Frequency Division Duplex Mode Research Articles

Virtual Angular-Domain Channel Estimation for FDD Based Massive MIMO Systems with Partial Orthogonal Pilot Design

This article proposes a virtual angular-domain channel estimation scheme for massive multiple-input multiple-output systems operating in frequency division duplex (FDD) mode. Different from the conventional scheme where orthogonal pilots are transmitted on different antennas, we propose to transfer the channel estimation problem to the virtual angular domain and utilize the channel sparsity to reduce the training and feedback overhead. An orthogonal matching pursuit with Gram-Schmidt orthogonalization algorithm is proposed to construct the unitary transformation between the spatial domain and the virtual angular domain, which achieves higher sparsity than the existing approaches. Furthermore, we propose to estimate the downlink (DL) dominant angular set, which captures most of the channel power with only a few elements, by utilizing the directional reciprocity of FDD systems, where a calibration algorithm is introduced to handle the different wavelengths of uplink and DL transmissions. Based on the estimated dominant sets, we introduce a partial orthogonal criterion for virtual angular-domain pilot design and further propose two pilot assignment algorithms which minimize pilot overhead and pilot-reuse interference, respectively. Theoretical analyses on pilot overhead and the mean square error (MSE) performance are also presented. Simulation results demonstrate that our proposed virtual angular-domain channel estimation scheme provides excellent MSE performance with much reduced pilot overhead and, consequently, enjoys much larger per-user achievable rate in comparison to the conventional schemes.

Reconsidering Design of Multi-Antenna NOMA Systems With Limited Feedback

We provide in this paper a comprehensive solution to the design, performance analysis, and optimization of a multi-antenna non-orthogonal multiple access (NOMA) system for multiuser downlink communications under a general limited channel state information (CSI) feedback framework for frequency division duplex mode. We design a general framework including user clustering, joint power and bits allocation, CSI quantization and feedback, signal superposition coding, transmit beamforming, and successive interference cancellation at receivers. Then, we conduct a mathematically strict performance analysis of the considered system, and obtain a closed-form lower bound on the ergodic rate of each user in terms of transmit power, CSI quantization accuracy and channel conditions. For exploiting the potentials of multiple-antenna techniques in NOMA systems, we jointly optimize two key parameters, i.e., transmit power and the number of feedback bits allocated to each user, and propose low-complexity closed-form solutions. Moreover, through asymptotic analysis, we reveal the interactions between the main system parameters and their impacts on the joint power and feedback bits allocation result, and hence show some guidelines on the system design. Finally, numerical results validate the correctness of our theoretical analysis and demonstrate the advantages of the proposed algorithms over the most related state of the art.

Performance evaluation of video streaming on LTE with coexistence of Wi-Fi signal

The continuous growth in mobile data traffic and limited license wireless spectrum have led to dramatically increase the demand of the radio spectrum. It is widespread the concern about the coexistence of long term evolution (LTE) and Wi-Fi in the unlicensed band. There are several techniques have been proposed to enable the coexistence of LTE and Wi-Fi in the unlicensed band, but these works are targeted on the impact of the LTE to the Wi-Fi network performance. An experiment is carried out in this work to evaluate the impact of Wi-Fi signal on the video streaming in the LTE network. The experimental test comprised of the national instrument (NI) universal software radio peripheral (USRP) 2953R that is controlled by the LabVIEW Communication LTE application framework. Extensiveexperiments are carried out under two scenarios, i.e. (1) Coexistence of LTE and Wi-Fi signal, (2) LTE signal only. Performance evaluations are carried out with different Modulation and coding schemes (MCS) values and different mode of operations, i.e. frequency division duplex (FDD) and time division duplex (TDD) mode. The results illustrated that the interference from Wi-Fi signal caused the performance degradation of the LTE network in throughput and the power received by user equipment (UE).

Exploiting Bi-Directional Channel Reciprocity in Deep Learning for Low Rate Massive MIMO CSI Feedback

Channel state information (CSI) feedback is important for multiple-input multiple-output (MIMO) wireless systems to achieve their capacity gain in frequency division duplex mode. For massive MIMO systems, CSI feedback may consume too much bandwidth and degrade spectrum efficiency. This letter proposes a learning-based CSI feedback framework based on limited feedback and bi-directional reciprocal channel characteristics. The massive MIMO base station exploits the available uplink CSI to help recovering the unknown downlink CSI from low rate user feedback. We propose two deep learning architectures, DualNet-MAG and DualNet-ABS, to significantly reduce the CSI feedback payload based on the multipath reciprocity. DualNet-MAG and DualNet-ABS can exploit the bi-directional correlation of the magnitude and the absolute value of real/imaginary parts of the CSI coefficients, respectively. The experimental results demonstrate that our architectures bring an obvious improvement compared with the downlink-based architecture.

Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks

We propose a method to improve the performance of the downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation new radio (NR) networks. The standards-compliant method is based on the construction of a surrogate CoMP trigger function using deep learning. The cooperating set is a single-tier of sub-6 GHz heterogeneous base stations operating in the frequency division duplex mode (i.e., no channel reciprocity). This surrogate function enhances the downlink user throughput distribution through online learning of non-linear interactions of features and lower bias learning models. In simulation, the proposed method outperforms industry standards in a realistic and scalable heterogeneous cellular environment.

Analysis of Quantized MRC-MRT Precoder For FDD Massive MIMO Two-Way AF Relaying

The maturing massive multiple-input multiple-output (MIMO) literature has provided asymptotic limits for the rate and energy efficiency (EE) of maximal ratio combining/ maximal ratio transmission (MRC-MRT) relaying on two-way relays (TWRs) using the amplify-and-forward (AF) principle. Most of these studies consider time-division duplexing and a fixed number of users. To fill the gap in the literature, we analyze the MRC-MRT precoder performance of an N-antenna AF massive MIMO TWR, which operates in a frequency-division duplex mode to enable two-way communication between 2M = [N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sup> ] single-antenna users, with α ∈ [0, 1), divided equally into two groups of M users. We assume that the relay has realistic imperfect uplink channel state information (CSI), and that quantized downlink CSI is fed back by the users relying on B ≥ 1 bits per-user per relay antenna. We prove that for such a system with α ∈ [0, 1), the MRC-MRT precoder asymptotically cancels the multi-user interference (MUI) when the supremum and infimum of large-scale fading parameters are strictly nonzero and finite, respectively. Furthermore, its per-user pairwise error probability converges to that of an equivalent AWGN channel, as both N and the number of users 2M = [N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α</sup> ] tend to infinity, with a relay power scaling of P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> = (2ME <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> /N) and E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> being a constant. We also derive upper bounds for both the per-user rate and EE. We analytically show that the quantized MRC-MRT precoder requires as few as B = 2 bits to yield a BER, EE, and per-user rate close to the respective unquantized counterparts. Finally, we show that the analysis developed herein to derive a bound on α for MUI cancellation is applicable both to Gaussian as well as to any arbitrary non-Gaussian complex channels.

FDD Cooperative Channel Estimation and Feedback for 3D Massive MIMO System

In this paper, a downlink cooperative channel estimation scheme is proposed for the three-dimensional massive multiple inputs multiple outputs (3D-mMIMO) system operating in the frequency division duplexing (FDD) mode. In the proposed cooperative scheme, users have to cooperate with each other via device-to-device (D2D) communication protocol to jointly exploit the sparsity structure property of the channel. Motivated by the sparsity property of the mMIMO channel in the angle-time domain, a parametric feedback scheme is proposed, where the feedback overhead is decreased by sending a limited version of the estimated coefficients rather than all the coefficients back to the BS. Then, a compressive sensing (CS) algorithm is proposed, which we named weighted fast iterative shrinkage thresholding (WFISTA). In the WFISTA, we first introduce new weights and threshold function to the original FISTA to enhance its sparsity-undersampling trade-off in the single measurement vector (SMV) case, then we extend the proposed WFISTA to the case of multiple measurement vector (MMV) problem by adopting ReMBo (reduce MMV and boost) strategy. The proposed WFISTA has the ability to estimate the mMIMO system's channel coefficients by exploiting the joint sparsity structure through cooperation among users' equipment (UEs). Complexity analysis and the probability of decreasing the feedback overhead are provided for the proposed cooperative estimation scheme. The simulation results verify the efficiency of the proposed cooperative algorithm scheme compared to several joint channel estimations.

