Channel State Information Mismatch Research Articles

Data Augmentation Empowered Neural Precoding for Multiuser MIMO With MMSE Model

Precoding design exploiting deep learning methods has been widely studied for multiuser multiple-input multiple-output (MU-MIMO) systems. However, conventional neural precoding design applies black-box-based neural networks which are less interpretable. In this letter, we propose a deep learning-based precoding method based on an interpretable design of a neural precoding network, namely iPNet. In particular, the iPNet mimics the classic minimum mean-squared error (MMSE) precoding and approximates the matrix inversion in the design of the neural network architecture. Specifically, the proposed iPNet consists of a model-driven component network, responsible for augmenting the input channel state information (CSI), and a data-driven sub-network, responsible for precoding calculation from this augmented CSI. The latter data-driven module is explicitly interpreted as an unsupervised learner of the MMSE precoder. Simulation results show that by exploiting the augmented CSI, the proposed iPNet achieves noticeable performance gain over existing black-box designs and also exhibits enhanced generalizability against CSI mismatches.

MHSA-EC: An Indoor Localization Algorithm Fusing the Multi-Head Self-Attention Mechanism and Effective CSI.

Channel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. The CSI signals collected by different fingerprint points have a high degree of discrimination due to the influence of multi-path effects. This multi-path effect is reflected in the correlation between subcarriers and antennas. However, in mining such correlations, previous methods are difficult to aggregate non-adjacent features, resulting in insufficient multi-path information extraction. In addition, the existence of the multi-path effect makes the relationship between the original CSI signal and the distance not obvious, and it is easy to cause mismatching of long-distance points. Therefore, this paper proposes an indoor localization algorithm that combines the multi-head self-attention mechanism and effective CSI (MHSA-EC). This algorithm is used to solve the problem where it is difficult for traditional algorithms to effectively aggregate long-distance CSI features and mismatches of long-distance points. This paper verifies the stability and accuracy of MHSA-EC positioning through a large number of experiments. The average positioning error of MHSA-EC is 0.71 m in the comprehensive office and 0.64 m in the laboratory.

Distributionally Robust Chance-Constrained Backscatter Communication-Assisted Computation Offloading in WBANs

Implementing wireless body area networks (WBANs) is very challenging, due to limited power supply, inadequate computation capability, and imperfect channel state information (CSI). In this paper, we propose a hybrid offloading scheme with backscatter communication (BackCom) under imperfect CSI, where each sensor firstly receives radio frequency (RF) energy and then offloads body data task via low-power BackCom to the access point (AP) for edge computing. Aiming to minimize the end-to-end system latency, we jointly optimize the computation speed of AP for processing computation tasks, the power of the signal transmitted by the AP, and the power reflection coefficient under energy and data rate chance constraints. To solve the proposed distributionally robust chance-constrained optimization problem, we approximate chance constraints by the Bernstein-type-inequality (BTI) method and Conditional value-at-risk (CVaR) method in the Gaussian distribution and arbitrary distribution of channel estimation errors, respectively. To tackle the NP-hard problem efficiently, the original problem can be decomposed into two subproblems, which are solved by successive linear programming and iterative algorithm, respectively. Simulation results show that the CVaR method outperforms the other methods for the non-Gaussian CSI mismatch, and the Bernstein method is more suitable for the Gaussian distribution of CSI errors.

Robust Transceiver Design for Multi-Hop AF MIMO Relay Multicasting From Multiple Sources

In this article, the transceiver design optimization problem is investigated for multi-hop multicasting amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems, where multiple source nodes broadcast their message to multiple destination nodes via multiple serial relay nodes. Multiple antennas are installed at the sources, relays, and the destination nodes. In the transceiver design, we consider the mismatch between the true and the estimated channel state information (CSI), where the CSI mismatch follows the Gaussian-Kronecker model. A robust transceiver design algorithm is developed to jointly optimize the source, relay, and destination matrices to minimize the maximal weighted mean-squared error (WMSE) of the received message at all destination nodes. In particular, the WMSE is made statistically robust against the CSI mismatch by averaging through the distributions of the true CSI. Moreover, the WMSE decomposition is exploited to reduce the computational complexity of the transceiver optimization. Numerical simulations show a better performance of the proposed robust transceiver design against the channel mismatch.

