Frequency Division Duplex Mode Research Articles

A Full-Duplex 60 GHz Transceiver with Digital Self-Interference Cancellation

This paper presents the design and measurement of an IEEE 802.11ad standard compatible RF transceiver for 60 GHz wireless communication systems. In addition to the traditional half-duplex (HD) mode, this work supports full-duplex (FD) operations to deliver better channel utilization and faster response times for the system. The isolation between the transmitter and receiver from the architecture design to system integration for FD operations has been fully considered. A digital self-interference cancellation (DSIC) is implemented in MATLAB to verify the FD performance. The super-heterodyne architecture with an intermediate frequency (IF) of 12 GHz is designed to suppress the image frequencies without using extra filters. A flexible phase-locked loop (PLL) synthesizer provides a local oscillator (LO) frequency with a 2 kHz resolution. Other than the time division duplex (TDD) mode used in the conventional 60 GHz system, a wide-bandwidth baseband digital variable-gain amplifier (DVGA) with a 3 dB bandwidth of more than 4 GHz also supports frequency division duplex (FDD) operations. The transceiver chip is fabricated using the Tower Jazz 0.18 µm SiGe BiCMOS process. With an on-board antenna, the transceiver covers all four channels in the 802.11ad standard, with MCS-12 (7.04 Gbps under 1.76 GSym/s and 16-QAM) under 1.5 m. In the proposed system design, the RF frontend-based self-interference (SI) suppression from the local transmitter to receiver LNA is around 54 dB. To achieve a practical FD application, the SI is further suppressed with the help of a digital SI compensation. The measured power consumption for the transmitter and receiver configurations are 194 mW and 231 mW, respectively, in HD mode and 398 mW for the FDD or FD operation mode.

ACTIVE TRANSMITTER SIGNAL CANCELLATION IN RECEIVE SIGNAL PATH FOR SDR

Elimination of receiver (RX) band-pass filters is actual task for construction of next generation New Radio Wireless Transceivers and other modern equipment (Multiple Input Multiple Output systems, radar applications, etc.) based on software-defined radio. Unfortunately, some new problems with electromagnetic compatibility arise. Main problem start up with Frequency Division Duplex mode, were part of high power transmitter (TX) signal came to high sensitivity RX input through duplexer with limited isolation. In presence of high TX signal RX mixer could come into non-linear working area and RX sensitivity is decreases. This problem may be solved by cancellation of TX signal from RX path. This article provides a preliminary approach to the problem of active TX signal cancellation in RX signal path with cancellation is at least 25 dB, and relatively small additional power consumption and noise. After critical parameters identifying, based on mathematical expressions, the requirements for cancellation blocks parameters are determined. Simplest implementations of the proposed structure are simulated.

Adaptive refined random orthogonal matching pursuit algorithm for FBMC/OQAM MIMO framework

The fifth generation of wireless communication is anticipated to provide improved quality of service and enhanced data rates to the end users. One such technology that stands out as a potential transmission scheme for 5G systems is Filter Bank Multicarrier using Offset Quadrature Amplitude Modulation (FBMC/OQAM) with an effective channel estimation technique for improved performance. However, due to the inherent imaginary interference, channel estimation methods relying on preamble structures in FBMC/OQAM systems exhibit sub-optimal performance, particularly within Multiple-Input Multiple-Output (MIMO) setups. For channel estimation schemes based on compressed sensing, the inherent sparsity of wireless channels can be exploited for accurate channel reconstruction and overall performance improvement.We propose a novel compressed sensing based algorithm namely, Adaptive Refined Random Orthogonal Matching Pursuit (ARROMP), for MIMO-FBMC system with Coordinated MultiPoint (CoMP) scheduling. This algorithm adaptively selects a support set by utilizing a double threshold for the minimization of mean squared error and for accurate channel reconstruction. The proposed algorithm's performance is compared with existing Orthogonal Matching Pursuit (OMP) schemes such as random OMP, refined random OMP, and least square-based estimation. The numerical simulations suggest that the proposed adaptive algorithm provides performance improvement in terms of reduced Mean Squared Error (MSE) of channel reconstruction and Bit Error Rate (BER). Moreover, the proposed ARROMP algorithm for MIMO-FBMC is rigorously tested with CoMP scheduling for a cellular network using frequency division duplex mode. The proposed system presents significant improvements in throughput and spectral efficiency for all types of cell users, including cell-edge users. The simulation results validate the improved performance of the proposed algorithm with CoMP scheduling over the existing single-cell system with no coordination.

