Frequency Division Duplex Research Articles

MRFNet: A Deep Learning-Based CSI Feedback Approach of Massive MIMO Systems

In frequency division duplex (FDD) networks, channel state information (CSI) is critical for massive multiple-input multiple-output (MIMO) systems, because the base station (BS) relies on accurate CSI to achieve the promising benefits of massive MIMO systems. Therefore, the user equipments (UEs) need to feedback CSI to the BS accurately. Since the feedback overhead is large in massive MIMO system due to the large number of antennas, compressing and recovering CSI more accurately are essential and urgent problems. In this letter, we propose a network named MRFNet. The MRFNet can recover fruitful features with different receptive fields and the large number of convolution channels for better CSI recovery. Moreover, we visualize the output of the last block and the final output of the MRFNet with a different number of convolution channels and multiple receptive fields to explain why MRFNet works. Simulation results show that the proposed MRFNet obtains the significant performance gain of CSI feedback for both indoor and outdoor scenarios with different compression rates.

Deep Learning Based CSI Compression and Quantization With High Compression Ratios in FDD Massive MIMO Systems

Generally, the downlink channel state information (CSI) used for precoding is conveyed back in massive multi-input multi-output (MIMO) systems with frequency division duplex (FDD) mode, which costs too much bandwidth. However, most of CSI compression algorithms including deep learning based schemes show poor performance with high compression ratio (CR) in ourdoor scenery. In this letter, we propose a new neural network that utilizes the deep neural network (DNN) to improve the learning ability of residual network and a novel activation function to adapt the neural network. Simulation results show that the proposed CsiNet+DNN method exhibits better performance than exsiting methods in recovery quality and accuracy, which also achieves remarkable robustness at noise situations.

Distributed Base Station: A Concept System for Long-Range Broadband Wireless Access

We propose a concept system termed distributed base station (DBS) which enables distributed transmit beamforming at large carrier wavelengths to achieve significant range extension and/or increased downlink data rate, providing a low-cost infrastructure for applications such as rural broadband. We consider a frequency division duplexed (FDD) system using feedback from the receiver to achieve the required phase coherence. At a given range, N cooperating transmitters can achieve N2-fold increase in received power compared to that for a single transmitters, and feedback-based algorithms with near-ideal performance have been prototyped. In this paper, however, we identify and address key technical issues in translating such power gains into range extension via a DBS. First, to combat the drop in per-node SNR with extended range, we design a feedback-based adaptation strategy that is suitably robust to noise. Second, to utilize available system bandwidth, we extend narrowband adaptation algorithms to wideband channels through interpolation over OFDM subcarriers. Third, we observe that the feedback channel may become a bottleneck unless sophisticated distributed reception strategies are employed, but show that acceptable performance can still be obtained with standard uplink reception if channel time variations are slow enough. We quantify system performance compactly via outage capacity analyses.

A Modified OMP Algorithm with Reduced Feedback Overhead for Massive MIMO System

Objectives: Massive Multiple-Input and Multiple-Output (MIMO) is the optimum way to enhance the bandwidth issue, in which the feedback overhead is a challenging concern when tested with Frequency Division Duplex (FDD) systems. Conventional greedy search algorithms like Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP) are based on the prior knowledge of the signal sparsity, which enhances overhead. So, the objective of this study is to develop a method with low complexity channel estimation and reduced feedback overhead. Methods: The proposed method is based on the residual vector which is updated after iteration and a threshold-based approach is utilized to decide the stopping criteria thereby controlling the number of iterations. The evaluation parameters used in the experimental analysis are Normalized Mean Square Error and channel capacity. Findings: The proposed method improves the effectiveness in signal recovery and reconstruction along with signal sparsity it also reduces the complexity of the estimation process and limits the feedback. This results in a high data rate and can be used in a massive MIMO system to optimize the channel estimation. Novelty : The proposed model is independent of the sparsity in the input vector and improves the effectiveness of the signal recovery and reduces the feedback burden. The proposed method is suitable for a 5G massive MIMO systems. Keywords: Feedback overhead; CSI; OMP; Residue vector

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

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

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

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

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

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

Design of broadband RF transceiver front-end using highly selective diplexer and high Q-factor hybrid resonator bandpass filters

In this paper, a development of low cost, miniature, highly integration broadband RF transceiver front-end using the high selectivity hybrid resonator RF bandpass filters, and diplexer integrated with shielding case is presented. The proposed RF transceiver frontend operates at the 2.7 GHz for transmitting and 2.4 GHz for receiving, supporting a channel bandwidth of 100 MHz with frequency division duplexing mode. The designed low loss, high selectivity hybrid resonator bandpass filters are utilized to channel selection and reject the interference signal. The high isolation diplexer is employed to separate the Tx and Rx signals and reject the interferences from other systems to the RF receiver as well as filter out the harmonics generated by the RF transmitter. The measured performance of the proposed RF transceiver front-end module is superior to the requirements as well as can support high spectrum efficiency and high data rate communications with low error vector magnitude.

