Perfect Channel State Information Research Articles

UAV-assisted ISAC network physical layer security based on Stackelberg game

This paper deals with the physical layer security for multi-unmanned aerial vehicle (UAV) assisted transmission of integrated sensing and communication (ISAC) signals from a dual-function base station (BS) to the ground user. Here, an active eavesdropper (Eve) on the ground eavesdrops on the information transmitted by the UAV network to the user. We propose a Stackelberg game model for the game problem of information security transmission between UAV and Eve, with perfect or imperfect channel state information (CSI) and cost information. To deal with the Stackelberg game problem, the dual optimization theory and KKT conditions are used to solve it, then we prove the existence and uniqueness of Stackelberg equilibrium (SE). At the same time, considering that the influence of ISAC signal’s sensing of Eve’s location on the distance between Eve and the user, and thus on the utility function of both sides of the game, this paper uses the Cramer–Rao bound (CRB) to represent the accurate performance of location sensing. Then the relaxation method is used for the non-convex optimization problem, and convex optimization tools can be used subsequently. Finally we notice that both operations have their own optimal resolution strategy, it is difficult to reach agreement, therefore an overall algorithm is proposed to solve the whole problem iteratively. In the simulation results, the effect of power on game and location sensing, and the effect of the distance between Eve and the user on the utility function of both sides of the game are given. In addition, we give the relationship between the CRB threshold and the transmit power, and verify the convergence of the game and overall algorithm.

Wavelet-based massive MIMO-NOMA with advanced channel estimation and detection powered by deep learning

This paper presents a massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) system’s channel estimation and detection method which uses a deep learning algorithm to address the issue of erroneous signal detection caused by imperfect channel state information (CSI), user interference and channel noise. The proposed network applies deep learning (DL) model to a discrete wavelet transform (DWT)-based orthogonal frequency-division multiplexing (OFDM) approach, aiming to reduce noise and inter-channel interference (ICI) while maintaining compatibility with the existing networks. A significant advantage of this network is the reduction of transmission overhead as it can estimate and detect symbols with and without pilot signals. The study shows that the DL-based channel estimation and detection method proposed in this paper enhances the successive interference cancellation (SIC) process compared to the conventional linear scheme-based SIC in the mMIMO-NOMA network. Additionally, the wavelet-based mMIMO-NOMA is compared to the traditional fast-Fourier-transform (FFT) based mMIMO-NOMA in terms of symbol error rate (SER) for both perfect and imperfect CSI. The simulation results show that the proposed method outperforms the traditional method.

Robust Linear Decentralized Tracking of a Time-Varying Sparse Parameter Relying on Imperfect CSI

Robust linear decentralized tracking of a time varying sparse parameter is studied in a multiple-input multiple-output (MIMO) wireless sensor network (WSN) under channel state information (CSI) uncertainty. Initially, assuming perfect CSI availability, a novel sparse Bayesian learning-based Kalman filtering (SBL-KF) framework is developed in order to track the time varying sparse parameter. Subsequently, an optimization problem is formulated to minimize the mean square error (MSE) in each time slot, followed by the design of a fast block coordinate descent (FBCD)-based iterative algorithm. A unique aspect of the proposed technique is that it requires only a single iteration per time slot to obtain the transmit precoder (TPC) matrices for all the sensor nodes (SNs) and the receiver combiner (RC) matrix for the fusion center (FC) in an online fashion. The recursive Bayesian Cramer Rao bound (BCRB) is also derived for benchmarking the performance of the proposed linear decentralized estimation (LDE) scheme. Furthermore, for considering a practical scenario having CSI uncertainty, a robust SBL-KF (RSBL-KF) is derived for tracking the unknown parameter vector of interest followed by the conception of a robust transceiver design. Our simulation results show that the schemes designed outperform both the traditional sparsity-agnostic KF and the state-of-the-art sparse reconstruction methods. Furthermore, as compared to the uncertainty-agnostic design, the robust transceiver architecture conceived is shown to provide improved estimation performance, making it eminently suitable for practical applications.