Implementation of SIC Algorithms in the 5G DL

In this paper, we have implemented successive interference cancellation algorithms in the 5G downlink. We have calculated the maximum throughput in Frequency Division Duplex (FDD) mode in the downlink, where we have obtained a value equal to 836932 b/ms. The transmitter is of type Multiple Input Multiple Output (MIMO) with eight transmitting and receiving antennas. Each antenna among eight transmits simultaneously a data rate of 104616 b/ms that contains the binary messages of the three users. In this case, the cyclic redundancy check CRC is negligible, the Block error rate BLER is null, the MIMO category is the spatial diversity. The technology used for this is called Non-Orthogonal Multiple Access (NOMA) with a Quadrature Phase Shift Keying (QPSK) modulation. The transmission is done in a Rayleigh fading channel with the presence of obstacles. The MIMO Successive Interference Cancellation (SIC) receiver with two transmitting and receiving antennas recovers its binary message without errors for certain values of transmission power such as 50 dBm, with 0.054485% errors when the transmitted power is 20 dBm and with 0.00286763% errors for a transmitted power of 32 dBm(in the case of user 1) as well as with 0.0114705% errors when the transmitted power is 20 dBm also with 0.00286763% errors for a power of 24 dBm(in the case of user2) by applying the steps involved in SIC.

CCA-Net: A Lightweight Network Using Criss-Cross Attention for CSI Feedback

To enhance the performance of a massive multiple-input multiple-output (MIMO) system, the downlink channel state information (CSI) should be compressed and fed back to the base station (BS) effectively in a frequency division duplex (FDD) mode. In contrast to traditional compression methods that rely on the particular prior assumptions, deep learning (DL) based CSI feedback has the potential to learn informatively from the hidden complex models behind the data. However, most of the current DL based methods stay in the early stage that uses DL modules directly from image processing, while the distinctive channel semantic information has been largely ignored. In this letter, considering the spatial-temporal correlation property of CSI matrices, we propose an architecture based on criss-cross attention, called CCA-Net, which extracts the channel features more precisely by leveraging the contextual information along the delay and angle domain. Meanwhile, a real and imaginary part combination module is used to assist in extracting the internal features of CSI. In addition, to reap the computational resources at BSs, a scalable decoder is designed to extend the reconstruction abilities. Experiments demonstrate that our network achieves better normalized mean square error with lower flops than existing state-of-the-art algorithms.

Deep-Unfolding-Based Bit-Level CSI Feedback in Massive MIMO Systems

In frequency division duplex mode, downlink channel state information (CSI) should be sent to the base station via a feedback link to obtain the benefits of the massive multiple-input multiple-output technology, leading to a substantial overhead. The existing deep-unfolding-based works combine the advantages of high performance and interpretability. In practical systems, the compressed CSI must be quantized at the user equipment before feedback. In this letter, a deep-unfolding-based bit-level CSI feedback is proposed to reduce the effects of quantization errors by using the shortcut and approximate quantization schemes. The shortcut scheme provides the unquantized encoder output directly to the decoder through an additional network branch, which can effectively help the decoder training. The approximate quantization scheme allows the gradient to be propagated correctly by approximating the real quantization operation. The two schemes are combined as a training strategy to reduce quantization errors further. Results show that the combined scheme can substantially reduce the influence of the quantization layer and improve the reconstruction performance while having some generality.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output (MIMO) in Frequency Division Duplex (FDD) mode can be fully exploited when the downlink Channel State Information (CSI) is available at base stations. However, the accurate CSI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas. In this paper, we propose a deep learning based joint channel estimation and feedback framework, which comprehensively realizes the estimation, compression, and reconstruction of downlink channels in FDD massive MIMO systems. Two networks are constructed to perform estimation and feedback explicitly and implicitly. The explicit network adopts a multi-Signal-to-Noise-Ratios (SNRs) technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels, while the implicit network directly compresses pilots and sends them back to reduce network parameters. Quantization module is also designed to generate data-bearing bitstreams. Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

Downlink Channel Estimation for FDD Massive MIMO Using Conditional Generative Adversarial Networks

