Dynamic TDD Research Articles

Performance Analysis of Simultaneous Collision-Free Duplexing Method in Point-to-Multipoint Environment

In this letter, we have studied how to use simultaneous collision-free duplexing (SCD) in point-to-multipoint environment. SCD is a method exchanging uplink and downlink signals simultaneously using the same resource for each link and has the advantages in that it does not need complicated self-interference cancellation unlike full duplexing. In addition, SCD is different from dynamic time division duplexing (D-TDD) in which interlink interference can be completely avoided. Therefore, to analyze the effect of interlink interference on SCD, we have derived the upper bound of link capacity and the required value of interlink isolation level for better SCD performance than D-TDD. We have verified the analyzed isolation level and confirmed that SCD has good performance even in practical point-to-multipoint environment through simulation.

비직교 다중접속 방식을 이용한 동적 시분할 듀플렉싱 시스템의 타임 슬롯 할당 기법

동적 시분할 듀플렉싱(D-TDD: Dynamic Time Division Duplex, 이하 D-TDD) 기술은 5세대 이동통신 시스템에서 빠르게 변화하는 상방향(uplink)과 하방향(downlink) 트래픽 수요에 적응적으로 대처할 수 있는 해법 중 하나이다. 그러나 셀 가장자리에서 심각한 간섭을 유발하는 ‘교차 슬롯 문제(crossed-slot problem)’를 야기한다. 기존의 자원할당 기법에서는 이웃 셀 간 교차 슬롯 상황을 회피하도록 무선자원을 할당했고, 전송 방향이 제한될 수 있었다. 본 논문에서는 비직교 다중접속(NOMA: non-orthogonal multiple access, 이하 NOMA) 기법을 활용해 수신기에서 ‘연속적 간섭제거(Successive Interference Cancellation, SIC)를 수행함으로써 신호 대 간섭 및 잡음비를 향상시키고자 한다. 이를 통해, 기존에는 사용하지 못했던 교차 슬롯을 셀 가장자리에서도 가용하게 함으로써 무선채널 자원의 사용 효율을 높이는 방법을 제안한다. 성능분석 결과, 특별히 셀 가장자리에서 사용자의 SINR이 최대 57.83 [dB] 개선되었으며 전송방향 (상/하)방향인 경우 각각 약 14%, 26% 통신가능 영역을 증가를 확인하였고, 전송 방향 변경을 용이하게 하여 무선자원의 사용 효율이 증대됨을 확인하였다.

Centralized Dynamic-Time Division Duplex Utilizing Interference Alignment

In this paper, we combine centralized dynamic-time division duplex (D-TDD) with interference alignment (IA) for a wireless network comprised of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> full duplex nodes. We maximize the performance of the proposed centralized D-TDD scheme utilizing IA in terms of rate region by optimizing the reception, transmission, simultaneous reception and transmission, and silence at each node in each time slot in addition to the choice of whether a node should treat the interference as noise or it should use IA. The problem of the rate region maximization of the wireless network is formulated as a non-convex optimization problem, whose optimal solution is found. The simulation results demonstrate that the proposed centralized D-TDD scheme utilizing IA achieves significant gains over the existing D-TDD schemes.

Game-Theoretic Mode Scheduling for Dynamic TDD in 5G Systems

Dynamic time-division duplexing (TDD) enables independent uplink/downlink mode scheduling at each cell, based on the local traffic. However, this creates cross-interference among cells. Thus, the joint power allocation and scheduling problem becomes mixed-integer non-convex and turns out to be NP-hard. We propose a low-complexity and decentralized solution, where power allocation and scheduling are decoupled. First, power is allocated in a decentralized fashion, and then modes are scheduled by a non-cooperative game to achieve the mixed-strategy Nash equilibrium. We consider two possible approaches to compute the payoffs in the game, according to the cross-interference power model and the entailed communication overhead among cells. Simulation results are presented for an outdoor dense small-cell scenario, showing that our approaches outperform static TDD significantly.

Throughput Analysis of Small Cell Networks Under D-TDD and FFR

Dynamic time-division duplex (D-TDD) has emerged as an effective solution to accommodate the unaligned downlink and uplink traffic in small cell networks. However, the flexibility of traffic configuration also introduces additional inter-cell interference. In this letter, we study the effectiveness of applying fractional frequency reuse (FFR) as an interference coordination technique for D-TDD small cell networks. We derive the analytical expressions of downlink and uplink mean packet throughput (MPT), then study a network parameter optimization problem to maximize MPT while guaranteeing each user's throughput. Numerical results corroborate the benefits of the proposed FFR-based D-TDD in terms of improving throughput.

