Channel State Information Feedback Overhead Research Articles

Deep data hiding‐based channel state information feedback within audio signals

AbstractDeep learning‐based channel state information (CSI) hiding within images has been introduced to eliminate the downlink CSI feedback overhead in frequency division duplexing systems. In this letter, a deep data hiding‐based CSI feedback framework (named Au_EliCsiNet), which hides/superimposes downlink CSI within the transmitted audio signals, is proposed. Convolution neural networks are adopted to extract CSI features, hide CSI within audio signals, and reconstruct CSI from the audio signals. Simulation results show that the proposed Au_EliCsiNet can feed back downlink CSI accurately with no (or fewer) effects on the original audio transmission.

Multi-Connectivity for URLLC and Coexistence With eMBB in Time-Varying and Frequency-Selective Fading Channels

Multi-connectivity enables a 5G cellular system to meet the challenging reliability requirements of downlink ultra-reliable low-latency communication (URLLC) data traffic. In it, multiple base stations (BSs) transmit to the URLLC user by pre-empting time-frequency resources assigned to enhanced mobile broadband (eMBB) users. We derive insightful expressions for achievability, which is the probability that the URLLC user’s block error rate (BLER) requirement is met by multi-connectivity. We do so for both joint transmission (JT) and orthogonal transmission (OT) modes of URLLC for the general case in which the transmissions occur over frequency-selective channels. We then propose a low-complexity algorithm to jointly select the set of cooperating BSs and their modulation and coding schemes (MCSs) to minimize the eMBB throughput loss. For time-varying channels with feedback delays, we present an alternate stochastic reliability requirement for URLLC traffic. The MCS selected on the basis of this requirement has a markedly higher probability of meeting the BLER target over the grid of URLLC user locations. Our results highlight the different trade-offs between URLLC achievability, eMBB throughput loss, and channel state information feedback overhead of OT and JT. They bring out the significant impact of feedback delays even at moderate Doppler spreads.

Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach

The increased throughput brought by MIMO technology relies on the knowledge of channel state information (CSI) acquired in the base station (BS). To make the CSI feedback overhead affordable for the evolution of MIMO technology (e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced to deal with the CSI compression task. In traditional communication systems, the compressed CSI bits is treated equally and expected to be transmitted accurately over the noisy channel. While the errors occur due to the limited bandwidth or low signal-to-noise ratios (SNRs), the reconstruction performance of the CSI degrades drastically. As a branch of semantic communications, deep joint source-channel coding (DJSCC) scheme performs better than the separate source-channel coding (SSCC) scheme—the cornerstone of traditional communication systems—in the limited bandwidth and low SNRs. In this paper, we propose a DJSCC based framework for the CSI feedback task. In particular, the proposed method can simultaneously learn from the CSI source and the wireless channel. Instead of truncating CSI via Fourier transform in the delay domain in existing methods, we apply non-linear transform networks to compress the CSI. Furthermore, we adopt an SNR adaption mechanism to deal with wireless channel variations. The extensive experiments demonstrate the validity, adaptability, and generality of the proposed framework.

Overview of Deep Learning-Based CSI Feedback in Massive MIMO Systems

Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user equipment) and feeding back to the transmitter. The overhead of CSI feedback occupies substantial uplink bandwidth resources, especially when the number of transmit antennas is large. Deep learning (DL)-based CSI feedback refers to CSI compression and reconstruction by a DL-based autoencoder and can greatly reduce feedback overhead. In this paper, a comprehensive overview of state-of-the-art research on this topic is provided, beginning with basic DL concepts widely used in CSI feedback and then categorizing and describing some existing DL-based feedback works. The focus is on novel neural network architectures and utilization of communication expert knowledge to improve CSI feedback accuracy. Works on joint design of CSI feedback with other communication modules are also introduced, and some practical issues, including bitstream generation, multirate feedback, imperfect feedback, NN complexity, training dataset collection, online training, and standardization effect, are discussed. At the end of the paper, some challenges and potential research directions associated with DL-based CSI feedback in future wireless communication systems are identified.

