Channel Correlation Information Research Articles

RIS-assisted communications in correlated Rayleigh channels: Comparison of training overhead reduction schemes

Channel training in reconfigurable intelligent surface (RIS)-assisted communications is usually conducted in an on-off manner, resulting in unaffordable training time overhead when the number of RIS elements is large. In this paper, for correlated Rayleigh channels, we compare three typical training overhead reduction schemes, namely RIS element selection (Scheme 1), element grouping (Scheme 2), and statistical CSI-based phase shifts design (Scheme 3). For Scheme 1 and Scheme 2, we propose two algorithms to select RIS elements (or form element groups) and determine the optimal number of activated elements (or formed groups), based on the channel correlation information only; for Scheme 3, we consider a semi-definite programming-based approach in the literature, and propose an alternative dominant eigenvector-based method for determining the RIS phase shifts vector. Via extensive simulations, we compare the achievable ergodic rates of these schemes versus the signal-to-noise ratio, the channel correlation level, and the element number-to-coherent time ratio, respectively, and discuss possible switching of the three schemes over these system parameters. At last, operation regions of the considered training overhead reduction schemes are shown in the plane characterized by the system parameters, which provides useful guidelines for practical scheme determination.

Deep Learning-Aided TR-UWB MIMO System

This paper presents a novel deep learning-aided scheme dubbed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PR\rho $ </tex-math></inline-formula> -net for improving the bit error rate (BER) of the Time Reversal (TR) Ultra-Wideband (UWB) Multiple Input Multiple Output (MIMO) system with imperfect Channel State Information (CSI). The designed system employs Frequency Division Duplexing (FDD) with explicit feedback in a scenario where the CSI is subject to estimation and quantization errors. Imperfect CSI causes a drastic increase in BER of the FDD-based TR-UWB MIMO system, and we tackle this problem by proposing a novel neural network-aided design for the conventional precoder at the transmitter and equalizer at the receiver. A closed-form expression for the initial estimation of the channel correlation is derived by utilizing transmitted data in time-varying channel conditions modeled as a Markov process. Subsequently, a neural network-aided design is proposed to improve the initial estimate of channel correlation. An adaptive pilot transmission strategy for a more efficient data transmission is proposed that uses channel correlation information. The theoretical analysis of the model under the Gaussian assumptions is presented, and the results agree with the Monte-Carlo simulations. The simulation results indicate high performance gains when the suggested neural networks are used to combat the effect of channel imperfections.

ACSL: Adaptive correlation-driven sparsity learning for deep neural network compression

Deep convolutional neural network compression has attracted lots of attention due to the need to deploy accurate models on resource-constrained edge devices. Existing techniques mostly focus on compressing networks for image-level classification, and it is not clear if they generalize well on network architectures for more challenging pixel-level tasks, e.g., dense crowd counting or semantic segmentation. In this paper, we propose an adaptive correlation-driven sparsity learning (ACSL) framework for channel pruning that outperforms state-of-the-art methods on both image-level and pixel-level tasks. In our ACSL framework, we first quantify the data-dependent channel correlation information with a channel affinity matrix. Next, we leverage these inter-dependencies to induce sparsity into the channels with the introduced adaptive penalty strength. After removing the redundant channels, we obtain compact and efficient models, which have significantly less number of parameters while maintaining comparable performance with the original models. We demonstrate the advantages of our proposed approach on three popular vision tasks, i.e., dense crowd counting, semantic segmentation, and image-level classification. The experimental results demonstrate the superiority of our framework. In particular, for crowd counting on the Mall dataset, the proposed ACSL framework is able to reduce up to 94% parameters (VGG16-Decoder) and 84% FLOPs (ResNet101), while maintaining the same performance of (at times outperforming) the original model.