Full Duplex and Dynamic TDD: Pushing the Limits of Spectrum Reuse in Multi-Cell Communications

Although in cellular networks full duplex and dynamic time-division duplexing promise increased spectrum efficiency, their potential is so far challenged by increased interference. While previous studies have shown that self-interference can be suppressed to a sufficient level, we show that the cross-link interference for both duplexing modes, especially from base station to base station, is the remaining challenge in multi-cell networks, restricting the uplink performance. Using beamforming techniques of low complexity, we show that this interference can be mitigated, and that full duplex and dynamic time-division duplexing can substantially increase the capacity of multi-cell networks. Our results suggest that if we can control the cross-link interference in full duplex, we can almost double the multi-cell network capacity as well as user throughput. Therefore, the techniques in this article have the potential to enable a smooth introduction of full duplex into cellular systems.

Decentralized Slot-Ordered Cross Link Interference Control Scheme for Dynamic Time Division Duplexing (TDD) in 5G Cellular System

Dynamic time-division duplexing (TDD) allows for flexible resource allocation for the varying downlink (DL) and uplink (UL) traffic demands in the 5G cellular network. It becomes more useful when the number of active users is highly limited, especially for the small cells in a ultra-dense network. However, traffic imbalance between DL and UL in adjacent cells incurs crosslink interference (CLI), which may seriously reduce the system throughput. In order to deal with CLI induced by dynamic TDD, full or partial information on slot assignment and/or mutual interference must be shared among all cells for coordinating dynamic slot assignment throughout the system. In this paper, we propose the fully decentralized slot assignment policies that can reduce the CLI without resorting to side information shared among the cells. It is based a bi-directional slot-ordered scheduling scheme in each subframe, which exploits implicit location-dependent information such as signal-to-interference-plus-noise ratio (SINR) and/or three-dimensional spatial angle for all UEs in each cell under a full-dimension multi-input and multi-output (FD-MIMO) operation. Our system-level performance evaluation in 5G system scenario has shown that the proposed approach can maintain total interference almost constant even subject to the CLI. Furthermore, it achieves the average system throughput as much as 95% and 91% of the performance that can be achieved by a centralized greedy search for DL and UL, respectively, without much compromising the lower 5% system throughput.

Interference Mitigation for Dynamic TDD Networks Employing Sounding Signals

The requirements of fifth-generation (5G) mobile communications include services with low delay and high throughput. Network densification is pointed to as a promising method to increase the network capacity. However, this solution brings new problems, such as the fast variation in traffic demands among access nodes (ANs) and between uplink and downlink, leading to high delays. To solve the traffic issues in dense networks, dynamic time division duplex (DTDD) is pointed as a possible solution. This strategy creates a new kind of interference between ANs and user equipments (UEs) called cross-interference. Therefore, obtaining channel state information (CSI) of cross-interference channels is essential for implementing interference mitigation methods, such as interference alignment, coordinated beamforming, resource schedulers, among others. Hence, this work proposes methods to estimate the intended and interfering channels based on sounding reference signal (SRS) and/or demodulation reference signal (DMRS). A coordinated scheme is developed to assign sounding signals in the network and reduce the interference perceived during the channel sounding, which improves the channel estimation quality. Furthermore, a refined successive interference cancellation (SIC) algorithm is proposed for estimating the channel. To assess system performance, a zero-forcing beamforming algorithm has been developed based on the CSI acquired with the proposed methods. This algorithm handles the degrees of freedom issues when ANs are operating in opposite directions. The numerical results show that the improved channel quality provided by the proposed estimation algorithm increases network capacity.

Dynamic-TDD interference tractability approaches and performance analysis in macro-cell and small-cell deployments

Meeting the continued growth in data traffic volume, Dynamic Time Division Duplex (D-TDD) has been introduced as a solution to deal with the uplink (UL) and downlink (DL) traffic asymmetry, mainly observed for dense heterogeneous network deployments, since it is based on instantaneous traffic estimation and provide more flexibility in resource assignment. However, the use of this feature requires new interference mitigation schemes capable of handling two additional types of interference between cells in opposite transmission direction: DL to UL and UL to DL interference. The aim of this work is to provide a complete analytical approach to model inter-cell interference in macro-cell and dense small-cell networks. We derive the explicit expressions of Interference to Signal Ratio (ISR) at each position of the network, in both DL and UL, to quantify the impact of each type of interference on the system performance. Also, we provide the explicit expressions of the coverage probability as functions of different system parameters by covering different scenarios. Finally, through system-level simulations, we analyze the feasibility of D-TDD implementation in both deployments and we compare its performance to the static-TDD (S-TDD) configuration.

