Frequency Division Duplex Research Articles

Downlink Precoding for DP-UPA FDD Massive MIMO via Multi-Dimensional Active Channel Sparsification

In this paper, we consider user selection and downlink precoding for an over-loaded single-cell massive multiple-input multiple-output (MIMO) system in frequency division duplexing (FDD) mode, where the base station is equipped with a dual-polarized uniform planar array (DP-UPA) and serves a large number of single-antenna users. Due to the absence of uplink-downlink channel reciprocity and the high-dimensionality of channel matrices, it is extremely challenging to design downlink precoders using closed-loop channel probing and feedback with limited spectrum resource. To address these issues, a novel methodology – active channel sparsification (ACS) – has been proposed recently in the literature for uniform linear array (ULA) to design sparsifying precoders, which substantially reduces channel feedback overhead. Pushing forward this line of research, we aim to facilitate the potential deployment of ACS in practical FDD massive MIMO systems, by extending it from ULA to DP-UPA with explicit user selection and making the current ACS implementation simplified. To this end, by leveraging Toeplitz matrix theory, we start with the spectral properties of channel covariance matrices from the lens of their matrix-valued spectral density function. Inspired by these properties, we extend the original ACS using scalar-weight bipartite graph representation to the matrix-weight counterpart. Building upon such matrix-weight bipartite graph representation, we propose a multi-dimensional ACS (MD-ACS) method, which is a generalization of original ACS formulation and is more suitable for DP-UPA antenna configurations. The nonlinear integer program formulation of MD-ACS can be classified as a generalized multi-assignment problem (GMAP), for which we propose a simple yet efficient greedy algorithm to solve it. Simulation results demonstrate the performance improvement of the proposed MD-ACS with greedy algorithm over the state-of-the-art methods based on the QuaDRiGa channel models.

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.

A Multipath Local Route Repair Scheme for Bidirectional Traffic in an Airborne Network of Multibeam FDD Nodes

A directional airborne network consisting of nodes equipped with multibeam antennas is considered. These nodes use the frequency division duplex mode of communication. This allows formation of multiple routes between a pair of source and destination nodes, where every forward and reverse route completely overlaps. Each of these routes supports the bidirectional traffic. These routes are formed using the bidirectional ad hoc on-demand multipath distance vector (BAOMDV) routing protocol. In this article, we propose a local route repair scheme, called BAOMDV-LR, when these routes break due to node mobility. The proposed route repair scheme preserves the overlapping and link-disjoint (or node-disjoint) characteristics of the routes, while reducing the need for expensive route rediscovery. The proposed scheme significantly improves the overall network performance for bidirectional traffic (in terms of packet delivery ratio, end-to-end delay, and routing overhead) as compared to the BAOMDV and other existing routing schemes, especially at higher node speeds and data rates.

Learning the CSI Recovery in FDD Systems

We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, the base station reconstructs the full channel state information in the downlink frequency range based on limited downlink channel state information feedback from the mobile terminal. The channel state information recovery is based on a convolutional neural network which is trained exclusively on collected channel state samples acquired in the uplink frequency domain. No acquisition of training samples in the downlink frequency range is required at all. Finally, after a detailed presentation and analysis of the proposed technique and its performance, the “reciprocity of distribution” assumption of the convolutional neural network that is central to the proposed approach is validated with an analysis based on the maximum mean discrepancy metric.

Feedback Overhead in the FDD Downlink Massive MIMO System

One of the fundamental problems in massive multiple input multiple output (MMIMO) systems based on the frequency division duplexing (FDD) mode is the acquisition of the channel state information at the base station (BS). Indeed, in these systems, the reciprocity of the uplink (UL) and downlink (DL) channels is not applicable. Thus, the users are supposed, after having estimated their subchannels, to feedback them to the BS. Since the size of the information to be returned is proportional to the number of base station antennas that is very large, then we witness an excess of the number to be returned for each user. It is therefore essential to find a way to reduce this quantity, which affects the overall performance of the system. In this article, we propose two new MMIMO-OFDM-FDD-DL schemas based on the Givens rotation quantization used in the 802.11ac standard to reduce the amount of information to be returned for each user. The first is a fusion between the Givens rotation (GR) and the arithmetic coding (AC) technique, and the second is based on the combination of the GR method and the universal compression technique, Lempel Ziv Welch (LZW). The proposed schemes named GR & AC and GR & LZW (with an improvement of this proposal based on a modified version of LZW (LZWM) named GR & LZWM) for the first and second, respectively, are compared and both can achieve a compression ratio of up to 95% (or even 98%) and 83%, considerably exceeding recent advanced techniques.

