CSI feedback for massive MIMO system with dual-polarized antennas

Aiming at the problem of high complexity and low feedback accuracy of existing channel state information (CSI) feedback algorithms for frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL), which is suitable for single-user and multi-user scenarios in massive MIMO systems. This algorithm considers the spatial correlation of massive MIMO channel and uses bidirectional long short-term memory (Bi-LSTM) and bidirectional convolutional long short-term memory (Bi-ConvLSTM) network to decompress and recover the CSI for single-user and multi-user, respectively. The proposed DL-based CSI feedback network is trained offline by massive MIMO channel data and could learn the structural characteristics of the massive MIMO channel by fully exploiting the channel information in the training samples. The simulation results show that compared with several classical CSI compression feedback algorithms, the proposed CSI compression feedback algorithm has lower computational complexity, higher feedback accuracy, and better system performance in 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.

This letter presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (VQ-VAE) framework while reducing its computational complexity based on shape-gain vector quantization. In this method, the magnitude of the latent vector is quantized using a non-uniform scalar codebook with a proper transformation function, while the direction of the latent vector is quantized using a trainable Grassmannian codebook. A multi-rate codebook design strategy is also developed by introducing a codeword selection rule for a nested codebook along with the design of a loss function. Simulation results demonstrate that the proposed method reduces the computational complexity of VQ-VAE while improving CSI reconstruction performance under a given feedback overhead.

Large language models (LLMs) have revolutionized natural language processing by learning rich representations from massive and diverse text data. While pre-trained LLMs have been widely customized for a variety of text-related tasks, their potential in wireless communications remains largely untapped. In this paper, we propose a novel channel state information (CSI) feedback framework for massive multiple-input multiple-output (MIMO) systems that leverages pre-trained LLMs to bridge the gap between natural language processing and wireless communication. To adapt LLMs for the CSI feedback task, we design specialized pre-processing, embedding, and post-processing modules that convert CSI data into token representations compatible with LLMs. Moreover, to address the limitations of a single model and enhance performance in diverse environments and under various compression ratios, we introduce a mixture-of-agents (MoA)based framework that integrates the complementary strengths of multiple LLMs. Simulation results demonstrate that our approach achieves superior CSI reconstruction performance, robust generalization to unseen scenarios, and significant training cost reduction even with limited data. These findings confirm that the transferable knowledge acquired from large-scale natural language pre-training can effectively enhance CSI feedback, offering a promising direction for intelligent and efficient nextgeneration wireless communication systems.

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

In this paper, we propose a cross-attention and self-attention Transformer-based network (CASA-TransNet) for channel state information (CSI) feedback in dual-polarized massive multiple-input multiple-output (MIMO) systems. In dualpolarized massive MIMO systems, the channels in the horizontal and vertical polarization directions have a high correlation, which can be exploited for joint compression and reconstruction of the dual-polarized channels to improve the CSI feedback performance. The proposed CASA-TransNet employs a twostream cross-attention Transformer architecture and extracts the correlation between horizontal and vertical polarization CSI via cross-stream exchange of the query embeddings. Furthermore, a self-attention Transformer is utilized to model long-term dependencies of the dual-polarized CSI. Simulation results show that the proposed CASA-TransNet outperforms other deep learningbased methods, achieving better CSI reconstruction performance even at high compression ratios. Compared to state-of-the-art schemes, CASA-TransNet achieves a performance gain of $3.47 \sim 5.74 \mathrm{~dB}$.

Multicell cooperative processing (MCP) can overcome the detrimental effects of inter-cell interference (ICI). However, since mobile terminals (MTs) need to estimate and feed back channel state information (CSI) related to several cooperative base stations (BSs), reductions in CSI feedback overhead and delay are more demanding in MCP enabled networks. In this paper, a novel overlaid-pilot based CSI feedback scheme for multicell cooperative networks is proposed. MTs directly overlay the received downlink pilot data of all the cooperative BSs on the uplink pilot data and then broadcast it to the cooperative BSs. Each cooperative BS can recover multiple CSIs from the overlaid pilot by a presented overlaid-pilot based channel estimation algorithm. Because the overlaid pilot is fed back on the uplink pilot channel, no extra uplink channel is occupied and the downlink CSI feedback process is with zero-overhead regardless of the number of cooperative BSs. In addition, as it just needs one broadcast to feed back multiple CSIs to BSs, the feedback delay is decreased significantly. Numerical simulations show that the performance of the proposed CSI feedback scheme outperforms the traditional feedback scheme.

