Quasi-static Channels Research Articles

Performance Analysis of SIMO–OFDM Systems With Pilot-Aided Channel Estimation

Pilot symbols are commonly modulated on uniformly spaced subcarriers to aid channel estimation, thereby facilitating data demodulation in practical orthogonal frequency-division multiplexing (OFDM) systems operating over quasi-static multipath channels, where the channel frequency response (CFR) remains static within one block. Aided by uniformly spaced pilot symbols, the direct least-square CFR estimation can enable reliable symbol-by-symbol data demodulation within the same OFDM block, based on the principle of generalized likelihood ratio test (GLRT). Such GLRT-based OFDM can be easily incorporated with single-input multiple-output (SIMO) antenna technique to enhance data demodulation over quasi-static channels exhibiting random multipaths. In this article, an upper bound to the average bit error probability (BEP) is analytically derived for this GLRT-based SIMO–OFDM system over quasi-static random multipath channels. The analysis is generally applicable to arbitrary two-dimensional component modulations when the quasi-static SIMO channel consists of independent but not necessarily identically distributed random channel paths with Rician or Rayleigh distributed path response magnitudes. The BEP upper bound facilitates the error performance characterization of the GLRT-based SIMO–OFDM system with respect to data-to-noise power ratio, data-to-pilot power ratio, antenna diversity, pilot density, and pilot power allocation.

Reinforcement Learning for Link Adaptation and Channel Selection in LEO Satellite Cognitive Communications

In this letter, we solve the link adaptation and channel selection problem in next generation satellite cognitive networks under dynamically varying channel availability and time-varying channel statistics. Primary user (PU) activity in Low Earth Orbit (LEO) satellite cognitive communications forces the set of available transmission channels for a secondary user (SU) to vary dynamically over time. We consider the scenario where the channel state varies in a piecewise-stationary mode, referred to as quasi-static (block-fading) channels. We formalize the problem as a reinforcement learning problem, and propose Discounted Structured and Sleeping Thompson Sampling (dSTS), which maximizes the SU’s throughput by selecting the optimum modulation and coding scheme (MCS) and the transmission channel under volatile and piecewise-stationary settings. When channel characteristics are unknown as well as piecewise-stationary, the proposed algorithm adapts the SU’s link-rate by exploiting the structure of the transmission success probability in transmission rates over the selected available channel. Furthermore, channel state information (CSI) is absent and feedback is limited to 1-bit (success/failure).

Performance of Quantized Massive MIMO With Fronthaul Rate Constraint Over Quasi-Static Channels

Performance of Quantized Massive MIMO With Fronthaul Rate Constraint Over Quasi-Static Channels

Physical Layer Key Generation for Secure OAM Communication Systems

In this paper, we propose a physical layer secret key (PLSK) generation scheme for secure encrypted transmissions in orbital angular momentum (OAM) communication systems which improves the secret key capacity via taking advantage of OAM orthogonal modes. Based on the line-of-sight (LoS) quasi-static channel conditions in OAM systems, we propose a mode-based active defense phase in the PLSK scheme to invalidate keys generated by an eavesdropper when it is extremely close to the legitimate user. Our simulation results highlight that the key information generated by the eavesdropper is almost zero in the case of highly correlated legitimate and eavesdropping channels under LoS conditions. In addition, our proposed OAM PLSK scheme achieves a higher key generation rate (KGR) than existing works since mode-based active defense can disrupt the process of PLSK by the eavesdropper.

Two-Stage Channel Estimation for Semi-Passive RIS-Assisted Millimeter Wave Systems

In a reconfigurable intelligent surface (RIS) assisted millimeter Wave (mmWave) communication system, the channel coefficient increases exponentially with the number of RIS elements which results in expensive pilot overhead. Most previous works have proposed some channel estimation algorithms for the estimation accuracy of cascaded channels, which have improved the estimation accuracy, but the pilot overhead is discouraging in the estimation process. To improve the channel estimation accuracy with reduced pilot overhead, we propose a two-stage channel estimation protocol by exploiting semi-passive elements and the coherent time difference of the channel, where the quasi-static channel between the base stations (BS) and RIS is estimated at the RIS, and the user (UE)-RIS time-varying channel is estimated at the BS. In the first stage, we formulate the BS-RIS channel estimation as a mathematical optimization problem by an iterative weighting method and then propose a gradient descent (GD)-based algorithm to solve it. In the second stage, we first transform the received the UE-RIS signal model into an equivalent parallel factor (PARAFAC) tensor model and estimate the UE-RIS channel by the least-squares (LS) algorithm. The simulation results show that the proposed method has better estimation accuracy than the LS, compression sensing (CS) and minimum mean square error (MMSE) methods with less pilot overhead, and the spectral efficiency is improved by at least 10.5% compared to the other three methods.