RF CMOS Duplexers for Frequency-Division Duplex Radios: The Most Recent Developments

One main challenge for modern mobile communication systems is simultaneous demand for higher data rates (performance) and lower power consumption and size (cost). One way to increase wireless data transfer capacity is to employ frequency-division duplex (FDD) wireless systems where both transmitter (Tx) and receiver (Rx) work at the same time but in two different frequency bands with sufficient amount of guard band between them. An FDD system requires sharing the antenna between Tx and Rx [ <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1(a)</xref> ]. Because of the simultaneous operation of Tx and Rx units, modern FDD front-end realizations face numerous challenges, as shown in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1(b)</xref> :

Binarized Aggregated Network With Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to be fed back from the user equipment to the base station in frequency division duplexing (FDD) mode. However, the overhead of the direct feedback is unacceptable due to the large antenna array in massive MIMO system. Recently, deep learning is widely adopted to the compressed CSI feedback task and proved to be effective. In this paper, a novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance with network aggregation and parametric rectified linear unit (PReLU) activation. The practical deployment of the feedback network in the communication system is also considered. Specifically, the elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations. Besides, the network binarization technique is combined with the feature quantization for lightweight and practical deployment. Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks, providing a neat feedback solution with high performance, low cost and impressive flexibility.

Joint Massive MIMO CSI Estimation and Feedback via Randomized Low-Rank Approximation

The acquisitionof channel state information at transmitter (CSIT) is crucial to attain the potential benefits of the frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. Traditionally, CSI estimation at receiver and feedback to transmitter are separately designed, failing to maximize the estimation accuracy while minimizing the complexity/overhead. In this work, we propose a novel computation and communication efficient CSIT acquisition scheme, by jointly designing downlink CSI estimation and uplink feedback, which is inspired by randomized matrix approximation techniques. In the process of CSI estimation, we represent a large channel matrix with three sub-matrices by exploiting its inherent low-rank characteristic, and then estimate these small matrices separately. Our new estimator significantly reduces the computation complexity in massive MIMO CSI acquisition, whilst attaining the high accuracy. The convergence as well as the error bound of our CSI estimator is derived theoretically. In the following CSI feedback process, only partial elements of sub-matrices are reported to transmitter without further compression, and finally the full CSI is recovered accurately. This novel CSI feedback scheme, relying on the estimated CSI with special structure, greatly reduces both the communication overhead. Numerical simulations are provided to valid our joint CSI estimation and feedback scheme, which would have great potential in massive MIMO communications.

Scheduling for Massive MIMO With Hybrid Precoding Using Contextual Multi-Armed Bandits

In this work we study different scheduling problems in the downlink of a Frequency Division Duplex multiuser wireless system that employs a hybrid precoding antenna architecture for massive Multiple Input Multiple Output. In this context, we propose a scheduling framework using Reinforcement Learning (RL) tools, namely Contextual Multi-Armed Bandits (CMAB), that can dynamically adapt themselves to solve three scheduling problems, which are: i) Maximum Throughput (MT); ii) Maximum Throughput with Fairness Guarantees (MTFG), and; iii) Maximum Throughput with QoS Guarantees (MTQG), which are well-known relevant problems. Before performing scheduling itself, we exploit statistical Channel State Information (CSI) to create clusters of spatially compatible User Equipmentss (UEss). This structure, combined with the usage of Zero-Forcing precoding, allows us to reduce the scheduler complexity by considering each cluster as an independent virtual RL scheduling agent. Next, we apply a new learning-based scheduler aiming to optimize the desired system performance metric. Moreover, only scheduled UEss need to feed back instantaneous equivalent CSI, which also reduces the signaling overhead of the proposal. The superiority of the proposed framework is demonstrated through numerical simulations in comparison with reference solutions.

