Channel Estimation Research Articles

Doppler frequency analysis for sound-field sampling with moving microphones

Moving microphones allow for the fast acquisition of sound-field data that encode acoustic impulse responses in time-invariant environments. Corresponding decoding algorithms use the knowledge of instantaneous microphone positions for relating the dynamic samples to a positional context and solving the involved spatio-temporal channel estimation problem subject to the particular parameterization model. Usually, the resulting parameter estimates are supposed to remain widely unaffected by the Doppler effect despite the continuously moving sensor. However, this assumption raises issues from the physical point of view. So far, mathematical investigations into the actual meaning of the Doppler effect for such dynamic sampling procedures have been barely provided. Therefore, in this paper, we propose a new generic concept for the dynamic sampling model, introducing a channel representation that is explicitly based on the instantaneous Doppler shift according to the microphone trajectory. Within this model, it can be clearly seen that exact trajectory tracking implies exact Doppler-shift rendering and, thus, enables unbiased parameter recovery. Further, we investigate the impact of non-perfect trajectory data and the resulting Doppler-shift mismatches. Also, we derive a general analysis scheme that decomposes the microphone signal along with the encoded parameters into particular subbands of Doppler-shifted frequency components. Finally, for periodic excitation, we exactly characterize the Doppler-shift influences in the sampled signal by convolution operations in the frequency domain with trajectory-dependent filters.

Experimental investigations on the applicability of LASSO based sparse recovery for compressively sensed OFDM channel estimation

Experimental investigations on the applicability of LASSO based sparse recovery for compressively sensed OFDM channel estimation

Multiple-Antenna Aided Aeronautical Communications in Air-Ground Integrated Networks: Channel Estimation, Reliable Transmission, and Multiple Access

To provide seamless coverage during all flight phases, aeronautical communications systems (ACS) have to integrate space-based, air-based, as well as ground-based platforms to formulate aviation-oriented space-air-ground integrated networks (SAGINs). In continental areas, L-band aeronautical broadband communications (ABC) are gaining popularity for supporting air traffic management (ATM) modernization. However, L-band ABC faces the challenges of spectrum congestion and severe interference due to the legacy systems. To circumvent these, we propose a novel multiple-antenna aided L-band ABC paradigm to tackle the key issues of reliable and high-rate air-to-ground (A2G) transmissions. Specifically, we first introduce the development roadmap of the ABC. Furthermore, we discuss the peculiarities of the L-band ABC propagation environment and the distinctive challenges of the associated multiple-antenna techniques. To overcome these challenges, we propose an advanced multiple-antenna assisted L-band ABC paradigm from the perspective of channel estimation, reliable transmission, and multiple access. Finally, we shed light on the compelling research directions of the aviation component of SAGINs.

Channel Estimation by Transmitting Pilots From Reconfigurable Intelligent Surface

Channel Estimation by Transmitting Pilots From Reconfigurable Intelligent Surface

Advanced hybrid algorithms for precise multipath channel estimation in next-generation wireless networks

Multipath channels continue to present challenges in wireless communication for both 5G and 6G networks. A multipath channel is a phenomenon in wireless communications where signals traverse from the sender to the receiver along various paths. This end occurs due to the reflection, diffraction, and refraction of signals of various objects and structures in the environment. Such pathways can cause symbol interference in the transmitted signal, leading to communication issues. To this end, our paper proposes the integration of three algorithms: teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and artificial neural networks (ANN). This combination effectively analyzes and stabilizes the transmission channel, minimizing symbol interference. We have developed, simulated, and evaluated this hybrid approach for multipath fading channels. We apply it to various coding schemes, including tail-biting convolutional code, turbo codes, low-density parity-check, and polar code. Additionally, we have explored various decoding methods such as Viterbi, maximum logarithmic maximum a posteriori, minimum sum, and cyclic redundancy check soft cancellation list. Our study encompasses new channel equalization schemes and coding gains derived from simulations and mathematical analysis. Our proposed method significantly enhances channel equalization, reducing interference and improving error correction in wireless communication systems.

Non-binary codes approach on the performance of short-packet full-duplex transmissions

This paper illustrates the enhancement of the performance of short-packet full-duplex (FD) transmission by taking the approach of non-binary low density parity check (NB-LDPC) codes over higher Galois field. For the purpose of reducing the impacts of self-interference (SI), high order of modulation, complexity, and latency decoder, a blind feedback process composed of channels estimation and decoding algorithm is implemented. In particular, this method uses an iterative process to simultaneously suppress SI component of FD transmission, estimate intended channel, and decode messages. The results indicate that the proposed technique provides a better solution than both the NB-LDPC without feedback and the binary LDPC feedback algorithms. Indeed, it can significantly improve the performance of overall system in two important factors, which are bit-error-rate (BER) and <a name="_Hlk155778233"></a>mean square error (MSE), especially in high order of modulation. The suggested algorithm also shows a robustness in reliability and power consumption for both short-packet FD transmissions and high order modulation communications.

