Inaccurate Channel State Information Research Articles

Channel temporal correlation-based optimization method for imperfect underwater acoustic channel state information

The rapid time variations and large channel estimation errors in underwater acoustic (UWA) channels mean that transmitters for adaptive resource allocation quickly become outdated and provide inaccurate channel state information (CSI). This results in poor resource allocation efficiency. To address this issue, this paper proposes an optimization approach for imperfect CSI based on a Gauss–Markov model and the per-subcarrier channel temporal correlation (PSCTC) factor. The proposed scheme is applicable to downlink UWA orthogonal frequency division multiple access systems. The proposed PSCTC factors are measured, and their long-term stability is verified using data recorded in real-world sea tests. Simulation and experimental results show that the optimized CSI effectively mitigates the effects of the temporal variability of UWA channels. It demonstrates that the resource allocation scheme using optimized CSI achieves a higher effective throughput and a lower bit error rate than both imperfect CSI and the CSI predicted by the recursive least-squares (RLS) algorithm.

IMRecoNet: Learn to Detect in Index Modulation Aided MIMO Systems With Complex Valued Neural Networks

Index modulation (IM) reduces the power consumption and hardware cost of the multiple-input multiple-output (MIMO) system by activating part of the antennas for data transmission. However, IM significantly increases the complexity of the receiver and needs accurate channel estimation to guarantee its performance. To tackle these challenges, in this paper, we design a deep learning (DL) based detector for the IM aided MIMO (IM-MIMO) systems. We first formulate the detection process as a sparse reconstruction problem by utilizing the inherent attributes of IM. Then, based on greedy strategy, we design a DL based detector, called IMRecoNet, to realize this sparse reconstruction process. Different from the general neural networks, we introduce complex value operations to adapt the complex signals in communication systems. To the best of our knowledge, this is the first attempt that introduce complex valued neural network to the design of detector for the IM-MIMO systems. Finally, to verify the adaptability and robustness of the proposed detector, simulations are carried out with consideration of inaccurate channel state information (CSI) and correlated MIMO channels. The simulation results demonstrate that the proposed detector outperforms existing algorithms in terms of antenna recognition accuracy and bit error rate under various scenarios.

Online Multicell Coordinated MIMO Wireless Network Virtualization With Imperfect CSI

We consider online coordinated precoding design for downlink wireless network virtualization (WNV) in a multi-cell multiple-input multiple-output (MIMO) network with imperfect channel state information (CSI). In our WNV framework, an infrastructure provider (InP) owns each base station that is shared by several service providers (SPs) oblivious of each other. The SPs design their precoders as virtualization demands for user services, while the InP designs the actual precoding solution to meet the service demands from the SPs. Our aim is to minimize the long-term time-averaged expected precoding deviation over MIMO fading channels, subject to both per-cell long-term and short-term transmit power limits. We propose an online coordinated precoding algorithm for virtualization, which provides a fully distributed semi-closed-form precoding solution at each cell, based only on the current imperfect CSI without any CSI exchange across cells. Taking into account the two-fold impact of imperfect CSI on both the InP and the SPs, we show that our proposed algorithm is within an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\delta)$ </tex-math></inline-formula> gap from the optimum over any time horizon, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> is a CSI inaccuracy indicator. Simulation results validate the performance of our proposed algorithm under two commonly used precoding techniques in a typical urban micro-cell network environment.

Robust transmission design for artificial noise aided multiuser broadcast system in inaccurate channel state information scenario with users of different importance

In this article, robust beamforming (BF) and artificial noise (AN) design method is proposed for multiuser broadcast secure transmission under secrecy outage probability (SOP) constraints. A special scenario is assumed that the transmitter cannot obtain the accurate channel state information (CSI) of the users' channel, and different users have different priorities and importance. This special scenario makes traditional BF algorithms and AN design methods unable to guarantee the effective and secure transmission of information. To deal with intractable non-convex problems in this new design, a series of optimization methods like Bernstein-type inequality (BTI) and S-Procedure are employed to transform these problems into solvable functions. Moreover, a search algorithm is explored for simplifying the computational complexity of the optimization algorithm. After that, the new robust algorithm is compared with former methods in inaccurate CSI scenarios by numerical simulation, which shows the advantages of our algorithm.

