CSI Estimation Research Articles

RZ‐PPM impulse tone‐based CSI estimation for adaptive threshold decision in FSO communications

RZ‐PPM impulse tone‐based CSI estimation for adaptive threshold decision in FSO communications

Learning-Based Intermittent CSI Estimation with Adaptive Intervals in Integrated Sensing and Communication Systems

Learning-Based Intermittent CSI Estimation with Adaptive Intervals in Integrated Sensing and Communication Systems

Heterogeneous Doppler Spread-Based CSI Estimation Planning for TDD Massive MIMO

Massive multi-input multi-output (MIMO) has been recognized as a key technology for fifth generation (5G) networks. Indeed, it enables to meet the challenging requirements of increased coverage, capacity and massive connectivity. Nevertheless, its performance is limited by channel estimation overhead which scales in time division duplexing (TDD) systems with the number of active users, consequently limiting the system’s efficiency. In this paper, we address the bottleneck of channel estimation in TDD Massive MIMO through optimized lean carrier. We propose an adaptive uplink training scheme that exploits the heterogeneous Doppler spreads of the different users in order to reduce the periodicity of uplink sounding signal transmission. The idea is to enable the network to plan its uplink training decisions for long time periods while taking into consideration user mobility. To this end, we formulate a two time-scale control problem that takes into account the different rates of the wireless channel and location changes. In the fast time scale, an optimized uplink training policy is derived based on estimated user locations. In the slow time scale, positioning decisions are optimized. Simulation results show that the optimized training policies provide considerable improvement in the system efficiency even with partial location knowledge.

Joint Massive MIMO CSI Estimation and Feedback via Randomized Low-Rank Approximation

The acquisitionof channel state information at transmitter (CSIT) is crucial to attain the potential benefits of the frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. Traditionally, CSI estimation at receiver and feedback to transmitter are separately designed, failing to maximize the estimation accuracy while minimizing the complexity/overhead. In this work, we propose a novel computation and communication efficient CSIT acquisition scheme, by jointly designing downlink CSI estimation and uplink feedback, which is inspired by randomized matrix approximation techniques. In the process of CSI estimation, we represent a large channel matrix with three sub-matrices by exploiting its inherent low-rank characteristic, and then estimate these small matrices separately. Our new estimator significantly reduces the computation complexity in massive MIMO CSI acquisition, whilst attaining the high accuracy. The convergence as well as the error bound of our CSI estimator is derived theoretically. In the following CSI feedback process, only partial elements of sub-matrices are reported to transmitter without further compression, and finally the full CSI is recovered accurately. This novel CSI feedback scheme, relying on the estimated CSI with special structure, greatly reduces both the communication overhead. Numerical simulations are provided to valid our joint CSI estimation and feedback scheme, which would have great potential in massive MIMO communications.

Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect

This work conceives dictionary-learning (DL)-based sparse channel estimation schemes for multi-user Terahertz (THz) hybrid MIMO systems incorporating also non-idealities such as hardware impairments and beam-squint effect. Due to the presence of large antenna arrays coupled with frequency selectivity, beam squint effect is significant in THz systems. Moreover, the manufacturing and calibration errors that inevitably arise during the production of antenna arrays result in hardware impairments such as irregular antenna spacing, mutual coupling and antenna gain/phase errors in practical THz systems. To overcome these problems, this work proposes a DL algorithm to determine the best sparsifying dictionary from the acquired observations for a single-carrier frequency domain equalization (SC-FDE)-based wideband THz system in the presence of hardware impairments as well as the beam squint effect. The dictionary thus obtained is subsequently employed to exploit the sparsity of the MIMO THz channel toward CSI estimation. Furthermore, the Cramér-Rao lower bound (CRLB) is also derived for the joint DL and CSI estimation algorithm, which acts as a benchmark for the mean-squared error (MSE) performance of the channel estimate obtained. The scheme is also extended to SC-FDE-based wideband THz MIMO systems with multiple antenna users. Simulation results are presented to corroborate our analytical findings and also demonstrate the improved performance with respect to the agnostic scheme that ignores the non-idealities.

