Dynamic TDD Research Articles

Throughput Maximization of Dynamic TDD Networks With a Full-Duplex UAV-BS

Throughput Maximization of Dynamic TDD Networks With a Full-Duplex UAV-BS

Performance analysis of joint transmission in TDD-based mm-wave networks with BS heights

Dynamic-time division duplexing (D-TDD) is used in millimeter-wave (mm-wave) networks to allocate time-frequency resources for both downlink (DL) and uplink (UL) transmissions. This scheduling mechanism adapts to varying data traffic to meet the different quality of service (QoS) requirements of heterogeneous services. However, blockage is the major bottleneck to millimeter wave (mm-Wave) networks since it causes signal outages. Moreover, the dense deployment of base stations (BSs) to reduce the signal outage problem may lead to severe interference due to solid line-of-sight (LoS) BSs. To solve the problem of less coverage, this paper utilizes a joint transmission mechanism in the DL of D-TDD-based mm-Wave networks. Further, examining the network performance also entails considering the elevation disparity between the Base Station (BS) and User Equipment (UE). The primary focus of this work is to study the effects of joint transmission in D-TDD-based DL mm-Wave networks with different BS heights and directional beamforming using the stochastic geometry framework. For D-TDD with Joint Transmission, numerical evaluations indicate that the SINR coverage probability reaches approximately 0.7 when the coverage radius (rc) is 100 m. Conversely, at a coverage radius of 200 m, the SINR coverage probability drops to around 0.25. The simulation results demonstrate using MATLAB 2019b tool, that D-TDD-based mm-Wave networks achieve superior coverage using the joint transmission scheme compared to single BS association. Besides, results show that optimal UL and DL split and load balancing are required to achieve better DL coverage.

Half-Duplex APs With Dynamic TDD Versus Full-Duplex APs in Cell-Free Systems

Half-Duplex APs With Dynamic TDD Versus Full-Duplex APs in Cell-Free Systems

Multi-Agent Deep Reinforcement Learning to Enable Dynamic TDD in a Multi-Cell Environment

Multi-Agent Deep Reinforcement Learning to Enable Dynamic TDD in a Multi-Cell Environment

Interference management in 5G and beyond networks: A comprehensive survey

During the last decade, wireless data services have had an incredible impact on people’s lives in ways we could never have imagined. The number of mobile devices has increased exponentially and data traffic has almost doubled every year. Undoubtedly, the rate of growth will continue to be rapid with the explosive increase in demands for data rates, latency, massive connectivity, network reliability, and energy efficiency. In order to manage this level of growth and meet these requirements, the fifth-generation (5G) mobile communications network is envisioned as a revolutionary advancement combining various improvements to previous mobile generation networks and new technologies, including the use of millimeter wavebands (mm-wave), massive multiple-input multiple-output (mMIMO) multi-beam antennas, network densification, dynamic Time Division Duplex (TDD) transmission, and new waveforms with mixed numerologies. New revolutionary features including terahertz (THz) communications and the integration of Non-Terrestrial Networks (NTN) can further improve the performance and signal quality for future 6G networks. However, despite the inevitable benefits of all these key technologies, the heterogeneous and ultra-flexible structure of the 5G and beyond network brings non-orthogonality into the system and generates significant interference that needs to be handled carefully. Therefore, it is essential to design effective interference management schemes to mitigate severe and sometimes unpredictable interference in mobile networks. In this paper, we provide a comprehensive review of interference management in 5G and Beyond networks and discuss its future evolution. We start with a unified classification and a detailed explanation of the different types of interference and continue by presenting our taxonomy of existing interference management approaches. Then, after explaining interference measurement reports and signaling, we provide for each type of interference identified, an in-depth literature review and technical discussion of appropriate management schemes. We finish by discussing the main interference challenges that will be encountered in future 6G networks and by presenting insights on the suggested new interference management approaches, including useful guidelines for an AI-based solution. This review will provide a first-hand guide to the industry in determining the most relevant technology for interference management, and will also allow for consideration of future challenges and research directions.