Codebook-Based Solutions for Reconfigurable Intelligent Surfaces and Their Open Challenges

Reconfigurable intelligent surfaces (RIS) is a revolutionary technology to cost-effectively improve the performance of wireless networks. We first review the existing framework of channel estimation and passive beamforming (CE & PBF) in RISassisted communication systems. To reduce the excessive pilot signaling overhead and implementation complexity of the CE & PBF framework, we conceive a codebook-based framework to strike flexible tradeoffs between communication performance and signaling overhead. Moreover, we provide useful insights into the codebook design and learning mechanisms of the RIS reflection pattern. Finally, we analyze the scalability of the proposed framework by flexibly adapting the training overhead to the specified quality-of-service requirements and then elaborate on its appealing advantages over the existing CE & PBF approaches. It is shown that our novel codebook-based framework can be beneficially applied to all RIS-assisted scenarios and avoids the curse of model dependency faced by its existing counterparts, thus constituting a competitive solution for practical RIS-assisted communication systems.

Unsupervised Learning for Ultra-Reliable and Low-Latency Communications With Practical Channel Estimation

Unsupervised Learning for Ultra-Reliable and Low-Latency Communications With Practical Channel Estimation

Adversarial Score-Based Generative Models for MMSE-Achieving AmBC Channel Estimation

Adversarial Score-Based Generative Models for MMSE-Achieving AmBC Channel Estimation

Innovative Joint CFO and Channel Estimation for RIS-Aided OFDM Systems

Innovative Joint CFO and Channel Estimation for RIS-Aided OFDM Systems

Machine Learning-Based Channel Estimation for 5G New Radio

Machine Learning-Based Channel Estimation for 5G New Radio

Temporal Correlation and Message-Passing-Based Sparse Bayesian Learning Channel Estimation for Underwater Acoustic Communications

Temporal Correlation and Message-Passing-Based Sparse Bayesian Learning Channel Estimation for Underwater Acoustic Communications

Covert performance enhancement in NOMA enabled CoMP network with optimal RIS selection

In this paper, we consider the covert rate maximization problem for the cell edge user in a reconfigurable intelligent surface (RIS) aided downlink non-orthogonal multiple access (NOMA) network with coordinated multipoint (CoMP) transmission in the presence of channel estimation errors. We derive the analytical expression for the detection error probability (DEP) at the adversary (Willie) assuming the worst case scenario where they employ optimal detection threshold. With the derived analytical expression, we propose an optimal RIS selection scheme to boost the covert rate. To further improve the covert performance, we optimize the NOMA power allocation coefficients and phase shifts of the selected RIS subject to the target rate and covertness constraints. Simulation results demonstrate the improvement in covert rate and robustness offered by the proposed scheme in the presence of channel estimation errors over other benchmarks.

Effect of Strip-Fin Height on Jet Impingement Heat Transfer in a Rectangular Channel at Two Jet-to-Target Surface Spacings

Abstract An experimental investigation of heat transfer performance in a rectangular impingement channel featuring staggered strip-fins was completed. Four configurations were considered to study the effects of varying the strip-fin height (H/d = 1.5 and 2.75) at two jet-to-target surface spacings (z/d = 3 and 6) on the heat transfer, pressure loss, and crossflow magnitude for a long impingement channel with in-line, 4 × 12 impinging jets. Also, the effect of the reference temperature choice, either jet inlet temperature or local bulk temperature, for calculating the local heat transfer coefficients was considered. The regionally averaged heat transfer coefficients were measured at seven Reynolds numbers, based on the jet diameter (10k–70k) utilizing the copper plate experimental method. The empirical correlations were expressed for the area averaged Nusselt number estimation of impingement channels with strip-fin or pin-fin roughness elements. The results showed that the long strip-fin channel with z/d = 3 provided the best thermal performance. The discharge coefficients are similar for all configurations between Rejet = 10k and 50k. The results are compared with the impingement channels with conventional pin-fins. They show that strip-fin channels provide lower pressure drop with marginally better heat transfer coefficients compared to the conventional pin-fin channels. However, when the channel weight is considered, strip-fins would increase the roughness material volume more than the conventional pin-fins.