Intelligent reflecting surface‐assisted beamforming‐NOMA networks for short‐packet communications: Performance analysis and deep learning approach

AbstractThis paper investigates intelligent reconfigurable surface‐assisted non‐orthogonal multiple access (NOMA) networks for short packet communications where the source with multiple antennas utilizes beamforming technology to serve two end users including a near user and a far user. The analysis system, closed‐form expressions of the average block error rates (BLERs), latency and reliability at all users are derived under assuming perfect channel state information over quasi‐static Rayleigh fading channels. For evaluating the improvement of the network system, a deep neural network (DNN) framework is designed to predict the performance parameters in two cases: (i) BLERs prediction of the users, (ii) transmit power allocation under the constraints of BLERs at each user which satisfy the extremely high reliability requirements of uRLLCs. The extensive results demonstrate that first, the DNN‐based estimation results are more accurate than Monte‐Carlo simulation results with low execution time. Second, the allocated power for each user can be accurately predicted at the high layers. Finally, two methods of evaluating the effectiveness of the DNN model, i.e. root mean square error and mean absolute percentage error, reach low error which verify the power of the designed DNN model for future analysis.

Integrating Intelligent Reflecting Surface Into Base Station: Architecture, Channel Model, and Passive Reflection Design

Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network’s capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS’s antenna radome to directly reconfigure the signal radiation to/from the BS’s antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS’s antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS’s reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS’s reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.

Data-Aided CSI Estimation Using Affine-Precoded Superimposed Pilots in Orthogonal Time Frequency Space Modulated MIMO Systems

An orthogonal affine-precoded superimposed pilot (AP-SIP)-based architecture is developed for the cyclic prefix (CP)-aided single input single output (SISO) and multiple input multiple output (MIMO) orthogonal time frequency space (OTFS) systems relying on arbitrary transmitter-receiver (Tx-Rx) pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler (DD)-domain followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver, the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided Bayesian learning (PA-BL) technique is conceived for the channel state information (CSI) estimation of SISO OTFS systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided Bayesian learning (DA-BL)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. In this scenario our sophisticated data detection rule also integrates the CSI uncertainty of channel estimation into our the linear minimum mean square error (LMMSE) detectors. The AP-SIP framework is also extended to MIMO OTFS systems, wherein the DD-domain input matrix is affine-precoded for each transmit antenna (TA). Then an EM algorithm-based PA-BL scheme is derived for simultaneous row-group sparse CSI estimation for this system, followed also by our data-aided DA-BL scheme that performs joint CSI estimation and data detection. Moreover, the Bayesian Cramer-Rao bounds (BCRBs) are also derived for both SISO as well as MIMO OTFS systems. Finally, simulation results are presented for characterizing the performance of the proposed CSI estimation techniques in a range of typical settings along with their bit error rate (BER) performance in comparison to an ideal system having perfect CSI.

Investigation of channel state information conditions in VL-NOMA using power allocation technique for 6G network

Abstract This paper investigates the channel state information (CSI) conditions in visible light non-orthogonal multiple access (VL-NOMA) using the power allocation technique (PA) for a sixth-generation (6G) network. One light-emitting diode (LED) is used as a carrier to transmit the signal to three-user-positioned at certain distances. The challenge of allocating power to the system is solved using the PA technique. The PA technique uses to assign signal powers to the three users. The VL-NOMA channel with CSI symbols is synchronized for the performance of signal propagation. We deployed perfect CSI, imperfect CSI, and partial CSI to coordinate the signal enhancement that results in an improved data rate, higher channel capacity, and improved signal-to-noise ratio (SNR). The synchronized system model guarantees successful signal enhancement where perfect CSI performs better than partial CSI and imperfect CSI. We have enabled interference plus noise to see how the system behaves. We notice low channel capacity region, low data rate performance, etc. Hence, we analyse the PA to determine the outage probability effect on the signal.

Cooperative jamming aided securing wireless communications without CSI of eavesdroppers

Physical layer security was designed as an effective solution to provide the security and confidentiality of information transmission in wireless networks, and secrecy transmission capacity (STC) played a key role in analyzing the impact of system parameters on physical layer security. Different from the previous works, which exploited cooperative jamming to improve the security with the assumption of perfect channel state information (CSI) of eavesdroppers (Eves), in this paper, without considering Eves’ CSI, we designed two secrecy improvement strategies aided by cooperative jamming with transmit power allocation scheme, i.e., connection guard zone (CGZ) and improved interferer protected zone (IIPZ). Specifically, we established an analytical framework of the STC by applying the tools of stochastic geometry and derived conditions that achieved a positive STC. In addition, we consider the impact of Random WayPoint (RWP) mobile receiver and active eavesdroppers on physical layer security. Simulations showed that IIPZ achieves the best reliability performance and CGZ makes the best secrecy performance than existing popular protections, and RWP mobile destination can achieve a higher level of reliability and STC than these achievable in static scenarios.