For implementation of massive multiple-input multiple-output (MIMO) cellular systems in frequency division duplex (FDD) mode, accurate estimation of downlink channel state information (CSI) is necessary, but full radio channel reciprocity between the uplink and downlink does not exist in that mode. Existing work on estimating downlink CSI in FDD massive MIMO systems has considered such approaches as angle-of-arrival reciprocity, compressive sensing, using second-order channel statistics (particularly the channel covariance matrix (CCM)), and machine learning using deep neural networks (DNNs). Typical DNN-based approaches are unsuitable for this problem because DNNs require large datasets, thousands of training epochs, and are susceptible to environmental variations. To overcome these shortcomings, we develop a conditional generative adversarial network (CGAN) approach to uplink-to-downlink mapping of both CCMs and CSI. To apply this method, we convert the uplink and downlink CCMs/CSI to images and employ CGAN techniques previously applied to image translation. The normalized mean square error performance of the proposed CGAN is evaluated for several array sizes for both CCM and CSI mapping. For uplink-to-downlink CSI mapping, we also examine the spectral efficiency performance of our CGAN-based method, as well as the impact of pilot reuse; both simulated and measured CSI data are considered. Our results demonstrate performance improvement over existing algorithms.

Changeable Rate and Novel Quantization for CSI Feedback Based on Deep Learning

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

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.

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.

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.

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.

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.

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.

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.

Reliability-optimal pilot-assisted transmission for URLLC over non-reciprocal MISO channels: TDD or FDD?

In this paper, we focus on the pilot-assisted transmission design for downlink URLLC over non-reciprocal channels, in which the multi-antenna controller sends mission-critical data signals to a single-antenna actuator. In this system, the prior knowledge of downlink channel state information (CSI) is a prerequisite for reliable data transmission. Generally, the acquisition of downlink CSI is completed either via the uplink pilot measurement exploiting channel reciprocity and time-division duplex (TDD) operation, or via the downlink pilot measurement with quantized feedback and frequency division duplex (FDD) operation. Inspired by this, we aim to investigate how the degree of channel non-reciprocity impacts the transmission reliability of our URLLC system, and the superiority between the TDD mode and FDD mode in terms of transmission reliability maximization. To describe the degree of reliability loss, we derive the closed-form approximations on the transmission error probability of URLLC in TDD and FDD modes, via leveraging the Gauss-Hermite and Gauss-Chebyshev quadrature rules. Following by the theoretical approximations, we demonstrate how to determine the optimal training pilot length and quantized feedback duration that maximize the transmission reliability under given latency constraint. Through numerical results, we validate the accuracy of theoretical approximations derived in this paper, and obtain some meaningful conclusions.

Deep Learning-Based Joint CSI Feedback and Hybrid Precoding in FDD mmWave Massive MIMO Systems.

In this paper, we propose an end-to-end deep learning approach to realize channel state information (CSI) feedback and hybrid precoding for millimeter wave massive multiple-input multiple-output systems in the frequency division duplexing mode. Different from conventional approaches that treat the CSI reconstruction and hybrid precoding as separate components, we propose a new end-to-end learning method bypassing the channel reconstruction phase, and design the hybrid precoders and combiners directly from the feedback codewords (a compressed version of the CSI). More specifically, we design a neural network composed of the CSI feedback and hybrid precoding. Experiment results show that our proposed network can achieve better performance than conventional hybrid precoding schemes that reserve channel reconstruction, especially when the feedback resources are limited.

CSI Feedback With Model-Driven Deep Learning of Massive MIMO Systems

In order to achieve reliable communication with a high data rate of massive multiple-input multiple-output (MIMO) systems in frequency division duplex (FDD) mode, the estimated channel state information (CSI) at the receiver needs to be fed back to the transmitter. However, the feedback overhead becomes exorbitant with the increasing number of antennas. In this letter, a two stages low rank (TSLR) CSI feedback scheme for millimeter wave (mmWave) massive MIMO systems is proposed to reduce the feedback overhead based on model-driven deep learning. Besides, we design a deep iterative neural network, named FISTA-Net, by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to achieve more efficient CSI feedback. Moreover, a shrinkage thresholding network (ST-Net) is designed in FISTA-Net based on the attention mechanism, which can choose the threshold adaptively. Simulation results show that the proposed TSLR CSI feedback scheme and FISTA-Net outperform the existing algorithms in various scenarios.