Time Division Duplex Research Articles

Spanning Tree Method for Over-the-Air Channel Calibration in 6G Cell-Free Massive MIMO

Cell-free massive multiple-input multiple-output (MIMO) is an attractive network in 6G communications that significantly increases the spectral efficiency. Operating in time-division duplex (TDD) mode, the downlink beamforming is achieved by the estimated uplink channel, which is equal to the downlink channel due to the property of channel reciprocity. However, the involvement of different radio frequency (RF) gains in transceiver antennas renders the whole channel non-reciprocal. Therefore, it is of great necessity to calibrate the bi-directional channel. In this paper, we focus on the issue of over-the-air channel calibration in cell-free system. Taking a toy scenario as an example, we examine the performance differences between the calibration methods of ‘Direct Process’ and ‘Indirect Process’. A novel low-cost calibration method based on spanning tree model is proposed specifically for this distributed AP scenario, where a calibration tree is established to calculate calibration coefficients. Numerical results manifest that higher accuracy of our method is achieved compared to the existing calibration methods in the literatures. Our method is less sensitive to the location of master AP compared to Argos, and practical applications under the impact of phase noise show the priority of our method compared to LS method.

Dual-Band Dual-Output Doherty Power Amplifier for Unsynchronized Time-Division Duplexing

Conventional multiband power amplifiers (PAs), when working in time-division duplex (TDD), require the transmit–receive intervals of all bands to be synchronized. This is an undesirable constraint in modern radio base stations. This article proposes a dual-output dual-band Doherty PA (DB-DPA) that enables the loading of one band to be switched on-and-off without impacting the other band in normal transmission. A thorough analysis of the proposed dual-output DB-DPA architecture is presented, and a dual-output DB-DPA prototype operating at 1.4 and 2.6GHz has been designed as a proof of concept. The large-signal measurements of the prototype DB-DPA indicate Doherty behavior at both frequencies with over 56% saturation efficiency and 45% efficiency at 6-dB power back-off (PBO). Furthermore, single-band and concurrent dual-band linearized modulated measurements, using 10-MHz new radio (NR) signals with 6-dB peak-to-average power ratio (PAPR), demonstrate the linearizability of the proposed DB-DPA and its usefulness for unsynchronized TDD applications.

Fingerprint-Based mmWave Positioning System Aided by Reconfigurable Intelligent Surface

Reconfigurable intelligent surface (RIS) is a promising technique for millimeter wave (mmWave) positioning systems. In this paper, we consider multiple mobile users (MUs) positioning problem in the multiple-input multiple-output (MIMO) time-division duplex (TDD) mmWave systems aided by the RIS. We derive the expression for the space-time channel response vector (STCRV) as a novel type of fingerprint. The STCRV fingerprint comprises the channel characteristics of the cascaded channel, such as the angle of arrival (AOA) at the RIS and the time delay from the MU to the RIS, which are indicative of the position of the MU. To process the STCRV with more features, we propose a novel residual convolution network regression (RCNR) learning algorithm to output the estimated three-dimensional (3D) position of the MU with higher accuracy. Specifically, the RCNR learninng algorithm includes a data processing block to process the input STCRV, a normal convolution block to extract the features of STCRV, four residual convolution blocks to further extract the features and protect the integrity of the features, and a regression block to estimate the 3D position. Extensive simulation results are also presented to demonstrate that the proposed RCNR learning algorithm outperforms the traditional convolution neural network (CNN).

Robust Joint Precoding/Combining Design for Multiuser MIMO Systems With Calibration Errors

We consider the downlink of a multiuser system operating in the time-division duplexing mode, for which base station (BS) and users are equipped with multiple antennas, and provide a robust precoding/combining design against imperfect channel state information (CSI) and calibration errors due to hardware mismatch. Towards this end, we first formulate a robust joint precoder and combiner design as a stochastic minimum mean squared error optimization problem. Then, employing an alternating optimization approach, we propose an algorithm to obtain the precoding and combining matrices assuming imperfect CSI and calibration errors at both the BS and the user sides. We also provide asymptotic closed-form expressions for the mean squared error (MSE) and the achievable sum-rate in the massive MIMO regime. The results indicate that while the MSE linearly increases with the calibration errors at the user side, the sum-rate is asymptotically independent of them. Extensive simulation results show that the proposed robust joint precoder/combiner outperforms the existing solutions while having the same order of complexity. Moreover, when the BS sends a quantized version of the combining coefficients to the users, it is observed that the proposed solution is more robust to the quantization errors than the existing algorithms.