The Analysis of Service Integrity on Video Streaming Services Using Time Division Duplex and Frequency Division Duplex Technology on LTE Networks

The growing use of cellular technology has a rapid impact on the development of technology and information. This development relates to the use of information and communication services that is tailored directly, practically and effectively manner. Responding to the issue, the researcher is interested in conducting research by looking at the parameters using Time Division Duplex (TDD) technology and Frequency Division Duplex (FDD) technology on video streaming services. Service integrity measurement results on LTE networks are carried out in real time in the field according to the research path and eNodeB installed in dedicated mode. Dealing with the results, the effect of DT parameters on service integrity can only be seen in the SINR and CQI parameters. When SINR measured 21.3 dB and CQI measured 13.5, the measured throughput was 3665.2 Kbps so that the measured modulation was 64 QAM, on the other hand, if SINR measured -0.2 dB and CQI measured 10, the measured throughput was 0.5 Kbps so that the modulation measured was small, namely QPSK. From the results obtained, LTE TDD has a better service integrity value, namely throughput has a value of 891.16 Kbps and a latency of 48 ms for Telkomsel while 882.14 Kbps and latency was 49 ms for Smartfren than LTE FDD which had a throughput value of 820.83 Kbps and a latency was 68 ms for Telkomsel while 831.21 Kbps and 77 ms latency were for Smartfren, thus LTE TDD is better in terms of throughput and measured latency.

The Analysis of Service Integrity on Video Streaming Services Using Time Division Duplex and Frequency Division Duplex Technology on LTE Networks

The growing use of cellular technology has a rapid impact on the development of technology and information. This development relates to the use of information and communication services that is tailored directly, practically and effectively manner. Responding to the issue, the researcher is interested in conducting research by looking at the parameters using Time Division Duplex (TDD) technology and Frequency Division Duplex (FDD) technology on video streaming services. Service integrity measurement results on LTE networks are carried out in real time in the field according to the research path and eNodeB installed in dedicated mode. Dealing with the results, the effect of DT parameters on service integrity can only be seen in the SINR and CQI parameters. When SINR measured 21.3 dB and CQI measured 13.5, the measured throughput was 3665.2 Kbps so that the measured modulation was 64 QAM, on the other hand, if SINR measured -0.2 dB and CQI measured 10, the measured throughput was 0.5 Kbps so that the modulation measured was small, namely QPSK. From the results obtained, LTE TDD has a better service integrity value, namely throughput has a value of 891.16 Kbps and a latency of 48 ms for Telkomsel while 882.14 Kbps and latency was 49 ms for Smartfren than LTE FDD which had a throughput value of 820.83 Kbps and a latency was 68 ms for Telkomsel while 831.21 Kbps and 77 ms latency were for Smartfren, thus LTE TDD is better in terms of throughput and measured latency.

Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems

This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels' sparsity is exploited for reducing the overhead. First, we consider the uplink channel estimation for time-division duplexing systems. To reduce the uplink pilot overhead for estimating high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Particularly, by exploiting the channels' structured sparsity from an a priori model and learning the integrated trainable parameters from the data samples, the proposed multiple-measurement-vectors learned approximate message passing (MMV-LAMP) network with the devised redundant dictionary can jointly recover multiple subcarriers' channels with significantly enhanced performance. Moreover, we consider the downlink channel estimation and feedback for frequency-division duplexing systems. Similarly, the pilots at the BS and channel estimator at the users can be jointly trained as an encoder and a decoder, respectively. Besides, to further reduce the channel feedback overhead, only the received pilots on part of the subcarriers are fed back to the BS, which can exploit the MMV-LAMP network to reconstruct the spatial-frequency channel matrix. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms state-of-the-art approaches.