Pilot Design and Allocation for Multi-Cell Massive MIMO Systems With Two-Tier Receiving

This paper investigates pilot design and allocation in multi-cell massive multiple-input multiple-output systems with two-tier receiving. As the length of pilot is limited, the pilots cannot be orthogonal and cause interference. The sum rate as well as the signal-to-interference-plus-noise ratio (SINR) of the system are firstly analyzed. The sum rate upper bound and the asymptotic SINR are derived. The analysis shows that the pilot correlation and the spatial correlation jointly affect the sum rate. Based on this observation, the discrete Fourier transform vectors are employed in designing pilots with various correlation values. Then, with the channel correlation information and the designed pilots, the pilots are allocated to the cells in a greedy way with the object of maximizing the sum rate upper bound. Moreover, the sum rate analysis, the pilot design, and the pilot allocation are extended to the scenario with grouped correlations. Simulation results show that the proposed pilot design and allocation performs better than other pilots and the derived upper bound is effective.

Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

In this paper, we propose a new method based on the dynamic mode decomposition (DMD) to find a distinctive contrast between the ictal and interictal patterns in epileptic electroencephalography (EEG) data. The features extracted from the method of DMD clearly capture the phase transition of a specific frequency among the channels corresponding to the ictal state and the channel corresponding to the interictal state, such as direct current shift (DC-shift or ictal slow shifts) and high-frequency oscillation (HFO). By performing classification tests with Electrocorticography (ECoG) recordings of one patient measured at different timings, it is shown that the captured phenomenon is the unique pattern that occurs in the ictal onset zone of the patient. We eventually explain how advantageously the DMD captures some specific characteristics to distinguish the ictal state and the interictal state. The method presented in this study allows simultaneous interpretation of changes in the channel correlation and particular information for activity related to an epileptic seizure so that it can be applied to identification and prediction of the ictal state and analysis of the mechanism on its dynamics.

Optimum Performance of Short Block Length Codes Under Multivariate Stationary Rayleigh Fading

The performance of short block length codes in the presence of multi-variate stationary Rayleigh fading coherent channels is studied. The channel model is inspired by multi-symbol OFDM transmissions for ultra-reliable low latency communication (URLLC) services, according to which information is sent in a small number of OFDM symbols to reduce latency. More specifically, the dispersion of the coherent fading channel is generalized from the scalar case, which is well known in the literature, to the multivariate one. The obtained expressions are then particularized to the Rayleigh fading statistics and expressed in closed form as a series expansion. Finally, a high SNR approximation of the ergodic capacity and channel dispersion is derived for this particular fading choice. Results show that the code performance depends on the time-frequency fading correlation through very simple functions of the channel correlation coefficients. In particular, it is shown that the channel dispersion converges to a constant plus the sum of the dilogarithms of the correlation coefficients of the channel process. These formulas provide a very useful tool to design physical layer system parameters from channel correlation information measured at the receiver.

Emotion Recognition From Multi-Channel EEG Signals by Exploiting the Deep Belief-Conditional Random Field Framework

Recently, much attention has been attracted to automatic emotion recognition based on multi-channel electroencephalogram (EEG) signals, with the rapid development of machine learning methods. However, traditional methods ignore the correlation information between different channels, and cannot fully capture the long-term dependencies and contextual information of EEG signals. To address the problems, this paper proposes a deep belief-conditional random field (DBN-CRF) framework which integrates the improved deep belief networks with glia chains (DBN-GC) and conditional random field. In the framework, the raw feature vector sequence is firstly extracted from the multi-channel EEG signals by a sliding window. Then, parallel DBN-GC models are utilized to obtain the high-level feature sequence of the multi-channel EEG signals. And the conditional random field (CRF) model generates the predicted emotion label sequence according to the high-level feature sequence. Finally, the decision merge layer based on K-nearest neighbor algorithm is employed to estimate the emotion state. According to our best knowledge, this is the first attempt that applies the conditional random field methodology to deep belief networks for emotion recognition. Experiments are conducted on three publicly available emotional datasets which include AMIGOS, SEED and DEAP. The results demonstrate that the proposed framework can mine inter correlation information of multiple-channel by the glia chains and catch inter channel correlation information and contextual information of EEG signals for emotion recognition. In addition, the classification accuracy of the proposed method is compared with several classical techniques. The results indicate that the proposed method outperforms most of the other deep classifiers. Thus, potential of the proposed framework is demonstrated.