Goodput Maximization With Quantized Feedback in the Finite Blocklength Regime for Quasi-Static Channels

In this paper, we study a quantized feedback scheme to maximize the goodput of a finite blocklength communication scenario over a quasi-static fading channel. It is assumed that the receiver has perfect channel state information (CSI) and sends back the CSI to the transmitter over a resolution-limited error-free feedback channel. With this partial CSI, the transmitter is supposed to select the optimum transmission rate, such that it maximizes the overall goodput of the communication system. This problem has been studied for the asymptotic blocklength regime, however, no solution has so far been presented for finite blocklength. Here, we study this problem in two cases: with and without constraint on reliability. We first formulate the optimization problems and analytically solve them. Iterative algorithms that successfully exploit the system parameters for both cases are presented. It is shown that although the achievable maximum goodput decreases with shorter blocklengths and higher reliability requirements, significant improvement can be achieved even with coarsely quantized feedback schemes.

Transforming Fading Channel From Fast to Slow: Intelligent Refracting Surface Aided High-Mobility Communication

Intelligent reflecting/refracting surface (IRS) has recently emerged as a promising solution to reconfigure wireless propagation environment for enhancing the communication performance by tuning passive signal reflection or refraction. In this paper, we study a new IRS-aided high-mobility communication system by employing the intelligent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">refracting</i> surface with a high-speed vehicle to aid its passenger’s communication with a remote base station (BS). Due to the environment’s random scattering and vehicle’s high mobility, a rapidly time-varying channel is typically resulted between the static BS and fast-moving IRS/user, which renders the channel estimation for IRS with a large number of passive refracting elements more challenging, as compared to that for the conventional slow fading IRS channels with low-mobility users. In order to reap the high IRS passive beamforming gain with low channel training overhead, we propose a new and efficient two-stage transmission protocol to achieve both IRS channel estimation and refraction optimization for data transmission. Specifically, by exploiting the quasi-static channel between the IRS and user both moving at the same high speed as well as the line-of-sight (LoS) dominant channel between the BS and IRS, the user first estimates the LoS component of the cascaded BS-IRS-user channel in Stage I, based on which IRS passive refraction is designed to maximize the corresponding IRS-refracted channel gain. Then, the user estimates the resultant IRS-refracted channel as well as the non-IRS-refracted channel in Stage II for setting an additional common phase shift at all IRS refracting elements so as to align these two channels for maximizing the overall channel gain for data transmission. Simulation results show that the proposed design can efficiently achieve the full IRS passive beamforming gain in the high-mobility communication scenario, which also converts the overall BS-user channel from fast to slow fading for more reliable transmission. The proposed on-vehicle IRS system is further compared with a baseline scheme of deploying fixed IRSs (intelligent reflecting surfaces) on the roadside to assist high-speed vehicular communications, which achieves significant rate improvement due to its greatly saved channel training time.

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.

Sparse, Group-Sparse, and Online Bayesian Learning Aided Channel Estimation for Doubly-Selective mmWave Hybrid MIMO OFDM Systems

Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier's quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequency-domain (FD) correlation of the channel's frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis.

Towards the optimisation of direct laser acceleration

Experimental measurements using the OMEGA EP laser facility demonstrated direct laser acceleration (DLA) of electron beams to (505 ± 75) MeV with (140 ± 30) nC of charge from a low-density plasma target using a 400 J, picosecond duration pulse. Similar trends of electron energy with target density are also observed in self-consistent two-dimensional particle-in-cell simulations. The intensity of the laser pulse is sufficiently large that the electrons are rapidly expelled along the laser pulse propagation axis to form a channel. The dominant acceleration mechanism is confirmed to be DLA and the effect of quasi-static channel fields on energetic electron dynamics is examined. A strong channel magnetic field, self-generated by the accelerated electrons, is found to play a comparable role to the transverse electric channel field in defining the boundary of electron motion.

Optimum Averaging of Superimposed Training Schemes in OFDM Under Realistic Time-Variant Channels

The current global bandwidth shortage in orthogonal frequency division multiplexing (OFDM)-based systems motivates the use of more spectrally efficient techniques. Superimposed training (ST) is a candidate in this regard because it exhibits no information rate loss. Additionally, it is very flexible to deploy and it requires low computational cost. However, data symbols sent together with training sequences cause an intrinsic interference. Previous studies, based on an oversimplified channel (a quasi-static channel model) have solved this interference by averaging the received signal over the coherence time. In this paper, the mean square error (MSE) of the channel estimation is minimized in a realistic time-variant scenario. The optimization problem is stated and theoretical derivations are presented to attain the optimum amount of OFDM symbols to be averaged. The derived optimal value for averaging is dependent on the signal-to-noise ratio (SNR) and it provides a better MSE, of up to two orders of magnitude, than the amount given by the coherence time. Moreover, in most cases, the optimal number of OFDM symbols for averaging is much shorter, about 90% reduction of the coherence time, thus it provides a decrease of the system delay. Therefore, these results match the goal of improving performance in terms of channel estimation error while getting even better energy efficiency, and reducing delays.

