mitigation and symbol detection in Orthogonal frequency division multiplexing (OFDM) systems. As the speed of mobile terminal increases, the channel changes rapidly, causing fast fading which will degrade the system performance. In this paper, a low complexity fast fading channel estimation method suited for scattered pilot OFDM systems is proposed. This method is characterized by estimating channel matrix directly in the frequency domain, which can significantly reduce the channel estimation complexity with good system performance. Simulation results verify its superiority over the existing estimation methods.

Capacity and BER analysis of BCH and LDPC coded FSO communication system for different channel conditions

Free space optical (FSO) communication is of paramount importance for the design of next-generation 5G+ wireless networks. These are, however, susceptible to degrading effects of atmospheric turbulence. The use of error correcting codes (ECCs) can mitigate the effects of fading caused by atmospheric turbulence. We evaluate the performance of uncoded and coded FSO communication system, where we use Bose Chaudhuri Hocquenghem (BCH) and low-density parity-check codes for the coded system. Both the systems under consideration are based on intensity-modulation and direct detection. We investigate the performance of a coded communication system under various channel conditions, viz., slow fading and fast fading gamma–gamma channels for different turbulence regimes and subsequently, obtain the coding gain. We derive the expressions of channel capacity for a fast fading channel and outage capacity for a slow fading channel. We conclude that ECCs deteriorate the bit error rate performance in a slow fading channel. However, for this kind of channel, the combination of ECCs with an interleaver reduces the burst errors and improves the performance which leads to positive coding gain. In a fast fading channel, coding gain is not much and it remains almost same in different turbulence conditions. The analytical results are supported by simulation study.

Outage prediction for ultra-reliable low-latency communications in fast fading channels

The addition of redundancy is a promising solution to achieve a certain quality of service (QoS) for ultra-reliable low-latency communications (URLLC) in challenging fast fading scenarios. However, adding more and more redundancy to the transmission results in severely increased radio resource consumption. Monitoring and prediction of fast fading channels can serve as the foundation of advanced scheduling. By choosing suitable resources for transmission, the resource consumption is reduced while maintaining the QoS. In this article, we present outage prediction approaches for Rayleigh and Rician fading channels. Appropriate performance metrics are introduced to show the suitability for URLLC radio resource scheduling. Outage prediction in the Rayleigh fading case can be achieved by adding a threshold comparison to state-of-the-art fading prediction approaches. A line-of-sight (LOS) component estimator is introduced that enables outage prediction in LOS scenarios. Extensive simulations have shown that under realistic conditions, effective outage probabilities of 10^{-5} can be achieved while reaching up-state prediction probabilities of more than {90}{%}. We show that the predictor can be tuned to satisfy the desired trade-off between prediction reliability and utilizability of the link. This enables our predictor to be used in future scheduling strategies, which achieve the challenging QoS of URLLC with fewer required redundancy.

A Simple Cooperative Diversity Method Based on Deep-Learning-Aided Relay Selection

Opportunistic relay selection (ORS) has been recognized as a simple but efficient method for mobile nodes to achieve cooperative diversity in slow fading channels. However, the wrong selection of the best relay arising from outdated channel state information (CSI) in fast time-varying channels substantially degrades its performance. With the proliferation of high-mobility applications and the adoption of higher frequency bands in 5G and beyond systems, the problem of outdated CSI will become more serious. Therefore, the design of a novel cooperative method that is applicable to not only slow fading but also fast fading is increasingly of importance. To this end, we develop and analyze a deep-learning-aided cooperative method coined predictive relay selection (PRS) in this article. It can remarkably improve the quality of CSI through fading channel prediction while retaining the simplicity of ORS by selecting a single opportunistic relay so as to avoid the complexity of multi-relay coordination and synchronization. Information-theoretic analysis and numerical results in terms of outage probability and channel capacity reveal that PRS achieves full diversity gain in slow fading wireless environments and substantially outperforms the existing schemes in fast fading channels.