A novel duplex deep reinforcement learning based RRM framework for next-generation V2X communication networks

Resource management in the next-generation vehicle-to-everything (V2X) communication networks is a demanding research problem. It is difficult to achieve the best results if the resources are not managed efficiently. To address this challenge and facilitate the dynamic movement of network demands, efficient management of resources is crucial. In this context, we propose a duplex deep reinforcement learning (DDRL)-based resource management framework for next-generation V2X communication networks. Our framework considers multiple network state parameters and utilizes these resources during the learning phase to address resource management problems effectively. The key objective was to select the forthcoming resource control state through the duplex deep reinforcement learning phase, thereby optimizing resource utilization efficiency. To achieve this, we jointly implement time division duplexing (TDD) and frequency division duplexing (FDD), which allow for adaptive management of TDD and FDD to meet high mobility requirements and efficiently utilize limited radio resources. This study introduces a dynamic duplex deep Q-learning-assisted sequential decision-making algorithm to manage band resources for the next-generation V2X network. By employing this algorithm, we enable the efficient allocation of resources, considering factors such as latency, system throughput, network utilization, busy channel rate (BCR), and packet received rate. To validate the performance of our proposed framework, we conducted simulation experiments using benchmarking techniques. The results demonstrate that our framework outperforms benchmark approaches across various performance parameters. It exhibits superior efficiency in terms of latency, system throughput, network utilization, BCR, and packet received rate.

Improved TDD operation on Software-Defined Radio platforms towards future wireless standards

Software-Defined Radio (SDR) platforms are valuable for research and development activities or high-end systems that demand flexible wireless protocols. While low-latency digital baseband processing can be achieved using a dedicated processing unit, like an FPGA or hardware accelerator, its multi-purpose Radio Frequency (RF) front-end often poses a limitation. Zero Intermediate Frequency (ZIF) transceivers are favorable for SDR, however, even for Time Division Duplex (TDD) systems, these transceivers suffer from self-interference when the transmitting and receiving Local Oscillator (LO) is set to the same frequency. To achieve low self-interference, switching from receiving to transmitting mode is needed. However, the time this takes (turnaround time, TT) for configurable RF front-ends often violates the strict timing requirements of protocols like Wi-Fi and 5G, which require response times in the order of microseconds. In this work, we first evaluate the advantages and disadvantages of several methods to suppress self-interference of a ZIF transceiver. Next, a novel approach is proposed, which can reduce the TT to as low as 640ns using the widely used AD9361 configurable ZIF RF front-end, while the noise floor is at the same level as achieved by the conventional way of switching between transmit and receive mode. We have realized and validated this approach using openwifi — an open-source Wi-Fi implementation on SDR. As a result, the receiver sensitivity is improved by up to 17dB in the 2.4GHz band and 9.5dB in the 5GHz band, for over-the-air transmissions.

Lightweight Machine Learning for Digital Cross-Link Interference Cancellation With RF Chain Characteristics in Flexible Duplex MIMO Systems

The flexible duplex (FD) technique, including dynamic time-division duplex (D-TDD) and dynamic frequency-division duplex (D-FDD), is regarded as a promising solution to achieving a more flexible uplink/downlink transmission in 5G-Advanced or 6G mobile communication systems. However, it may introduce serious cross-link interference (CLI). For better mitigating the impact of CLI, we first present a more realistic base station (BS)-to-BS channel model incorporating the radio frequency (RF) chain characteristics, which exhibit a hardware-dependent nonlinear property, and hence the accuracy of conventional channel modelling is inadequate for CLI cancellation. Then, we propose a channel parameter estimation based polynomial CLI canceller and two machine learning (ML) based CLI cancellers that use the lightweight feedforward neural network (FNN). Our simulation results and analysis show that the ML based CLI cancellers achieve notable performance improvement and dramatic reduction of computational complexity, in comparison with the polynomial CLI canceller.

Transmit Antenna Selection Based on SNR prediction in TDD Systems Using Convolutional Neural Network

This paper proposes a method for predicting future signal-to-noise ratio (SNR) in a time-division-duplexing (TDD) mobile communication environments using a convolutional neural network (CNN). The communication system uses multiple receive antennas and transmit using only one or two antennas among them. A CNN model is proposed to predict the SNR at a future transmission time based on past SNRs received from multiple antennas. The probability of reception at a certain is set to 10-100%. In case that SNR cannot be measured due to the absence of reception, linear interpolation is performed using two adjacent recorded SNRs. If even two adjacent SNRs do not exist, the SNR is set to 0dB. Comparing the predicted SNRs at multiple antennas, the antenna with the highest SNR value is selected for future transmission. To verify antenna selection accuracy, computer simulation is conducted. The simulation results substantiate the superiority of the proposed method over conventional method in single antenna selection. Regarding multi-antenna selection, the proposed method demonstrates diminished accuracy relative to conventional methods at lower speeds. Nevertheless, a comprehensive evaluation considering the root mean square error (RMSE) demonstrates the overall superiority of the proposed method across all speeds.