Deep Learning for CSI Feedback Based on Superimposed Coding

Massive multiple-input multiple-output (MIMO) with frequency division duplex (FDD) mode is a promising approach to increasing system capacity and link robustness for the fifth generation (5G) wireless cellular systems. The premise of these advantages is the accurate downlink channel state information (CSI) fed back from user equipment. However, conventional feedback methods have difficulties in reducing feedback overhead due to significant amount of base station (BS) antennas in massive MIMO systems. Recently, deep learning (DL)-based CSI feedback conquers many difficulties, yet still shows insufficiency to decrease the occupation of uplink bandwidth resources. In this paper, to solve this issue, we combine DL and superimposed coding (SC) for CSI feedback, in which the downlink CSI is spread and then superimposed on uplink user data sequences (UL-US) toward the BS. Then, a multi-task neural network (NN) architecture is proposed at BS to recover the downlink CSI and UL-US by unfolding two iterations of the minimum mean-squared error (MMSE) criterion-based interference reduction. In addition, for a network training, a subnet-by-subnet approach is exploited to facilitate the parameter tuning and expedite the convergence rate. Compared with standalone SC-based CSI scheme, our multi-task NN, trained in a specific signal-to-noise ratio (SNR) and power proportional coefficient (PPC), consistently improves the estimation of downlink CSI with similar or better UL-US detection under SNR and PPC varying.

Deep Learning for Massive MIMO CSI Feedback

In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery {mechanism} that learns to effectively use channel structure from training samples. CsiNet learns a transformation from CSI to a near-optimal number of representations (or codewords) and an inverse transformation from codewords to CSI. We perform experiments to demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain.

Interference Coordination for 3-D Beamforming-Based HetNet Exploiting Statistical Channel-State Information

In this paper, we investigate the inter-tier interference coordination for a heterogeneous network (HetNet), where both macro and small cells work in the frequency-division duplexing mode and share the same spectrum. A 2-D large-scale antenna array is deployed at a macro-base station (BS), while conventional multiple antenna array is deployed at each small cell. First, we derive an approximation of macro-users’ signal-to-interference-plus-noise ratio (SINR), under the assumption of only statistical channel-state information (CSI) at macro-BS, and a 3-D beamforming transmission scheme is applied for macro-users. A lower-bound of small-cell users’ expected SINR is also derived using the property of complex Wishart matrix. Based on these, simple metrics are derived to measure the inter-tier interference. Then, new interference coordination algorithms are proposed to achieve a good tradeoff for the performance and traffic between macro-cells and small cells. The proposed algorithms require only a few additional statistical CSI. Moreover, we derive tractable ergodic rate approximation of both the macro-cell and small-cell users which are shown to match well with the Monte Carlo results.

Alternative direction for 3D orthogonal frequency division multiplexing massive MIMO FDD channel estimation and feedback

In this study, downlink channel estimation of three-dimensional massive multiple-input multiple-output (3D-MIMO) system operating in the frequency division duplexing (FDD) mode is considered. Inspired by the channel sparsity property, this study proposes a compressive sensing algorithm to exploit the channel sparsity structure in the angle-time domain. The proposed algorithm, named AMP-ADM, combines the multiple approximate message passing (M-AMP) algorithm with the alternative direction of multiplier (ADM) technique to efficiently exploit the sparsity structure of the 3D massive MIMO channel. First, the proposed AMP-ADM is implemented in the case of the conventional estimation for the FDD protocol where the channel is estimated individually at each user equipment. Then, building on this algorithm, a low complexity feedback AMP-ADM-T scheme at the transmitting base station (BS) side is proposed. In the proposed feedback AMP-ADM-T technique the users' channels are jointly estimated at the BS to fully exploit the common sparsity basis. Complexity and convergence analyses are provided for both the AMP-ADM and feedback AMP-ADM-T algorithms. Simulation results prove the improved performance of the proposed feedback AMP-ADM-T algorithm compared to different state-of-the-art joint channel estimation techniques.

Joint Optimization of Hybrid Beamforming for Multi-User Massive MIMO Downlink

Considering the design of two-stage beamformers for the downlink of multi-user massive multiple-input multiple-output systems in frequency division duplexing mode, this paper investigates the case where both the link ends are equipped with hybrid digital/analog beamforming structures. A virtual sectorization is realized by channel-statistics-based user grouping and analog beamforming, where the user equipment only needs to feedback its intra-group effective channel, and the overall cost of channel state information (CSI) acquisition is significantly reduced. Under the Kronecker channel model assumption, we first show that the strongest eigenbeams of the receive correlation matrix form the optimal analog combiner to maximize the intra-group signal to inter-group interference plus noise ratio. Then, with the partial knowledge of instantaneous CSI, we jointly optimize the digital precoder and combiner by maximizing a lower bound of the conditional average net sum rate. Simulations over the propagation channels obtained from geometric-based stochastic models, ray tracing results, and measured outdoor channels, demonstrate that our proposed beamforming strategy outperforms the state-of-the-art methods.