Centralized and Decentralized Channel Estimation in FDD Multi-User Massive MIMO Systems

We design a centralized and a decentralized variational Bayesian learning (C- and D-VBL) algorithms for the base station (BS) of a frequency division duplex massive multiple input multiple output (mMIMO) cellular system, wherein users send compressed information for it to estimate their downlink channels. The BS in the decentralized algorithm consists of multiple processing units (PUs), and each PU separately estimates the channels of a group of users, by employing the proposed D-VBL algorithm. To reduce channel estimation error, the PUs exploit the structured sparsity inherent in multi-user mMIMO channels by exchanging information among themselves. We investigate the proposed C-VBL and low-complexity D-VBL algorithms and show that i) they substantially outperform the state-of-the-art centralized and decentralized algorithms in terms of the normalized mean squared error and the bit error rate. This is because they beneficially exploit the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inherent</i> channel sparsity, while the existing state-of-the-art solutions fail to do so. The proposed D-VBL is also robust to PU failures, and provides a similar performance as its centralized counterpart (C-VBL), but with a much reduced complexity.

Environment Knowledge-Aided Massive MIMO Feedback Codebook Enhancement Using Artificial Intelligence

The autoencoder empowered by artificial intelligence has shown considerable potential in solving channel state information (CSI) feedback problems in frequency-division duplexing systems. However, this method needs to completely change the existing feedback schemes, which is difficult to deploy in the next few years. This paper proposes an environment knowledge-aided codebook-based CSI feedback framework, which retains the existent codebook-based scheme while introducing environment knowledge to feedback process through neural networks (NNs) at the base station. Only an NN-based refining operation is added after the common standardized feedback approach. The NNs learn to automatically extract environment features and utilize the channel statistics through large volumes of recorded data. The NNs also use the partial correlation between bidirectional channels to further improve feedback performance. In addition, to deal with downlink channel estimation errors, we propose two strategies to reduce their effects using an NN-based denoise module. The proposed framework can be easily embedded in most existing codebook-based feedback methods, such as random vector quantization. Two channel datasets generated by QuaDRiGa and measured in practical systems are adopted to evaluate the proposed methods. Results show that the proposed method offers over 100% increase in the throughput compared with the baseline codebook because of more accurate feedback.

Sum-Rate-Optimal Statistical Precoding for FDD Massive MIMO Downlink With Deterministic Equivalents

Statisticalprecoding is considered as a promising technique to release the channel state information (CSI) acquisition overhead. This article investigates a linear precoder design for frequency-division duplexing (FDD) massive MIMO downlink with only statistical CSI. We use a beam-based statistical channel model to capture the spatial correlation characteristics of the channels. The objective of the precoder design is to maximize the ergodic sum-rate under total power constraint. Based on the minorize-maximize (MM) algorithm, a stationary solution of the ergodic sum-rate maximization problem can be obtained. The stationary solution is shown to be the same as the optimal solution to a stochastic weighted minimum mean square error (SWMMSE) problem. Further, we establish the approximations for rate expressions with deterministic equivalent. The deterministic equivalents of ergodic rates are only related to the precoding matrices and statistical CSI. According to these closed-form rate expressions, we propose a linear precoder design algorithm and obtain tractable expressions for precoding matrices. Numerical comparisons with the existing precoding approach demonstrate the significant advantages of the developed algorithm.

Performance evaluation of frequency division duplex (FDD) massive multiple input multiple output (MIMO) under different correlation models.