Channel Estimation for Reconfigurable Intelligent Surface-Assisted Wireless Communications Considering Doppler Effect

In wireless systems aided by reconfigurable intelligent surfaces (RISs), channel state information plays a pivotal role in achieving the performance gain of RISs. Mobility renders accurate channel estimation (CE) more challenging due to the Doppler effect. In this letter, we propose two practical wideband CE schemes incorporating Doppler shift adjustment (DSA) for multi-path and single-path propagation environments, respectively, for RIS-assisted communication with passive reflecting elements. For the multi-path scenario, ordinary CE is first executed assuming quasi-static channels, followed by DSA realized via joint RIS reflection pattern selection and transformations between frequency and time domains. The proposed CE necessitates only one more symbol incurring negligible extra overhead compared with the number of symbols required for the original CE. For the single-path case which is especially applicable to millimeter-wave and terahertz systems, a novel low-complexity CE method is devised capitalizing on the form of the array factors at the RIS. Simulation results demonstrate that the proposed algorithms yield high CE accuracy and achievable rate with low complexity, and outperform representative benchmark schemes.

Multimode Integer-Forcing Receivers for Block Fading Channels

Integer-forcing (IF) receivers are a class of linear multiple-input multiple-output (MIMO) receivers to achieve near-optimal performance under quasi-static channels, with almost the same complexity as that of conventional linear receivers. Instead of trying to recover the transmitted messages directly, an IF receiver recovers their integer-linear combinations corresponding to a chosen integer-valued effective channel matrix, and then obtains them. Recently, the IF strategy has been extended to a block fading scenario. In this paper, we propose a novel IF receiver for block fading channels, called a multimode IF (M-IF) receiver. Unlike conventional IF receivers, the proposed M-IF receiver employs several integer-valued effective channel matrices for IF decoding. We also propose an efficient search algorithm for selecting such matrices with the aid of Gaussian elimination. We then show that for any channel realizations, the proposed M-IF receiver has better performance in terms of the effective noise variance than conventional IF receivers. Simulation results demonstrate that it works really well in various environments.

Absolute Amplitude Differential Phase Spatial Modulation and Its Non-Coherent Detection Under Fast Fading Channels

Amplitude phase shift keying (APSK) aided differential spatial modulation (APSK-DSM) is a multiple-input-multiple-output wireless transmission technique which not only has the desirable features of differential spatial modulation (DSM) but also has a higher spectrum efficiency than DSM. However, the error propagation problem and the violation of the quasi-static channel assumption in conventional APSK-DSM design will cause significant performance loss, especially in undesirable channel conditions. Besides, the maximum-likelihood detection based on the traditional block-by-block search has a complexity that grows exponentially with the block size, which becomes intractable in systems with large transmit antenna numbers. To overcome those drawbacks, we propose a novel transmission scheme named the absolute amplitude differential phase spatial modulation (AADP-SM) in this paper. AADP-SM is able to alleviate the error propagation problem and achieve a near-coherent performance by invoking multiple previous blocks in the current detection. By taking into account the channel fading rate, AADP-SM is also more robust in fast-fading channels than APSK-DSM. A novel non-coherent detection algorithm is proposed for AADP-SM to reduce the exponential detection complexity to a polynomial order. Finally, we show through simulation that AADP-SM is tolerable to imperfect information about the channel statistics.

Improving Performance of Bell Labs Layered Space-Time Systems by Multiframe Space-Time Interleavers

Turbo-BLAST (T-BLAST) is a multiple-input multiple-output (MIMO) wireless communication system. It is based on three main concepts: Bell laboratories layered space-time (BLAST) architecture, random space-time interleaving, and the turbo principle. T-BLAST system has many advantages including high data rate, excellent performance, and low complexity. These advantages make T-BLAST one the most important choices for future wireless communication systems. In this article, the authors study the effect of the space-time interleaver on the performance of T-BLAST system. This interleaver is split into two separated stages, time interleaving followed by space interleaving. They show that, in a quasi-static channel, the enhancement in performance provided by the time interleavers is very small compared to the gain provided by space interleavers. They also propose a new technique to improve the system performance, called multiframe space-time interleaving (MUSTI). Simulation results show an improvement in performance up to 6 dB at BER=10-4 for a (2,2) configuration.