Performance of NOMA-Enabled Cognitive Satellite-Terrestrial Networks With Non-Ideal System Limitations

Satellite-terrestrial networks (STNs) have received significant attention from research and industry due to their capability of providing a stable connection to rural and distant areas, where the allocation of terrestrial infrastructures is uneconomical or difficult. Moreover, the STNs are considered as a promising enabler of fifth-generation communication networks. However, expected massive connectivity in future communication networks will face issues associated with spectrum scarcity. In this regard, the integration of cognitive radio and non-orthogonal multiple access (NOMA) techniques into STNs is considered as a promising remedy. Thereafter, in this article, we investigate NOMA-assisted cognitive STN under practical system conditions, such as transceiver hardware impairments, channel state information mismatch, imperfect successive interference cancellation, and interference noises. Generalized coverage probability formulas for NOMA users in both primary and secondary networks are derived considering the impact of interference temperature constraint and its correctness is verified through Monte Carlo simulation. Furthermore, to achieve performance fairness among the users, power allocation factors based on coverage fairness for primary and secondary NOMA users are provided. Moreover, the numerical results demonstrate superior performance compared to the ones obtained from an orthogonal multiple access scheme and examine the imperfection's impact on the system performance in terms of coverage and throughput.

Impact of imperfect channel estimation and antenna correlation on quantised massive multiple‐input multiple‐output systems

This study examines the uplink performance of large-constellation multi-user massive multiple-input multiple-output systems with low-resolution analogue-to-digital converters (ADCs) in the presence of channel correlation and imperfect channel state information (CSI). The base station (BS) employs a large number of antennas for multiplexing and demultiplexing co-channel users with each antenna element having a dedicated radio frequency chain and two low-resolution ADCs. While such ADCs cause data loss due to coarse quantisation, the large number of antennas can be exploited not only to alleviate such a problem but also to make it possible to utilise large-constellation modulation schemes. The results provide an insight into the trade-off between various performance metrics and the number of quantisation bits under a wide range of realistic conditions. It will be shown that 1-bit quantisation provides sufficient resolution with 100 BS antennas to communicate with ten user equipments using quadrature phase shift keying, but the number of quantisation bits must be increased for larger constellations particularly to overcome CSI mismatch and channel correlation. The results also consider the trade-off between average mutual information and power consumption of the low-resolution ADCs. It will be shown that 16-quantum amplitude modulation with 2-bit quantisation may provide a good compromise between energy efficiency and average mutual information.

Interference alignment in MIMO interference broadcast channels with imperfect CSI

In this study, the authors propose four interference alignment (IA) schemes for multiple-input multiple-output (MIMO) interference broadcast channels (IBCs) under imperfect channel state information (CSI). They propose two new algorithms based on minimising the leakage of the interference signals under a generalised imperfect CSI model from two new points of view. The first algorithm is based on alternating minimisation algorithm. The second proposed algorithm is an improved version of minimum weighted leakage interference algorithm with a faster convergence rate. Inspired by the CSI uncertainty model, the authors present two robust IA algorithms based on exploiting stochastic properties of the CSI mismatch. The first one is based on joint signal and IA which reduces the leakage of the interference signals outside the interference subspace and forces the intended signal to fall into the orthogonal complement of the interference subspace. The second algorithm maximises the signal-to-interference-plus-noise ratio (SINR) in a per-user-based approach, while the existing maximum SINR (MSINR) algorithm in literature is a per-stream-based approach. The authors illustrate that the proposed MSINR algorithm requires less channel information and has less computational costs compared to the existing MSINR. The superiority of the proposed schemes over the existing algorithms are confirmed by simulation examples.

