Channel State Information Estimation Error Research Articles

Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems

Hybrid beamforming (HBF) has been regarded as one of the most promising technologies in millimeter Wave (mmWave) communication systems. In order to guarantee the communication quality in non-line-of-sight (NLOS) scenarios, joint HBF design for the mmWave amplify-and-forward (AF) relay communication system is studied in this paper. The ideal case is first considered where the mmWave half-duplex (HD) AF relay system operates with channel state information (CSI) accurately known. In order to tackle the non-convex problem, a manifold optimization (MO)-based alternating optimization algorithm is proposed, where an optimization problem containing only constant modulus constraints in Euclidean space can be converted to an unconstrained optimization problem in a Riemann manifold. Furthermore, considering more practical cases with estimation errors of CSI, we investigate the robust joint HBF design with the system operating in full-duplex (FD) mode to obtain higher spectral efficiency (SE). A null-space projection (NP) based self-interference cancellation (SIC) algorithm is developed to attenuate the self-interference (SI). Different from the traditional SI suppression algorithm, there’s no limit on the number of RF chains. Numerical results reveal that our proposed algorithms has a good convergence and can effectively deal with the influence of different CSI estimation errors. A significant performance improvement can be achieved in contrast with other approaches.

Rate-Region Characterization and Channel Estimation for Cell-Free Symbiotic Radio Communications

Cell-free massive MIMO and symbiotic radio communication have been recently proposed as the promising beyond fifth-generation (B5G) networking architecture and transmission technology, respectively. To reap the benefits of both, this paper studies cell-free symbiotic radio communication systems, where a number of cell-free access points (APs) cooperatively send primary information to a receiver, and simultaneously support the passive backscattering communication of the secondary backscatter device (BD). We first derive the achievable communication rates of the active primary user and passive secondary user under the assumption of perfect channel state information (CSI), based on which the transmit beamforming of the cell-free APs is optimized to characterize the achievable rate-region of cell-free symbiotic communication systems. Furthermore, to practically acquire the CSI of the active and passive channels, we propose an efficient channel estimation method based on two-phase uplink-training, and the achievable rate-region taking into account CSI estimation errors is further characterized. Simulation results are provided to show the effectiveness of our proposed beamforming and channel estimation methods.

Robust Joint Beamforming Optimization for RISs-Empowered Cell-Free Networks

Reconfigurable intelligent surfaces (RISs) have emerged as a promising economical solution to implement cell-free networks. In this paper, the joint active and passive beamforming optimization of access points (APs) and RISs for the RISs-empowered cell-free network in the presence of channel state information (CSI) estimation errors is investigated. By taking the CSI estimation errors into account, we start with deriving the expression of a tight lower bound on the exact sum-rate, which is taken as the optimization criteria. Then, a robust joint beamforming optimization algorithm is proposed to obtain the highest sum-rate by using the lower bound, so as to obtain the best result out of the worst-case. In addition, a counter-intuitive but interesting result can be concluded via rigorous mathematical derivations that CSI estimation errors always impact the optimization of active transmitting beamforming matrices of APs, but surprisingly have no impact on the optimization of passive reflecting beamforming matrices of RISs when the covariance matrices of CSI estimation errors seen from two sides of each channel are proportional to an identity matrix. Simulation results demonstrate that RISs can considerably improve the sum-rate of the conventional cell-free network, and confirm the resilience of the proposed algorithm against CSI uncertainty.

Robust Beamforming Design for IRS-Aided URLLC in D2D Networks

Intelligent reflecting surface (IRS) and device-to-device (D2D) communication are two promising technologies for improving transmission reliability between transceivers in communication systems. In this paper, we consider the design of reliable communication between the access point (AP) and actuators for a downlink multiuser multiple-input single-output (MISO) system in the industrial IoT (IIoT) scenario. We propose a two-stage protocol combining IRS with D2D communication so that all actuators can successfully receive the message from AP within a given delay. The superiority of the protocol is that the communication reliability between AP and actuators is doubly augmented by the IRS-aided first-stage transmission and the second-stage D2D transmission. A joint optimization problem of active and passive beamforming is formulated, which aims to maximize the number of actuators with successful decoding. We study the joint beamforming problem for cases where the channel state information (CSI) is perfect and imperfect. For each case, we develop efficient algorithms that include convergence and complexity analysis. Simulation results demonstrate the necessity and role of IRS with a well-optimized reflection matrix, and the D2D network in promoting reliable communication. Moreover, the proposed protocol can enable reliable communication even in the presence of stringent latency requirements and CSI estimation errors.