MCS Selection Based on Convolutional Neural Network in TDD System

In this paper, a convolutional neural network (CNN) is proposed for selecting modulation and coding schemes (MCSs) at the time of future transmission in time-division-duplex (TDD) systems. The proposed method estimates the signal-to-noise ratio (SNR) obtained by the average of the equalizer’s output in the orthogonal frequency division multiplexing (OFDM) system and records it to select the most suitable MCS for future transmission. Two methods are proposed: one that directly selects an MCS and one that predicts the SNR first before selecting an MCS. The conventional method commonly used is to select an MCS based on the SNR of the most recently received signal. Computer simulations show that the outage probability and throughput of all proposed methods (direct and indirect) are superior to conventional methods (recent value). Shorter SNR sampling periods perform better than longer ones, and the accuracy of MCS selection decreases as mobile speed increases

Downlink channel estimation of intelligent reflective surface aided MU-MIMO system

Obtaining channel status information (CSI) is one of the main problems of the intelligent reflective surface (IRS) assisted communication system. This is because IRS has only a large number of passive reflective components and cannot process signals. In this paper, we aim to estimate downlink channel of IRS assisted multi-user multiple-input multiple-output (MU-MIMO) communication system. In time division duplex (TDD) mode, the downlink channel is estimated according to the uplink pilot by using channel reciprocity. We propose a parallel factor analysis based on projection subspace (PSS-PARAFAC) channel estimation scheme. In the first step, the common sparsity among multiple users is used for signal subspace projection to reduce noise energy and complexity. The second step is to convert each user signal received of base station (BS) into the PARAFAC problem. The iterative method of alternating least squares (ALS) is used to solve this problem. From the analysis of simulation results, the channel estimation performance of the proposed algorithm is better.

Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free Wireless Networks

We consider a cell-free wireless system operated in Time Division Duplex (TDD) mode with user-centric clusters of remote radio units (RUs). Since the uplink pilot dimensions per channel coherence slot is limited, co-pilot users might incur mutual pilot contamination. In the current literature, it is assumed that the long-term statistical knowledge of all user channels is available. This enables Minimum Mean-Square Error channel estimation or simplified dominant subspace projection, which achieves significant pilot decontamination under certain assumptions on the channel covariance matrices. However, estimating the channel covariance matrix or even just its dominant subspace at all RUs forming a user cluster is not an easy task. In fact, if not properly designed, a piloting scheme for such long-term statistics estimation will also be subject to the contamination problem. In this paper, we propose a new channel subspace estimation scheme explicitly designed for cell-free wireless networks. Our scheme is based on 1) a sounding reference signal (SRS) using latin squares wideband frequency hopping, and 2) a subspace estimation method based on robust Principal Component Analysis (R-PCA). The SRS hopping scheme ensures that for any user and any RU participating in its cluster, only a few pilot measurements will contain strong co-pilot interference. These few heavily contaminated measurements are (implicitly) eliminated by R-PCA, which is designed to regularize the estimation and discount the “outlier” measurements. Our simulation results show that the proposed scheme achieves almost perfect subspace knowledge, which in turns yields system performance very close to that with ideal channel state information, thus essentially solving the problem of pilot contamination in cell-free user-centric TDD wireless networks.

Channel Hardening: A Comparison Between Concentrated and Distributed Massive MIMO

In this letter, a comparative analysis of the hardening effect for concentrated and distributed massive multiple-input multiple-output channels (C-mMIMO and D-mMIMO respectively) is presented. The analysis is carried out in two buildings with similar structural characteristics, considering two frequency bands of interest for 5G deployments, 3.5 and 26 GHz; taking into account both experimental data at 3.5 GHz and simulation results obtained in the 26 GHz band using a rigorous and well-tested Ray-Tracing method (RT). Both, measurements and simulations emulated C-mMIMO and D-mMIMO systems in an indoor cell in the framework of a time division duplex (TDD) - orthogonal frequency division multiplexing (TDD-OFDM) system. To quantify the level of hardening that the specific channels under analysis offer in the frequency domain, the standard deviation of the gain of the channels is used, as well as its evolution as the number of active antennas at the base station (BS) grows. The results obtained for both mMIMO systems show that a sufficiently high level of hardening occurs in indoor environments to contribute to the reliability of communication systems or sensor networks.

