Frequency Division Duplex Research Articles

Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes.

Combining Reciprocity and CSI Feedback in MIMO Systems

Reciprocity-based time-division duplex (TDD) Massive MIMO (multiple-input multiple-output) systems utilize channel estimates obtained in the uplink to perform precoding in the downlink. However, this method has been criticized of breaking down, in the sense that the channel estimates are not good enough to spatially separate multiple user terminals, at low uplink reference signal signal-to-noise ratios, due to insufficient channel estimation quality. Instead, codebook-based downlink precoding has been advocated for as an alternative solution in order to bypass this problem. We analyze this problem by considering a "grid-of-beams world" with a finite number of possible downlink channel realizations. Assuming that the terminal accurately can detect the downlink channel, we show that in the case where reciprocity holds, carefully designing a mapping between the downlink channel and the uplink reference signals will perform better than both the conventional TDD Massive MIMO and frequency-division duplex (FDD) Massive MIMO approach. We derive elegant metrics for designing this mapping, and further, we propose algorithms that find good sequence mappings.

Quan-Transformer Based Channel Feedback for RIS-Aided Wireless Communication Systems

Reconfigurable intelligent surface (RIS) has become one of the key technologies for future 6G communications due to its characteristics of an intelligent and controllable wireless environment. In the frequency division duplex mode, the user equipment feeds back the downlink channel state information (CSI) to the base station through the feedback link, to obtain potential gains for the RIS-aided wireless communication system. A large number of RIS reflection elements greatly increase the feedback link cost of the system. In this letter, Quan-Transformer, a sample network framework for CSI compression and reconstruction, is proposed based on Transformer network. The proposed framework can not only compress CSI effectively but also recover CSI with high precision, which improves the performance of the RIS-aided wireless communication system. According to the experimental results, compared with the CsiNet scheme, the achievable sum-rate and normalized mean squared error of the proposed scheme are increased by 6.57% and 3.32%, respectively.

Self-Supervised Learning for CSI Compression in FDD Massive MIMO Systems

In frequency division duplexing systems, the user equipment frequently reports the channel state information (CSI) to the base station. This information is crucial for link adaptation and beamforming tasks in massive MIMO systems. However, it consumes a large amount of bandwidth when the number of antennas and subcarriers increases. Currently, compression techniques have been used to reduce this bandwidth overhead. In this letter, we propose a novel learning-based technique for CSI compression. Our method exploits the bias/variance tradeoff for CSI compression based on a shallow neural network to approximate a sufficient statistic function for each sampled channel. Unlike prior work that adopts projection-based techniques for compression, our method interprets the model weights as the compressed representation. The experimental results confirm the advantages of the proposed method compared with other state-of-the-art models.

Cell-Free MIMO 시스템에서 Rician 인자에 기반한 사용자 선택 및 피드백 비트 할당 통합 설계

최근 5G/6G 이동통신 시스템에서 한 사용자가 여러 분산된 access point(AP)로부터 효율적으로 서비스받을 수 있도록 사용자 관점에서 셀 개념이 사라진 cell-free MIMO 시스템이 주목받고 있다. 또한, cell-free 네트워크와 더불어 고주파수 대역 통신과 비지상 네트워크에서 물리적인 특성인 line-of-sight(LoS) 성분이 반영된 LoS 통신이 폭넓게 고려되고 있다. 사용자가 많은 MIMO 통신 시스템에서는 사용자 선택이 필요하며, frequency-division duplex(FDD) 방식의 제한된 피드백 환경에서는 피드백 비트 할당이 주요 이슈로써 다뤄진다. 본 논문에서는 AP간 채널 정보 공유가 이루어지지 않는 상황에서 각 AP가 독립적으로 제로-포싱 빔포밍을 수행할 때, LoS 통신을 반영한 Rician 페이딩 채널 모델에서 Rician 인자를 고려하여 사용자 선택과 피드백 비트 할당 통합 설계 기법을 제안한다. Rician 페이딩 채널에 특화된 밴드 구조의 비균질 코드북을 활용했을 때, 전송률의 하한과 피드백 비트할당의 영향을 분석하고 사용자 선택과 피드백 비트 할당을 순차적으로 수행하여 낮은 복잡도를 가진 최적에 근접한 피드백 비트 할당을 도출한다. 모의실험을 통해 제안하는 기법이 기존 기법들과 비교해 달성할 수 있는 평균합 전송률을 크게 향상할 수 있음을 확인한다.