Improving secret key generation for wireless communications in FDD mode

SummaryMost recent research on channel‐based key generation oriented to time division duplex system because the channel reciprocity feature is applied directly for secret key generation. Most of commercial cellular systems depend on frequency division duplex (FDD) systems. In this paper, we investigate the impact of uplink and downlink of FDD systems for the generation of shared secret keys between two parties in the presents of passive eavesdropper. In addition, we are considering improving the rate of the secret key for wireless communication in FDD mode. The main idea is to use the fading coefficient of the channels between the relay and other parties as an additional random common source for the secret key generation. Also, explore the using of channel estimation techniques to reduce the channel training sequence and study its effect on the generation of shared key for wireless communications in FDD mode. We derive the upper bound of the generated shared key rate for four scenarios and give numerical examples to reveal the performance of our suggested improvement approaches.

Performance Analysis of Secret Precoding-Aided Spatial Modulation With Finite-Alphabet Signaling

The precoding-aided spatial modulation (PSM) is first characterized with respect to its secrecy performance, which shows that the PSM is a secrecy embedded communication scheme when operated in the time-division duplex mode, but experiences security risk under frequency-division duplex mode. Therefore, we extend the PSM to a secret PSM (SPSM) that is suitable for operation in any communications scenarios experiencing passive eavesdropping. In the proposed SPSM, the secrecy performance is enhanced by using a part of transmit power to interfere the eavesdropper’s reception. In this paper, the secrecy performance of SPSM with discrete inputs is comprehensively investigated in terms of both bit error rate and secrecy rate and by both analysis and simulations. Relying on the asymptotic analysis, we also derive the upper and lower bounds for the error performance and secrecy rate of SPSM. Furthermore, we study the power allocation between information and interference transmission in order to maximize SPSM’s secrecy performance. Our studies result in a range of expressions, which are validated by Monte Carlo simulations. Furthermore, our studies demonstrate that the analytical formulas are beneficial to the optimization of SPSM systems for maximizing its security performance.

Adaptive RF Front-Ends Using Electrical-Balance Duplexers and Tuned SAW Resonators

This paper proposes an adaptive RF front-end (RFFE) architecture that uses an electrical-balance duplexer (EBD) and tuned surface acoustic wave (SAW) resonators to enable adaptive dual-frequency isolation suited for LTE’s frequency-division duplex (FDD) mode of operation. Alternatively, in the in-band full-duplex (IBFD) mode, the EBD cancels the in-band TX self-interference directly at RF for increased channel capacity. Furthermore, the EBD’s balance network is optimized to reduce the TX insertion loss (IL) below the nominal 3-dB limit in the FDD mode. A 0.18- $\mu \text{m}$ SOI CMOS prototype implements the EBD, low-noise amplifier, and SAW-tuning capacitor banks. The chip is mounted to a module substrate together with the SAWs and matching components. The RFFE achieves >50-dB dual-frequency isolation and 2.6–3.4-dB TX IL in FDD mode, has +58/+42-dBm TX/RX-path IIP3 for an increased IBFD-link budget and handles up to +27.5-dBm TX power in both modes.

A Scalable Framework for CSI Feedback in FDD Massive MIMO via DL Path Aligning

Unlike the time-division duplexing (TDD) systems, the downlink (DL) and uplink (UL) channels are not reciprocal anymore in the case of frequency-division duplexing (FDD). However, some long-term parameters, e.g. the time delays and angles of arrival (AoAs) of the channel paths, still enjoy reciprocity. In this paper, by efficiently exploiting the aforementioned limited reciprocity, we address the DL channel state information (CSI) feedback in a practical wideband massive multiple-input multiple-output (MIMO) system operating in the FDD mode. With orthogonal frequency-division multiplexing (OFDM) waveform and assuming frequency-selective fading channels, we propose a scalable framework for the DL pilots design, DL CSI acquisition, and the corresponding CSI feedback in the UL. In particular, the base station (BS) can transmit the FFT-based pilots with the carefully-selected phase shifts. Then the user can rely on the so-called time-domain aggregate channel (TAC) to derive the feedback of reduced imensionality according to either its own knowledge about the statistics of the DL channels or the instruction from the serving BS. We demonstrate that each user can just feed back one scalar number per DL channel path for the BS to recover the DL CSIs. Comprehensive numerical results further corroborate our designs.