LensFD: Using Lenses for Improved Sub-6 GHz Massive MIMO Full-Duplex

Massive MIMO has emerged as a key-enabler to deliver the high data-rates in current and next-generation systems. Many massive MIMO deployments use time-division duplex (TDD), thereby opening the door for the potential use of full-duplex at base-stations. In this paper, we present LensFD, a scalable method to enable cost-effective massive MIMO full-duplex operation in sub-6 GHz frequencies with a focus on the CBRS band of 3.5 GHz. LensFD uses a low-cost lens array combined with transmit precoding to reshape the effective beam pattern of antennas in massive MIMO arrays to reduce self-interference. We tested three lens configurations in LensFD experimentally with a software-defined 40-antenna base-station and demonstrate a significant improvement in self-interference reduction and hence overall increased system capacity to 1.6× over the TDD counterpart. As an important contribution, we will open-source the large measurement dataset from our experiments.

Underlay Spectrum Sharing in Massive MIMO Systems

We consider an underlay spectrum sharing scheme, where a multi-user massive MIMO network (referred to as the primary network (PN)) allows another multi-user massive MIMO network (called the secondary network (SN)) to use the spectrum, owned by the PN, to communicate with the SN’s users. For such a scheme, we devise joint power allocation and beamforming schemes at the SN for both conventional time-division duplexing (C-TDD) and reverse time-division duplexing (R-TDD) protocols. This is accomplished by maximizing the weighted uplink and downlink sum-rates of the SN, while protecting the PN from the SN’s interference. Computationally efficient solutions are presented for both protocols. Our simulation results show that our solution for the R-TDD protocol mostly outperforms the solution to the C-TDD mode in terms of the SN’s total sum-rate.

Reconfigurable Intelligent Surface for FDD Systems: Design and Optimization

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology for wireless communications, which intelligently controls the phase shift of each unit cell to form desired beams. Most prior works on RIS consider time-division duplexing (TDD) systems, in which the same phase shifts can be applied to both uplink and downlink due to the channel reciprocity. However, for frequency-division duplexing (FDD) systems, using the same phase shifts will result in beam misalignment, thereby leading to performance degradation. To address this issue, in this paper, we study the practical RIS design and beamforming optimization for FDD systems. By representing the phase shifts of RIS with the equivalent circuit model which includes the resistance, inductances, and tunable capacitance, we propose a methodology to design the circuit parameters (i.e., inductances and capacitance) to meet the desired reflection requirements (i.e., phase tuning range, reflectivity, and zero phase slope) of both the uplink and downlink transmissions in FDD systems. Given the designed inductances, a practical binary RIS reflection model corresponding to two reflection states is then proposed. Furthermore, based on the proposed reflection model, a problem is formulated to jointly optimize the active and passive beamforming such that the minimum array response gain of the uplink and the downlink is maximized. An efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results show that our proposed RIS design outperforms those benchmarks which design the circuits by only optimizing either uplink or downlink.

Joint Energy Harvesting and Transmission Optimization for Cell-Free Massive MIMO With Network-Assisted Full Duplexing

This paper investigates the problem of joint energy harvesting (EH) and transmission optimization for cell-free massive MIMO with network-assisted full duplexing (NAFD), where each access point (AP) works in full-duplex mode, half-duplex mode or another flexible mode and is connected to a central processing unit (CPU) via compressed fronthaul links. The high demand on the fronthaul links and series cross-link interference (CLI) are the two main problems in cell-free massive MIMO with NAFD. Therefore, in our scheme, we propose a joint strategy of EH, fronthaul compression, and CLI cancelation to maximize the energy efficiency (EE) of the system under constraints of guaranteed quality-of-service (QoS), EH and compressed fronthaul requirements. A two-stage strategy of EH and transceiver design is proposed to solve the complicated optimization problem. Specifically, in the first stage, we propose an EH/information receiving (IR) antenna selection algorithm to design the operation mode of the antennas for uplink users and receiving APs. In the second stage, with the antennas selected in the first stage, we further attempt to solve the EE maximization problem. Furthermore, to solve the highly nonconvex EE maximization problem, we propose a fractional programming (FP)-based two-tier iterative algorithm in which a series of nonconvex–convex approximation methods, such as FP, iterative convex approximation and equivalent formulation, are used. Simulation results demonstrate that the proposed algorithms yield a higher EE gain than the traditional time-division duplexing (TDD) and co-frequency co-time full duplexing (CCFD) schemes.