Deep learning (DL)-based channel state information (CSI) feedback improves the capacity and energy efficiency of massive multiple-input multiple-output (MIMO) systems in frequency division duplexing mode. However, multiple neural networks with different lengths of feedback overhead are required by time-varying bandwidth resources. The storage space required at the user equipment (UE) and the base station (BS) for these models increases linearly with the number of models. In this paper, we propose a DL-based changeable-rate framework with novel quantization scheme to improve the efficiency and feasibility of CSI feedback systems. This framework can reutilize all the network layers to achieve overhead-changeable CSI feedback to optimize the storage efficiency at the UE and the BS sides. Designed quantizer in this framework can avoid the normalization and gradient problems faced by traditional quantization schemes. Specifically, we propose two DL-based changeable-rate CSI feedback networks CH- CsiNetPro and CH- DualNetSph by introducing a feedback overhead control unit. Then, a pluggable quantization block (PQB) is developed to further improve the encoding efficiency of CSI feedback in an end-to-end way. Compared with existing CSI feedback methods, the proposed framework saves the storage space by about 50% with changeable-rate scheme and improves the encoding efficiency with the quantization module.

In frequency division duplex (FDD) massive MIMO systems, the feedback of channel state information (CSI) increases greatly with the number of antennas raising. Therefore, it is a hot-spot to research how to reduce the feedback overhead. It is considered that massive MIMO channel is sparse and in actual situation the sparsity is unknown, so the sparse adaptive matching pursuit (SAMP) algorithm is introduced to cope with these problems. Aiming at solving the shortcomings of SAMP, including the fixed step size and too much iterations, the modified SAMP (M-SAMP) is proposed in this paper. We combine the signal segmenting, the initial sparsity estimating and variable step size to reconstruct the signal quickly and accurately. The simulation results show that M-SAMP is superior than the SAMP algorithm both in reconstruction accuracy and computation time. In addition, compared with the orthogonal matching pursuit (OMP), subspace tracking (SP), and SAMP algorithms, the better normalized mean squared error (NMSE) performance of M-SAMP could be witnessed, which demonstrates the practicability of M-SAMP in massive MIMO systems.

The work of cooperative perception realized by mmWave Vehicle to everything (V2X) [3] has pointed out that vehicles require much network bandwidth to exchange sensor information for safe automated driving; e.g., they require higher than 1Gbps V2V bandwidth to ensure safe velocity of 70 km/h. On the other hand, some research works have shown that channel state information (CSI) feedback is able to mitigate interference for 5G frequency division duplexing (FDD) mmWave massive MIMO systems. Existing approaches either do not consider the characteristics of FDD mmWave V2X systems or assume all channels are ideal, i.e., uncorrelated and identical. To achieve higher throughput and low overhead for FDD mmWave V2X systems, there are two major issues: (1) how to acquire an accurate CSI under the environment of non-identical channels, and (2) how to effectively use such information to improve overall system throughput. Our idea is to jointly consider the characteristics of the angle of arrival (AoA) method and Khler manifolds to design of codebook feedback approach for mmWave V2X massive systems. That is, to get an accurate CSI, we leverage the measurement method with the idea of the AoA approach. With this AoA measurement, we propose a novel Khler manifolds codebook feedback approach and determine an optimal bit allotment over each mmWave V2X link for achieving better interference reduction as well as lower overhead for V2X CSI exchange. In addition, our analytic results validate that proposed AoA Khler manifolds codebook feedback scheme is more adequate than the traditional CSI feedback with regard to interference reduction. Simulations show that the proposed AoA Khler manifolds codebook feedback scheme can significantly improve the vehicle rate and reduce the overhead for V2V CSI exchange.

Most learning-based channel state information (CSI) feedback efforts concentrate on enhancing feedback accuracy through innovative neural network (NN) designs and exploiting correlations. This paper introduces an integrated learning framework for CSI feedback and localization designed to synergistically improve both tasks. We present a novel unified approach for CSI feedback and downlink CSI-based localization, where feedback is facilitated by an autoencoder, and the downlink CSI-based localization uses the feedback codeword directly without requiring reconstruction. The goal is to simultaneously minimize feedback and localization errors. Additionally, for users with access to coarse position data, we propose a refined framework that integrates this information into both the feedback mechanism and localization processes. This coarse positional knowledge is incorporated into the encoding and decoding stages to reduce feedback errors and is inputted into the localization NN to enhance localization accuracy. The improved framework is refined through an end-to-end training strategy, focusing on concurrently reducing feedback and localization errors. Simulation results using ray tracing channel datasets demonstrate that our proposed method not only enables feedback and localization tasks to mutually benefit but also shows that incorporating coarse positional data significantly increases the accuracy of both CSI feedback and CSI-based localization.