Eliminating CSI Feedback Overhead via Deep Learning-Based Data Hiding

Channel state information (CSI) plays a crucial role in the capacity of multiple-input and multiple-output systems, but CSI feedback occupies substantial precious transmission resources in frequency-division duplexing (FDD) systems. In this work, we propose a data hiding-based CSI feedback framework, namely, EliCsiNet, to eliminate the CSI feedback overhead in FDD systems through deep learning. The key idea is to hide/superimpose CSI in transmitted messages (e.g., images) with no transmission resource occupation and few effects on message semantics. Concretely, we introduce a novel neural network framework in which the user extracts and hides CSI features in images, and the base station recovers the CSI from the transmitted images. However, the essential source coding (e.g., JPEG compression) before data transmission causes two problems in the proposed EliCsiNet framework when applied in practical systems. First, the compression inevitably disturbs the information of the hidden CSI in images and affects the CSI reconstruction accuracy. Therefore, a two-stage separable training strategy, which includes coding-free end-to-end and coding-aware decoder-only training, is adopted to reduce these effects. Second, the bit length of the images coded via JPEG is unpredictable and uncontrollable, and CSI superimposition may lead to an increase in the bit length of the coded images. To avoid this issue, we divide a full image into several sub-blocks and select the one with the smallest length increment. Image entropy is also introduced to accelerate block selection. Simulation results demonstrate that the proposed EliCsiNet framework can eliminate the CSI feedback overhead with few effects on the features properties of transmitted images, including image quality and bit length.

Closed-Form Sum-Rate Analysis of Interference Alignment with Limited Feedback Based on Scalar Quantization and Random Vector Quantization

Interference alignment (IA) is a promising interference management technique to achieve the theoretical optimal degree of freedom (DoF) performance in multi-user cooperation scenarios. However, the effective achievable sum-rate performance of IA is largely affected by the feedback overhead and accuracy of channel state information (CSI) and decoding information (DI). Therefore, it is critical to establish the exact relationship between feedback overhead and the achievable sum-rate of IA to obtain the optimal effective performance. Most existing IA performance analysis approaches focus on the vector quantization (VQ)-based feedback strategy, but the implementation complexity of VQ will be excessive when more quantization bits are required to achieve the expected quantization accuracy for larger-sized matrices or higher signal-to-noise ratio (SNR) regimes. Moreover, the obtained achievable sum-rate formulas are too complicated for quick performance evaluation. In this paper, a new sum-rate performance analysis method for IA under different quantization and feedback strategies is proposed to achieve a trade-off between accuracy and complexity, and the closed-form achievable sum-rate expressions are derived. First, in the IA case with random vector quantization (RVQ)-based CSI feedback, the quantization error of RVQ is transformed into the equivalent VQ error of the Gaussian channel error, based on which the achievable sum-rate formula is obtained. Second, in the IA case with scalar quantization (SQ)-based CSI feedback, the relationship between the effective sum-rate and SQ bits is established. Third, in the IA case with SQ-based CSI feedback and RVQ-based DI feedback, the achievable sum-rate formula is derived by combining these two kinds of quantization errors. Finally, the simulation results confirm that the theoretical results are accurate enough, which can help to determine the optimal CSI feedback overhead in practical channel conditions. Moreover, the theoretical and simulation results demonstrate that RVQ may be more applicable to IA scenarios with fewer receiving antennas and low SNR regimes.

A Novel Approach Using Convolutional Transformer for Massive MIMO CSI Feedback

In frequency division duplex model, the massive multiple-input multiple-output (MIMO) systems rely on feedback of channel state information (CSI) to perform precoding operations. This can increase the potential transmission gain of the system. The use of large scale antennas increases the feedback overhead of CSI exponentially. Therefore, CSI must be compressed for feedback. However, convolution based feedback methods lack long-range dependency modeling of the CSI, resulting in limited effects at high compression ratios. In this letter, we propose a novel neural network based on the convolutional transformer architecture to improve the performance of CSI compression and reconstruction at high compression rates. The experimental results show that the average accuracy of ours is improved by 5.39% over the state-of-the-art method at a high compression rate of 1/64. At the same time, the overall performance of the system has been improved.