Performance analysis and comparison of approximate detectors with linear detectors in massive MIMO systems under imperfect channel state information

SummaryMassive MIMO systems are a key technology for enhancing the capacity and reliability of wireless communication systems. However, to unlock the full potential of massive MIMO systems, accurate channel state information is crucial at the receivers. In practice, channel estimation errors are inevitable, leading to significant performance degradation in traditional massive MIMO equalizers that assume perfect channel state information. In this work, we investigate the performance of approximate massive MIMO detectors, specifically the Gauss‐Seidel and Neumann series, over a time‐invariant channel with imperfect channel state information. We derive the expressions of signal‐to‐interference‐plus‐noise ratio and average bit error rate for approximate massive MIMO detectors in a unicellular environment using M‐ary quadrature amplitude modulation. Additionally, we compare the performance of linear detectors, including zero‐forcing, minimum‐mean‐squared‐error, and maximal‐ratio combining, with that of the approximate detectors. Through extensive Monte Carlo numerical simulations and various system configurations, we demonstrate that the Gauss‐Seidel detector, under imperfect channel state information, achieves comparable bit error rate performance to the zero‐forcing and minimum‐mean‐squared‐error detectors while requiring less computational complexity. Our study suggests that the Gauss‐Seidel detector has the potential to be used in massive MIMO systems in the presence of imperfect channel state information, highlighting its practical relevance in real‐world scenarios.

A novel multicarrier index keying‐aided non‐orthogonal multiple access systems in presence of uncertain channel state information

SummaryThis paper investigates the performance of novel multicarrier index keying‐aided non‐orthogonal multiple access (MCIK‐NOMA) system. Unlike the classical scheme, the proposed system sends the information through both index domain and constellation domain symbols thereby increases the spectral efficiency. The impact of channel state information (CSI) uncertainty on the system's performance is investigated. More specifically, the performance of MCIK‐NOMA under three different CSI conditions is analyzed: perfect CSI, MMSE‐based variable CSI, and fixed CSI uncertainty. This paper also discusses the optimum power difference needed for the successful separation of symbols at the SIC receiver by alternating the power ratio in the proposed system. The influence of selecting different active subcarriers and modulation techniques on the system's performance is studied in detail. The simulation results show that the proposed system achieves better BER and spectral efficiency and outperforms the existing systems.

Enhanced deep soft interference cancellation for multiuser symbol detection

AbstractThe detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model‐based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data‐driven methods perform slightly worse than model‐based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning‐based method by improving a state‐of‐the‐art data‐driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error‐propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model‐based methods in perfect CSI environments and the best performance in imperfect CSI environments.

Outage-Minimization Coordinated Multi-Point for Millimeter-Wave OFDM With Random Blockages

We consider millimeter-wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems subjected to random propagation path blockages and propose a new Coordinated multi-point (CoMP) transmission scheme that minimizes the outage probability of users with respect to given target data rates. To this end, a stochastic sum-outage-probability minimization problem is formulated for joint beamforming design, data rate allocation, and power allocation over subcarriers. In order to solve this problem efficiently, a block statistic learning approach is introduced using training data generated from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of path blockage probabilities. To initialize the stochastic learning solver, the novel initial beamforming is also proposed based on the upper bound of the original objective function, which improves convergence without tuning hyper-parameters. Numerical results confirm the effectiveness of the proposed block stochastic learning approach in terms of both convergence behavior and outage probability. Furthermore, these results confirm that the proposed approach with only blockage probabilities is comparable to the outage performance of a CoMP sum rate maximization (SRM) transmission scheme with perfect channel state information (CSI) and perfect knowledge of blockages.