Massive multiple-input multiple-output systems deploying a large number of antennas at the base station considerably increase the spectrum efficiency by serving multiple users simultaneously without causing severe interference. However, the advantage relies on the availability of the downlink channel state information (CSI) of multiple users, which is still a challenge in frequency-division-duplex transmission systems. This paper aims to solve this problem by developing a full transceiver framework that includes downlink channel training (or estimation), CSI feedback, and channel reconstruction schemes. Our framework provides accurate reconstruction results for multiple users with small amounts of training and feedback overhead. Specifically, we first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP) algorithm to extract the frequency-independent parameters (i.e., downtilts, azimuths, and delays) from the uplink. Then, by leveraging the information from these frequency-independent parameters, we develop an efficient downlink training scheme to estimate the downlink channel gains for multiple users. This training scheme offers an acceptable estimation error rate of the gains with a limited pilot amount. Numerical results verify the precision of the eNOMP algorithm and demonstrate that the sum-rate performance of the system using the reconstructed downlink channel can approach that of the system using perfect CSI.

In frequency division duplex mode of massive multiple-input multiple-output systems, the downlink channel state information (CSI) must be sent to the base station (BS) through a feedback link. However, transmitting CSI to the BS is costly due to the bandwidth limitation of the feedback link. Deep learning (DL) has recently achieved remarkable success in CSI feedback. Realizing high-performance and low-complexity CSI feedback is a challenge in DL-based communication. We develop a DL-based CSI feedback network in this study to complete the feedback of CSI effectively. However, this network cannot be effectively applied to the mobile terminal due to its excessive number of parameters and high computational complexity. Therefore, we further propose a new lightweight CSI feedback network based on the developed network. Simulation results show that the proposed CSI network maintains a few parameters and parameter complexity while exhibiting better reconstruction performance than existing works. These findings suggest the feasibility and potential of the proposed techniques.

For massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) feedback is critical for exploiting the performance potentials. Recognized by the 3rd Generation Partnership Project as a key use case for the artificial intelligence-native air interface, deep learning (DL)-based CSI feedback has attracted extensive research from academia and industry. However, one of the most significant obstacles to its practical deployment is the limited generalization capabilities of DL models. In this article, we first illustrate the generalization challenge faced by DL-based CSI feedback with the main factors. Then, we systematically analyze its feasible solutions from two perspectives: initial model acquisition and efficient online updating. Finally, to address knowledge-forgetting problems in online updating, a novel alternating optimization framework is proposed to further improve the generalization ability through knowledge review. Hopefully, this article can give inspiring insights for the optimization of deployment and generalization for DL-based CSI feedback systems.

주파수 분할 하향링크 다중 안테나 시스템에서 사용되는 코드북 기반의 채널 정보 피드백 기법을 거대 다중 안테나 시스템에 그대로 적용할 경우 다음의 두 가지 문제가 생기게 된다. 먼저, 코드북 형성에 필요한 자원이 안테나 수에 지수적으로 필요하기 때문에 안테나 수가 매우 많은 거대 다중 안테나 시스템에서는 코드북 형성이 어려워진다. 뿐만 아니라, 사용자의 코드북 검색 시간이 안테나 수에 비례하기 때문에 채널 정보를 피드백 하는데 긴 시간이 필요하게 된다. 본 논문에서는 DFT 코드북의 계수를 안테나 별로 찾음으로써 코드북 형성에 필요한 자원을 안테나 수에 선형적으로 만드는 코드북 형성 기법을 제안한다. 그리고 일정 조건 하에 코드북 검색을 도중에 멈춤으로써 검색 시간을 감소시키는 기법을 제안한다. 실험 결과들은 제안 기법들이 기존 기법에 가까운 성능을 갖고 있음에도 코드북 형성 및 검색에 필요한 자원을 대폭 감소시킬 수 있음을 보여준다. Applying channel state information feedback scheme based on codebook for FDD downlink MIMO system to massive MIMO system directly causes following two problems. First, codebook generation becomes hard because resources needed for codebook generation increases exponentially in the number of antennas. In addition, long time is needed for channel state information feedback because users's codebook search time increases linearly in the number of antennas. This paper proposes a codebook generation scheme and a codebook search scheme which can reduce resource for codebook generation and codebook search time drastically though they have similar performance as conventional approaches.