Performance Analysis for RIS-Aided Wireless Systems With Imperfect CSI

The impact of imperfect channel state information (CSI) on a reconfigurable intelligent surface (RIS)-aided wireless system is investigated in this letter. More specifically, considering the imperfect CSI from the RIS to the destination, we develop the cumulative distribution function (CDF) of the end-to-end signal-to-interference-plus-noise ratio (SINR). Then, using the CDF, we derive the closed-form expressions of the outage probability (OP), average bit error rate (BER), and average capacity. Furthermore, we also derive the asymptotic OP, average BER, and average capacity at high signal-to-noise ratio (SNR). Simulation results show that inaccurate CSI results in zero diversity order and a floor phenomenon. Also, numerical results demonstrate that the derived closed-form expressions match the simulated results.

Tractable Modelling and Robust Coordinated Beamforming Design With Partially Accurate CSI

Coordinated Beamforming (CB) is an effective technique to maximize weighted sum rate (WSR) targeting at on-demand network capacity. However, the channel state information (CSI) of the base stations (BSs) far away can’t be measured accurately, which will lead to the invalidation of the traditional WSR-maximization beamformer design and eventually result in capacity decrease. This letter proposes a tractable user-centric CSI model and a robust CB design to maximize WSR with partially accurate CSI. Specifically, the CSI model of the coordinated BSs is built for each user, where the inaccurate CSI of BSs far away from this user is modeled according to its static statistic characteristic. To derive a robust beamformer, deterministic equivalent method is adopted to obtain an approximate closed-form expression of the ergodic sum rate. Moreover, the performance of the robust CB design with different model parameters is considered to provide guidance for practical network deployment. Simulation results show that the proposed robust CB design has 30% capacity gain through careful interference management compared with traditional beamforming.

An improvement to hybrid beamforming precoding scheme for mmWave massive MIMO systems based on channel matrix

&lt;p&gt;The massive multiple-input multiple-output (MIMO) technology has been applied innew generation wireless systems due to growing demand for reliability and high datarate. Hybrid beamforming architectures in both receiver and transmitter, includinganalog and digital precoders, play a significant role in 5G communication networksand have recently attracted a lot of attention. In this paper, we propose a simple andeffective beamforming precoder approach for mmWave massive MIMO systems. Wefirst solve an optimization problem by a simplification subject, and in the second step,we use the covariance channel matrixfCk=Cov(Hk)andBk=HkHHkinstead of chan-nel matrixHk. Simulation results verify that the proposed scheme can enjoy a highersum rate and energy efficiency than previous methods such as spatially sparse method,analog method, and conventional hybrid method even with inaccurate Channel StateInformation (CSI). Percentage difference of the achievable rate ofCk=Cov(Hk)andBk=HkHHkschemes compared to conventional methods are 2.51% and 48.94%, re-spectively.&lt;/p&gt;

Is NOMA Efficient in Multi-Antenna Networks? A Critical Look at Next Generation Multiple Access Techniques