Cooperative Multiple-Access Channels With Distributed State Information

This paper studies a memoryless state-dependent multiple access channel (MAC) where two transmitters wish to convey a message to a receiver under the assumption of causal and imperfect channel state information at transmitters (CSIT) and imperfect channel state information at receiver (CSIR). In order to emphasize the limitation of transmitter cooperation between physically distributed nodes, we focus on the so-called distributed CSIT assumption, i.e., where each transmitter has its individual channel knowledge, while the message can be assumed to be partially or entirely shared a priori between transmitters by exploiting some on-board memory. Under this setup, the first part of the paper characterizes the common message capacity of the channel at hand for arbitrary CSIT and CSIR structure. The optimal scheme builds on Shannon strategies, i.e., optimal codes are constructed by letting the channel inputs be a function of current CSIT only. For a special case when CSIT is a deterministic function of CSIR, the considered scheme also achieves the capacity region of a common message and two private messages. The second part addresses an important instance of the previous general result in a context of a cooperative multi-antenna Gaussian channel under i.i.d. fading operating in frequency-division duplex mode, such that CSIT is acquired via an explicit feedback of perfect CSIR. The capacity of the channel at hand is achieved by distributed linear precoding applied to Gaussian codes. Surprisingly, we demonstrate that it is suboptimal to send a number of data streams bounded by the number of transmit antennas as typically considered in a centralized CSIT setup. Finally, numerical examples are provided to evaluate the sum capacity of the binary MAC with binary states as well as the Gaussian MAC with i.i.d. fading.

Dual-Band CMOS RF Front-End Employing an Electrical-Balance Duplexer and N-Path LNA for IBFD and FDD Radios

This article proposes a dual-band complementary metal-oxide-semiconductor (CMOS) radio frequency (RF) front end employing a hybrid transformer-based electrical-balance duplexer (EBD) and N-path filter embedded low-noise amplifier (LNA) for both in-band full duplexing (IBFD) and frequency-division duplexing (FDD) radios. To securely obtain sufficient self-interference cancellation (SIC) or transmitter (TX) leakage rejection in IBFD and FDD modes of the EBD at both low-band (LB) (0.7-1 GHz) and mid-band (MB) (1.8-2.2 GHz) frequency ranges, a band-switchable hybrid transformer is designed with a finely adjustable balance network to control the antenna impedance variations. In addition, the four-phase N-path LNA is utilized to further offer TX leakage rejection in the FDD operation. This RF front-end is fabricated using a 65-nm CMOS technology and is mainly characterized by long-term evolution frequency bands. The demonstrated design achieves SIC and TX leakage rejection greater than 55 dB for IBFD and FDD modes of all measured frequency bands, which has a maximum power handling capability of +25 dBm. Furthermore, the design attains voltage gains greater than 8.7 dB for LB and 3.7 dB for MB, noise figures of 10.4 dB for LB and 12.1 dB for MB, out-of-band (OB) P1dBs greater than +21.4 dBm for LB and +21.5 dBm for MB, and OB IIP3s greater than +39.7 dBm for LB and +43.1 dBm for MB. It draws a bias current of 23.8 mA from the LNA and output buffer with a nominal supply of 1.2 V and has an active area of approximately 2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Deep learning based user grouping for FD-MIMO systems exploiting statistical channel state information

The joint spatial division and multiplexing (JSDM) is a two-phase precoding scheme for massive multiple-input-multiple-output (MIMO) system under frequency division duplex (FDD) mode to reduce the amount of channel state information (CSI) feedback. To apply this scheme, users need to be partitioned into groups so that users in the same group have similar channel covariance eigenvectors while users in different groups have almost orthogonal eigenvectors. In this paper, taking the clustered user model into account, we consider the user grouping of JSDM for the downlink massive MIMO system with uniform planar antenna array (UPA) at base station (BS). A deep learning based user grouping algorithm is proposed to improve the efficiency of the user grouping process. The proposed grouping algorithm transfers the statistical CSI of all users into a picture, and utilizes the deep learning enabled objective detection model you look only once (YOLO) to divide the users into different groups rapidly. Simulation results show that the proposed user grouping scheme can achieve higher sum rate with less time delay.

Deep Learning Based Downlink Channel Covariance Estimation for FDD Massive MIMO Systems

Obtaining the downlink channel state information in frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems is challenging due to the overwhelming training and feedback overhead. In this letter, motivated by the existence of mapping characteristics between uplink and downlink, we propose a covariance variational auto-encoder network (CVENet) to approximate the mapping function. Different from normal auto-encoder, the CVENet extracts the uplink channel covariance to a latent distribution space and then predicts the downlink channel covariance by the sample of the space. Simulation results demonstrate that the CVENet performs better than the conventional dictionary pairs algorithm. And the CVENet still achieves robustness in a circumstance where the channel environment of the training stage is different from the deployment stage, which shows its practical applicability.