Data Aided Quasistatic and Doubly-Selective CSI Estimation Using Affine-Precoded Superimposed Pilots in Millimeter Wave MIMO-OFDM Systems

This paper proposes affine-precoded superimposed pilot (SIP) design, followed by channel state information (CSI) estimation techniques for millimeter wave (mmWave) MIMO-OFDM systems. Toward this end, a multiple measurement vector (MMV) sparse Bayesian learning (SBL)-based SIP (M-SIP) technique is initially derived to exploit the simultaneous-sparsity of the beamspace channel vector across the subcarriers, thereby leading to improved performance in comparison to the existing single measurement vector (SMV)-based SBL and other techniques. This is subsequently extended to the data-aided J-SIP technique that performs joint channel estimation and data detection, and is shown to ultimately yield a better mean squared error (MSE) of the channel estimate and bit error-rate (BER). The SIP-based simultaneous-sparse channel estimation framework is extended to time-selective wideband, i.e. doubly-selective mmWave MIMO channels, via the MMV sparse Kalman filtering-based SIP (MK-SIP) technique for tracking the CSI, and subsequently also to the joint Kalman filtering-based SIP (JK-SIP) for data-aided CSI acquisition. Bayesian Cramér-Rao bounds are determined both for quasistatic and doubly-selective mmWave MIMO-OFDM channel estimation. Simulation results, which also incorporate practical channel realizations, are provided to demonstrate performance improvement considering various metrics, such as MSE and BER.

Secure TDD MIMO Networks Against Training Sequence Based Eavesdropping Attack

Multi-User MIMO (MU-MIMO) has attracted much attention due to its significant advantage of increasing the utilization ratio of wireless channels. However, Frequency-Division Duplex (FDD) systems are vulnerable to eavesdropping, since the explicit CSI feedback can be manipulated. In this paper, we show that Time-Division Duplex (TDD) systems are insecure as well. In particular, we show that it is possible to eavesdrop on other users' downloads by tuning training sequences. In order to defend MU-MIMO against such threats, we propose a secure CSI estimation scheme, which can provide correct estimates of CSI when adversarial users are in presence. We prove that our scheme is secure against training sequence based eavesdropping attack. We have implemented our scheme for TDD MU-MIMO systems and performed a series of experiments. Results demonstrate that our secure CSI estimation scheme is highly effective in protecting TDD MIMO networks against eavesdropping attack. Furthermore, we extend our scheme to support massive MU-MIMO networks, with a carefully redesigned uplink protocol and optimized power allocation to achieve higher spectral efficiency. To be more practical, we also take mismatch channel issue into our consideration. An enhancement scheme is proposed and we show that our scheme with enhancement is secure and correct under mismatch channel.

Probability of Pilot Interference in Pulsed Radar-Cellular Coexistence: Fundamental Insights on Demodulation and Limited CSI Feedback

This letter considers an underlay pulsed radar-cellular spectrum sharing scenario, where the cellular system uses pilot-aided demodulation, statistical channel state information (S-CSI) estimation and limited feedback schemes. Under a realistic system model, upper and lower bounds are derived on the probability that at least a specified number of pilot signals are interfered by a radar pulse train in a finite CSI estimation window. Exact probabilities are also derived for important special cases which reveal operational regimes where the lower bound is achieved. Using these results, this letter (a) provides insights on pilot interference-minimizing schemes for accurate coherent symbol demodulation, and (b) demonstrates that pilot-aided methods fail to accurately estimate S-CSI of the pulsed radar interference channel for a wide range of radar repetition intervals.