In the past few years, a large body of literature has been created on downlink Non-Orthogonal Multiple Access (NOMA), employing superposition coding and Successive Interference Cancellation (SIC), in multi-antenna wireless networks. Furthermore, the benefits of NOMA over Orthogonal Multiple Access (OMA) have been highlighted. In this paper, we take a critical and fresh look at the downlink Next Generation Multiple Access (NGMA) literature. Instead of contrasting NOMA with OMA, we contrast NOMA with two other multiple access baselines. The first is conventional Multi-User Linear Precoding (MU-LP), as used in Space-Division Multiple Access (SDMA) and multi-user Multiple-Input Multiple-Output (MIMO) in 4G and 5G. The second, called Rate-Splitting Multiple Access (RSMA), is based on multi-antenna Rate-Splitting (RS). It is also a non-orthogonal transmission strategy relying on SIC developed in the past few years in parallel and independently from NOMA. We show that there is some confusion about the benefits of NOMA, and we dispel the associated misconceptions. First, we highlight why NOMA is inefficient in multi-antenna settings based on basic multiplexing gain analysis. We stress that the issue lies in how the NOMA literature, originally developed for single-antenna setups, has been hastily applied to multi-antenna setups, resulting in a misuse of spatial dimensions and therefore loss in multiplexing gains and rate. Second, we show that NOMA incurs a severe multiplexing gain loss despite an increased receiver complexity due to an inefficient use of SIC receivers. Third, we emphasize that much of the merits of NOMA are due to the constant comparison to OMA instead of comparing it to MU-LP and RS baselines. We then expose the pivotal design constraint that multi-antenna NOMA requires one user to fully decode the messages of the other users. This design constraint is responsible for the multiplexing gain erosion, rate and spectral efficiency loss, ineffectiveness to serve a large number of users, and inefficient use of SIC receivers in multi-antenna settings. Our analysis and simulation results confirm that NOMA should not be applied blindly to multi-antenna settings, highlight the scenarios where MU-LP outperforms NOMA and vice versa, and demonstrate the inefficiency, performance loss, and complexity disadvantages of NOMA compared to RSMA. The first takeaway message is that, while NOMA is suited for single-antenna settings (as originally intended), it is not efficient in most multi-antenna deployments. The second takeaway message is that another non-orthogonal transmission framework, based on RSMA, exists which fully exploits the multiplexing gain and the benefits of SIC to boost the rate and the number of users to serve in multi-antenna settings and outperforms both NOMA and MU-LP. Indeed, RSMA achieves higher multiplexing gains and rates, serves a larger number of users, is more robust to user deployments, network loads and inaccurate channel state information and has a lower receiver complexity than NOMA. Consequently, RSMA is a promising technology for NGMA and future networks such as 6G and beyond.

Multiuser Full-Duplex Relaying: Enabling Dual Connectivity via Impairments-Aware Successive Interference Cancellation

In this article, we consider the downlink of a cellular communication network, where dual-connectivity at the end-users is enabled with the assistance of a full-duplex (FD) massive-multiple-input-multiple-output (mMIMO) relay. In particular, the base station transmits separate data streams through the cochannel direct link as well as the FD relay channel, utilizing the successive-interference-cancellation (SuIC) capability at the receiver. As a result, the downlink communication data can be interchangeably loaded to separate subcarriers, employing an orthogonal multicarrier strategy, or to different available links, i.e., direct or FD relay link, employing the nonorthogonal SuIC at the receiver. In order to reliably model the SuIC operation at the receiver, the collective sources of impairments, including the nonlinear transmit and receiver chain distortions as well as the channel state information inaccuracy are incorporated. An optimization problem for joint subcarrier and power allocation is then devised in order to maximize system sum-rate, which belongs to the class of smooth difference-of-convex problems. An iterative optimization solution is then proposed, utilizing successive inner approximation framework, which converges to the point that satisfies Karush–Kuhn–Tucker optimality conditions. Numerical results show performance and robustness gain of proposed SuIC scheme in terms of sum-rate compared to previously proposed single-connectivity and half-duplex relaying schemes.