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.

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.

On the Sum Spectral Efficiency of Dynamic TDD-Enabled Cell-Free Massive MIMO Systems

We examine the sum spectral efficiency (SE) performance of a cell-free massive multiple-input multiple-output (CF-mMIMO) system, where each access point (AP) can operate either in the uplink or downlink mode in each slot, corresponding to dynamic time-division duplexing (DTDD) across the APs. We derive the sum SE of the system under a weighted combining of the signals received at the central processing unit. We show that the sum SE is a sub-modular function of the subset of active APs. We exploit this to develop a novel, low-complexity, greedy algorithm for choosing the mode of operation of the APs which is guaranteed to achieve within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1-1/e)$ </tex-math></inline-formula> of the sum SE attained via a full-complexity brute-force search. Our results show that DTDD with greedy AP mode selection can nearly double the sum SE compared to a TDD based CF-system where all APs operate in the uplink or downlink modes simultaneously. Thus, it is a promising duplexing scheme for beyond 5G communications.

Interference in Dynamic TDD: Effect of MIMO Rank on DoF and Transceiver Design

Interference in Dynamic TDD: Effect of MIMO Rank on DoF and Transceiver Design

Interference in dynamic TDD: effect of MIMO rank on DoF and transceiver design

Interference in dynamic TDD: effect of MIMO rank on DoF and transceiver design

Hopfield Neural Network Based Uplink/Downlink Transmission Order Optimization for Dynamic Indoor TDD Femtocells

Hopfield Neural Network Based Uplink/Downlink Transmission Order Optimization for Dynamic Indoor TDD Femtocells

QoE-Driven Distributed Resource Optimization for Mixed Reality in Dynamic TDD Systems

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes.

Can Dynamic TDD Enabled Half-Duplex Cell-Free Massive MIMO Outperform Full-Duplex Cellular Massive MIMO?

We consider a dynamic time division duplex (DTDD) enabled cell-free massive multiple-input multiple-output (CF-mMIMO) system, where each half-duplex (HD) access point (AP) is scheduled to operate in the uplink (UL) or downlink (DL) mode based on the data demands of the user equipments (UEs), with the goal of maximizing the sum UL-DL spectral efficiency (SE). We develop a new, low complexity, greedy algorithm for the combinatorial AP scheduling problem, with an optimality guarantee theoretically established via showing that a lower bound of the sum UL-DL SE is sub-modular. We also consider pilot sequence reuse among the UEs to limit the channel estimation overhead. In CF systems, all the APs estimate the channel from every UE, making pilot allocation problem different from the cellular case. We develop a novel algorithm that iteratively minimizes the maximum pilot contamination across the UEs. We compare the performance of our solutions, both theoretically and via simulations, against a full duplex (FD) multi-cell mMIMO system. Our results show that, due to the joint processing of the signals at the central processing unit, CF-mMIMO with dynamic HD AP-scheduling significantly outperforms cellular FD-mMIMO in terms of the sum SE and 90&#x0025; likely SE. Thus, DTDD enabled HD CF-mMIMO is a promising alternative to cellular FD-mMIMO, without the cost of hardware for self-interference suppression.

Joint Beamforming and Power Allocation for M2M/H2H Co-Existence in Green Dynamic TDD Networks: Low-Complexity Optimal Designs

Coexistence and interference management issues for machine-to-machine (M2M) and human-to-human (H2H) communications are crucial for the Internet of Things (IoT). This article considers beamforming and power allocation for M2M/H2H coexistence networks adopting the dynamic time division duplex (TDD) spectrum sharing scheme and energy harvesting (EH). The design objective is total system power minimization with device Quality-of-Service (QoS) constraints as well as EH constraints. Since the dynamic TDD introduces new types of interference, i.e., uplink/downlink cross-interference, the considered problem is a challenging nonconvex coupled problem. We first consider a simplified problem without the EH considerations. We propose a novel low-complexity algorithm based on uplink-downlink duality (UDD) and alternating optimization (AO) to tackle this problem. Then, we propose a second-order cone programming (SOCP) relaxation-based AO low-complexity algorithm to deal with the general problem. In the simulation, we study the performance of the QoS, the number of antennas, the number of users, and the power splitting ratio. Finally, the performance of the proposed algorithms have low-complexity than the classical convex optimization method.