Optimal ADMM-Based Spectrum and Power Allocation for Heterogeneous Small-Cell Networks with Hybrid Energy Supplies

Powering cellular networks with hybrid energy supplies is not only environment-friendly but can also reduce the on-grid energy consumption, thus being emerging as a promising solution for green networking. Intelligent management of spectrum and power can increase the network utility in cellular networks with hybrid energy supplies, usually at the cost of higher energy consumption. Unlike prior studies on either the network utility maximization or on-grid energy cost minimization, this paper studies the joint spectrum and power allocation problem that maximizes the system revenue in a heterogeneous small-cell network with hybrid energy supplies. Specifically, the system revenue is considered as the difference between the network utility and on-grid energy cost. By developing the convexity of the optimization problem through transformation and reparameterization, we propose a joint spectrum and power allocation algorithm based on the primal-dual arguments to obtain the optimal solution by iteratively solving the primal and dual sub-problems of the convex optimization problem. To solve the primal sub-problem, we further propose the Lagrangian maximization based on the alternating direction method of multipliers (ADMM), and derive the optimal solution in the closed-form expression at each iteration. It is shown that the proposed joint spectrum and power allocation algorithm approaches the global optimality at the rate of 1=n with n being the number of iterations. Also, the proposed ADMM-based Lagrangian maximization algorithm approaches the primal optimal solution with the time complexity of O(1=ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> ) iterations with ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> being the termination parameter. Simulation results show that in comparison with the power control with equal frequency allocation algorithm and frequency allocation with equal power allocation algorithms the proposed algorithm increases the system revenue by over 20 and 60 percent without consuming more on-grid energy when the proportional fairness utility and the weighted sum rate utility are considered with the approximate system parameter settings, respectively. Meanwhile, in comparison with the full frequency reuse case, the proposed algorithm increases the system revenue by 20 percent at least in terms of the weighted sum rate utility, although it achieves the similar system revenue when considering the proportional fairness utility. Simulation results also show that our proposed algorithm can perform well under the realistic fast fading channel conditions.

Analysis of LDPC Codes in FSO Communication System Under Fast Fading Channel Conditions

The free-space optical (FSO) communication represents a promising futuristic technology due to several benefits, which has attracted significant attention recently for various applications. FSO communication is susceptible to fading due to atmospheric turbulence. FSO channel, being a slow fading channel, inherently does not allow the efficient use of error-correcting codes (ECCs). ECCs have shown to have good performance in a fast fading channel. A way to convert the slow fading channel into a fast fading channel is by using techniques like diversity. Diversity makes the bits pass through many channels simultaneously, and thus the fast fading channel effect is observed at the receiver. In this paper, we first derive the capacity and bit error rate performance of the low-density parity-check (LDPC) codes in a fast fading channel. We then study the performance of LDPC codes in receiver diversity systems. Finally, we see the impact of using LDPC coded multiple-input multiple-output (MIMO) systems in the FSO channel. We use regular (3,6) LDPC code and irregular LDPC codes of rate 0.5, where the desired degree distribution is optimized for the channel condition.

Outage Aware Power Control for Vehicular Wireless Energy Transfer Over Dynamic Channels

Wireless powered devices suffer from dynamic energy arrival in case of fast fading channel conditions caused by high mobility of the energy base station (BS), which may lead to energy outage for practical applications executed at the wireless device (WD). We use effective bandwidth theory to investigate the tail distribution of the WD's energy queue, from which the energy outage probability (EOP) is derived. Then, a power control problem is proposed to minimize the mobile vehicular BS's transmit power while guaranteeing the EOP for the WD, which can be equivalently addressed by solving a single equation. The match between simulation results and theoretical analysis demonstrates the validity of the proposed EOP analysis. Furthermore, the proposed scheme can reduce the power consumption of the vehicular BS while guaranteeing EOP requirement of the WD.