PCA-Based Adaptive Training-Feedback Scheme in Time-Varying FDD Massive MIMO Systems

For massive multiple-input multiple-output (MIMO) systems, the real-time channel state information (CSI) acquisition is crucial but difficult in fast time-varying scenarios, especially for downlink (DL) channels in frequency division duplex (FDD) systems. This paper proposes an adaptive training-feedback scheme to estimate the CSI of DL channels. Specifically, first, base station (BS) determines a subspace containing uplink (UL) channel and an orthogonal normal basis of this subspace by using received signals in UL channel and the principle component analysis (PCA) technique. Second, by using the spatial reciprocity, it is found that the obtained subspace above also contains the DL channel vector. Thus, regarding the basis as pilots, the BS transmits pilots to mobile user (MU), and the user estimates received signals, feeds back them to the BS. Finally, according to information of the feedback, the BS can construct the DL channel vector. In this scheme, the pilots are adaptive to the change of the UL channel. Furthermore, the times of training is the dimension of the subspace, and feedback overhead is the coefficients of linear combination of DL channel vector under the basis only. Thus, cost of training and feedback can be greatly reduced. The simulation results show that the performance of the proposed scheme can approach the optimal scheme with very few training times and feedback overhead at high speed.

Joint Spatial Division and Multiplexing for FDD in Intelligent Reflecting Surface-Assisted Massive MIMO Systems

Channel reciprocity, assumed in fifth-generation (5G) systems such as massive multiple-input-multiple-output (mMIMO), appears to be questioned in practice by recent studies on intelligent reflecting surface (IRS). Hence, contrary to previous works, we consider frequency division duplexing (FDD) to study the performance of an IRS-assisted mMIMO system. However, FDD is not suitable for large number of antennas architectures. For this reason we employ the joint spatial division and multiplexing (JSDM) approach exploiting the structure of the correlation of the channel vectors to reduce the channel state information (CSI) uplink feedback, and thus, allowing the use even of a large number of antennas at the base station. Specifically, we derive the sum spectral efficiency (SE) based on statistical CSI in terms of large-scale statistics by using the deterministic equivalent (DE) analysis while accounting for correlated Rayleigh fading. Subsequently, we formulate the optimization problem concerning the sum SE with respect to the reflecting beamforming matrix (RBM)and the total transmit power, which can be performed at every several coherence intervals by taking advantage of the slow time variation of the large-scale statistics. This notable property contributes further to the decrease of the feedback overhead. Numerical results, verified by Monte-Carlo (MC) simulations, enable interesting observations by elucidating how fundamental system parameters such as the rank of the covariance matrix and the number of groups of UEs affect the performance. For example, the selection of a high rank improves the channel conditioning but increases the feedback overhead.

Learning How to Transfer From Uplink to Downlink via Hyper-Recurrent Neural Network for FDD Massive MIMO

In order to unlock the full advantages of massive multiple-input multiple-output (MIMO) in the downlink, the base station (BS) must leverage information about the downlink fading channels. However, in frequency division duplex (FDD) systems, full channel reciprocity does not hold, and acquiring information about the downlink channels generally requires downlink pilot transmission followed by uplink feedback. Prior work proposed to design pilot transmission, feedback, and channel state information (CSI) estimation, or directly downlink beamforming, via deep learning in an end-to-end manner. While previous work only used downlink pilots in a single slot, in this work, we introduce an enhanced end-to-end design that leverages partial uplink-downlink reciprocity and temporal correlation of the fading processes by utilizing jointly downlink and uplink pilots across multiple time slots. The proposed method is based on a novel deep learning architecture – HyperRNN – that combines hypernetworks and recurrent neural networks (RNNs) to optimize the transfer of long-term invariant channel features from uplink to downlink. Simulation results demonstrate that the HyperRNN achieves a lower normalized mean square error (NMSE) performance in terms of channel estimation, and that it attains a larger achievable sum-rate when applied to multi-user beamforming, as compared to the state of the art.

Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems With Implicit CSI

In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi-user hybrid beamforming with a limited pilot and feedback overhead is challenging. To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI). For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network. While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network. Different from conventional approaches separately processing different modules, the proposed solution simultaneously optimizes all modules with the sum rate as the optimization object. Therefore, by perceiving the inherent property of air-to-ground massive MIMO-OFDM channel samples, the DL-based E2E neural network can establish the mapping function from the channel to the beamformer, so that the explicit channel reconstruction can be avoided with reduced pilot and feedback overhead. Besides, practical low-resolution phase shifters (PSs) introduce the quantization constraint, leading to the intractable gradient backpropagation when training the neural network. To mitigate the performance loss caused by the phase quantization error, we adopt the transfer learning strategy to further fine-tune the E2E neural network based on a pre-trained network that assumes the ideal infinite-resolution PSs. Numerical results show that our DL-based schemes have considerable advantages over state-of-the-art schemes.

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.

Dilated Convolution Based CSI Feedback Compression for Massive MIMO Systems

Although the frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system can offer high spectral and energy efficiency, it requires to feedback the downlink channel state information (CSI) from users to the base station (BS), in order to fulfill the precoding design at the BS. However, the large dimension of CSI matrices in the massive MIMO system makes the CSI feedback very challenging, and it is urgent to compress the feedback CSI. To this end, this paper proposes a novel dilated convolution based CSI feedback network, namely <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ilated <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> hannel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> econstruction <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Net</b> work (DCRNet). Specifically, the dilated convolutions are used to enhance the receptive field (RecF) of the proposed DCRNet without increasing the convolution size. Moreover, advanced encoder and decoder blocks are designed to improve the reconstruction performance and reduce computational complexity as well. Numerical results are presented to show the superiority of the proposed DCRNet over the conventional networks. In particular, compared to the state-of-the-arts (SOTA) networks, the proposed DCRNet can achieve almost the same performance while reduce floating point operations (FLOPs) by about 30%.

Clustering Algorithm-Based Quantization Method for Massive MIMO CSI Feedback

Deep learning-based channel state information (CSI) feedback can provide high downlink throughput for massive multiple-input multiple-output (MIMO) system in frequency division duplex mode. The compression and quantization of CSI greatly reduce the feedback overhead. However, existing quantization methods design quantization levels without matching the distribution of feedback codewords, which impairs the reconstruction accuracy of CSI. In this letter, we propose a k-means clustering algorithm-based quantizer, named KMQBlock, which can find the appropriate quantization levels according to the distribution of feedback codewords by using scalar and vector quantization. To illustrate the efficacy of KMQBlock, we firstly propose a deep learning-based CSI feedback network MRNet to improve the CSI reconstruction accuracy. Then, we apply KMQBlock to MRNet to quantize the feedback codewords. Experimental results show that KMQBlock can provide at most 8.20 dB performance improvement for MRNet compared with conventional quantization methods.

Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement Matrices

For massive multiple-input multiple-output systems operating in frequency-division duplex mode, downlink channel state information (CSI) acquisition will incur large overhead. This overhead is substantially reduced when sparse channel estimation techniques are employed, owing to the channel sparsity in the angular domain. When a sparse channel estimation method is implemented, the measurement matrix, which is related to the pilot matrix, is essential to the channel estimation performance. Existing sparse channel estimation schemes widely adopt random measurement matrices, which have been criticized for their suboptimal reconstruction performance. This paper proposes novel data-driven solutions to design the measurement matrix. Model-based autoencoders are customized to optimize the measurement matrix by unfolding the classical basis pursuit algorithm. The obtained data-driven measurement matrices are applied to existing sparse reconstruction algorithms, leading to flexible hybrid data-driven implementations for sparse channel estimation. Numerical results show that the proposed data-driven measurement matrices can achieve more accurate reconstructions and use fewer measurements than the existing random matrices, thereby leading to a higher achievable rate for CSI acquisition. Moreover, compared with existing pure deep learning-based sparse reconstruction methods, the proposed hybrid data-driven scheme, which uses the novel data-driven measurement matrices with conventional sparse reconstruction algorithms, can achieve higher reconstruction accuracy.