Coverage analysis of dynamic TDD‐based downlink mmWave network with shadowed fading and BS heights

In this study, the authors analyse the downlink (DL) performance of dynamic-time division duplexing (D-TDD) enabled millimetre-wave (mmWave) network with different base station (BS) heights using a channel model that incorporates the effect of shadowed fading. Modelling the combined effect of small and large (shadowing) scale fadings with k − μ shadowed fading, they provide a general framework based on stochastic geometry that comprehensively includes the smallest path loss BS association with either a line-of-sight (LoS) path or a non-LoS (NLoS) path. For the precise analysis of the network, they further consider different PL distributions for NLoS and LoS links which are associated with the 3D distance, blockage model that includes outage, and directional beamforming at the transmitter and the receiver. Specifically, they assume the directional beamforming at the transmitter and the receiver using scanning angle and number of antennas. Through simulation results, they first illustrate the performance efficiency of D-TDD over the static-TDD for DL mmWave networks. Later, they show the impact of k − μ shadowed fading parameters on the network performance under the both LoS and NLoS propagations, and only NLoS propagation. Next, the effect of BS heights on the coverage of the network is investigated to provide more insights on the performance in more realistic scenarios. Finally, the coverage of the network is analysed using the varying number of transmitting antennas, scanning angle and BS heights.

Joint Modeling of TDD and Decoupled Uplink/Downlink Access in 5G HetNets With Multiple Small Cells Deployment

Due to highly variant traffic in downlink (DL) and uplink (UL) in heterogeneous networks (HetNets), dynamic time-division duplexing (TDD) is proposed to dynamically allocate UL and DL resources. Under the same circumstances, downlink and uplink decoupled access (DUDA) is introduced to balance between UL and DL transmissions and to further improve the system performance. Rather than belonging to a specific cell, a mobile user can receive the downlink traffic from one base station (BS) and send uplink traffic through another BS. In this article, we analytically investigate a joint TDD and DUDA statistical model with multiple small cells deployment. This model is based on a geometric probability approach. Taking all possible TDD subframes combinations between the macro and small cells, coupled and decoupled cell associations strategies are investigated in details. We derive analytical expressions for the capacity and the interference, considering a network of one macro cell and multiple small cells. We build on the derived capacity expressions to measure the decoupling gain and thus, identify the location of the interferer small cell where the decoupled mode maintains a higher gain in both DL and UL. Monte-Carlo simulations results are presented to validate the accuracy of the statistical model.

Dynamic TDD Systems for 5G and Beyond: A Survey of Cross-Link Interference Mitigation

Dynamic time division duplex (D-TDD) dynamically allocates the transmission directions for traffic adaptation in each cell. D-TDD systems are receiving a lot of attention because they can reduce latency and increase spectrum utilization via flexible and dynamic duplex operation in 5G New Radio (NR). However, the advantages of the D-TDD system are difficult to fully utilize due to the cross-link interference (CLI) arising from the use of different transmission directions between adjacent cells. This paper is a survey of the research from academia and the standardization efforts being undertaken to solve this CLI problem and make the D-TDD system a reality. Specifically, we categorize and present the approaches to mitigating CLI according to operational principles. Furthermore, we present the signaling necessary to apply the CLI mitigation schemes. We also present information-theoretic performance analysis of D-TDD systems in various environments. As topics for future works, we discuss the research challenges and opportunities associated with the CLI mitigation schemes and signaling design in a variety of environments. This survey is recommended for those who are in the initial stage of studying D-TDD systems and those who wish to develop a more feasible D-TDD system as a baseline for reviewing the research flow and standardization trends surrounding D-TDD systems and to identify areas of focus for future works.