Network-assisted full-duplex cell-free mmWave networks: Hybrid MIMO processing and multi-agent DRL-based power allocation

This paper investigates the network-assisted full-duplex (NAFD) cell-free millimeter-wave (mmWave) networks, where the distribution of the transmitting access points (T-APs) and receiving access points (R-APs) across distinct geographical locations mitigates cross-link interference, facilitating the attainment of a truly flexible duplex mode. To curtail deployment expenses and power consumption for mmWave band operations, each AP incorporates a hybrid digital–analog structure encompassing precoder/combiner functions. However, this incorporation introduces processing intricacies within channel estimation and precoding/combining design. In this paper, we first present a hybrid multiple-input multiple-output (MIMO) processing framework and derive explicit expressions for both uplink and downlink achievable rates. Then we formulate a power allocation problem to maximize the weighted bidirectional sum rates. To tackle this non-convex problem, we develop a collaborative multi-agent deep reinforcement learning (MADRL) algorithm called multi-agent twin delayed deep deterministic policy gradient (MATD3) for NAFD cell-free mmWave networks. Specifically, given the tightly coupled nature of both uplink and downlink power coefficients in NAFD cell-free mmWave networks, the MATD3 algorithm resolves such coupled conflicts through an interactive learning process between agents and the environment. Finally, the simulation results validate the effectiveness of the proposed channel estimation methods within our hybrid MIMO processing paradigm, and demonstrate that our MATD3 algorithm outperforms both multi-agent deep deterministic policy gradient (MADDPG) and conventional power allocation strategies.

Symbol-level precoding scheme robust to channel estimation errors in wireless fading channels

Most studies on symbol-level (SL) precoding have assumed that channel estimation is perfect. However, because interference signals or additive white Gaussian noise exist in the received signal for channel estimation, channel estimation errors always exist. In this paper, we propose an SL precoding scheme that is robust to channel estimation errors. First, using the characteristics of the channel estimation errors, we derive an equation for the worst-case mean squared error (MSE) which is the maximum of the MSE. Then by designing the SL precoding to minimize the worst-case MSE, it has robust characteristics against channel estimation errors.

Robust Beamforming for Downlink Multi-Cell Systems: A Bilevel Optimization Perspective

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming technique that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties.

Energy‐aware power allocation using Ladybug Beetle Honey Badger Optimization in multiple‐input multiple‐output‐non‐orthogonal multiple access with multiple users

SummaryThe non‐orthogonal multiple access (NOMA) is highly capable of serving multiple users at similar times and frequencies. The power allocation (PA) is widely considered as a main factor in NOMA for efficient communication. Here, the application of multiple‐input multiple‐output (MIMO) is added to NOMA to fulfill demands of enriched spectral efficiencies and extra user data. In this research, the Ladybug Beetle Honey Badger Optimization (LBHBO) is proposed for efficient PA in MIMO‐NOMA. Initially, the received signals from the user are modulated for amplitude and frequency. Then, user grouping is conducted by fuzzy local information c‐means (FLICM) clustering followed by using PA done by proposed LBHBO. This power is then moved to the transmitter and then to the channel estimation process. Moreover, cyclic prefix (CP) removal is carried out that tends to discrete Fourier transform (DFT). Finally, quadrature amplitude modulation (QAM) demodulation is performed for reallocated output. Furthermore, LBHBO is formed by combining Ladybug Beetle Optimization (LBO) and Honey Badger Algorithm (HBA). The performance offered by the LBHBO‐PA with maximal values with energy efficiency (EE) of 25.38 Mbits/s, sum rate of 1.29 Mbits/s, and achievable rate of 100.47 Mbits/s.

A joint estimation algorithm for single-input multiple-output underwater acoustic communications

In single-input multiple-output (SIMO) underwater acoustic (UWA) communications, the receiver based on passive time reversal (PTR) combined with decision feedback equalizer (DFE) is widely used but has a limited performance. A multi-channel joint estimation algorithm based on sparse Bayesian learning (MJSBL) is proposed in this paper to exploit the diverse gain from multi-channels, where reasonable prior distribution functions are selected for parameters in the probabilistic model. Afterwards, the algorithm is derived by the mean-field variational inference (VI), iteratively updating the estimation of symbols, channels and noise variation. As a result, the maximum likelihood estimation of the dictionary matrix, as well as the maximum posterior estimation of the channel vectors and noise variance, can be approximated. Simulation and experimental results verify that compared to typical single-channel and multi-channel algorithms, the systems always have lower bit error rates (BERs) and symbol error rates (SERs) with the MJSBL algorithm for different communications distances and symbol block lengths.

Dissociation of Hydrofluorocarbon Molecules after Electron Impact in Plasma.

The process of dissociation for two hydrofluorocarbon molecules in low triplet states excited by electron impact in plasma is investigated by ab initio molecular dynamics (AIMD). The interest in the dissociation of hydrofluorocarbons in plasma is motivated by their role in plasma etching in microelectronic technologies. Dissociation of triplet states is very fast, and the reaction products can be predicted. In this work, it was found that higher triplet states relax into the lowest triplet state within a few femtoseconds due to nonadiabatic dynamics, such that the simplest ab initio MD on the lowest triplet state seems to give a reasonable estimate of the reaction channels branching ratios. We provide evidence of the existence of simple rules for the dissociation of hydrofluorocarbon molecules in triplet states. For molecules with a double bond, the bonds adjacent to the double bond dissociate faster than the other bonds.