A Distributionally Robust Linear Receiver Design for Multi-Access Space-Time Block Coded MIMO Systems

A receiver design problem for multi-access space-time block coded multiple-input multiple-output systems is considered. To hedge the mismatch between the true and the estimated channel state information (CSI), several robust receivers have been developed in the past decades. Among these receivers, the Gaussian robust receiver has been shown to be superior in performance. This receiver is designed based on the assumption that the CSI mismatch has Gaussian distribution. However, in real-world applications, the assumption of Guassianity might not hold. Motivated by this fact, a more general distributionally robust receiver is proposed in this paper, where only the mean and the variance of the CSI mismatch distribution are required in the receiver design. A tractable semi-definite programming (SDP) reformulation of the robust receiver design is developed. To suppress the self-interferences, a more advanced distributionally robust receiver is proposed. A tight convex approximation is given and the corresponding tractable SDP reformulation is developed. Moreover, for the sake of easy implementation, we present a simplified distributionally robust receiver. Simulations results are provided to show the effectiveness of our design by comparing with some existing well-known receivers.

Improved multiple feedback successive interference cancellation algorithms for near‐optimal MIMO detection

In this study, the authors propose an improved multiple feedback successive interference cancellation (IMF-SIC) algorithm and an ordered IMF-SIC (OIMF- SIC) algorithm for near-optimal multiple-input multiple-output (MIMO) detection. In particular, the multiple feedback (MF) strategy in successive interference cancellation (SIC) detector is based on the concept of shadow region, where, if a decision falls in the shadow region, then multiple neighbouring constellation points are used in the decision feedback loop followed by the SIC technique, and the best candidate symbol is selected by using maximum likelihood cost. However, while deciding the best symbol, the shadow condition is not checked in the subsequent layers which may result in an unreliable decision. Thus, to improve the accuracy of a decision, the authors propose an improved MF strategy where the shadow region condition is checked recursively. Further, the authors also propose an OIMF-SIC algorithm where the log likelihood ratio based dynamic ordering is utilised for ordering the detection sequence. Simulation results validate superiority of the proposed algorithms over the other SIC based detection techniques. In addition, to validate robustness of the proposed algorithms, BER performance is computed and compared under channel state information mismatch.

Optimization of a MIMO amplify-and- forward relay system with channel state information estimation error and feedback delay

This paper addresses the robust design of a multiple-input multiple-output amplify-and-forward relay system against channel state information (CSI) mismatch due to estimation error and feedback delay. The estimation error and feedback delay are expressed using appropriate models, from which we derive the conditional mean square error (MSE) between the desired and the received signals upon the estimated CSI. The conditional MSE is then minimized to optimize the relay beamforming matrix with relay transmission power constraint. It is shown that the proposed optimization problem reduces to the conventional minimum MSE problem when CSI mismatch vanishes. By analyzing the structure of the optimal beamforming matrix, the optimization problem is simplified so that it can be directly solved using the genetic algorithm (GA). To further reduce the computational load, we develop a relaxed version of the optimization problem. It is found that the relaxation enables us to efficiently solve the problem using water filling strategy. Computer simulations show that both GA and water filling solutions are superior to conventional solutions without CSI mismatch consideration, while the water filling is 1000 times faster than the GA.

Achievable sum‐rate maximization using relay/antenna selection for MIMO two‐way AF relaying with channel uncertainties

SummaryThis paper focuses on the layered relay‐and‐antenna selection (LRAS) for achievable sum‐rate (ASR) maximization while considering the impacts of residual self‐interference due to channel estimation errors in multiple‐input multiple‐output two‐way amplify‐and‐forward relaying systems. Two LRAS algorithms, namely, the Gram–Schmidt and the adaptive discrete stochastic approximation selection techniques, are investigated based on the ASR maximization under an equal power allocation. To alleviate the complexity burden of the LRAS strategies, the optimal relay and the subset of transmit‐and‐receive antenna pairs are determined by a two‐stage selection mechanism. By taking two LRAS strategies and correlated channel uncertainties into account, the development of a two‐way multiple‐input multiple‐output multi‐amplify‐and‐forward‐relay system is able to provide improved robustness against the channel state information mismatch and the residual self‐interference. Copyright © 2016 John Wiley & Sons, Ltd.