A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems

A novel grant-free random access scheme with a high success rate is proposed to support massive access for machine-to-machine communications in massive multiple-input–multiple-output (MIMO) systems. This scheme allows active user equipments (UEs) to transmit their modulated uplink messages and super pilots consisting of multiple subpilots to a base station (BS). Then, the BS performs channel state information (CSI) estimation and uplink message decoding by utilizing a proposed graph combined clustering independent component analysis (GCICA) decoding algorithm and then employs the estimated CSIs to detect active UEs by using the characteristic of asymptotic favorable propagation of massive MIMO channel. We call this proposed scheme as the GCICA-based random access (GCICA-RA) scheme. We analyze the successful access probability, missed detection probability, and uplink throughput of the GCICA-RA scheme. Numerical results show that the GCICA-RA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability, and provides low CSI estimation error at the same time.

ML-Based Massive MIMO Channel Prediction: Does It Work on Real-World Data?

Accurate channel state information (CSI) acquisition is hindered by CSI estimation errors, compression, feedback, and processing delays. We propose a machine learning (ML)-based massive multiple-input multiple-output (mMIMO) channel predictor (CP), which can work on the estimated channel and the compressed version of the estimated channel as well. While existing work has evaluated the performance of ML algorithms by only using the artificially generated channel realizations, this letter reports the results of the ML algorithm using the real-world channel realizations from a measurement campaign performed at Nokia Bell-Labs. The results corroborate the validity of the proposed ML-based CP.

Statistical Beamforming Techniques for Power Domain NOMA System

Power-domain non-orthogonal multiple access (NOMA) assigns different power levels for near and far users in order to discriminate their signals by employing successive interference cancellation (SIC) at the near user. In this context, multiple-input-single-output NOMA (MISO-NOMA), where the base station (BS) is equipped with multiple antennas while each mobile user has a single antenna receiver, is shown to have a better overall performance by using the knowledge of instantaneous channel state information (CSI). However, this requires prior estimation of CSI using pilot transmission, which increases the transmission overhead. Moreover, its performance is severely degraded in the presence of CSI estimation errors. In this work, we provide statistical beamforming solutions for downlink power-domain NOMA that utilize only knowledge of statistical CSI, thus reducing the transmission overhead significantly. First, we derive the outage probabilities for both near and far users in the multi-user NOMA system without imposing strong assumptions, such as Gaussian or Chi-square distribution. This is done by employing the exact characterization of the ratio of indefinite quadratic form (IQF). Second, this work proposes two techniques to obtain the optimal solution for beam vectors which rely on the derived outage probabilities. Specifically, these two methods are based on (1) minimization of total beam power while constraining the outage probabilities to the QoS threshold, and (2) minimization of outage probabilities while constraining the total beam power. These proposed methods are non-convex function of beam vectors and, hence, are solved using numerical optimization via sequential quadratic programming (SQP). Since the proposed methods do not require pilot transmission for channel estimation, they inherit better spectral efficiency. Our results validate the theoretical findings and prove the supremacy of the proposed method.

Secrecy enhancing for SSK‐based communications in the presence of imperfect CSI estimation

In this letter, a new security enhancement scheme for space shift keying (SSK) systems under imperfect channel state information (CSI) is proposed. In this scheme, the legitimate receiver is always accompanied by an eavesdropper, and the secrecy enhancing of the SSK system is realized by adding the designed artificial noise (AN). Since the AN is designed by the CSI of the legitimate channel, it can be canceled at the legitimate receiver and remained at the eavesdropper. In addition, the impacts of the CSI estimation error on the secrecy rate (SR) and bit error ratio (BER) of the SSK system have also been taken into account. The simulation results show that compared with the traditional SSK under imperfect CSI environment, the proposed scheme improves the SR and BER. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

