Channel State Information Estimation Research Articles

Simple and Robust Log-Likelihood Ratio Calculation of Coded MPSK Signals in Wireless Sensor Networks for Healthcare

The simple and robust log-likelihood ratio (LLR) computation of coded Multiple Phase Shift Keying (MPSK) signals in Wireless Sensor Networks (WSNs) is considered under both phase noncoherent and Rayleigh fading channels for healthcare applications. We first simplify the optimal LLR for phase noncoherent channel, the estimation of the instantaneous channel state information (CSI) for both the fading amplitude and the additive white Gaussian noise (AWGN) is successfully avoided, and the complexity-intensive process for zero-order Bessel function of the first kind is also perfectly eliminated. Furthermore, we also develop the simplified LLR under Rayleigh fading channel. Correspondingly, the variance estimation for both AWGN and the statistical characteristic of the fading amplitude is no longer required, and the complicated process for implementation of the exponential function is also successfully avoided. Compared to the calculation of optimal LLR with full complexity, the proposed method is implementation-friendly, which is practically desired for energy-limited WSNs. The simulations are developed in the context of low-density parity-check (LDPC) codes, and the corresponding results show that the detection performance is extremely close to that of the full-complexity LLR metrics. That is, the performance degradation is efficiently prevented, whereas complexity reduction is also successfully achieved.

A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback

Channel state information (CSI) plays a vital role in scheduling and capacity-approaching transmission optimization of massive MIMO communication systems. In frequency division duplex (FDD) MIMO systems, forward link CSI reconstruction at transmitter relies on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent application of recurrent neural networks (RNN) has demonstrated promising results of massive MIMO CSI feedback compression. However, the cost of computation and memory associated with RNN deep learning remains high. In this work, we exploit channel temporal coherence to improve learning accuracy and feedback efficiency. Leveraging a Markovian model, we develop a deep convolutional neural network (CNN)-based framework called MarkovNet to efficiently encode CSI feedback to improve accuracy and efficiency. We explore important physical insights including spherical normalization of input data and deep learning network optimizations in feedback compression. We demonstrate that MarkovNet provides a substantial performance improvement and computational complexity reduction over the RNN-based work. We demonstrate MarkovNet’s performance under different MIMO configurations and for a range of feedback intervals and rates. CSI recovery with MarkovNet outperforms RNN-based CSI estimation with only a fraction of computational cost.

Exploiting angular spread in channel estimation of millimeter wave MIMO system

Millimeter wave (mmWave) frequency spectrum can mitigate severe spectrum shortage caused by the explosive growth of mobile data demand. To overcome the high propagation loss of mmWave signals, massive multi-input multi-output (MIMO) and hybrid architecture are employed. As in microwave communication systems, channel state information (CSI) is essential to fully achieve the advantages of mmWave communication. However, due to the massive number of antennas and hybrid architecture, the CSI acquisition is challenging. The sparsity of mmWave channel can be utilized to reduce the training overhead. In addition to sparsity, real-world measurements in dense urban propagation environments reveal that the mmWave channel may spread in form of cluster of paths over the angular domains, namely the angular spread. In this paper, it is utilized to formulate the channel estimation as a block-sparse signal recovery problem. The block orthogonal matching pursuit (BOMP) is used to validate the model. Then, block fast Bayesian matching pursuit (BFBMP) algorithm is proposed to solve the above problem. Compared with other existing channel estimation methods, simulation results show that the angular spread feature and the proposed BFBMP can considerably improve the CSI estimation with less complexity.