Optimal Multi-User Computation Offloading Strategy for Wireless Powered Sensor Networks

Wireless sensors network (WSN) is widely used for environmental monitoring, surveillance, healthcare, and security services. One of the most critical challenges that WSN facing is the heavy computation requirements and limited energy storage. The recent development of radio frequency-based (RF) wireless power transfer (WPT) and mobile computation offloading to cloud provide a promising solution to overcome these shortcoming. In this paper, we consider a multi-user Mobile/Multi-Access edge computing (MEC) aided WSN powered by the WPT, where BSs are equipped with multiple antennas jointly service the whole sensors, the sensor node is powered by the WPT only. An optimal multi-user computation offloading strategy is proposed, where sensor nodes choose to operate in either the offloading or the local computing. In addition, perfect channel state information (CSI) and imperfect CSI are taken into account. We study the optimization of computation task offloading in MEC aided WSN under the conditions of perfect CSI and imperfect CSI. In particular, the optimal dynamic time division duplex (D-TDD) factor is investigated. Numerical results confirm the advantages of the proposed computation offloading strategy over the conventional local computation design in handing computationally heavy tasks.

Online Radio Pattern Optimization Based on Dual Reinforcement-Learning Approach for 5G URLLC Networks

The fifth generation (5G) radio access technology is designed to support highly delay-sensitive applications, i.e., ultra-reliable and low-latency communications (URLLC). For dynamic time division duplex (TDD) systems, the real-time optimization of the radio pattern selection becomes of a vital significance in achieving decent URLLC outage latency. In this study, a dual reinforcement machine learning (RML) approach is developed for online pattern optimization in 5G new radio TDD deployments. The proposed solution seeks to minimizing the maximum URLLC tail latency, i.e., min-max problem, by introducing nested RML instances. The directional and real-time traffic statistics are monitored and given to the primary RML layer to estimate the sufficient number of downlink (DL) and uplink (UL) symbols across the upcoming radio pattern. The secondary RML sub-networks determine the DL and UL symbol structure which best minimizes the URLLC outage latency. The proposed solution is evaluated by extensive and highly-detailed system level simulations, where our results demonstrate a considerable URLLC outage latency improvement with the proposed scheme, compared to the state-of-the-art dynamic-TDD proposals.

Bidirectional Sum-Power Minimization Beamforming in Dynamic TDD MIMO Networks

Employing dynamic time division duplexing can increase the system-wide spectral efficiency of applications with varying and unbalanced uplink and downlink data traffic requirements. However, in order to achieve this efficiency gain, it is necessary to manage the effects of cross-link interference, which are generated among cells transmitting in opposite link directions. This paper considers bidirectional sum-power minimization beamforming as a means to deal with this cross-link interference, by forcing a minimum signal-to-interference-plus-noise ratio constraint for both uplink and downlink. We propose two iterative approaches to solve this beamforming problem. The first approach assumes centralized processing and requires the availability of global channel state information. The second approach is performed in a decentralized manner, based on the alternating direction method of multipliers and requires only local channel state information and reduced signaling load. Both approaches are shown to converge to a minimum network power expenditure, whereas close-to-optimum performance can be obtained when limiting the number of iterations.

MSE-Based Downlink and Uplink Joint Beamforming in Dynamic TDD System Based on Cloud-RAN

In this paper, we consider a dynamic time division duplex system based on a cloud radio access network (RAN) to support the ultimate goal of efficient resource utilization. To optimize the performance of both remote radio heads (RRHs) and user equipments (UEs) which have multiple antennas, we propose an iterative scheme consisting of four types of beamforming, namely downlink (DL) transmit beamforming, DL receive beamforming, uplink (UL) transmit beamforming, and UL receive beamforming, in an attempt to minimize mean squared error (MSE). The proposed beamforming scheme makes it possible to allow DL and UL transmission be supported simultaneously by managing interference between RRHs and UEs. Our simulation results show that the MSE and the bit error rate (BER) are greatly improved under the proposed beamforming algorithm compared to conventional schemes. From intensive system level simulations, the proposed algorithm points out more than 3 dB performance improvement in terms of the required signal-to-noise-ratio (SNR) to achieve the BER less than $10^{-3}$ compared to conventional schemes.

Dynamic Time Division Duplexing for Downlink/Uplink Decoupled Millimeter Wave-Based Cellular Networks

The upcoming 5G cellular network’s standard from 3GPP is expected to support the millimeter-wave (mmwave)-based small cell base stations (SCBSs) to increase the data rate and network capacity. Furthermore, the downlink/uplink decoupling (DUDe) has been proposed in 5G to compensate for the transmit power imbalance between the macro-base stations and SCBSs. In this letter, joint uplink and downlink scheduling and resource allocation in a dynamic time division duplex (TDD) system is formulated as an optimization problem. Given a generalized $\alpha $ -fair scheduler, the dynamic TDD and the user scheduling time fractions are derived for a relaxed problem. The derived dynamic TDD results are shown to be a function of the system load. Simulation results are presented, which match with the derived results and show 17% gain in throughput for certain scenarios.