Efficient coordination of radio frames to mitigate cross-link interference in 5G D-TDD systems

Dynamic time division duplexing (D-TDD) is widely used in 5G to support various services and short-latency communications, where uplink (UL) and downlink (DL) traffic may be highly asymmetric and fast-varying. In particular, D-TDD allows each cell to arrange subframes in terms of selecting link directions, which betters the utilization of spectrum resources based on instantaneous traffic demand. However, the inter-cell subframe misalignment, which is caused by different link directions on the same frequency band in neighboring cells, leads to notable cross-link interference (CLI). Especially, CLI has a negative impact on UL performance. Hence, this paper proposes a cluster-based radio frame coordination using codebooks (CRCC) framework to mitigate CLI efficiently. CRCC first clusters cells and formulates a codebook of radio frame configuration for each cluster, where cells pick their configuration of subframes. Then, the cluster is divided into subclusters, where the cells in each subcluster adjust subframes to reduce misalignments. Finally, CRCC combines subclusters by deciding the subframes of those cells located on the boundaries between subclusters. Simulation results reveal that CRCC greatly increases UL throughput and keeps high DL throughput, thereby improving the performance of a 5G D-TDD system.

A low‐complexity transmit combining method for downlink of massive multiuser multiple input multiple output systems

AbstractMultiuser multiple input multiple output (MU‐MIMO) systems can provide high spectral efficiency (SE) that is achieved with transmit/receive combining schemes at the base station (BS) for downlink (DL) and uplink (UL). So far, various combining schemes for DL were proposed, with the regularized zero forcing (RZF) and Tomlinson–Harashima precoding (THP) being usually considered. In a case of massive MU‐MIMO systems, with a large number of antennas, the matrix inversion operation used in the RZF and THP schemes causes high computational complexity that significantly aggravates implementation and raises its costs. The authors propose a low‐complexity transmit combining scheme for the massive MU‐MIMO systems with time division duplex (TDD), devised in a similar manner as the THP scheme, based on the gram‐Schmidt orthogonalization, and show that it achieves slightly better SE than the THP scheme for the estimated channel state information (CSI), but with a much lower complexity than the THP and RZF schemes.

Hierarchical-Absolute Reciprocity Calibration for Millimeter-Wave Hybrid Beamforming Systems

In time-division duplexing (TDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the reciprocity mismatch severely degrades the performance of the hybrid beamforming (HBF). In this work, to mitigate the detrimental effect of the reciprocity mismatch, we investigate reciprocity calibration for the mmWave-HBF system with a fully-connected phase shifter network. To reduce the over-head and computational complexity of reciprocity calibration, we first decouple digital radio frequency (RF) chains and analog RF chains with beamforming design. Then, the entire calibration problem of the HBF system is equivalently decomposed into two subproblems corresponding to the digital-chain calibration and analog-chain calibration. To solve the calibration problems efficiently, a closed-form solution to the digital-chain calibration problem is derived, while an iterative-alternating optimization algorithm for the analog-chain calibration problem is proposed. To measure the performance of the proposed algorithm, we derive the Cramér-Rao lower bound on the errors in estimating mismatch coefficients. The results reveal that the estimation errors of mismatch coefficients of digital and analog chains are uncorrelated, and that the mismatch coefficients of receive digital chains can be estimated perfectly. Simulation results are presented to validate the analytical results and to show the performance of the proposed calibration approach.

Deep Learning for Parametric Channel Estimation in Massive MIMO Systems

Massive Multiple-Input Multiple-Output (MIMO) communication with a low bit error rate depends upon the availability of accurate Channel State Information (CSI) at the base station. The massive MIMO systems can be either deployed using time division duplexing with channel reciprocity assumption or by availing frequency division duplexing, which requires closed-loop feedback for CSI acquisition. The channel reciprocity simplifies transmission in time division duplexing; however, it suffers a bottleneck due to pilot contamination, whereas transmission in frequency division duplexing is challenged by channel estimation complexity, CSI feedback, and overall delay in CSI transfer. This paper proposes a simplified parametric channel model, its deep neural network aided estimation along with pilot decontamination for time division duplexing and a low rate parametric feedback and improved precoding for frequency division duplexing based massive MIMO systems. This novel framework integrates the massive MIMO parametric estimation and deep learning for improved estimation and precoding. Our proposed model also offers a unified approach for CSI acquisition with a performance bound on channel correlation in fast time-varying conditions. A theoretical model has been presented using Gaussian assumptions and validated by Monte-Carlo simulations. The results show total nullification of pilot contamination and high-performance gains when the proposed technique is employed for estimation.