Fast Accurate Beam and Channel Tracking for Two-Dimensional Phased Antenna Arrays

The sparsity and the severe attenuation of millimeter-wave (mmWave) channel imply that highly directional communication is needed. The narrow beam produced by large array requires accurate alignment, which is difficult to achieve when serving fast-moving users. In this paper, we focus on accurate two-dimensional (2D) beam and channel tracking problem aiming at minimizing exploration overhead and tracking error. Using a typical frame structure with periodic exploration and communication, a proven minimum overhead of exploration is provided first. Then tracking algorithms are designed for three types of channels with different dynamic properties. It is proved that the algorithms for quasi-static channels and channels in Dynamic Case I are optimal in approaching the minimum Cramer-Rao lower bound (CRLB). The computational complexity of our algorithms is analyzed showing their efficiency, and simulation results verify their advantages in both tracking error and tracking speed.

Ring Compute-and-Forward Over Block-Fading Channels

The Compute-and-Forward protocol in quasi-static channels normally employs lattice codes based on the rational integers $\mathbb{Z}$, Gaussian integers $\mathbb{Z}\left[i\right]$ or Eisenstein integers $\mathbb{Z}\left[\omega\right]$, while its extension to more general channels often assumes channel state information at transmitters (CSIT). In this paper, we propose a novel scheme for Compute-and-Forward in block-fading channels without CSIT, which is referred to as Ring Compute-and-Forward because the fading coefficients are quantized to the canonical embedding of a ring of algebraic integers. Thanks to the multiplicative closure of the algebraic lattices employed, a relay is able to decode an algebraic-integer linear combination of lattice codewords. We analyze its achievable computation rates and show it outperforms conventional Compute-and-Forward based on $\mathbb{Z}$-lattices. By investigating the effect of Diophantine approximation by algebraic conjugates, we prove that the degrees-of-freedom (DoF) of the optimized computation rate is ${n}/{L}$, where $n$ is the number of blocks and $L$ is the number of users.

Performance analysis of physical layer security over Weibull/lognormal composite fading channel with MRC reception

With the advent of denser network and internet of things (IoT) applications for fifth generation (5G) communication, there is a need to understand and define the security limitations. This paper proposes closed-form expressions for different physical layer security (PLS) metrics for millimeter wave (mmWave) communication for the 5G systems and IoT applications. The information-theoretic formulation is considered in which licit users can impart their data over a quasi-static channel and eavesdroppers perceive their communication through another quasi-static channel, where the channel is represented as Weibull/lognormal (WL) composite fading channel in mmWave. To this end, the closed-form expressions for the probability of density function (PDF) and cumulative distribution function (CDF) are firstly derived. This set of results are then used to derive the closed-form expressions for the lower bound of secure outage probability (SOPL) and strictly positive secrecy capacity (SPSC) with maximal ratio combining (MRC) reception. In addition, average secrecy capacity (ASC) is also analyzed and expressed in closed-form. Furthermore, the derivation is extended for the performance metrics for the two eavesdroppers case to provide perspective for more complex yet practical situation.

Comparing Various Channel Estimation Techniques for OFDM Systems Using MATLAB

This paper compares the performance of various channel estimation techniques for OFDM systems over quasi-static channels using MATLab. It compares the performance of five channel estimation techniques, these are: decision directed (DD), linear interpolation, second-order interpolation, discrete Fourier transform (DFT) interpolation, minimum mean square error (MMSE) interpolation. The performance is evaluated in terms of two widely-used performance measures, namely, bit-error rate (BER) and the mean square error (MSE) for different levels of signal-to-noise ratio (SNR). The OFDM model is explained and implemented using MATLab to run different simulations. The simulation results demonstrate that the DD channel estimation provides the lowest BER and MSE as compared to interpolation techniques, at the cost of extra processing delay and comparatively sensitive to channel variations between OFDM symbols. Also, the MMSE interpolation outperforms all other interpolation techniques.

Sliced Sensing System: Toward 5G Cognitive Radio Applications Under Fast Time-Varying Channels

Cognitive radio technology is an effective tool to alleviate spectrum scarcity problems for future wireless communication systems. Spectrum sensing is the core technique of cognitive radio, because it directly influences the utilization efficiency of spectrum resources. However, the fifth generation communications will encounter many scenarios with fast time-varying channels, in which traditional sensing systems cannot maintain a satisfactory performance as that in traditional quasi-static channels. Motivated by this circumstance, in this paper, we propose a novel sensing system, i.e., sliced sensing system, to dedicatedly solve the challenge above. The proposed system includes an operational concept, an architecture, six specific core modules, and detailed workflows for all the modules. Numerical results are provided to testify the effectiveness of sliced sensing system. It demonstrates that the proposed system can obviously strengthen the sensing performance under fast time-varying channels. Furthermore, it also reveals that the sensing performance of the proposed system is robust, even with inaccurate channel information.