SIABR: A Structured Intra-Attention Bidirectional Recurrent Deep Learning Method for Ultra-Accurate Terahertz Indoor Localization

High-accuracy localization technology has gained increasing attention in gesture and motion control and many diverse applications. Due to multi-path fading and blockage effects in indoor propagation, 0.1m-level precise localization is still challenging. Promising for 6G wireless communications, the Terahertz (THz) spectrum provides multi-GHz ultra-broad bandwidth. Applying the THz spectrum to indoor localization, the channel state information (CSI) of THz signals, including angle of arrival (AoA), received power, and delay, has unprecedented resolution that can be explored for positioning. In this paper, a Structured Intra-Attention Bidirectional Recurrent (SIABR) deep learning method is proposed to solve the CSI-based three-dimensional (3D) THz indoor localization problem with significantly improved accuracy. As a two-level structure, the features of individual multi-path rays are first analyzed in the recurrent neural network with the attention mechanism at the lower level. Furthermore, the upper-level residual network (ResNet) of the constructed SIABR network extracts hidden information to output the geometric coordinates. Simulation results demonstrate that the 3D localization accuracy in the metric of mean distance error is within 0.25m. The developed SIABR network has very fast convergence and is robust against THz indoor line-of-sight blockage, multi-path fading, channel sparsity and CSI estimation error.

Power-Tolerant NOMA Using Data-Aware Adaptive Power Assignment for IoT Systems

Nonorthogonal multiple access (NOMA) is a promising candidate for future wireless networks due to its ability to improve the spectral efficiency and network connectivity. Nevertheless, the error rate performance of NOMA depends significantly on the power assignment for each user, which requires accurate knowledge of the channel state information (CSI) at the transmitter, which can be challenging for several applications, such as wireless sensor networks (WSNs) and Internet of Things (IoT). Therefore, this article proposes a power-tolerant NOMA by adaptively changing the signal power of each user to reduce the system sensitivity to inaccurate power assignment. The power adaptation in the power-adaptive NOMA (PANOMA) is performed based on the transmitted data, and it does not require accurate CSI. To quantify its potential, the bit error rate (BER) and the lower bound capacity performance, over Rayleigh fading channels, are derived in exact closed forms for two and three users scenarios. The results demonstrate that PANOMA provides a tangible BER performance improvement over conventional power-domain NOMA when both schemes use suboptimal power assignment, which is typically experienced in practical scenarios involving channel time variation and CSI estimation errors. Specifically, it will be shown that both schemes provide similar BERs using optimal assignment, but the PANOMA offers BER reduction by a factor of 10 for certain scenarios when suboptimal power values are assigned. The integrity of the analytical results is verified via matching extensive Monte Carlo simulation experiments.

Dimensionality reduction of interference subspace for multiuser massive MIMO null-space expansion

This paper proposes a dimensionality reduction of interference subspace to improve the interference suppression capability of null-space expansion (NSE) for multiuser massive multiple-input-multiple-output (MIMO) systems. The fundamental concept of the NSE is to nullify the interference subspace generated by the combined multiple past channel state information (CSI) vectors. It can suppress inter-user interference (IUI) leaked due to outdated CSI in a mobility environment. It is enabled by plentiful degrees of freedom (DoF) brought by massive antenna elements the base station has, and hence NSE can outperform existing channel prediction based precoding approaches. However, additive noise-induced CSI estimation error limits its effectiveness; expanded dimension of null-space cannot be appropriately spanned to interference subspace. The key idea of our new proposal is to extract principal interference subspace channel. Null-space can be appropriately expanded with reduced its dimension, and remaining DoF can be assigned to beamforming. It results in improving overall signal-to-interference-plus-noise power ratio (SINR) performance which is verified via numerical simulation.

Zero-Forcing Precoding With Partial CSI in Multibeam High Throughput Satellite Systems

Precoding has emerged as a prominent technique to mitigate interbeam interference (IBI) in the forward link of a multibeam high throughput satellite system. In this paper, we consider zero-forcing (ZF) precoding to mitigate IBI. To reduce the channel state information (CSI) feedback from the user terminals, which consequently reduces the latency and signaling overheads, we design a ZF precoder with matrix and vector normalizations using partial CSI and analyze its ergodic SR performance. We consider a practical scenario where the links between the satellite and user terminals are affected by rain fading attenuation and derive an analytical expression, upper and lower bounds, and asymptotic (in terms of transmit power) approximations for the ergodic SR achieved by this ZF precoder. Many important insights are obtained from these results. Also, the robustness of this ZF precoder to CSI estimation errors is numerically analyzed.