Phase Calibration for Intelligent Reflecting Surfaces Assisted Millimeter Wave Communications

In this paper, wepropose a novel two-stage transmission framework for an intelligent reflecting surface (IRS) assisted millimeter wave (mmWave) system in the presence of phase error caused by hardware impairments. Specifically, in Stage I, we introduce a pilot-based phase calibration task and reformulate it as an alternating optimization program. Building on this formulation, an efficient iterative algorithm is developed to jointly estimate the phase error, which needs to be calibrated, and the channels corresponding to the first user. Stage II consists of two blocks, channel estimation and data transmission. During the channel estimation block, by leveraging on the sparsity of mmWave channels, we propose a fast Fourier transform based scheme to estimate the IRS-BS channel and user-IRS channels. During the data transmission block, based on the estimated channel state information, base station precoders and IRS phase shifts are jointly optimized to maximize the sum signal-to-leakage-plus-noise ratio of all users. To handle this non-convex problem, we further develop a low-complexity joint beamforming algorithm by exploiting fractional programming techniques. Moreover, to examine the performance of the proposed phase calibration scheme, we derive the Cramer-Rao lower bound for the intended phase error estimation. Simulation results validate the effectiveness of our proposed transmission framework and the necessity of IRS phase calibration.

Deep Expectation-Maximization for Joint MIMO Channel Estimation and Signal Detection

To overcome the influence of channel estimation error on signal detection, this paper presents a model-driven deep learning method for joint channel estimation and signal detection in multiple-input multiple-output (MIMO) wireless communication systems. To improve the robustness of the method, we use the Student's <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -distribution to model the environment noise, and model the joint problem probabilistically by taking the channel state information (CSI) as a latent variable, then derive a generalized expectation maximization (GEM) algorithm to fit the model. In GEM, the expectation step is used for robust CSI estimation while the maximization step detects the transmitted signal with the estimated CSI. To reduce the computational complexity of GEM, we unfold it into a deep neural network. The network contains only a few trainable parameters, which is easy to train and has low space complexity. Based on our experimental results, we find that the proposed GEM algorithm outperforms a variety of signal detection algorithms which take the CSI estimation and signal detection as independent modules. Further, we find empirically that the proposed GEM network outperforms state-of-the-art deep learning based joint channel estimation and signal detection methods and has a good generalization ability against the number of antennas, the modulation mode, the level of signal-to-noise ratio and channel correlation.

Bayesian Learning Aided Simultaneous Row and Group Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated MIMO Systems

A sparse channel state information (CSI) estimation model is proposed for reducing the pilot overhead of orthogonal time frequency space (OTFS) modulation aided multiple-input multiple-output (MIMO) systems. Explicitly, the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI that leads to a reduction of the pilot overhead, training duration and pre-processing complexity. Furthermore, it completely avoids placing multiple DD-domain guard intervals corresponding to each transmit antenna within the same OTFS frame, while keeping the training duration flexible, hence increasing the bandwidth efficiency. A unique benefit of the proposed CSI estimation model is that it can efficiently handle fractional Dopplers also. The resultant DD-domain CSI becomes simultaneously row and group (RG)-sparse. To exploit this compelling property, an orthogonal matching pursuit (OMP)-based RG-OMP technique is developed, conveniently complemented by an enhanced Bayesian learning (BL)-based RG-BL framework, both of which substantially outperform the state-of-the-art methods. Furthermore, low-complexity linear detectors are designed for the ensuing data detection phase, which directly employ the estimated DD-domain sparse CSI, without assuming any further knowledge concerning the number of dominant multipath components. Finally, simulation results are provided to demonstrate performance improvement of the proposed BL-based schemes over the OMP and the state-of-the-art schemes.

Cooperative Mode Switching-Based Cognitive NOMA With Transmit Antenna and User Selection