A Survey of FDD-Based Channel Estimation Schemes With Coordinated Multipoint

New enabling technologies that include millimeter wave (mmWave) communication, massive multiple-input-multiple-output (MIMO), and ultradense networks are set to transform the wireless communication systems. These emerging technologies contribute toward the evolution of fifth generation (5G) systems that promise greater connectivity, throughput, mobility, and reliability for communication networks. This article presents a comprehensive overview of techniques for channel estimation, particularly, frequency division duplex (FDD)-based estimation techniques for a communication network. An exhaustive literature survey of FDD-based estimation schemes is presented, with a comprehensive review of the coordinated multipoint (CoMP) strategies in 5G radio networks, and its reliance on acquisition of channel state information (CSI). Since accurate CSI promotes the adoption of channel precoding, and reliable signal detection in FDD-based 5G systems, therefore, channel estimation for CSI in conjunction with CoMP transmission is preferable. We also present future research directions for FDD-based channel estimation and CoMP in 5G.

A Partial Channel Reciprocity-Based Codebook for Wideband FDD Massive MIMO

The acquisition of channel state information (CSI) in Frequency Division Duplex (FDD) massive MIMO has been a formidable challenge. In this paper, we address this problem with a novel CSI feedback framework enabled by the partial reciprocity of uplink and downlink channels in the wideband regime. We first derive the closed-form expression of the rank of the wideband massive MIMO channel covariance matrix for a given angle-delay distribution. A low-rankness property is identified, which generalizes the well-known result of the narrow-band uniform linear array setting. Then we propose a partial channel reciprocity (PCR) codebook, inspired by the low-rankness behavior and the fact that the uplink and downlink channels have similar angle-delay distributions. Compared to the latest codebook in 5G, the proposed PCR codebook scheme achieves higher performance, lower complexity at the user side, and requires less feedback. We derive the feedback overhead necessary to achieve asymptotically error-free CSI feedback. Two low-complexity alternatives are also proposed to further reduce the complexity at the base station side. Simulations with the practical 3GPP channel model show the significant gains over the latest 5G codebook, which prove that our proposed methods are practical solutions for 5G and beyond.

Mixed-Timescale Deep-Unfolding for Joint Channel Estimation and Hybrid Beamforming

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital beamforming is an essential technique for exploiting the potential array gain without using a dedicated radio frequency chain for each antenna. However, due to the large number of antennas, the conventional channel estimation and hybrid beamforming algorithms generally require high computational complexity and signaling overhead. In this work, we propose an end-to-end deep-unfolding neural network (NN) joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the system sum rate in time-division duplex (TDD) massive MIMO. Specifically, the recursive least-squares (RLS) algorithm and stochastic successive convex approximation (SSCA) algorithm are unfolded for channel estimation and hybrid beamforming, respectively. In order to reduce the signaling overhead, we consider a mixed-timescale hybrid beamforming scheme, where the analog beamforming matrices are optimized based on the channel state information (CSI) statistics offline, while the digital beamforming matrices are designed at each time slot based on the estimated low-dimensional equivalent CSI matrices. We jointly train the analog beamformers together with the trainable parameters of the RLS and SSCA induced deep-unfolding NNs based on the CSI statistics offline. During data transmission, we estimate the low-dimensional equivalent CSI by the RLS induced deep-unfolding NN and update the digital beamformers. In addition, we propose a mixed-timescale deep-unfolding NN where the analog beamformers are optimized online, and extend the framework to frequency-division duplex (FDD) systems where channel feedback is considered. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms with reduced computational complexity and signaling overhead.