Distributed IRS With Statistical Passive Beamforming for MISO Communications

Intelligent reflecting surface (IRS) has recently been identified as a prominent technology with the ability of enhancing wireless communication by dynamically manipulating the propagation environment. This letter investigates a multiple-input single-output (MISO) system deploying distributed IRSs. For practical considerations, we propose an efficient design of passive reflecting beamforming for the IRSs to exploit statistical channel state information (CSI) and analyze the achievable rate of the network taking into account the impact of CSI estimation error. The ergodic achievable rate is derived in a closed form, which provides insightful system design guidelines. Numerical results confirm the accuracy of the derived results and unveil the performance superiority of the proposed distributed IRS deployment over the conventional centralized deployment.

Optimal Pilot Design and Error Rate Analysis of Block Transmission Systems in the Presence of Channel State Information (CSI) Estimation Error

This work proposes novel techniques toward the design of optimal pilot sequences to perform channel estimation in block transmission systems over wideband frequency selective wireless fading channels. The framework developed is based on minimization of the Bayesian Cramer-Rao bound (BCRB) for the mean squared error (MSE) of the channel state information (CSI) estimate. Optimal pilot signals are determined for the four predominant classes of block transmission systems, viz. single carrier zero padding (SC-ZP), multi-carrier zero padding (MC-ZP), single carrier cyclic prefix (SC-CP) and multi-carrier cyclic prefix (MC-CP) systems. This makes the techniques developed general in nature and thus applicable in a wide variety of block transmission systems. As part of this study, succinct expressions and results are also derived to characterize the error rate performance, incorporating also the effect of CSI estimation error resulting due to the proposed algorithms. Finally, numerical results obtained via Monte-Carlo simulation are presented to illustrate and compare the CSI acquisition performance of optimal pilot designs with that of conventional designs and also validate the theoretical analysis for the error rate performance.

Energy-Efficient Channel Estimation

We investigate the energy-efficient channel estimation in wireless networks, where one pilot is inserted for every several data slots to estimate the channel coefficients. Both the channel state information estimation error and the time-varying model are considered. We first formulate the energy-efficient channel estimation problem into a mixed integer nonlinear programming (MINLP) problem, where the variables include the transmitted power and the number of data slots. Due to the NP-hard nature, we degenerate the MINLP problem into a series of non-concave optimization problems without integer variables. Then we solve these problems using successive convex approximation, geometric programming, and the Dinkelbach algorithm to obtain a point satisfying the Karush-Kuhn-Tucker (KKT) conditions. Furthermore, we develop a low-complexity sub-optimal scheme through binary variable relaxation to obtain a solution, and the convergence point satisfies the KKT conditions of the relaxed non-concave problem. Simulation results demonstrate the convergence and effectiveness of our proposed schemes.

An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems

A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code (STBC) based multiple-input multiple-output (MIMO) systems. We exploit the zero-forcing equalization technique to modify the typical average likelihood ratio test (ALRT) function. The proposed ALRT function has a low computational complexity compared to existing ALRT functions for MIMO systems classification. The proposed approach is analyzed for blind channel scenarios when the receiver has imperfect channel state information (CSI). Performance analysis is carried out for scenarios with different numbers of antennas. Alamouti-STBC systems with 2 × 2 and 2 × 1 and space-time transmit diversity with a 4 × 4 transmit and receive antenna configuration are considered to verify the proposed approach. Some popular modulation schemes are used as the modulation test pool. Monte-Carlo simulations are performed to evaluate the proposed methodology, using the probability of correct classification as the criterion. Simulation results show that the proposed approach has high classification accuracy at low signal-to-noise ratios and exhibits robust behavior against high CSI estimation error variance.