The paper presents performance analysis of a multiuser Cooperative Non-Orthogonal Multiple Access (CNOMA) network that is underlay to a primary network. A selected secondary Full-Duplex (FD) Near User (NU) cooperates with a selected secondary Far User (FU) by apportioning the Interference Temperature Limit (ITL) with an ITL Apportioning Parameter (ITLAP). This Cooperative Mode (CM) is exercised only when NU meets its own performance criteria, else the network switches to a Non-Cooperative Mode (NCM). The network is therefore made adaptive in such a way that performance of the NU is same as in a network without the FU. Performance of the network is further improved using Maximum Transmit Power-based Transmit Antenna Selection (MTP-TAS), Best NU-Best FU (BNBF) selection, and optimal power allocation, thereby also motivating NOMA even in power-constrained underlay environments where transmit powers are not always high. The selection mechanisms are designed in a way that additional overheads for Channel State Information (CSI) estimation and feedback are not imposed. Analytical expressions for outage probability are presented for both end users, assuming only statistical knowledge of channel gains under the assumption of imperfect Successive Interference Cancellation (SIC) at the NU. Outage minimizing optimal values of the NOMA Power Allocation Parameter (NPAP) and ITLAP are derived that minimize FU outage. Simulations validate the derived expressions, and demonstrate superiority of the proposed scheme over FD NU-based conventional NOMA as well as conventional OMA signalling.

Deep learning for fast channel estimation in millimeter-wave MIMO systems

Channel estimation has been considered as a key issue in the millimeter-wave (mmWave) massive multi-input multioutput (MIMO) communication systems, which becomes more challenging with a large number of antennas. In this paper, we propose a deep learning (DL)-based fast channel estimation method for mmWave massive MIMO systems. The proposed method can directly and effectively estimate channel state information (CSI) from received data without performing pilot signals estimate in advance, which simplifies the estimation process. Specifically, we develop a convolutional neural network (CNN)-based channel estimation network for the case of dimensional mismatch of input and output data, subsequently denoted as channel (H) neural network (HNN). It can quickly estimate the channel information by learning the inherent characteristics of the received data and the relationship between the received data and the channel, while the dimension of the received data is much smaller than the channel matrix. Simulation results show that the proposed HNN can gain better channel estimation accuracy compared with existing schemes.

Robust Tensor-Based Algorithm for UAV-Assisted IoT Communication Systems via Nested PARAFAC Analysis

In this paper, we propose a robust tensor-based strategy to jointly estimate channel state information (CSI) and detect information symbols for unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) communication systems. First, a superimposed signal transmission scheme is designed for each IoT device, and the superimposed signals are transmitted to UAVs simultaneously. Then, UAVs amplify and forward the received signals to the base station (BS), where a combined nested parallel factor (PARAFAC) tensor model is constructed. Finally, a robust tensor-based algorithm is derived to estimate full knowledge of CSI and detect information symbols. Simulation results show that the proposed algorithm offers superior performance compared with the competitive methods.

Pilot Contamination Mitigation in Massive MIMO Cloud Radio Access Networks

Massive multiple-input multiple-output (MIMO) technology is expected to achieve significant gains in both signal-to-noise-plus-interference ratio (SINR) and throughput for 5G cellular wireless networks. Efficient and highly accurate channel state information (CSI) acquisition at the base stations (BS) is essential to achieve the potential benefits of massive MIMO systems. However, the inadequate number of orthogonal pilot sequences used for CSI estimation leads to erroneous channel estimation as it causes interference between pilot sequences. This phenomenon is coined pilot contamination and severely limits the system performance. Therefore, we address in this paper the pilot contamination problem in massive MIMO Cloud-Radio Access Networks systems. Leveraging on a real telecom operator data delivered by Call Detail Records (CDR), our objective is to maximize the average uplink achievable rate by reducing the effect of pilot contamination in massive MIMO Cloud-Radio Access Networks (C-RAN). As such a problem is a non-linear integer problem that has no solution in polynomial time, we develop a two-stage solution to solve it. First, a coalition game is proposed where Remote Radio Heads (RRHs) gather into clusters with random user-pilot allocation. Then, two greedy heuristics are applied to match each user in the cluster with a given pilot. The goal is to further improve the average uplink rate achieved in the first stage. Simulation results show that our heuristic solutions for pilot contamination mitigation outperform the traditional pilot allocation solution and a state-of-the-art pilot allocation scheme based on large-scale fading in terms of average uplink achievable rate and SINR.

