Dynamic TDD Research Articles

Interference Management Using Crossed-Slot in Dynamic Time Division Duplexing Systems

Dynamic time division duplex is a promising 5th generation (5G) mobile communication technology that can improve spectrum efficiency by meeting the demands of asymmetric and rapidly varying uplink and downlink traffic. However, when two adjacent cells have opposite transmission directions and share a time slot, it causes serious interference called crossed-slot interference. In this paper, we show that a crossed time slot shared by two adjacent cells can improve the signal-to-interference ratio (SIR) depending on the location of the user equipment (UE). We mathematically derive a beneficial UE region and show through simulations that the opportunistic usage of crossed-slot transmissions can mitigate interference and achieve a high SIR.

Coverage Probability Analysis for mmWave Communication Network With ABSF-Based Interference Management by Stochastic Geometry

Millimeter wave (mmWave) communication technology has been widely discussed as a promising part to be used in 5th Generation (5G) fig: interference networks. However, mmWave signals are sensitive to blockage compared with traditional spectrums used for cellular networks, which proposes new challenges. In addition, 5G networks will support Time-Division Duplex (TDD) mode, which is able to provide flexible and dynamic uplink-downlink (UL-DL) configurations. However, dynamic adjustment will then introduce UL-DL interference and will further influence system performance. This paper will focus on the coverage probability of mmWave communication network with dynamic TDD configuration based on stochastic geometry analysis. Both the Line-of-Sight (LoS) link and Non-Line-of-Sight (NLoS) link are modeled, since the transmissions of mmWave signals will be greatly influenced by different link types. Coverage probabilities for downlink and uplink are analyzed, respectively. Furthermore, in order to mitigate the UL-DL interference introduced by dynamic TDD, the Almost-Blank-SubFrame (ABSF) scheme is studied in this work. In ABSF scheme, a base station with DL transmission will mute temporarily to avoid severe DL-UL interference to nearby base stations with LoS links and UL transmissions. Possible improvement after introducing ABSF is studied by updating downlink and uplink coverage probability expressions. Numerical results under different configurations are obtained and discussed. It is shown that uplink coverage probability is greatly influenced by downlink interference, which provides basis for applying ABSF. Improvements by ABSF are validated by corresponding results.

Distributed Coordinated Transmission with Forward-Backward Training for 5G Radio Access

CoMP transmission and reception have been considered in cellular networks for enabling larger coverage, improved rates, and interference mitigation. To harness the gains of coordinated beamforming, fast information exchange over a backhaul connecting the cooperating BSs is required. In practice, the bandwidth and delay limitations of the backhaul may not be able to meet such stringent demands. These impairments motivate the study of cooperative approaches based only on local CSI that require minimal or no information exchange between the BSs. To this end, several distributed approaches are introduced for CB-CoMP. The proposed methods rely on the channel reciprocity and iterative spatially precoded over-the-air pilot signaling. We elaborate how F-B training facilitates distributed CB by allowing BSs and UEs to iteratively optimize their respective transmitters/receivers based on only locally measured CSI. The trade-off due to the overhead from the F-B iterations is discussed. We also consider the challenge of dynamic TDD where the UE-UE channel knowledge cannot be acquired at the BSs by exploiting channel reciprocity. Finally, standardization activities and practical requirements for enabling the proposed F-B training schemes in 5G radio access are discussed.

Bi-Directional Beamformer Training for Dynamic TDD Networks

In dynamic time-division-duplexing networks, the available resources per cell can be freely allocated to either uplink (UL) or downlink (DL) depending on the instantaneous traffic demand. Hence, complicated UL–DL and DL–UL interference scenarios arise due to simultaneous UL and DL data transmission in adjacent cells. In this paper, decentralized iterative beamformer designs are obtained for several traffic aware network optimization objectives such that only minimal information exchange is required among the coordinated base stations (BS) and user equipment (UE). Bi-directional forward–backward training via spatially precoded over-the-air pilot signaling is used to facilitate coordinated beamforming. This allows BSs and UEs to iteratively optimize their respective transmitters/receivers based on only locally measured reverse link pilot measurements. Novel bi-directional beamformer training strategies and methods for direct estimation (DE) of the stream specific beamformers are developed for each intermediate beamformer update in a limited and noisy pilot environment. The proposed signaling and DE schemes allow for non-orthogonal and overlapping pilots, which considerably reduces the resource coordination effort. Also, the decontamination ability of the proposed strategies are analyzed with limited pilot resources. The numerical examples illustrate the superior system performance of the proposed training and estimation framework in comparison to both the traditional stream-specific channel estimation method and an uncoordinated system.

A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks

Recently, deep learning has emerged as a state-of-the-art machine learning technique with promising potential to drive significant breakthroughs in a wide range of research areas. The application of deep learning for network traffic control, however, remains immature due to the difficulty in uniquely characterizing the network traffic features as an appropriate input and output dataset to the learning structures. The network traffic features are anticipated to be even more dynamic and complex in the UDNs of the emerging 5G networks with high traffi c demands coupled with beamforming and massive MIMO technologies. Therefore, it is critical for 5G network operators to carry out radio resource control in an efficient manner instead of adopting the simple conventional F/TDD. This is because the conventional uplink-downlink configuration change in the existing dynamic TDD method, typically used for resource assignment in beamforming and massive-MIMO-based UDNs, is prone to repeated congestion. In this article, we address this issue and discuss how to leverage the deep LSTM learning technique to make localized prediction of the traffic load at the UDN base station (i.e., the eNB). Based on localized prediction, our proposed algorithm executes the appropriate action policy a priori to avoid/alleviate the congestion in an intelligent fashion. Simulation results demonstrate that our proposal outperforms the conventional method in terms of packet loss rate, throughput, and MOS.