Learning and Data-Driven Beam Selection for mmWave Communications: An Angle of Arrival-Based Approach

This paper investigates how angle-of-arrival (AoA) information can be exploited by deep-/machine-learning approaches to perform beam selection in the uplink of a mmWave communication system. Specifically, we consider a hybrid beamforming setup comprising an analog beamforming (ABF) network with adjustable beamwidth followed by a zero-forcing baseband processing block. The goal is to select the optimal configuration for the ABF network based on the estimated AoAs of the various user equipments. To that aim, we consider 1) two supervised machine-learning approaches: k -nearest neighbors (kNN) and support vector classifiers (SVC); and 2) a feed-forward deep neural network: the multilayer perceptron. We conduct an extensive performance evaluation to investigate the impact of the quality of CSI estimates (AoAs and powers) obtained via the Capon or MUSIC methods, fluctuations in the received power, the size of the training dataset, the total number of analog beamformers in the codebook, their beamwidth, or the number of active users. The computer simulations reveal that performance, in terms of classification accuracy and sum-rate is very close to that achievable via exhaustive search.

Achievable Rate of Spectrum Sharing Cognitive Radio Multiple-Antenna Channels

We investigate the spectral efficiency gain of an uplink cognitive radio (CR) multi-input-multi-output system in which the secondary user (SU) is allowed to share the spectrum with the primary user (PU) using a specific precoding scheme to communicate with a common receiver. The proposed scheme exploits, at the same time, the free eigenmodes of the primary channel after a space alignment procedure and the interference threshold tolerated by the PU. At the common receiver, we adopt a successive interference cancellation (SIC) technique to eliminate the effect of the detected primary signal transmitted through the exploited eigenmodes. Furthermore, we analyze the SIC operation inaccuracy as well as the CSI estimation imperfection on the PU and SU throughputs. Numerical results show that our proposed scheme enhances considerably the cognitive achievable rate. For instance, in case of a perfect detection of the PU signal, the CR rate remains non-zero for high signal to noise ratio, which is usually impossible when we only employ a space alignment technique. We show that a modified water-filling power allocation policy at the PU can increase the secondary rate with a marginal degradation of the primary rate. Finally, we investigate the behavior of the PU and SU rates through the study of the rate achievable region.

Performance Analysis for Analog Network Coding with Imperfect CSI in FDD Two Way Channels

AbstractA time-division duplex (TDD) two-way channel exploits reciprocity to estimate the forward channel gain from the reverse link. Many previous works explore outage probabilities in the TDD system, based on the reciprocity property. However, a frequency-division duplex (FDD) system has no reciprocity property. In this letter, we investigate the impact of CSI estimation error on the performance of non-orthogonal and orthogonal analog network coding protocols in an FDD two-way system, where channel gains are independent of each other. Considering imperfect CSI, the closed-form expressions of outage probabilities by two protocols are derived in the high signal-to-noise ratio (SNR) regime, respectively. It is shown that the derived outage probabilities match results of Monte Carlo simulations in different communication scenarios. It is interesting that ANC in the FDD two-way channel is proved to outperform that in the TDD channel by the computer simulation.

Adaptive CSI and feedback estimation in LTE and beyond: a Gaussian process regression approach