Adaptive channel estimation for cognitive fully adaptive radar

The paper addresses channel state information estimation for a cognitive radar system. The underlying radar signal model considered in this paper is based on a stochastic Green's function approach. In the radar model, the clutter returns can be expressed as a convolution of the stochastic Green's function impulse response with the transmit radar waveform. The paper proposes and solves constrained channel estimation algorithms that exploit the cosine similarity constraint and the inner product constraint to improve conventional least squares solutions. Using the fact that adjacent channel transfer functions have a close relationship in terms of a cosine similarity metric, the cosine similarity constraint can be exploited in the least squares problem. It turns out that the derived optimisation problem under the cosine similarity constraint is non-convex. However, since strong duality holds for the non-convex optimisation problem, it can be solved using the semi-definite relaxation (SDR) approach. The paper also proposes a new optimisation problem with an additional inner product constraint, which results in a convex second-order cone programming (SOCP) and is robust to the signal-to-noise ratio (SNR) in terms of the cosine similarity measurement. Numerical simulations are provided to verify the performance of the proposed methods on a challenge dataset generated using the high-fidelity modelling and simulation tool, RFView®.

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.

Secure energy efficient power allocation in massive multiple‐input multiple‐output systems with an eavesdropper using cell division technique

SummaryIn this paper, we study energy‐efficient power allocation (PA) to secure the confidential information of massive multiple‐input multiple‐output (MIMO) systems in the presence of an eavesdropper. First a training scheme is presented in the uplink using the MMSE method. Based on the estimated channel state information (CSI), the base station (BS) encrypts the confidential information. Next, the ergodic secret rate is obtained at the downlink. In this paper, in order to maximize the ergodic secrecy rate, the allocation of common power in the uplink and downlink is proposed by considering the constraints such as the transmit power constraint in the uplink, the transmit power constraint in the downlink, and the minimum ergodic secret rate constraint. The objective function in the proposed scenario is a nonconvex problem, which first becomes a convex problem and then, by using the Lagrange dual function method, the problem becomes an unconstraint problem. We also divide the cell into two areas using the cell division method. A practical energy‐efficient PA algorithm is presented which deals with the adverse effect of the eavesdropper. Simulations are carried out to demonstrate the effectiveness of the proposed methods.

Computation Offloading in MIMO Based Mobile Edge Computing Systems Under Perfect and Imperfect CSI Estimation

Intelligent offloading of computation-intensive tasks to a mobile cloud server provides an effective mean to expand the usability of wireless devices and prolong their battery life, especially for low-cost internet-of-things (IoT) devices. However, realization of this technology in multiple-input multiple-output (MIMO) systems requires sophisticated design of joint computation offloading and other network functions such as channel state information (CSI) estimation, beamforming, and resource allocation. In this paper, we study the computation task offloading and resource allocation optimization in MIMO based mobile edge computing systems considering perfect/imperfect-CSI estimation. Our design aims to minimize the maximum weighted energy consumption subject to practical constraints on available computing and radio resources and allowable latency. The optimal and low-complexity algorithms are proposed to solve the underlying mixed integer non-linear problems (MINLP). For the perfect-CSI, we employ bisection search to find the optimal solution. The low-complexity algorithms are developed by decomposing the original optimization problem into the offloading optimization (OP) and power allocation (PA) subproblems and solve them iteratively. Moreover, the difference of convex functions (DC) method is employed to deal with non-convex structure of (PA) subproblems in the imperfect-CSI scenario. Numerical results confirm the advantages of proposed designs over conventional local computation strategies in energy saving and fairness.