Adaptive feedback bits and power allocation for dynamic TDD systems

In next-generation wireless communication systems, a dynamic time division duplex (TDD) system is promising to support simultaneous uplink (UL) and downlink (DL) transmissions. We herein consider a fixed number of feedback bits per DL user, where each DL user reports its quantized channel direction information (CDI) of the interference channel to a serving DL base station via its dedicated feedback channel. Based on the quantized CDI feedback, we design the UL and DL transmit zero-forcing beamforming (ZFBF) to mitigate the interference signals caused by the coexistence of the UL and DL transmissions. To maximize the DL data rate, the challenge of interference cancellation lies in how many feedback bits are adaptively allocated for the transmission of the quantized CDI of each channel link. This paper proposes an adaptive feedback bits allocation scheme which minimizes the mean rate loss between perfect channel knowledge and limited feedback system. We also propose the UL transmit power allocation scheme under the interference power constraint from the UL user to the DL user when the number of feedback bits for the quantized CDI is given. To jointly solve the feedback bits allocation and the UL transmit power allocation problems, we use an iterative scheme to maximize the DL data rate while satisfying the constraint of interference power. Finally, we investigate how many feedback bits per DL user are required to maintain a constant mean rate loss. The simulation results show that the proposed adaptive feedback bits allocation scheme significantly increases the DL data rate compared with the conventional schemes.

Reaping the Benefits of Dynamic TDD in Massive MIMO

Recent advances in massive multiple-input multiple-output (MIMO) communication show that equipping base stations (BSs) with large antenna arrays can significantly improve the performance of cellular networks. Massive MIMO has the potential to mitigate the interference in the network and enhance the average throughput per user. On the other hand, dynamic time-division duplexing (TDD), which allows neighboring cells to operate with different uplink (UL) and downlink (DL) subframe configurations, is a promising enhancement for the conventional static TDD. Compared with static TDD, dynamic TDD can offer more flexibility to accommodate various UL and DL traffic patterns across different cells, but may result in additional interference among cells transmitting in different directions. Based on the unique characteristics and properties of massive MIMO and dynamic TDD, we propose a marriage of these two techniques, i.e., to have massive MIMO address the limitation of dynamic TDD in macrocell (MC) networks. Specifically, we advocate that the benefits of dynamic TDD can be fully extracted in MC networks equipped with massive MIMO, i.e., the BS-to-BS interference can be effectively removed by increasing the number of BS antennas. We provide detailed analysis using random matrix theory to show that the effect of the BS-to-BS interference on UL transmissions vanishes as the number of BS antennas per user grows infinitely large. Last but not least, we validate our analysis by numerical simulations.

QoE-aware Q-learning based approach to dynamic TDD uplink-downlink reconfiguration in indoor small cell networks

The continuing rise of the amount of mobile traffic is daunting, but the deploying of indoor small cells provides exciting opportunities to boost network capacity, extend cell coverage, and eventually thrive on an increased level of customers’ quality of experience (QoE). Unfortunately, in current wireless systems, traffic exhibits great variations in uplink and downlink directions, which introduces challenges of efficient resource allocation. Through using a dynamic time-division duplexing (TDD) method, network operators can flexibly adapt to such variations. However, cross-link interference appears in a dynamic TDD network and seriously suppresses uplink transmission. In this work, we proposed a decentralized QoE-aware reinforcement learning based approach to dynamic TDD reconfiguration. The objective is to maximize the utility function of the users’ QoE in an indoor small cell network. This was done by empowering each base station to select the best configuration to avoid the occurrence of cross-link interference while maintaining as many users that can enjoy their service at a satisfactory QoE as possible. At each episode, after collecting local reports of the QoE state and traffic load of the users, every base station dynamically chooses the best configuration according to the learning model. The learning process repeats itself until convergence. We implemented a simulator to evaluate the performances of the proposed algorithms. The results show that the proposed strategy achieves the best utility of QoE in comparison with other approaches, especially in the direction of the uplink transmission. The study demonstrates the great potential of harnessing reinforcement learning algorithms to attain higher QoE in small cell networks.