Physical Layer Security in Multi-Antenna Cellular Systems: Joint Optimization of Feedback Rate and Power Allocation

This paper comprehensively studies the physical layer security in frequency division duplex multi-antenna cellular systems, where the multi-antenna base stations (BSs), legitimate users (LUs), and eavesdroppers are all randomly located. Each BS employs artificial-noise (AN)-aided multi-user linear beamforming with limited channel state information feedback. Based on the stochastic geometry theory, we first derive an analytical expression of a lower bound on the ergodic secrecy rate (ESR) of the typical LU without assuming asymptotes for any system parameter. We then develop a tight closed-form approximation on the optimal number of feedback bits to maximize a lower bound on the per-user net ESR, which takes into consideration the cost of uplink spectral efficiency for limited feedback. Moreover, the power allocation coefficient between message-bearing signals and AN can be optimized by using a bisection search method. Our main finding is that, the optimum number of feedback bits scales linearly with the path-loss exponent and the number of antennas, and scales logarithmically with the channel coherence time. The derived analytical results can also provide system-level insights into the ESR performance of the multi-antenna random cellular networks with limited feedback and the optimal system design. Numerical results are also presented to verify the obtained results.

Domain selective two-stage beamforming in 3D massive MIMO

In this paper, a two-stage beamforming scheme called domain selective neighbor-based joint spatial division and multiplexing (DS-NJSDM) scheme is proposed to reduce the overhead of pilot training and uplink feedback in Frequency Division Duplexing (FDD) three-dimensional (3D) massive multi-input multi-output (MIMO) systems. Inspired by the Kronecker product structure of the 3D channel model, a DS pre-beamformer is proposed to sparsify the channel matrix, which selects horizontal or vertical pre-beamformer to mitigate the interference from non-neighbors. The signal space can be effectively utilized with the DS pre-beamformer, and the complexity of beamforming is significantly reduced. After the pre-beamformer, the zero entries of the downlink channel matrix are randomly distributed, making pilot design difficult. Then, a pilot allocation scheme is proposed to design the pilot matrix. Simulation results are provided to validate the superiority of the proposed scheme in enhancing the spectral efficiency over the existing schemes.

Wireless Communication Network Security System Based on Big Data Information Transmission Technology

This paper establishes a unified perception information transmission model for the difference in the number of cognitive radio users in wireless communication network systems. The model can effectively improve the utilization of spectrum resources. At the same time, this paper introduces a cyclic delay full diversity sensing information coding transmission scheme for the security risks of wireless communication networks. We established a system simulation model to compare performance under different correlation coefficients. The study found that the total interference intensity in the cyclic delay diversity system is lower than that of the frequency division duplex and time division duplex. Its antijamming attack capability also becomes stronger accordingly.

A Compressive Sensing and Deep Learning-Based Time-Varying Channel Estimation for FDD Massive MIMO Systems

To achieve the performance gains of massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) must be acquired at the base station (BS). In frequency division duplexing (FDD) massive MIMO systems, the BS always first transmits downlink pilot symbols so that the user equipment (UE) can estimate CSI and then feedback to the BS. However, the huge number of antennas at the BS will lead to overwhelming feedback overhead. Moreover, time-varying caused by high mobility of user terminals makes the priori channel knowledge of the channels to change from one slot to another so that CSI aquisition is hard. To simultaneously reduce the overhead of overwhelming downlink pilot signaling and uplink feedback in time-varying massive MIMO systems, we propose a channel estimation scheme based on compressive sensing (CS) and deep learning (DL) in frequency division duplexing (FDD) massive MIMO systems. Specifically, we first develop a new CS-based algorithm for sparse channel estimation, which requires no priori knowledge of channel statistics. After obtaining the innitial channel estimation, we utilize two DL-based networks, named DnNet and DnLSTM respectively for denoising. Simulation results demonstrate that the proposed method can considerably reduce the training and feedback overhead and outperform the existing classical algorithms.