Optimal downlink beamforming for statistical CSI with robustness to estimation errors

We consider the problem of worst-case robust downlink beamforming in a multiuser Multiple-input–single-output (MISO) network with statistical channel state information (CSI) that is erroneous. In previous beamforming techniques, robustness is usually incorporated by employing generic error models, in which the mismatch between the presumed and the true instantaneous channel vectors, or the corresponding channel covariance matrices, is bounded by the Frobenius norm. A shortcoming of these methods is that these strongly rely on the assumption that the exact noise variance of the terminals and bounds on the channel mismatch are available. If this assumption is violated, then the beamforming approach may result in either insufficient robustness or overly conservative solutions. This assumption is particularly difficult as different sources of CSI error are generally not considered. In our proposed robust approach, we consider CSI mismatch from estimation errors in the noise power and the channel plus noise covariance matrix. Addressing both estimation errors separately allows us to derive meaningful threshold values for the uncertainty sets. In contrast to previous robust approaches based on Frobenius norm, we propose to use confidence intervals with given confidence levels to model the uncertainty sets. The simulation results verify the effectiveness of the proposed techniques.

Channel-Aware Spectrum Sensing and Access for Mobile Cognitive Radio Ad Hoc Networks

In hardware-constrained cognitive radio (CR) ad hoc networks, secondary users (SUs) with limited sensing capabilities strive to discover and share available spectrum resources without impairing primary-user (PU) transmission. Sensing strategy design objectives include high CR network throughput, resolved SU competition, distributed implementation, and reliable performance under node mobility. However, these objectives have not been realized by previously investigated sensing strategies. A novel sensing strategy is analyzed, where the reward is adapted to the SU-link channel state information (CSI) prior to sensing, thus randomizing sensing decisions and boosting the network throughput. Moreover, CSI-aided sensing is combined with a novel first-come-first-served (FCFS) medium access control (MAC) scheme that resolves SU competition prior to sensing. Finally, a pilot-based CSI prediction method is developed to enable the proposed CSI-aided sensing strategies for mobile scenarios. Analytical and numerical results demonstrate that the proposed sensing and access methods significantly outperform nonadaptive sensing strategies for practical mobile CR scenarios with CSI mismatch and pilot overhead.

Unitary Beamformer Designs for MIMO Interference Broadcast Channels

Interference broadcast channel (IBC) is a generalized scenario for interference channel (IC) where each base station (BS) transmits to multiple users in its own cell while sharing same time-frequency resources with other adjacent BSs. In this case, it has been shown that by using interference alignment (IA) and under the availability of perfect channel state information (CSI), the achievable degrees of freedom (DoFs) can be linearly scaled up with the number of users. However, in the presence of imperfect CSI, the sum rate becomes degraded, and the achievability of full DoF can not be guaranteed. In this paper, considering a multiple-input-multiple-output (MIMO) IBC with constant channel coefficients, we propose IA algorithms that result in unitary beamformers. First, we consider two improved beamformer designs based on minimizing the weighted-leakage interference (Min-WLI) and maximizing the signal-to-interference-plus-noise ratio (Max-SINR). Second, we propose a novel minimum-mean-square-error (MMSE)-based IA algorithm. Both the improved Max-SINR and the MMSE-based IA schemes are optimized upon the knowledge of the channel estimation error variance so as to achieve better performance under CSI mismatch. We then demonstrate that these two schemes outperform Min-WLI and the conventional Max-SINR IA under perfect and imperfect CSI. Furthermore, it is shown that the MMSE-based IA achieves the same performance trend as the improved Max-SINR IA does; however, the former needs less CSI to be available and has less computational complexity compared to the latter. Moreover, it is established that the MMSE-based IA can result in diagonalized, decoupled subchannels under perfect CSI. The superiority of the proposed algorithms over the conventional schemes is corroborated by numerical simulations.