Impacts of Asynchronous Reception on Cell-Free Distributed Massive MIMO Systems

Multiple remote antenna units (RAUs) cooperate to serve a certain user in cell-free distributed massive multiple-input multiple-output (MIMO) systems. Due to the different distances between each RAU and the user, asynchronous reception effects will occur. This paper studies the impacts of asynchronous reception effects on cell-free distributed massive MIMO systems. We first derive the minimum-mean-square error (MMSE) channel estimation under the asynchronous reception effects. Given the estimated channel state information, we derive the closed-form expressions of the user achievable rates with maximum ratio transmission (MRT) and regularized zero-forcing (RZF) precoders, which are further used to analyze the impacts of asynchronous reception effects. The accuracy of the closed-form expressions are verified. Simulation results demonstrate that asynchronous reception effects cause a non-negligible drop on the system spectral efficiency. Moreover, the performance degradation is proportional to the subcarrier sequence number and even more serious with the increasing of signal-to-noise ratio (SNR) or the number of antennas per RAU.

Intelligent Reflecting Surfaces: Sum-Rate Optimization Based on Statistical Position Information

In this paper, we consider a multi-user multiple-input multiple-output (MIMO) system aided by multiple intelligent reflecting surfaces (IRSs) that are deployed to increase the coverage and, possibly, the rank of the channel. We propose an optimization algorithm to configure the IRSs, which is aimed at maximizing the network sum-rate by exploiting only the statistical characterization of the locations of the mobile users. As a consequence, the proposed approach does not require the estimation of either instantaneous channel state information (CSI) or second-order channel statistics for IRS optimization, thus significantly relaxing (or even avoiding) the need of frequently reconfiguring the IRSs, which constitutes one of the most critical issues in IRS-assisted systems. Numerical results confirm the validity of the proposed approach. It is shown, in particular, that IRS-assisted wireless systems that are optimized based on statistical position information still provide large performance gains as compared to the baseline scenarios in which no IRSs are deployed.

Satellite-Assisted Cell-Free Massive MIMO Systems with Multi-Group Multicast.

In this paper, we use satellite-assisted and multi-group multicast mechanisms to relieve ground traffic pressure and improve data transmission efficiency of cell-free massive MIMO systems. We propose to estimate channel state information (CSI) by common pilot scheme. Given the estimated CSI, we derive the closed-form expressions of achievable rate with maximum ratio transmission (MRT) and zero-forcing (ZF) precoding. The correctness of the closed-form expressions is verified through simulations. The results show that with the help of satellite and multicast, the average system spectrum efficiency (SE) can be significantly improved.

ADMM Based Channel Estimation for RISs Aided Millimeter Wave Communications

In reconfigurable intelligent surfaces (RISs) aided millimeter wave communication systems, accurate channel state information (CSI) is particularly crucial to achieve high passive beamforming gain. However, the complexity of channel estimation increases significantly due to the high-dimensional channel matrix and the additional RISs' assisted channels. To tackle this challenge, an alternating direction method of multipliers (ADMM) based channel estimation method is proposed to improve the CSI estimation accuracy and reduce the training overhead. By converting the cascaded channel matrix into a sparse matrix recovery problem, both low rank and sparsity properties are exploited by the ADMM algorithm to solve the sparse matrix recovery problem. The simulation results verify that the proposed ADMM algorithm can significantly increase the accuracy of channel estimation and reduce the training overhead, compared with the well-known competing channel estimation algorithms.

Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach.

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.

Bayesian Learning Aided Sparse Channel Estimation for Orthogonal Time Frequency Space Modulated Systems

A novel sparse channel state information (CSI) estimation scheme is proposed for orthogonal time frequency space (OTFS) modulated systems, in which the pilots are directly transmitted over the time-frequency (TF)-domain grid for estimating the delay-Doppler (DD)-domain CSI. The proposed CSI estimation model leads to a reduction in the pilot overhead as well as the training duration required. Furthermore, it does not require a DD-domain guard interval between the pilot and data symbols, hence increasing the bandwidth efficiency. A novel Bayesian learning (BL) framework is proposed for CSI acquisition, which exploits the DD-domain sparsity for improving the estimation accuracy in comparison to the conventional minimum mean squared error (MMSE)-based scheme. A low-complexity linear MMSE detector is used in the subsequent data detection phase. Our simulation results demonstrate the performance improvement of the proposed BL-based scheme over the conventional MMSE-based scheme as well as over other existing sparse estimation schemes.