The constant increase in wireless handheld devices and the prospect of billions of connected machines has compelled the research community to investigate different technologies which are able to deliver high data rates, lower latency and better reliability and quality of experience to mobile users. One of the problems, usually overlooked by the research community, is that more connected devices require proportionally more signalling overhead. Particularly, acquiring users’ channel state information is necessary in order for the base station to assign frequency resources. Estimating this channel information with full resolution in frequency and in time is generally impossible, and thus, methods have to be implemented in order to reduce the overhead. In this paper, we propose a channel quality estimation method based on the concept of Gaussian process regression to predict users’ channel states for varying user mobility profiles. Furthermore, we present a dual-control technique to determine which is the most appropriate prediction time for each user in order to keep the packet loss rate below a pre-defined threshold. The proposed method makes use of active learning and the exploration-exploitation paradigm, which allow the controller to choose autonomously the next sampling point in time so that the exploration of the control space is limited while still reaching an optimal performance. Extensive simulation results, carried out in an LTE-A simulator, show that the proposed channel prediction method is able to provide consistent gain, in terms of packet loss rate, for users with low and average mobility, while its efficacy is reduced for high-velocity users. The proposed dual-control technique is then applied, and its impact on the users’ packet loss is analysed in a multicell network with proportional fair and maximum throughput scheduling mechanisms. Remarkably, it is shown that the presented approach allows for a reduction of the overall channel quality signalling by over 90 % while keeping the packet loss below 5 % with maximum throughput schedulers, as well as signalling reduction of 60 % with proportional fair scheduling.

On Symbol-Wise Variational Bayesian CSI Estimation and Detection for Distributed Antenna Systems Subjected to Multiple Unknown Jammers

This letter develops a novel iterative joint channel state information estimation (CSIE), jammer estimation (JE) and data detection (DD) algorithm for orthogonal frequency division multiplexing (OFDM)-based distributed antenna systems in the presence of multiple unknown jammers. In contrast to existing methods that only heuristically combine CSIE, JE and DD, the proposed algorithm rigorously casts the three problems under a unified variational Bayesian inference framework. Simulation results demonstrate that the proposed algorithm converges rapidly, and after convergence, the bit-error-rate performance of the proposed algorithm is very close to that of the ideal case which supposes perfect knowledge of CSI and jammer. In addition, it is shown that the proposed algorithm significantly outperforms other state-of-the-art methods.

Downlink beamforming through relays: imperfect CSI and coordinated transmission

Future wireless systems face challenges in supporting high-rate multimedia streaming with wide coverage area and at low power. Cooperative relaying is investigated in the following context: a single base station with multiple antennas simultaneously transmits different data streams to single-antenna destinations through a set of single-antenna fixed relays, with no direct link transmission between base stations and destinations. Transmission is via space-division multiple access with per-user quality-of-service constraints. The base station performs transmit beamforming (precoding) and relays cooperatively perform distributed beamforming. To address minimum-power source precoding and relay beamforming, the source precoder design with a fixed relay beamformer is first considered. Precoding is also generalized to multiple cooperating base stations. Next, the relay beamformer is optimized for a given source precoder and the process is iterated. The formulation is generalized to account for CSI estimates obtained from pilot symbol training. Simulation results quantify tradeoffs, including numbers of base station antennas and relays, effect of CSI quality on performance, as well as the impact of cooperating base stations.

Capacity maximization in eigen-MIMO with channel estimation and CSI feedback-link throughput constraint

Water-filled eigenchannels offer the highest multi-input multi-output MIMO information-theoretic capacity, but digital techniques such as quadrature amplitude modulation and finite block lengths will degrade the capacity from the Shannon limit to the capacity of a digital link. Furthermore, eigen-MIMO requires channel overheads, such as estimating the channel state information CSI and feeding it back to the transmitter, which further compromise the capacity. In this paper, the joint influence of channel estimation and imperfect feedback on the information-theoretic capacity and the practicable capacity is analyzed. The channel is modeled as static over a MIMO channel block. In each block, the forward channel is used for CSI estimation and for the payload data transmission. In the back direction, the channel is used to feed back a quantized form of the CSI to the transmitter with a throughput constraint. These three channel usages are combined into an effective simplex channel simplifying the capacity analysis. The capacities are formulated as functions of the link parameters, enabling optimization of the number of training symbols, the feedback duration, and the power allocation for training and data transfer, with the criterion of maximum capacity. The results presented are subject to the usual approximations used in communications theory. Copyright © 2012 John Wiley & Sons, Ltd.