Adaptive LS- and MMSE-Based Beamformer Design for Multiuser MIMO Interference Channels

In the presence of perfect channel state information (CSI), the achievable degrees of freedom (DoF) in wireless interference networks can be linearly scaled up with the number of users. Achievability is based on the idea of interference alignment (IA). However, in the presence of imperfect CSI, the sum rate becomes degraded, and full DoF may no longer be achievable. In this paper, the authors propose novel least squares (LS)- and minimum mean square error (MMSE)-based IA schemes that adaptively design beamformers by relying on the availability of imperfect CSI and knowledge of the channel estimation error variance in advance. Interestingly and unlike the other robust algorithms, the proposed adaptive schemes do not impose extra computational complexity compared to their nonadaptive counterparts. It is shown that the LS-based IA is able to outperform interference leakage minimization algorithms under both perfect and imperfect CSI. Furthermore, the authors compare the performance of the proposed MMSE-based IA with maximum signal-to-interference-plus-noise ratio (Max-SINR) algorithm. The authors show that while under perfect CSI, the MMSE-based IA achieves the same performance as that of Max-SINR, the former outperforms the latter under CSI mismatch. Meanwhile, it is shown that the proposed MMSE-based IA needs less CSI to be available and has less computational complexity compared to Max-SINR.

Channel inversion and regularization revisited

In multiuser multiple-input-single-output (MISO) downlink, linear precoders like channel inversion (CI) and regularized CI (RCI) are more desirable than their nonlinear counterparts due to their reduced complexity. To achieve the full benefits of linear precoding, the availability of perfect channel state information (CSI) at base stations (BSs) is necessary. Since in practice, having access to perfect CSI is not pragmatic, in this paper, we evaluate the performance of CI and RCI under a generalized, imperfect CSI model where the variance of the channel estimation error depends on the signal-to-noise ratio (SNR) and thus covers digital and analog feedbacks as two special cases. Then, based on this imperfect CSI model, we quantify the asymptotic mean loss in sum rate and the achievable degrees of freedom (DoFs) by deriving the received signal-to-interference-plus-noise ratio (SINR) of each user. For example, it is shown that the achievable DoF is directly related to the SNR exponent of the channel estimation error variance. Also, two asymptotic gaps to capacity for the analog feedback are derived: mean loss in sum rate and power loss. In addition, we propose an adaptive RCI technique by deriving an appropriate regularization parameter as a function of the error variance and without imposing any restrictions on the number of users or antennas. It is shown that in the presence of CSI mismatch, while the comparative improvement of the standard RCI to CI becomes negligible, the adaptive RCI compensates this degraded performance of the standard RCI without introducing any extra computational complexity.

Adaptively Regularized Phase Alignment Precoding for Multiuser Multiantenna Downlink

In multiantenna downlink, linear precoders like channel inversion (CI), regularized CI (RCI), and phase alignment (PA) are more desirable than their nonlinear counterparts due to their reduced complexity. However, to exploit the full benefits of multiuser downlink, the availability of perfect channel state information (CSI) at the base station (BS) is mandatory. Since such an assumption is not realistic in practice, subject to CSI mismatch, the downlink precoders may undergo a severe degradation in performance. Therefore, an adaptive linear precoder is required to achieve better performance under the availability of imperfect CSI at the BS. In this paper, we propose such a linear precoder by judiciously picking up an optimized regularization parameter based on the knowledge of the channel estimation error variance in advance. To do so, we first introduce a generalized CSI mismatch model where the variance of the channel estimation error is a function of the signal-to-noise ratio (SNR) and is thus able to accommodate a variety of distinct scenarios such as reciprocal channels and CSI feedback. It is shown that the proposed precoding scheme, dubbed adaptively regularized PA (RPA), is capable of achieving lower bit error rates (BERs) compared to standard linear precoders under both perfect and imperfect CSI.