Distributionally Robust Optimization for Secure Transmission With Assisting Jammer in MISO Downlink Networks

This letter investigates secure transmission with imperfect channel state information (CSI) in multi-input single-output downlink wireless networks that consist of a transmitter, a desired receiver, one eavesdropper, and an assisting jammer. Specifically, only rough first- and second-order moment information is obtained for the estimation errors of CSI. We aim to minimize the total transmit power by jointly designing the beamforming vector at transmitter and artificial noise covariance at jammer under the reliability and secrecy constraints for all the possible distributions of CSI errors. Since the chance-constrained optimization problem is non-convex, we reformulate it using a Lagrangian duality theory and semidefinite relaxation. The simulation results illustrate the validity of the proposed distributionally robust transmit scheme.

UAV-Aided Secure Transmission in MISOME Wiretap Channels With Imperfect CSI

In this paper, we study secure cooperative transmission for the multiple-input-single-output multiple-eavesdropper (MISOME) systems, where a source (Alice) aims to send confidential messages to a legitimate user (Bob) with the aid of an unmanned aerial vehicle (UAV) enabled friendly jammer (Charlie) in the presence of multiple eavesdroppers (Eves). To further guarantee the secrecy performance, artificial noise (AN) beamforming together with cooperative Jamming (CJ) has been utilized in this considered system with only location and statistical channel state information of the Eves. Specifically, taking imperfect channel state information (CSI) between Charlie and Bob into account, we derive the exact closed-form expression of the secrecy outage probability (SOP) and develop an efficient scheme to solve the secrecy rate maximization (SRM) problem which has been verified as a convex one under certain SOP. Moreover, we propose an optimal UAV placement strategy to further enhance secrecy performance. The numerical results are provided to validate that the system security would seriously degenerate with channel uncertainty. Besides, the proposed scheme is verified to outperform the terrestrial CJ scheme in improving the secrecy rate and secure energy efficiency (EE) performance under perfect CSI or tolerable channel estimation errors. Finally, the effectiveness of the proposed UAV placement strategy in enhancing transmission security and the applicability of the proposed scheme has also been demonstrated.

Simple <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math> </inline-formula>–Test-Based Spectrum Sensing Techniques for Multi-Antenna Cognitive Radios

An F-test detector with a simple analytical false alarm threshold expression is considered an alternative to the blind detectors which exhibit complicated analytical expressions. However, the existing F-test requires the channel state information (CSI) as a prior knowledge and is known to be sensitive to CSI estimation errors. In this paper, we present and evaluate simple F-test-based spectrum sensing techniques that do not require the knowledge of CSI for multi-antenna cognitive radios. Exact and asymptotic analytical performance closed-form expressions are derived for the presented detectors. Simulations assess the performance of the presented detectors and validate the derived closed-form expressions. For an additive noise exhibiting the same variance across multiple-antenna frontends, simulations also corroborate that the presented detectors are constant false alarm rate detectors which are robust against noise uncertainty.

Multi-Cell Massive MIMO Systems With Hardware Impairments: Uplink-Downlink Duality and Downlink Precoding

In this paper, we propose a new framework for uplink-downlink duality in multi-cell multi-user multiple-input multiple-output systems suffering from residual hardware impairments (HWIs) at the base stations and the user terminals. We apply the proposed uplink-downlink duality framework to derive a multi-cell interference and HWI aware minimum mean square error (MCHA-MMSE) precoder which also takes channel state information estimation errors into account. We use results from random matrix theory to derive an analytical expression for the downlink power allocation for the proposed MCHA-MMSE precoder in the large system limit, which only depends on the channel's second order statistics. In contrast to the conventional MMSE precoder, the proposed MCHA-MMSE precoder takes inter-cell interference, pilot contamination, and residual HWIs into account and therefore achieves substantially higher sum rates. The proposed MCHA-MMSE precoder exploits statistical channel knowledge, but does not require data exchange between base stations via backhaul links. In order to reduce the computational complexity, the matrix inversion required for the computation of the MCHA-MMSE precoder is approximated by a matrix polynomial leading to a new polynomial-expansion MCHA-MMSE precoder. Using results from the random matrix theory, we derive closed-form expressions for the asymptotically optimal coefficients of the matrix polynomial, which only depend on the channel's second order statistics.