Efficient Kalman Filter-Based Precoder Tracking for Time-Varying Massive MIMO-OFDM Systems

The inaccuracy of channel state information (CSI) and high computational complexity for pseudo-inverse calculation in time-varying massive multiple-input multiple-output (MIMO) systems can hinder the widespread utility of regularized zero-forcing (RZF) precoding. In this letter, a novel framework is proposed for directly tracking the RZF coefficients using a Kalman filter to improve the underlying precoding performance. Specifically, a temporal autocorrelation function (ACF) of RZF coefficients is derived for the proposed tracking. Simulation results show that our proposed algorithm can almost reach the performance of the existing benchmark involving pseudo-inverse computation and thus, significantly reduces the underlying complexity by more than 32%.

A Two-Timescale Approach for Network Slicing in C-RAN

Network slicing is a promising technique for cloud radio access networks (C-RANs). It enables multiple tenants (i.e., service providers) to reserve resources from an infrastructure provider. However, users' mobility and traffic variation result in resource demand uncertainty for resource reservation. Meanwhile, the inaccurate channel state information (CSI) estimation may lead to difficulties in guaranteeing the quality of service (QoS). To this end, we propose a two-timescale resource management scheme for network slicing in C-RAN, aiming at maximizing the profit of a tenant, which is the difference between the revenue from its subscribers and the resource reservation cost. The proposed scheme is under a hierarchical control architecture, which includes long timescale resource reservation for a slice and short timescale intra-slice resource allocation. To handle traffic variation, we utilize the statistics of users' traffic. Moreover, to guarantee the QoS under CSI uncertainty, we apply the uncertainty set of CSI for resource allocation among users. We formulate the profit maximization as a two-stage stochastic programming problem. In this problem, long timescale resource reservation for a slice is performed in the first stage with only the statistical knowledge of users' traffic. Given the decision in the first stage, short timescale intra-slice resource allocation is performed in the second stage, which is adaptive to real-time user arrival and departure. To solve the problem, we first transform the stochastic programming problem into a deterministic optimization problem. We further apply semidefinite relaxation to transform the problem into a mixed integer nonconvex optimization problem, which can be solved by combining branch-and-bound and primal-relaxed dual techniques. Simulation results show that our proposed scheme can well adapt to traffic variation and CSI uncertainty. It obtains a higher profit when compared with several baseline schemes.

Deep Learning for Fading Channel Prediction

Channel state information (CSI), which enables wireless systems to adapt their transmission parameters to instantaneous channel conditions and consequently achieve great performance boost, plays an increasingly vital role in mobile communications. However, getting accurate CSI is challenging due mainly to rapid channel variation caused by multi-path fading. The inaccuracy of CSI imposes a severe impact on the performance of a wide range of adaptive wireless systems, highlighting the significance of channel prediction that can combat outdated CSI effectively. The aim of this article is to shed light on the state of the art in this field and then go beyond by proposing a novel predictor that leverages the strong time-series prediction capability of deep recurrent neural networks incorporating long short-term memory or gated recurrent unit. In addition to an analytical comparison of computational complexity, performance evaluation in terms of prediction accuracy is carried out upon multi-antenna fading channels. Numerical results reveal that deep learning brings a notable performance gain compared with the conventional predictors built on shallow recurrent neural networks.

Inverse Extrapolation for Efficient Precoding in Time-Varying Massive MIMO–OFDM Systems

Regularized zero forcing (RZF) precoding is an efficient linear precoding scheme for combating interference in a single-cell massive multiple-input-multiple-output (MIMO) systems. Inaccurate channel state information (CSI) due to channel aging will reduce the performance of the precoder over time. The channel aging determines how often we need to estimate the channels, and thus how frequently we need to send pilots in order to maximize the overall data rate. Channel prediction is one way to improve the CSI accuracy in the downlink, without having to send new pilots but it requires frequent re-computation of the matrix inverse in the RZF precoder, which has high-computational complexity. In this paper, we consider massive MIMO-OFDM systems and propose an algorithm called inverse extrapolation that extrapolates the channel and inverse matrix coefficients separately. The RZF coefficients are then obtained with comparably low complexity with no need for matrix inversion. We compare this algorithm with the traditional way of computing the RZF coefficients through prediction of the channel matrix followed by matrix inversion. The simulation results show that the two predictors have the same performance when the number of antennas is large, and thus the proposed scheme is preferable since it can reduce the complexity. For example, a scenario is shown, where the complexity is reduced by 61.84% without a significant degradation in performance.