Massive multiple-input multiple-output (massive-MIMO) is considered as the key technology to meet the huge demands of data rates in the future wireless communications networks. However, for massive-MIMO systems to realize their maximum potential gain, sufficiently accurate downlink (DL) channel state information (CSI) with low overhead to meet the short coherence time (CT) is required. Therefore, this article aims to overcome the technical challenge of DL CSI estimation in a frequency-division-duplex (FDD) massive-MIMO with short CT considering five different physical correlation models. To this end, the statistical structure of the massive-MIMO channel, which is captured by the physical correlation is exploited to find sufficiently accurate DL CSI estimation. Specifically, to reduce the DL CSI estimation overhead, the training sequence is designed based on the eigenvectors of the transmit correlation matrix. To this end, the achievable sum rate (ASR) maximization and the mean square error (MSE) of CSI estimation with short CT are investigated using the proposed training sequence design. Furthermore, this article examines the effect of channel hardening in an FDD massive-MIMO system. The results demonstrate that in high correlation scenarios, a large loss in channel hardening is obtained. The results reveal that increasing the correlation level reduces the MSE but does not increase the ASR. However, exploiting the spatial correction structure is still very essential for the FDD massive-MIMO systems under limited CT. This finding holds for all the physical correlation models considered.

A Computationally Efficient Gradient Algorithm for Downlink Training Sequence Optimization in FDD Massive MIMO Systems

Future wireless networks will require advance physical-layer techniques to meet the requirements of Internet of Everything (IoE) applications and massive communication systems. To this end, a massive MIMO (m-MIMO) system is to date considered one of the key technologies for future wireless networks. This is due to the capability of m-MIMO to bring a significant improvement in the spectral efficiency and energy efficiency. However, designing an efficient downlink (DL) training sequence for fast channel state information (CSI) estimation, i.e., with limited coherence time, in a frequency division duplex (FDD) m-MIMO system when users exhibit different correlation patterns, i.e., span distinct channel covariance matrices, is to date very challenging. Although advanced iterative algorithms have been developed to address this challenge, they exhibit slow convergence speed and thus deliver high latency and computational complexity. To overcome this challenge, we propose a computationally efficient conjugate gradient-descent (CGD) algorithm based on the Riemannian manifold in order to optimize the DL training sequence at base station (BS), while improving the convergence rate to provide a fast CSI estimation for an FDD m-MIMO system. To this end, the sum rate and the computational complexity performances of the proposed training solution are compared with the state-of-the-art iterative algorithms. The results show that the proposed training solution maximizes the achievable sum rate performance, while delivering a lower overall computational complexity owing to a faster convergence rate in comparison to the state-of-the-art iterative algorithms.

Air-Induced PIM Cancellation in FDD MIMO Transceivers

In this letter, air-induced passive intermodulation (PIM) modeling and cancellation schemes are presented in a multiple-input multiple-output (MIMO) frequency-division duplexing (FDD) transceiver context. PIM is distortion generated by nonlinear passive devices either within the transmitter chain or outside the transceiver system, as is the case in air-induced PIM. In FDD systems, the PIM products may lie on the receiver band, thus possibly desensitizing the receiver chain. Unlike previous PIM cancellation works, we consider a challenging rank-2 dual-carrier MIMO transceiver scenario, with two spatially multiplexed signals per component carrier (CC). Stemming from the PIM modeling, we first present a cancellation method based on a complete set of identified basis functions (BFs). Additionally, to relax the processing complexity, we propose an alternative canceller solution with a reduced number of BFs, inspired by the problem modeling. RF measurements conducted with real-life equipment indicate favorable PIM suppression levels of up to 20 dB using both introduced techniques.

Transmit Correlation Diversity: Generalization, New Techniques, and Improved Bounds

When the users in a MIMO broadcast channel experience different spatial transmit correlation matrices, a class of gains is produced that is denoted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transmit correlation diversity.</i> This idea was conceived for channels in which transmit correlation matrices have mutually exclusive eigenspaces, allowing non-interfering training and transmission. This paper broadens the scope of transmit correlation diversity to the case of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partially and fully overlapping</i> eigenspaces and introduces techniques to harvest these generalized gains. For the two-user MIMO broadcast channel, we derive achievable degrees of freedom (DoF) and achievable rate regions with/without channel state information at the receiver (CSIR). When CSIR is available, the proposed achievable DoF region is tight in some configurations of the number of receive antennas and the channel correlation ranks. We then extend the DoF results to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -user case by analyzing the interference graph that characterizes the overlapping structure of the eigenspaces. Our achievability results employ a combination of product superposition in the common part of the eigenspaces, and pre-beamforming (rate splitting) to create multiple data streams in non-overlapping parts of the eigenspaces. Massive MIMO is a natural example in which spatially correlated link gains are likely to occur. We study the achievable downlink sum rate for a frequency-division duplex massive MIMO system under transmit correlation diversity.