Efficient skin lesion segmentation using separable-Unet with stochastic weight averaging

Background and objectiveEfficient segmentation of skin lesion in dermoscopy images can improve the classification accuracy of skin diseases, which provides a powerful approach for the dermatologists in examining pigmented skin lesions. However, the segmentation is challenging due to the low contrast of skin lesions from a captured image, fuzzy and indistinct lesion boundaries, huge variety of interclass variation of melanomas, the existence of artifacts, etc. In this work, an efficient and accurate melanoma region segmentation method is proposed for computer-aided diagnostic systems. MethodA skin lesion segmentation (SLS) method based on the separable-Unet with stochastic weight averaging is proposed in this work. Specifically, the proposed Separable-Unet framework takes advantage of the separable convolutional block and U-Net architectures, which can extremely capture the context feature channel correlation and higher semantic feature information to enhance the pixel-level discriminative representation capability of fully convolutional networks (FCN). Further, considering that the over-fitting is a local optimum (or sub-optimum) problem, a scheme based on stochastic weight averaging is introduced, which can obtain much broader optimum and better generalization. ResultsThe proposed method is evaluated in three publicly available datasets. The experimental results showed that the proposed approach segmented the skin lesions with an average Dice coefficient of 93.03% and Jaccard index of 89.25% for the International Skin Imaging Collaboration (ISIC) 2016 Skin Lesion Challenge (SLC) dataset, 86.93% and 79.26% for the ISIC 2017 SLC, and 94.13% and 89.40% for the PH2 dataset, respectively. The proposed approach is compared with other state-of-the-art methods, and the results demonstrate that the proposed approach outperforms them for SLS on both melanoma and non-melanoma cases. Segmentation of a potential lesion with the proposed approach in a dermoscopy image requires less than 0.05 s of processing time, which is roughly 30 times faster than the second best method (regarding the value of Jaccard index) for the ISIC 2017 dataset with the same hardware configuration. ConclusionsWe concluded that using the separable convolutional block and U-Net architectures with stochastic weight averaging strategy could enable to obtain better pixel-level discriminative representation capability. Moreover, the considerably decreased computation time suggests that the proposed approach has potential for practical computer-aided diagnose systems, besides provides a segmentation for the specific analysis with improved segmentation performance.

A Novel Low-Complexity Adaptive Bit Mapping Scheme for Spatial Modulation

We propose a novel low-complexity adaptive bit mapping scheme for spatial modulation (SM) called the nearest point method (NPM). In this scheme, the SM symbols are first sorted according to a certain sorting order, then bit sequences that are different only in 1 bit are assigned to subsequent SM symbols according to that sorting order. To determine the sorting order, NPM starts with the pair of SM symbols that has the minimum distance. From one of the SM symbols in that pair, it identifies the nearest SM symbol and continues to do so until it visits all the SM symbols without visiting the same SM symbol more than once. Simulation results show that NPM achieves performance close to the lower bound of the BER for SM and space shift keying (SSK) using full channel state information at the transmitter. It also achieves BER improvement for SSK using the channel distribution and correlation information. The complexity of the proposed NPM is on the order $\mathcal {O}(K^2)$ , where $K$ is the number of symbols, which is remarkably low.

Estimation of MIMO Channel with Imperfect Channel Correlation Information

Conventional channel estimation schemes may require for the transmission of orthogonal pilot signal for each antenna in frequency-division duplex (FDD) transmission systems. It is of great concern to get channel information with an affordable signaling overhead in FDD massive multi-input multi-output (m-MIMO) transmission environments. The signaling overhead for the channel estimation can be reduced by exploiting the channel correlation matrix (CCM) of m-MIMO channel. When the m-MIMO channel is correlated in spatial domain, the channel information can be estimated with reduced pilot signaling overhead. However, the estimation performance may seriously be affected by the accuracy of CCM. In this paper, we investigate the effect of CCM accuracy on the m-MIMO channel estimation and verify it by computer simulation.