Robust Beamforming for IRS-Aided Multi-Cell mmWave Communication Systems

Intelligent reflecting surface (IRS) has recently been envisioned to enhance the power of the desired received signal or suppress the interference signal by deploying low-cost passive reflection elements. This paper investigates an IRS-aided multi-cell millimeter wave (mmWave) communication system for suppressing inter-cell interference (ICI) to assist the downlink transmission of cell-edge users. We aim for maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) through jointly optimizing the active beamforming vectors of mmWave base stations (MBSs), the phase shifts of the IRS, and the location of the IRS. Especially since it is challenging to obtain the perfect channel state information (CSI) related to the IRS links, we also study the performance of IRS-aided mmWave communication in the case of imperfect CSI. First, in the case of perfect CSI, to tackle the challenging and non-convex minimum weighted SINR maximization problem, we develop an alternating optimization (AO)-based beamforming algorithm via updating the active beamforming vectors at MBSs, the phase shifts at the IRS, and the location of the IRS alternately. Then, in the case of imperfect CSI, we propose a low-complexity majorization-minimization (MM)-based robust beamforming algorithm to obtain the benchmark performance. Moreover, the proposed algorithms are also extended to multi-IRS-aided multi-cell mmWave scenarios. Finally, the simulation results demonstrate the advantages in terms of the SINR, effective sum rate as well as energy efficiency after introducing the IRS to mitigate the ICI.

Uplink Multiple Access With Semi-Grant-Free Transmission in Integrated Satellite-Aerial-Terrestrial Networks

This paper investigates a semi-grant-free (SGF) based transmission strategy to provide a flexible connectivity for various kinds of users in an integrated satellite-aerial-terrestrial network (ISATN). Herein, a high-altitude platform (HAP) termed as a grant-based user (GBU), which serves multiple mobile terminals (MTs) through space division multiple access (SDMA), wants to access a satellite network with multiple earth stations (ESs) termed as grant-free users (GFUs) simultaneously via non-orthogonal multiple access (NOMA) assisted SGF. To this end, we first propose two SGF-based uplink transmission schemes for both perfect channel state information (CSI) and imperfect CSI cases. When perfect CSI is available, a zero-forcing based beamforming (BF) scheme is used in HAP network while an adaptive transmit power allocation (ATPA) approach is adopted for SGF transmission. When only imperfect CSI is available, BF scheme employing the derived channel correlation matrix of HAP-MT link is proposed to achieve SDMA, and a novel ATPA strategy with rate probability constraint is proposed to guarantee quality-of-service of the GBU. Next, we derive the closed-form throughput expressions to evaluate the performance of the considered ISATN with the proposed two SGF-based schemes. Finally, computer simulations are conducted to validate the theoretical performance analysis and show the superiority of the proposed schemes over the related works. Moreover, our numerical results not only demonstrate a satisfactory performance of the proposed SGF-based scheme using imperfect CSI, but also reveal the impact of CSI errors on the system performance.

Full-Duplex MIMO Relay-Assisted Interference Alignment Algorithm in K-user Interference Channels

In this paper, we investigate the transceiver design schemes for the full-duplex multiple-input multiple-output relay-assisted K-user single-input multiple-output interference channels. Firstly, we propose an iterative optimized reference vector for IA (IORV-IA) algorithm in the perfect channel state information (CSI) scenario. The proposed IORV-IA algorithm not only achieves the alignment of interference signals at each receiver, but also iteratively optimizes the IA reference vector by orthogonalizing the directions of the interference signals and the desired signal. With the optimized IA reference vector, the relay processing matrix and the receiving filter vectors are designed to further improve the system performance. Considering that the relay cannot obtain perfect CSIs in practice due to many factors, and the performance of the IA scheme is very sensitive to this error. Furthermore, we propose a robust transceiver design scheme based on mean square error (MSE) in the imperfect CSI scenario, which minimizes the sum of MSEs in the worst case through iteration. The proposed algorithms are evaluated in terms of the average sum rate and bit error rate performance and the simulation results show the advantages of the proposed algorithms over existing centralized IA and centralized zero-forcing algorithms.

Robust Adversarial Attacks on Deep Learning-Based RF Fingerprint Identification

Deep learning (DL)-based radio frequency fingerprint identification (RFFI), despite its state-of-the-art capability in improving the security performance of communication networks, is still vulnerable to carefully crafted and imperceptible adversarial attack. However, conventional radio frequency (RF) adversarial attacks ignore the impact of the practical fading channels between the attacker and the sensor. In this letter, a generation adversarial perturbations (GAP) problem is formulated in a DL-based RFFI network. Both the case of ideally perfect channel state information (CSI) and the practical imperfect CSI are considered. The closed-form expression of the adversarial example based on the perfect CSI is provided. In order to address the intractable non-convex robust optimization problem under the imperfect CSI, a spoofing attack algorithm is proposed by exploiting the S-procedure. Simulation results demonstrate that our proposed scheme achieves a superior spoofing ratio compared with other benchmark schemes.