A low complexity burst channel estimation algorithm for FDD massive MIMO systems

To fully attain array gains of massive multiple-input multiple-output (MIMO) and its energy and spectral efficiency, deriving channel state information (CSI) at the base station (BS) side is essential. However, CSI estimation of frequency-division duplex (FDD) based massive MIMO is a challenging task owning to the required pilots, which are proportional to the number of antennas at the BS side. Therefore, the pilot overhead should be inevitably mitigated in the process of downlink channel estimation of FDD technique. In this paper, we propose a novel compressed sensing (CS) algorithm which takes advantage of correlation between the received and transmitted signals to estimate the channel with high precision, and moreover, to reduce the computational complexity imposed on the BS side. The main idea behind the proposed algorithm is to sort the specific number of maximum correlations as a common support in each iteration of the algorithm. Simulation results indicate that the proposed algorithm is capable of estimating downlink channel better than the counterpart algorithms in terms of mean square error (MSE) and the computational complexity. Meanwhile, the complexity of the proposed method linearly grows up when the number of BS antennas increases.

Triple-Structured Sparsity-Based Channel Feedback for RIS-Assisted MU-MIMO System

Reconfigurable intelligent surface (RIS) has attracted tremendous research attention in recent years. However, for RIS-assisted multi-user multiple-input multiple-output (MU-MIMO) system, downlink channel feedback in frequency division duplex (FDD) mode is quite a huge challenge due to the enlarged cascaded channel dimension. Consequently, feedback overhead turns unaffordable for RIS-assisted FDD model but limited studies focus on this puzzle. In this letter, we exploit the specific triple-structured sparsity of beamspace cascaded channel and propose a novel overhead-reduced feedback scheme. The common parameters shared by all users, i.e., path angles at BS side, offset values and amplitude ratios are transmitted back via partial active users, while the remaining user-specific information is compressed and quantized for efficient feedback. Simulation results show that under the same sum-rate requirement, the feedback overhead is further reduced by 56.8% compared with the previous studies.

CVLNet: A Complex-Valued Lightweight Network for CSI Feedback

The deep learning-based (DL-based) channel state information (CSI) feedback in the frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system has demonstrated its potential and efficiency. However, conventional neural networks cannot fully utilize the complex-valued nature of the downlink channel. In addition, multi-scale and multi-resolution features of CSI can be further explored. In this letter, we present a complex-valued lightweight neural network for CSI feedback named CVLNet. The CVLNet adopts the complex-valued neural network components in a multi-scale feature augmentation encoder and a multi-resolution X-shaped reconstruction decoder with a series of lightweight details. The experiment results show that the proposed CVLNet maintains the same-level parameters of the encoder with state-of-the-art (SOTA) lightweight networks while outperforming them with at most a 33.4% improvement in accuracy under severe compression rates.

TransNet: Full Attention Network for CSI Feedback in FDD Massive MIMO System

Channel state information (CSI) is a key aspect of massive multi-input multi-output (MIMO) system. It depicts important properties of transmission channels such as scattering, fading, the attenuation of power with distance, etc. The quality and cost of CSI feedback between user equipment (UE) and base station (BS) play vital roles in the quality of the whole communication system. In this letter, a new deep learning (DL) method based on Google&#x2019;s famous Transformer architecture is presented for CSI feedback in frequency division duplex (FDD) massive MIMO system. Simulation results show that the presented inception network named TransNet outperforms other DL methods on the quality of CSI feedback.