Truncated Channel Inversion Power Control to Enable One-Way URLLC With Imperfect Channel Reciprocity

We propose to use channel inversion power control (CIPC) to achieve one-way ultra-reliable and lowlatency communications (URLLC), where only the transmission in one direction requires ultra reliability and low latency. Based on channel reciprocity, our proposed CIPC schemes guarantee the power of received signal that is used to decode the information to be a constant value <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> , by varying the transmit signal and power, which relaxes the assumption of knowing channel state information (CSI) at the user. Thus, the CIPC schemes eliminate the overhead of CSI feedback, reduce communication latency, and explore the benefits of multiple antennas to significantly improve transmission reliability. We derive analytical expressions for the packet loss probability of the proposed CIPC schemes, based on which we determine a closed interval and a convex set for optimizing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> in CIPC with imperfect and perfect channel reciprocity, respectively. Our results show that CIPC is an effective means to achieve one-way URLLC. The tradeoff among reliability, latency, and required resources (e.g., transmit antennas) is further revealed, which provides novel principles for designing one-way URLLC systems.

Adaptive Lightweight CNN-Based CSI Feedback for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for a user equipment (UE) to achieve a high data rate. However, massive MIMO requires channel state information (CSI) at the transmitter and the CSI overhead fed back by UEs exponentially increases as the number of antennas increases. In the last years, many studies have been conducted to solve the problem of enormous CSI feedback overhead by utilizing deep learning. In this letter, we propose an adaptive lightweight convolutional neural network (CNN) in the deep learning-based MIMO CSI feedback. The proposed network adaptively finds the compression ratio to be used in the network and reduces the computational complexity of the network. Simulation results show that the proposed lightweight CNN significantly reduces the computational complexity in comparison with the conventional CsiNet while achieving the equivalent performance; and moreover the proposed network converges faster.

SALDR: Joint Self-Attention Learning and Dense Refine for Massive MIMO CSI Feedback With Multiple Compression Ratio

The advantages of massive multiple-input multiple-output (MIMO) techniques depend heavily on the accuracy of channel state information (CSI). In frequency division duplexing (FDD) massive MIMO systems, the user equipment (UE) needs to feed downlink CSI back to the base station (BS) through the feedback link. The excessive feedback overheads and low reconstruction accuracy are the main obstacles for actual deployment of FDD massive MIMO systems. In recent years, deep learning (DL) has been widely used to address the above problems. In this letter, we propose a neural network by utilizing the self-attention learning and dense refine (SALDR), which improves the accuracy of CSI feedback. Furthermore, a unified decoder named SALDR-U is designed to realize different compression ratios for CSI feedback without changing any parameter. Simulation results show that the proposed SALDR and SALDR-U outperform the state-of-the-art network in terms of accuracy and overhead of CSI feedback. The source code for all the experiments is available at GitHub.The code of this letter can be downloaded from GitHub link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XS96/SALDR</uri> .

Two-Stage Adaptive and Compressed CSI Feedback for FDD Massive MIMO

Channel state information (CSI) feedback is a key technical challenge for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. We propose a two-stage adaptive and compressed CSI feedback scheme to reduce the CSI feedback overhead and improve the feedback accuracy. Specifically, a binary angle-of-departure (AoD) vector and the associated angular-domain channel coefficients are first extracted from the massive MIMO channel. Then, by exploiting the dynamic burst-sparsity of the massive MIMO channel in the joint time-angular domain, a two-stage differential feedback scheme is proposed to compress and feed back the AoD vector, where the feedback overhead is adaptively adjusted according to the AoD changing rate. In addition, an adaptive-codebook-based feedback scheme is proposed for the angular-domain channel coefficients. Compared with the 5G New Radio (NR) Type-2 feedback scheme in 3GPP R15, the proposed scheme can significantly reduce the feedback overhead and achieve better block error rate (BLER) performance.

Compressive Sampled CSI Feedback Method Based on Deep Learning for FDD Massive MIMO Systems

Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality.