Robust Interference Alignment in Multiuser MIMO Interference Channels with Imperfect Channel-State Information

In this paper, we propose a robust transceiver design algorithm based on joint signal and interference alignment (JSIA) method for a multiple-input multiple-output interference network with stochastic channel error (SCE) model. Our proposed algorithm, namely JSIA-SCE, exploits the stochastic properties of the channel-state information (CSI) measurement error to overcome the performance loss in the system due to inaccurate CSI. The JSIA-SCE reduces both the leakage of the interference signals outside the interference subspace and the leakage of the desired signal outside the orthogonal complement of the interference subspace simultaneously, all in the case of imperfect CSI. Simulation results illustrate that the proposed JSIA-SCE algorithm offers better performance and is more robust in comparison with the existing algorihms in the case of SCE.

Dynamic Transmit Covariance Design in MIMO Fading Systems With Unknown Channel Distributions and Inaccurate Channel State Information

This paper considers dynamic transmit covariance design in point-to-point multiple-input multiple-output fading systems with unknown channel state distributions and inaccurate channel state information subject to both long-term and short-term power constraints. First, the case of instantaneous but possibly inaccurate channel state information at the transmitter (CSIT) is treated. By extending the drift-plus-penalty technique, a dynamic transmit covariance policy is developed and is shown to approach optimality with an $O(\delta)$ gap, where $\delta $ is the inaccuracy measure of CSIT, regardless of the channel state distribution and without requiring knowledge of this distribution. Next, the case of delayed and inaccurate channel state information is considered. The optimal transmit covariance solution that maximizes the ergodic capacity is fundamentally different in this case, and a different online algorithm based on convex projections is developed. The proposed algorithm for this delayed-CSIT case also has an $O(\delta)$ optimality gap, where $\delta $ is again the inaccuracy measure of CSIT.

Novel Markov channel predictors for interference alignment in cognitive radio network

In cognitive radio (CR) network, how to mitigate interference between different users is a key task. Interference alignment (IA) is a promising technique to tackle the multi-user interference in communication system. Compared with other interference management methods (such as zero-forcing), IA can not only effectively eliminate the interference, but also greatly increase the system capacity. However, the perfect channel state information (CSI) is required for both transmitters and receivers to apply the IA algorithm, which is hard to achieve in practical applications. In this paper, the effect of imperfect CSI on IA in CR system is analyzed in terms of signal to interference plus noise ratio and achievable sum rate. A linear finite state Markov chain (LFSMC) predictor, which incorporates the finite state Markov chain into the AR predictor, is proposed to reduce the impact of imperfect CSI on system performance of CR network. Moreover, for the sake of simplifying the initialization of LFSMC predictor, a simplified LFSMC (S-LFSMC) predictor is provided. Simulation results indicate that both of the LFSMC and S-LFSMC predictor can greatly improve the performance of IA system with the inaccurate CSI. Specifically, the LSFMC predictor can achieve satisfied performance compared with other predictors mentioned in this paper. And the LSFMC predictor which is simpler and its performance is still much better than traditional predictors. Therefore, we can choose a suitable predictor (LAFMC or S-LSFMC) based on the different requirements.