Performance Analysis of IA Techniques in the MIMO IBC With Imperfect CSI

In this work we consider the multiple-input multiple-output (MIMO) interference broadcast channel (IBC) and analyse the performance of interference alignment (IA) under imperfect channel state information (CSI), where the variance of the CSI error depends on the signal-to-noise ratio (SNR). First, we derive an upper bound on asymptotic mean loss in sum rate compared to the perfect CSI case and then we quantify the achievable degrees of freedom (DoF) with imperfect CSI. Both sum rate loss and achievable DoF are found to be dependent on the number of cells in the system and the amount of users per cell, in addition to the CSI error parameters themselves. Results show that when error variance is inversely proportional to SNR, full DoF are achievable and the asymptotic sum rate loss is bounded by a derived value. Additionally if the CSI imperfection does not disappear for asymptotically high SNR, then the full DoF gain promised by IA cannot be achieved; we quantify this loss in relation to the CSI mismatch itself. The analytically derived bounds are validated via system simulation, with the cellular counterparts of the maximum signal-to-interference-plus-noise ratio (Max-SINR) and the minimum weighted leakage interference (Min-WLI) algorithms being the IA techniques of choice. Secondly, inspired by the CSI mismatch model used to derive the bounds, we present a novel Max-SINR algorithm with stochastic CSI error knowledge (Max-SINR-SCEK) for the MIMO IBC. Simulations show that the proposed algorithm improves performance over the standard one under imperfect CSI conditions, without any additional computational costs.

Robust collaborative relay beamforming design for two-way relay systems with reciprocal CSI

In this paper, we propose a robust collaborative relay beamforming scheme in the presence of imperfect channel state information (CSI) for a multiple single-antenna two-way relays network with reciprocal channels. Many existing beamforming schemes have been investigated using perfect CSI for relay-aided systems. However, in practical communication systems the channel estimation is always inaccurate at relay nodes, leading to the deviation between the true CSI and the estimated CSI. Aiming to tackle the performance degradation caused by the CSI mismatch, a constrained optimization problem corresponding to the robust relay beamforming vector is formulated, in which the channel estimation error is characterized by the stochastic error model. Herein the commonly used performance metric, expected weighted sum mean squared error, is adopted as design objective to evaluate the overall link reliability. It is found that due to the intractable expression of the cost function, we can not resort to the traditional methods for solving the involved optimization problem. Whereas, by exploring the lower bound of the cost function using Jensen's inequality and the Taylor series expansion, the original optimization problem is recast to a convex optimization in the form of Rayleigh---Ritz ratio, which results in a closed-form robust relay beamforming solution. Numerical results verify the robustness and superiority of the proposed scheme against CSI errors in terms of system sum rate and average BER.

Robust Design for Amplify-and-Forward MIMO Relay Systems With Direct Link and Imperfect Channel Information

In this paper, we propose statistically robust design for multiple-input multiple-output (MIMO) relay systems with the direct source-destination link and imperfect channel state information (CSI). The minimum mean-squared error (MMSE) of the signal waveform estimation at the destination node is adopted as the design criterion. We develop two iterative methods to solve the nonconvex joint source, relay, and receiver optimization problem. In particular, we derive the structure of the optimal relay precoding matrix and show the effect of CSI mismatch on the structure of the optimal robust source and relay matrices. The proposed algorithms generalize the transceiver design of MIMO relay systems with the direct link to the practical scenario of imperfect CSI knowledge. Simulation results demonstrate an improved performance of the proposed algorithms with respect to the conventional methods at various levels of CSI mismatch.