Energy-efficient time and energy resource allocation in non-selfish symbiotic cognitive relaying sensor network with privacy preserving for smart city

Energy efficiency and privacy preserving have become essential for the wireless sensor networks. In this paper, a joint energy and time resource allocation problem for the cognitive users (CUs) in a non-selfish symbiotic cognitive relaying scheme (NSCRS) is considered. We aim to maximize the total energy efficiency (EE) of the primary user and CUs with the consideration of information privacy under the total energy constraints of CUs. With full channel state information (CSI), an optimal energy and time resource allocation algorithm is proposed based on the exhaustive searching. Besides, in order to reduce the overhead of CSI feedback, a suboptimal algorithm, in which only the partial instantaneous CSI is required, is additionally proposed. Simulation results demonstrate the EE of primary and CUs in the NSCRS with consideration of information privacy can be greatly improved by the proposed algorithms.

Energy-Efficient Millimeter-Wave Cell-Free Systems Under Limited Feedback

MmWave cell-free systems where multiple base stations (BSs) cooperatively serve user using the mmWave band signal have gained much attention recently due to its capability to dramatically improve the system capacity. One potential drawback of mmWave cell-free systems is that an intensive deployment of BSs increases the energy consumption of network substantially. To improve the energy efficiency, acquisition of accurate downlink channel state information (CSI) at the BSs is essential. However, this task is not easy since the CSI feedback overhead scales linearly with the number of antennas as well as the number of BSs. In this paper, we propose an approach to maximize the energy efficiency of mmWave cell-free systems under the limited feedback. Key idea of the proposed energy-efficient dominating path selection (EE-DPS) algorithm is to choose a small number of paths in the angular domain channel and then exploit the channel information of chosen paths in the data precoding and power allocation. By choosing the dominating paths maximizing the energy efficiency and then feeding back the components of chosen paths, we achieve a considerable reduction in the feedback overhead. Numerical results demonstrate that EE-DPS achieves more than 80% energy efficiency improvement over the conventional CSI feedback-based schemes.

Deep learning‐based massive multiple‐input multiple‐output channel state information feedback with data normalisation using clipping

Massive multiple-input multiple-output (MIMO) can provide real-time high-capacity data transmission service to the user equipment (UE). However, the use of massive MIMO exponentially increases the channel state information (CSI) feedback overhead. Deep learning-based approaches have been proposed to reduce CSI feedback overhead with significant CSI accuracy. Data normalisation is a very important part of deep learning, because the performance of the deep learning highly depends on the data normalisation. In this letter, an efficient data normalisation method for deep learning-based CSI feedback in a massive MIMO system is proposed, where the proposed method uses a clipping technique based on the received signal strength. Simulation results show that the proposed normalisation with the clipping decreases the CSI feedback error and increases the accuracy of the beamforming vector in deep learning-based CSI feedback.

Feedback Interval Optimization for MISO LiFi Systems

Utilizing the densely deployed light-emitting diodes (LEDs) as transmitters, multiple-input-single-output (MISO) becomes an attractive technique to improve the transmission throughput of light-fidelity (LiFi) systems. To fully exploit the benefits of joint transmission of multiple LEDs, beamforming techniques are usually employed to improve the downlink throughput, which requires the knowledge of channel state information (CSI) at transmitter. Since frequent CSI feedback leads to heavy overhead, the optimization of feedback interval, defined as the time period between two adjacent feedbacks, is investigated in this paper, which aims at maximizing the weighted bidirectional transmission throughput. Additionally, a quasi-optimal solution is calculated as a well approximation of the exact optimal solution in order to reduce the computational complexity. Simulation results show that, the proposed feedback interval optimization is capable of improving the weighted bidirectional throughput considerably thanks to the reduced CSI feedback overhead, compared with its conventional counterparts using classical feedback mechanisms. Besides, by employing the obtained quasi-optimal feedback interval, the weighted bidirectional throughput approaches that of the optimal interval with much lower computational complexity.

Massive MIMO CSI reconstruction using CNN‐LSTM and attention mechanism

In massive multiple-input multiple-output (MIMO) systems, the channel state information (CSI) feedback enables performance gain in frequency division duplex networks. However, with the increase in the number of antennas, the feedback overhead of CSI will also enhance. To this end, this study addresses the issue of massive MIMO CSI reconstruction using convolutional neural network (CNN), long short-term memory (LSTM) and attention mechanism, and proposes an efficient network architecture (denoted as CNN-LSTM-A). To achieve a compromise between performance and complexity, the proposed method significantly reduces the number of training parameters by utilising a single-stage network rather than a multiple-stage network. Finally, simulation results show that the authors method can reduce the feedback overhead of CSI effectively, and achieves better performance in terms of CSI compression and recovery accuracy compared with existing state-of-the-art methods.