Effects of non‐uniform quantisation on the interference mitigation using multi‐cell multiple‐input and multiple‐output‐coordinated beamforming

In recent years, coordinated beamforming (CBF) has attracted considerable attention because of its effectiveness in intercell interference (ICI) mitigation. To facilitate the operations of CBF for the frequency-division duplex mode, efficient channel state information (CSI) exchanges through backhaul links are necessary. In previous studies, uniform quantisation of CSI has been generally assumed in feedback schemes. Statistically, however, uniform quantisation may not be optimal from the viewpoint of the ICI in CBF, which is mainly caused by the quantisation error (QE). This study proposes a low-complexity cumulative distribution function (CDF)-based non-uniform quantisation method with a limited number of feedback bits for applying more quantisation levels to represent feedback CSI, which occurs with higher probability. This is because a more accurate representation of frequent CSI can lead to a lower QE for reducing the ICI level in most cases. To prove the effectiveness of the proposed CDF-based quantisation scheme, the ICI in the considered two-cell CBF scenario was analysed using several feedback bits. The simulation results proved the higher transmission rate, particularly in cases with fewer feedback bits.

A Multipath Extraction-Based CSI Acquisition Method for FDD Cellular Networks With Massive Antenna Arrays

Unlocking the true potential of massive antenna arrays for cellular networks operating in frequency division duplex (FDD) mode is a challenging task as inadequate channel state information (CSI) by conventional limited-size UE feedback forces crude beamforming in downlink. This paper develops a new technique for the CSI acquisition problem of FDD massive multiple-input multiple-output (MIMO) networks based on estimating the reciprocal characteristics of the multipath channel directly from uplink, while merely the nonreciprocal properties of the dominant propagation paths are estimated and fed back from UE. The main benefits of this technique over compressive-sensing methods include allowing single-beam pilot transmission during downlink training and relaxed UE-side computational complexity, as the full knowledge of CSI vector is no longer required at UE. The design can be accomplished by measuring the direction-of-arrival information and average powers of the dominant paths from uplink, indicating the target dominant paths to corresponding UEs, estimating and subsequently signaling the random phase information of the target paths from each UE, and reconstructing the aggregate CSI vector at eNB. The numerical evaluation results demonstrate that the proposed mechanism offers promising potential for reliable FDD operation in next generation cellular networks equipped with massive antenna arrays.

Channel Tracking and Transmit Beamforming With Frugal Feedback

Channel state feedback is a serious burden that limits deployment of transmit beamforming systems with many antennas in frequency-division duplex (FDD) mode. Transmit beamforming with limited feedback systems estimate the channel at the receiver and send quantized channel state or beamformer information to the transmitter. A different approach that exploits the spatio-temporal correlation of the channel is proposed here. The transmitter periodically sends a beamformed pilot signal in the downlink, while the receiver quantizes the corresponding received signal and feeds back the bits to the transmitter. Assuming an autoregressive (AR) channel model, Kalman filtering (KF) based on the sign of innovations (SOI) is proposed for channel tracking, and closed-form expressions for the channel estimation mean-squared error (MSE) are derived under certain conditions. For more general channel models, a novel tracking approach is proposed that exploits the quantization bits in a maximum a posteriori (MAP) formulation. Simulations show that close to optimum performance can be attained with only 2 bits per channel dwell time block, even for systems with many transmit antennas. This clears a hurdle for transmit beamforming with many antennas in FDD mode—which was almost impossible with the prior state-of-art.

English

Digital Mobile Communication Systems are prevalent worldwide for providing data/voice services. Second generation systems are limited to the maximum data rate they can support. Third generation Mobile communication systems provide more advanced services such as broadband internet access, video telephony and video conferencing services. It integrates a wide variety of communication services such as high speed data, video and multimedia traffic as well as voice signals. In the Third Generation (3G) Cellular Systems, one of the most important and complex parts in the receiver is a carrier synchronization unit. The data is transmitted in a frame by frame basis through a time varying channel. The phase and frequency of the transmitted signal may also be shifted because of fading and the Doppler frequency shift. In receiver side, carrier frequency of the receiver should be synchronized with the incoming signal. In this paper, we designed, low complexity, adaptive baseband receiver carrier synchronization architecture for uplink wide band code division multiple access (WCDMA) system based on 3rd Generation Partnership Project(3GPP) radio access network Frequency Division Duplex (FDD) mode specification and its Field Programmable Gate Array ( FPGA) Simulation is presented.