Coherent Versus Non-Coherent Decode-and-Forward Relaying Aided Cooperative Space-Time Shift Keying

Motivated by the recent concept of Space-Time Shift Keying (STSK), we propose a novel cooperative STSK family, which is capable of achieving a flexible rate-diversity tradeoff, in the context of cooperative space-time transmissions. More specifically, we first propose a Coherent cooperative STSK (CSTSK) scheme, where each Relay Node (RN) activates Decode and-Forward (DF) transmissions, depending on the success or failure of Cyclic Redundancy Checking (CRC). We invoke a bit to-STSK mapping rule, where according to the input bits, one of the Q pre-assigned dispersion vectors is activated to implicitly convey log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Q bits, which are transmitted in combination with the classic log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> L-bit modulated symbol. Additionally, we introduce a beneficial dispersion vector design, which enables us to dispense with symbol-level Inter-Relay Synchronization (IRS). Further more, the Destination Node (DN) is capable of jointly detecting the signals received from the source-destination and relay destination links, using a low-complexity single-stream-based Maximum Likelihood (ML) detector, which is an explicit benefit of our Inter-Element Interference (IEI)-free system model. More importantly, as a benefit of its design flexibility, our cooperative CSTSK arrangement enables us to adapt the number of the RNs, the transmission rate as well as the achievable diversity order. Moreover, we also propose a Differentially-encoded cooperative STSK (DSTSK) arrangement, which dispenses with CSI estimation at any of the nodes, while retaining the fundamental benefits of the cooperative CSTSK scheme.

Olfactory sensitivity in medical laboratory workers occupationally exposed to organic solvent mixtures

Published epidemiological information relating the effects of occupational exposure to organic solvents (OS) to olfaction is limited. The objectives of this pilot study were to measure the chemosensory abilities of medical laboratory employees occupationally exposed to OS mixtures, to compare these with control workers employed within the same occupational setting and to correlate chemosensory performance with OS exposure history and with employees' hedonic (pleasantness) perceptions about workplace OS odors. Twenty-four medical laboratory employees (OS-exposed technicians plus control workers minimally exposed to OS) completed a health-related questionnaire, a test of pyridine odor detection threshold, along with a gustatory detection threshold test involving aqueous quinine solutions. Estimates of cumulative hours of OS exposure (CSI) were calculated from self-reports. OS-exposed laboratory technicians detected weaker concentrations of pyridine odor. Positive correlations were detected between CSI estimates to both pyridine detection and the degree that participants reported that OS odors were present in the workplace. However, no association was detected between pyridine detection and how unpleasant workplace OS odors were perceived. The OS-exposed participants were able to detect weaker concentrations of quinine. Compared to controls, OS-exposed workers complained more of experiencing several symptoms while working, including headaches, nasal irritation and mild cognitive impairment. The results of this cross-sectional pilot study indicated that, compared to controls, medical laboratory technicians exposed to low-level OS mixtures displayed evidence of elevated olfactory sensitivity (hyperosmia) to pyridine odor. The relation of this study's results to chemical intolerance warrants further investigation.

Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI

This paper presents a Bayesian approach to the design of transmit prefiltering matrices in closed-loop schemes robust to channel estimation errors. The algorithms are derived for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Two different optimization criteria are analyzed: the minimization of the mean square error and the minimization of the bit error rate. In both cases, the transmitter design is based on the singular value decomposition (SVD) of the conditional mean of the channel response, given the channel estimate. The performance of the proposed algorithms is analyzed, and their relationship with existing algorithms is indicated. As with other previously proposed solutions, the minimum bit error rate algorithm converges to the open-loop transmission scheme for very poor CSI estimates.

Modified pre-FFT OFDM adaptive antenna array with beam-space channel estimation

A modified pre-FFT OFDM adaptive antenna array with max-SNIR criterion is proposed, in which the CSI estimation is made on the beam-space. Simulation results show that the proposed scheme can outperform the element-space scheme, especially when strong cochannel interference is present.