A User Cooperation Approach for Interference Cancellation in FDD Massive MIMO Systems

The performance of a massive multi-user Multi-Input Multi-Output (MIMO) system, operating in Frequency Division Duplex (FDD) mode, severely degrades under imperfect Channel State Information (CSI). Among the main challenges toward the acquisition of sufficiently accurate CSI at the transmitter is the issue of enormous CSI feedback overhead. In this paper, a novel interference cancellation strategy is proposed to alleviate the overhead. The concept of a device-to-device based interference cancellation strategy was hinted in some prior works but has not been fully exploited in the multi-user MIMO systems, especially when the number of antennas becomes large. Hence, this paper aims to exploit the potential of User Equipment (UE) cooperation to reduce the dependency of precoder at the transmitter to the accuracy of CSI. To do so, adjacent pieces of UE that experience correlated CSI are clustered in a similar group, jointly adjusting their receive antenna combining the weight vector to maximize the channel vector orthogonality. Simulation results show that the proposed strategy reduces the dependency of system performance on the accuracy of CSI feedback; moreover, compared to the conventional limited feedback strategy, a larger number of antennas can be deployed at the transmitter.

Precoding Design and Optimization for Multi-Antenna Systems With Limited Feedback

We consider a multi-antenna wireless system consisting of a source transmitting to its destination with limited feedback, where the channel state information (CSI) is considered to be quantized and fed back from the destination to the source in the face of channel quantization errors (CQE). We propose a multi-antenna precoding (MAP) scheme to mitigate an adverse effect of the CQE, which is called the CQE oriented MAP and denoted CQE-MAP for short. Typically, using more channel quantization bits enhances the accuracy of quantized CSI acquired at the source and improves the data rate of the source-destination transmission, which, however, results in an increase of the CSI feedback overhead. We define an effective throughput as the difference of the data transmission rate and the CSI feedback rate that is used for characterizing the system overhead of sending the quantized CSI. An optimization analysis of the CQE-MAP scheme is carried out in terms of maximizing the effective throughput with regard to the number of quantization bits per channel. It is proved that the conventional maximal ratio transmission (MRT) based MAP method denoted by MRT-MAP could be a special case of the proposed CQE-MAP scheme for centralized antenna systems. Simulation results demonstrate that the CQE-MAP generally achieves a higher effective throughput than the MRT-MAP for distributed antenna systems, especially in the high signal-to-noise ratio (SNR) region. It is also illustrated that the effective throughput of CQE-MAP scheme can be further maximized through an optimization of the number of quantization bits per channel. Moreover, with an increase of the SNR or a decrease of the terminal moving speed, an increased number of quantization bits per channel is needed for the sake of maximizing the effective throughput.

Downlink Pilot Precoding and Compressed Channel Feedback for FDD-Based Cell-Free Systems

Cell-free system where a group of base stations (BSs) cooperatively serves users has received much attention as a promising technology for the future wireless systems. In order to maximize the cooperation gain in the cell-free systems, acquisition of downlink channel state information (CSI) at the BSs is crucial. While this task is relatively easy for the time division duplexing (TDD) systems due to the channel reciprocity, it is not easy for the frequency division duplexing (FDD) systems due to the CSI feedback overhead. This issue is even more pronounced in the cell-free systems since the user needs to feed back the CSIs of multiple BSs. In this paper, we propose a novel feedback reduction technique for the FDD-based cell-free systems. Key feature of the proposed technique is to choose a few dominating paths and then feed back the path gain information (PGI) of the chosen paths. By exploiting the property that the angles of departure (AoDs) are quite similar in the uplink and downlink channels (this property is referred to as angle reciprocity), the BSs obtain the AoDs directly from the uplink pilot signal. From the extensive simulations, we observe that the proposed technique can achieve more than 60% reduction in feedback overhead over the conventional CSI feedback scheme.