Joint Atomic Norm Based Estimation of Sparse Time Dispersive SIMO Channels with Common Support in Pilot Aided OFDM Systems

We consider the problem of estimation of sparse time dispersive Single Input Multiple Output (SIMO) channels, using a single transmit and multiple receive antennas in pilot aided OFDM systems. The channels we consider are with a continuous time delays and sparse, and we assume a common support of the channel coefficients of the SIMO channels associated with different antennas, resulting from the same scatterer. To exploit these properties, we propose a new channel estimation algorithm based on the atomic norm minimization for the Multiple Measurement Vector (MMV) model. A joint estimation of the delays corresponding to the same scatterer is obtained using the combination of the atomic norm regularized minimization for the MMV model and the MUSIC method. Then, based on the availability of the channel correlation information, the path gains are estimated using the LS or the MMSE method. Additionally, we derive a theoretical estimate of the channel estimate Mean Square Error for the asymptotically increasing number of receive antennas. To evaluate the proposed algorithm, we compare its performance with other state of the art algorithms.

Power Consumption Minimization for MIMO Systems &amp;#x2014; A Cognitive Radio Approach

This paper shows how cognitive radio (CR) can help to optimize system power consumption of multiple input multiple output (MIMO) communication systems. Leveraging results from information theory and capabilities of a CR (e.g., the awareness of the component capabilities and characteristics), a theoretical framework is developed to minimize the system power consumption of MIMO systems while still considering radiated power. This paper mathematically formulates the system power consumption minimization problem under a sum rate constraint for MIMO systems. The impact of channel correlation and partial channel state information at the transmitter is considered. Numerical algorithms are developed to solve the constrained optimization problem. The simulation results show that significant power savings (e.g., up to 75% for a 4 × 4 MIMO system with Class A power amplifiers) can be achieved compared to conventional power allocation schemes. The results also show that the more computationally efficient suboptimal heuristic algorithms can achieve power savings comparable to the exhaustive search algorithm.

Capacity Limits of Multi-Antenna Multicasting Under Correlated Fading Channels

Physical layer multicasting in wireless networks has been proposed to efficiently send an identical message to multiple users simultaneously. In this paper, we consider multiple antenna multicasting where the transmitter is equipped with Mt antennas and data is transmitted to K single-antenna users. When the downlink channels are assumed to follow an uncorrelated Rayleigh distribution, the multicasting capacity scaling for the large K asymptote is known. On the other hand, the effect of channel spatial correlation on the capacity performance has not been well addressed. Therefore, we investigate the effect of correlation using the channel correlation information at the transmitter. Using extreme value theory, it is shown that signaling using uniformly allocated transmit powers on the spatial channel correlation matrix's eigenvectors with non-zero eigenvalues approaches the multicasting capacity for the large K asymptote. Compared to the performance of uncorrelated fading channels with a constraint on the trace of the spatial correlation matrix, it is shown that the channel correlation degrades the performance. Specifically, if the correlation matrix is full rank the asymptotic outage (average) capacity ratio, which is defined as the ratio of the outage (average) capacities of the correlated fading channel and the uncorrelated fading channel as K goes to infinity, is equivalent to the geometric mean of the eigenvalues of the transmit channel correlation matrix of the correlated channel. On the other hand, if the correlation matrix is not full rank, the capacity ratios become zero. To assess the value of knowledge of the channel correlation information at the transmitter, we compare the asymptotic performances with and without knowledge of the channel correlation information. Specifically, the asymptotic performance improvement due to this correlation knowledge becomes (Mt/M)1/M for the large K asymptote, where M is the rank of the transmit channel correlation matrix. In addition, we discuss the issues of nonidentical transmit channel correlations among the users and correlation between users' channels.

Limited Feedback Multiuser Scheduling of Spatially Correlated Broadcast Channels

This paper investigates the transmission scheduling of users under spatially correlated multiple-input-multiple-output (MIMO) channels. By maximizing an upper bound of the sum capacity, we propose a scheduling scheme that requires only scalar feedback from each receiving user. To simplify user information feedback and better exploit the spatial channel correlation, we further develop a more efficient scheduling method that only requires a 1-bit indicator from each user and selects users based on the MIMO channel correlation information. Additionally, we then extend the proposed scheduling schemes for the system with imperfect (finite-rate) channel information feedback. Numerical results finally show that the proposed scheduling schemes can effectively improve the system sum capacity performance by exploiting the statistic channel-correlation information.