Multi-Agent Reinforcement Learning Based Fully Decentralized Dynamic Time Division Configuration for 5G and B5G Network.

Future network services must adapt to the highly dynamic uplink and downlink traffic. To fulfill this requirement, the 3rd Generation Partnership Project (3GPP) proposed dynamic time division duplex (D-TDD) technology in Long Term Evolution (LTE) Release 11. Afterward, the 3GPP RAN#86 meeting clarified that 5G NR needs to support dynamic adjustment of the duplex pattern (transmission direction) in the time domain. Although 5G NR provides a more flexible duplex pattern, how to configure an effective duplex pattern according to services traffic is still an open research area. In this research, we propose a distributed multi-agent deep reinforcement learning (MARL) based decentralized D-TDD configuration method. First, we model a D-TDD configuration problem as a dynamic programming problem. Given the buffer length of all UE, we model the D-TDD configuration policy as a conditional probability distribution. Our goal is to find a D-TDD configuration policy that maximizes the expected discount return of all UE’s sum rates. Second, in order to reduce signaling overhead, we design a fully decentralized solution with distributed MARL technology. Each agent in MARL makes decisions only based on local observations. We regard each base station (BS) as an agent, and each agent configures uplink and downlink time slot ratio according to length of intra-BS user (UE) queue buffer. Third, in order to solve the problem of overall system revenue caused by the lack of global information in MARL, we apply leniency control and binary LSTM (BLSTM) based auto-encoder. Leniency controller effectively controls Q-value estimation process in MARL according to Q-value and current network conditions, and auto-encoder makes up for the defect that leniency control cannot handle complex environments and high-dimensional data. Through the parallel distributed training, the global D-TDD policy is obtained. This method deploys the MARL algorithm on the Mobile Edge Computing (MEC) server of each BS and uses the storage and computing capabilities of the server for distributed training. The simulation results show that the proposed distributed MARL converges stably in various environments, and performs better than distributed deep reinforcement algorithm.

Joint Optimization of Fractional Frequency Reuse and Cell Clustering for Dynamic TDD Small Cell Networks

In dense small cell networks, dynamic time-division duplex (D-TDD) technology has emerged as a promising solution to accommodate the fast variants of volatile traffic conditions because it allows each cell to dynamically configure the uplink and downlink transmission directions. However, the flexibility of traffic configuration introduces additional inter-cell interference, which largely deteriorates network throughput. This paper proposes an interference coordination technology for D-TDD small cell networks by integrating fractional frequency reuse (FFR) with cell clustering. To evaluate the system performance, we develop a theoretical framework to analytically characterize the mean packet throughput (MPT) performance by considering the impact of spatio-temporal traffic. The analytical model can be extended to further study the FFR-based D-TDD, clustered D-TDD, and traditional D-TDD networks. We verify the accuracy of our analysis through simulations and whereby explore the effect of different network parameters. Numerical results demonstrate that the proposed scheme outperforms clustered D-TDD and traditional D-TDD for both the downlink and uplink spatially averaged MPT, and can significantly improve the performance in uplink while slightly decreasing that in downlink compared with FFR-based D-TDD. Furthermore, by jointly optimizing network parameters, the spatially averaged MPT can be maximized while enduring MPT per user.