Self-Interference Cancellation in Full Duplex Communication using Steepest Descent Method

The present-day communication system uses Frequency Division Duplex (FDD) to emulate the benefits of Full Duplex Communication. But it requires more bandwidth as the cost of the spectrum is very high it becomes a major limitation. To overcome this problem implementation of Full Duplex Communication is the best solution. Implementation of full duplex communication is difficult because of a significant problem called self-interference. while transmitting and receiving signals on the same frequency band, receiving signal is interfered with the transmitted signal this phenomenon is called self-interference. The objective of this project is to minimize that self-interference signal from the received signal by using signal processing technique, LMS echo cancellation. Least Mean Square (LMS) echo canceller whose coefficients are updated iteratively is used to cancel the self-interference. An algorithm based on steepest descent method is used to obtain coefficients that change iteratively with varying step size to solve Weiner-Hopfs equation.

A Tunable Multimode Quadrature Balanced N-Path Diplexer With Nonlinear Cross-Modulation Distortion Correction

A tunable active diplexer based on quadrature balanced N-path mixers for single-antenna frequency-division duplex (FDD), half-duplex (HD), and carrier aggregation (CA) wireless is proposed. The balanced structure provides wideband TX noise cancellation in the RX band and low sensitivity to antenna VSWR variations, while sharp filtering between adjacent channels is achieved by the highly frequency-dependent input matching of the N-Path mixers. The diplexer's performance is enhanced by nonlinear digital algorithms accounting for cross-modulation distortion between the two channels and maintaining optimal performance at high TX power levels.

Randomized Low-Rank Approximation Based Massive MIMO CSI Compression

Massive multiple-input multiple-output (MIMO) is regarded as one enabling technique to improve channel capacity and energy/spectrum efficiency of 5G communications. To attain such potential benefits, accurate channel information is critical to the transmitter, which yet remains a challenging task for frequency division duplexing (FDD) systems, i.e., the channel state information (CSI) feedback tends to be resource-demanding especially for massive MIMO communications. In this work, we propose a novel CSI feedback method with low complexity and high precision, which is inspired by randomized matrix approximation. Our approach exploits the inherent low-rank characteristic of a large channel matrix, and approximates it by small sub-matrices which are then reported to transmitter to recover a CSI matrix. Theoretical bounds of the recovered CSI in both error-free and error cases are derived. Simulation results demonstrate our method could recover CSI accurately via an extremely low complexity and yet achieve a largely reduced compression ratio (or feedback overhead), compared to other schemes. It thus has the great potential in the emerging massive MIMO FDD communications.

Deep unfolding based hyper‐parameter optimisation for self‐interference cancellation in LTE‐A/5G‐transceivers

Deep unfolding is a very promising concept that allows to combine the advantages of traditional estimation techniques, such as adaptive filters, and machine learning approaches, like artificial neural networks. Focusing on a challenging self-interference problem occurring in frequency-division duplex radio frequency transceivers, namely modulated spurs, it is shown that deep unfolding enables remarkable performance gains. Based on the hyper-parameter optimisation of several least-mean squares (LMS) variants and the recursive-least squares algorithm, the importance of a well-chosen loss function are highlighted. Especially the variable step-size LMS and the transform-domain LMS vastly benefit without increased runtime complexity.

Multiuser joint downlink channel reconstruction based on spatial consistency

Massive multiple-input multiple-output (MIMO) systems working in frequency division duplex (FDD) transmission mode are facing great challenges because of the heavy training and feedback overhead. In this paper, we propose a novel algorithm for joint downlink channel reconstruction of multiple close-by users with reduced complexity and feedback overhead. Based on the spatial consistency, we extend the orthogonal matching pursuit algorithm to the multiuser case to jointly estimate the common channel parameters of these users. Specifically, a hierarchical dictionary is introduced to iteratively identify the common paths with reduced computational complexity. On another hand, based on the spatial reciprocity, the common path parameters are frequency-independent and can be obtained in the uplink, and only frequency-dependent channel parameters are then estimated in the downlink with reduced downlink training and feedback overhead. Numerical results verify the effectiveness and robustness of the proposed algorithm, and demonstrate that the proposed scheme can reduce the feedback overhead of channel estimation of adjacent users.