A low-complexity iterative channel estimation and detection technique for doubly selective channels

In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key to the proposed technique is a segment-by-segment processing strategy under the assumption that the channel is approximately static within a short segment of a data block. Through a virtual zero-padding technique, the proposed segment-by-segment equalization approach inherits the low-complexity advantage of the conventional frequency domain equalization (FDE), but does not need the assistance of guard interval (for cyclic-prefixing or zero-padding), thereby avoiding the spectral and power overheads. Furthermore, we develop a low-complexity bidirectional channel estimator, where the Gaussian message passing (GMP) technique is used to exploit the channel correlation information, and the intermediate channel estimation results in the iterative process are employed to perform inter-tap interference cancellation. Simulation results demonstrate the effectiveness of the proposed detection and channel estimation algorithms.

Efficient Capacity-Based Joint Transmit and Receive Antenna Selection Schemes in MIMO Systems

In this letter, we propose two different joint transmit and receive antenna subset selection schemes for multiple-input multiple-output (MIMO) systems on the basis of capacity maximization criterion. We assume that perfect channel state information (CSI) is known at the receiver but unknown to the transmitter. As the selection signaling is perfectly fed back to the transmitter, we propose a flexible two-step selection algorithm (TSSA) in practical MIMO channel scenarios. Computer simulations show that TSSA can maximize the capacity at low computation cost in most scenarios. It performs well in terms of capacity, computational complexity and flexibility. Furthermore, we propose a simplified algorithm based on the correlation matrix when the channel correlation information (CCI) is known to the transmitter. Simulation results show that the proposed correlation matrix based selection algorithm is only slightly inferior to an optimal selection algorithm.

Adaptive downlink multi-user MIMO wireless systems for correlated channels with imperfect CSI

Multiple-input multiple-output (MIMO) wireless antenna systems provide increases in capacity without the need for additional spectrum or power. However the capacity increase is limited when the number of antennas at the receiver is fixed or restricted (due to mobile size constraints for example). To overcome this limitation multi-user MIMO can be used, which allows several users to be served simultaneously in frequency and time. A disadvantage of these multi-user MIMO systems, when used in the downlink however, is that they need accurate channel state information (CSI) at the transmitter and also uncorrelated channels among users. In this paper we investigate methods to address the problems of multi-user MIMO systems in spatially correlated channels. We adopt the concept of angle between subspaces to characterize the inter-user spatial correlation and adapt the algorithm to those conditions. We also investigate the impact of the accuracy of CSI at the transmitter (CSIT) and whether more limited CSI such as channel correlation information alone can be used to provide good multi-user MIMO performance. Results are presented as various simulated capacity measures and we use them to make comparisons between the various multi-user MIMO configurations

Joint Antenna Selection and Link Adaptation for MIMO Systems

Multi-input multi-output (MIMO) systems, with multiple antennas at both the transmitter and the receiver, are anticipated to be widely employed in future wireless networks due to their predicted tremendous system capacity. To protect the transmitted data against random channel impairment, it is desirable to consider link adaptation, such as rate adaptation and power control, to improve the system performance and guarantee certain quality of service. Based on the observation that link adaptation and antenna selection problems are often coupled, we propose a joint antenna subset selection and link adaptation study for MIMO systems. After the formulation of the multidimensional joint optimization problem, the main contribution of this paper lies in the design of efficient algorithms approaching the optimal solution for both uncorrelated and correlated MIMO channels. Specifically, we propose one simplified antenna selection and link adaptation rule based on the expected optimal number of active antennas for uncorrelated MIMO with Rayleigh fading and one for correlated MIMO channels only based on the slowly varying channel correlation information. Our proposed algorithms are verified through numerical results, demonstrating significant gains over traditional MIMO signaling, while feasible for practical implementation.