Joint Access Configuration and Beamforming for Cell-Free Massive MIMO Systems With Dynamic TDD

We address the trade-off between system throughput and user equipment (UE) fairness in dynamic time division duplex (TDD) cell-free (CF)-massive multiple-input multiple-output (mMIMO) systems, developing to that end a joint access point (AP) access configuration, power allocation, and beamforming design scheme. Unlike state-of-the-art (SotA) methods, which mostly focus on sum-rate maximization or max-min worst case optimization, our design approach is to maximize the geometric mean of the UEs’ throughput performance under a constrained transmit power, which is shown to yield an excellent compromise between system throughput and user fairness. The direct reformulation of the resulting optimization problem is, however, hard to solve due not only to the non-convexity of the objective function, but also to the binary constraint induced by the AP access configuration. We thus present an efficient ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i.e$ </tex-math></inline-formula> ., polynomial time complexity) solution for the problem via a combination of fractional programming (FP) and convex–concave procedure (CCP), assisted by a negative entropy regularizer that promotes a binary solution. Numerical simulations are offered to evaluate the throughput performance and fairness index of the proposed algorithm in comparison not only to conventional TDD-based solutions, but also to recent dynamic TDD SotA designs, which illustrate the effectiveness of the proposed approach over existing methodologies both in throughput and fairness.

Resource Allocation Algorithm Based on Power Control and Dynamic Transmission Protocol Configuration for HAPS-IMT Integrated System

The high altitude platform station (HAPS) system is an essential component of the air-based network. It can shorten transmission delay and make a better user experience compared with satellite networks, and it can also be easily deployed and cover a larger area compared with international mobile telecommunications (IMT). In order to meet the needs of users with asymmetric and random data flow, the spectrum sharing and dynamic time division duplexing (TDD) mode are used in HAPS-IMT heterogeneous network. However, the cross-link interference brought by TDD mode will lead to the degradation of system performance. In this paper, a resource allocation algorithm based on power control and dynamic transmission protocol configuration is proposed. Firstly, a specific timeslot, “low power almost-bank subframe (LP-ABS)”, is introduced into the frame structure of the HAPS physical layer. The transmission protocol designing could mitigate inter-layer interference efficiently by power control in “LP-ABS”. Secondly, the utilization function is adopted for assessing the system performance, which gives attention to both diversified requirements on the quality of services (QoS) and the throughput of the HAPS-IMT system. Simulation results show that power control and resource allocation technologies proposed in this paper can effectively improve system performance and user satisfaction.

Coverage and Spectral Efficiency of Network Assisted Full Duplex in a Millimeter Wave System

To cope with the growing trend of asymmetric data traffic, we introduce a novel network assisted full duplex (NAFD) for a millimeter wave system. NAFD can dynamically allocate the number of remote radio heads in the uplink mode or in the downlink mode, which can facilitate simultaneous uplink and downlink communications. In this manuscript, we use stochastic geometry to analyze the distribution of the signal-to-interference-plus-noise ratio and the data rate in a NAFD system. The numerical results verify the analysis and show that the NAFD outperforms the dynamic time division duplex system and the traditional flexible duplex system in terms of spectral efficiency.

Deep Reinforcement Learning-Based Hierarchical Time Division Duplexing Control for Dense Wireless and Mobile Networks

Future wireless and mobile network services must accommodate highly dynamic downlink and uplink traffic asymmetry. To fulfill this requirement, the third-generation partnership project (3GPP) introduced the enhanced interference mitigation and traffic adaptation strategy in addition to dynamic time division duplexing (TDD). In this study, we develop a reinforcement learning (RL)-based dynamic TDD framework that effectively controls interference and serves various traffic demands. First, we introduce an interference-penalty model that evaluates interference indirectly based on the duplexing policy. This can significantly reduce overhead for measuring and exchanging channel information in a dense network. Second, we design a new mixed-reward model that consists of the achievable data rate and the implicit interference penalty. Third, we implement deep RL algorithms that base station (BSs) use to train their radio frame configurations (RFCs). The training process at each BS takes into account the traffic demand and the RFCs of the surrounding BSs. The BSs are coordinated in a single-leader multi-follower Stackelberg game, which achieves a global RFC setup that maximizes the data rate and minimizes the interference. Extensive simulations show that the proposed framework stably converges in various environments and provides near-optimal performance equivalent to 95% or more of the full-search-based optimal performance, which is 48.84%, 41.92%, and 62.11% higher than the currently utilized random RFC, fixed RFC, and traffic-matched RFC approaches.