Hybrid Beamforming With Sub-Connected Structure for MmWave Massive Multi-User MIMO Relay Systems

In this paper, we investigate the hybrid beamforming design with sub-connected structure for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) amplify-and-forward (AF) relay systems. Considering the constant-modulus and block-diagonal constraints, an iterative algorithm is proposed to sequentially design the analog beamforming of the base station, relay, and users. Next, the relay baseband combiner is designed by transforming the highly complicated non-convex mutual information maximization problem into an easily tractable weighted minimum mean squared error one. To mitigate the inter-user interference, a successive serial interference cancellation based and a piecewise successive approximation based methods are developed for the uniform sparse distribution and dense distribution scenarios, respectively. Simulation results demonstrate that the two proposed hybrid beamforming schemes can achieve good performance in terms of sum-rate and energy efficiency, and outperform benchmarks significantly. In addition, the two proposed schemes are shown to be robust to imperfect channel state information (CSI), even though they are studied based on perfect CSI.

Learning a Gaussian Mixture Model From Imperfect Training Data for Robust Channel Estimation

In this letter, we propose a gmm-based channel estimator which is learned on imperfect training data, i.e., the training data are solely comprised of noisy and sparsely allocated pilot observations. In a practical application, recent pilot observations at the bs can be utilized for training. This is in sharp contrast to state-of-the-art ml techniques where a training dataset consisting of perfect csi samples is a prerequisite, which is generally unaffordable. In particular, we propose an adapted training procedure for fitting the gmm which is a generative model that represents the distribution of all potential channels associated with a specific bs cell. To this end, the necessary modifications of the underlying em algorithm are derived. Numerical results show that the proposed estimator performs close to the case where perfect csi is available for the training and exhibits a higher robustness against imperfections in the training data as compared to state-of-the-art ml techniques.

Fluid Antenna Enabling Secret Communications

Recent researches have revealed that fluid antenna, a new position-switchable antenna technology, can make use of the spatial moments of deep fades of the interference signal occurred naturally due to multipath for multiple access. In this letter, this phenomenon is exploited in a physical layer security setup where a base station (BS) transmits an information-bearing signal to a legitimate user and an artificial noise (AN) signal to a potential eavesdropper while the user is equipped with a fluid antenna to overcome the AN signal. The proposed approach needs no effort from the base station (BS) to avoid the AN signal from harming the legitimate user. Trying to enhance the secrecy rate, we study the power allocation of the information-bearing and AN signals if the channel state information (CSI) is available at the BS. Both perfect and imperfect CSI scenarios are investigated. Simulation results demonstrate that the proposed system, with a single fluid antenna with one radio-frequency (RF) chain at the user, achieves the secrecy rate that is achievable by the user utilizing maximal ratio combining (MRC) with many antennas instead, and is the only approach robust to CSI uncertainties of the eavesdropper, requiring no power allocation nor beamforming at the BS.

IRS-Based Integrated Location Sensing and Communication for mmWave SIMO Systems

In this paper, we establish an integrated sensing and communication (ISAC) system based on a distributed semi-passive intelligent reflecting surface (IRS), which allows location sensing and data transmission to be conducted on the same time-frequency resources. The detailed working process of the proposed IRS-based ISAC system is designed, including the transmission protocol, location sensing and beamforming optimization. Specifically, each coherence block consists of the channel estimation period, the ISAC period with two time blocks, and the pure communication (PC) period. During the channel estimation period, the low-dimensional effective user-BS channel is estimated. During each time block of the ISAC period, data transmission and user positioning are carried out simultaneously. The estimated user location in the first time block will be used for beamforming design in the second time block. During the PC period, only data transmission is conducted, by invoking the user location estimated in the second time block of the ISAC period for beamforming design. Simulation results show that a millimeter-level positioning accuracy can be achieved by the proposed location sensing scheme. Besides, the proposed two beamforming schemes based on the estimated location achieve similar performance to the benchmark schemes assuming perfect channel state information, verifying the effectiveness of beamforming design using sensed location information.