Impact of Mobility on the Uplink Sum Rate of MIMO-OFDMA Cellular Systems

With the fast development of vehicular technologies, the number of mobile terminals as well as their moving speeds has increased conspicuously, making it meaningful to re-examine the impact of mobility on communication performance of the existing cellular systems. In this paper, the uplink sum rate of a multiple-input multiple-output orthogonal frequency-division multiple access (MIMO-OFDMA) cellular system with nodes moving randomly at different speeds is studied. In such a multi-user environment, mobility not only causes inter sub-carrier interferences (ICI) among different users, but also brings the so-called channel aging which together with the above interferences incur outdated and inaccurate channel estimation and thus entail additional pilot overhead. Here, we first derive the relationship between the users’ mobility and the ICI power, characterize the channel aging, and derive the overall multi-user interference caused by the outdated and inaccurate channel state information as a function of mobility. Then, by capitalizing on the above results, the sum rate of the MIMO-OFDMA uplink is theoretically analyzed. Moreover, an adaptive transmission scheme in which the users adjust the pilot percentage according to mobility statistics is proposed to maximize the sum rate. Finally, numerical results are provided to validate our analyses.

Unraveling the Rank-One Solution Mystery of Robust MISO Downlink Transmit Optimization: A Verifiable Sufficient Condition via a New Duality Result

This paper concentrates on a robust transmit optimization problem for the multiuser multi-input single-output (MISO) downlink scenario and under inaccurate channel state information (CSI). This robust problem deals with a general-rank transmit covariance design, and it follows a safe rate-constrained formulation under spherically bounded CSI uncertainties. Curiously, simulation results in previous works suggested that the robust problem admits rank-one optimal transmit covariances in most cases. Such a numerical finding is appealing because transmission with rank-one covariances can be easily realized by single-stream transmit beamforming. This gives rise to a fundamentally important question, namely, whether we can theoretically identify conditions under which the robust problem admits a rank-one solution. In this paper, we identify one such condition. Simply speaking, we show that the robust problem is guaranteed to admit a rank-one solution if the CSI uncertainties are not too large and the multiuser channel is not too poorly conditioned. To establish the aforementioned condition, we develop a novel duality framework, through which an intimate relationship between the robust problem and a related maximin problem is revealed. Our condition involves only a simple expression with respect to the multiuser channel and other system parameters. In particular, unlike other sufficient rank-one conditions that have appeared in the literature, ours is verifiable. The application of our analysis framework to several other CSI uncertainty models is also discussed.

Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks

This paper discusses robust link adaptation for a downlink precoded multiple input single output system, for guaranteeing ultra-reliable (99.999%) transmissions to mobile users (e.g., slowly moving machines in a factory) served by a small cell network. The proposed technique compensates the effect of inaccurate channel state information (CSI) caused by user mobility, as well as the variation of precoders in the interfering cells. Both of these impairments translate into instability of the received signal-to-noise plus interference ratios (SINRs), and may lead to CSI mispredictions and potentially erroneous transmissions. We show that, by knowing the statistics of the propagation channels and the precoders variations, it is possible to compute a backoff that guarantees robust link adaptation. The backoff value is based on the statistics of realized SINR, and is consequently used to adapt the transmissions according to current channel state. Theoretical analysis accompanied by simulation results show that the proposed approach is suitable for attaining 5G ultra-reliability targets in realistic settings.

Multi-cell massive MIMO transmission with coordinated pilot reuse

In this paper, we propose a coordinated pilot reuse (CPR) approach to reduce the pilot overhead for multi-cell massive multi-input multi-output transmission. Unlike the conventional multi-cell pilot reuse approach in which pilots can only be reused among different cells, the proposed CPR approach allows pilots to be reused among both inter-cell and intra-cell user equipments, and thus, pilot overhead can be efficiently reduced. For spatially correlated Rayleigh fading channels, we first present a CPR-based channel estimation method and a low complexity pilot allocation algorithm. Because CPR might lead to additional pilot interference, we develop a statistically robust uplink receiver and downlink precoder that takes channel estimation errors into account. The proposed uplink receiver and downlink precoder are robust to channel state information inaccuracy, and thus, can guarantee a certain transmission performance. Monte-Carlo simulations illustrate the significant performance improvement in net spectral efficiency offered by the proposed CPR approach.