Performance Analysis of Dense Small Cell Networks With Dynamic TDD

Small cell networks (SCNs) are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to quick variations and burstiness of DL/UL traffic. The study of dynamic TDD is particularly important because it serves as the predecessor of the full duplex transmission technology, which has been identified as one of the candidate technologies for the 5th-generation (5G) networks. In this paper, we study the performance of the synchronous dynamic TDD from a media access control layer viewpoint, which has been widely adopted in the existing 4G systems. Furthermore, we analyze the coverage probability and the area spectral efficiency in the DL and UL of dense SCNs considering the synchronous dynamic TDD transmissions, and the performance impact of dynamic TDD transmissions on the ASE in the DL and UL of dense SCNs is discussed. Moreover, the performance impact of interference cancellation is also explored. Our analytical results shed new light on the performance of dynamic TDD in future synchronous 5G networks.

Interference-Cancelled Asymmetric Traffic Cellular Networks: Dynamic TDD Meets Massive MIMO

Over the last decade, the ever-growing demand for asymmetric uplink (UL) and downlink (DL) traffic in cellular networks have made the necessity of dynamic time-division duplex (TDD). However, it is plagued by the cross-link interference of adjacent cells. To suppress it, we consider dynamic TDD with massive multiple-input multiple-output (MIMO). For a highly realistic system model, we consider the transceiver noise at the base station (BS), which would further aggravate the coupling between DL and UL. By using random matrix theory, we derive tight deterministic equivalents of the UL and DL achievable rates with a matched filtering receiver and maximum ratio transmission precoder. Exploiting these deterministic equivalents further, we analyze cross-link interference and transceiver noise, and then prove that their effects both scale down as $\mathcal O(M^{-1})$ , thus their elimination is possible by massively increasing the number of antennas. Consequently, the UL–DL decoupling would be achieved in dynamic TDD systems by combining massive MIMO, which is a desirable feature in future cellular networks. Based on the analysis results, we also propose a power control scheme for maximizing the total sum rate, which is decoupled into two tractable geometric programming problems. The effectiveness of this power control is validated by extensive simulation results.

Analysis of Packet Throughput in Small Cell Networks Under Clustered Dynamic TDD

Small cell networks under dynamic time-division duplex (D-TDD) transmission have emerged as a promising solution to accommodate the varied uplink (UL) and downlink (DL) traffic in next generation cellular mobile communication networks. By allowing each cell to individually configure its communication direction, D-TDD allocates resources to accommodate whichever transmission direction needs it most. However, with unaligned transmissions, the interference increases and limits the performance of mean packet throughput (MPT). In this paper, we study the small cell networks under D-TDD with cell clustering being the interference mitigation technique (clustered D-TDD). By leveraging stochastic geometry and queuing theory, we develop an analytical framework that captures both spatial and temporal randomness. We study the MPT whose analytical expression is verified via simulation, and based on the analysis, we explore the impact from different network and service parameters. In particular, numerical results show that there is an optimal cluster size for DL MPT, while UL MPT always benefits from increasing cluster size. By grouping cells into clusters, the clustered D-TDD can provide the flexible service compared with static time-division duplex (S-TDD), and provide significant improvement over a traditional D-TDD in terms of UL MPT at a small cost of DL MPT.

Traffic‐aware resource allocation scheme for mMTC in dynamic TDD systems

The asymmetry traffic between downlink (DL) and uplink (UL) in massive machine-type communication (mMTC) systems is so prominent that it makes the traditionally fixed frame protocols insufficient to handle. Meanwhile, the dynamic time-division duplexing (D-TDD) is a promising and attractive technology since its number of time slots for the DL and UL can be asymmetric and adjusted dynamically. In the cellular and mMTC co-existing network, to balance the discrepancy of the UL/DL ratio and alleviate the interference as well, in this study, the authors design a D-TDD-based transmission frame structure to first fulfil the basic transmission requirements of the human-type communication (HTC) users with a low power almost blank subframe. Herein, the stochastic geometry methods are adopted to calculate spectral efficiencies of the HTC user equipment. Then, focusing on the worst queue state in mMTC, they devise the slot allocation problem with the min–max objective of the UL/DL queues and utilise the sub-gradient descent (SGD) method for a solution. Simulation results show that the proposed traffic aware sub-frame configuration is more appropriate for the dynamical asymmetry environment. Meanwhile, the adopted dynamic step size SGD algorithm can achieve a trade-off between the worst-case queue and the network throughput.

Traffic Matching in 5G Ultra-Dense Networks

Driven by enormous traffic demand, traditional cellular networks are evolving into an ultra-dense architecture. To take full advantage of such an ultra-dense architecture and efficiently serve the traffic with spatiotemporal fluctuation, the transmission mechanisms should be redesigned under the constraints of backhaul and energy consumption. In this article, we summarize and classify the spatiotemporal arrival properties of different traffic in ultra-dense networks, and optimize several promising technologies, such as dynamic time-division duplexing and full duplex radio, to adapt the network service to match the traffic. A new approach based on combining stochastic geometry and queueing theory is proposed to provide a useful guidance for the design of ultra-dense networks when the traffic is spatiotemporally fluctuating. Successive efforts, such as the analysis of different QoS requirements and more complicated but practical scenarios, still require attention along this line of research.

Distributed Interference Alignment for Multi-Antenna Cellular Networks With Dynamic Time Division Duplex

In this letter, we propose a distributed interference alignment (DIA) technique in a two-cell multiple-input multiple-output (MIMO) network with dynamic time division duplex (TDD) mode under local channel state information assumption. The dynamic TDD operation introduces new types of interference, such as downlink-to-uplink [base station to base station (BS–BS)] and uplink-to-downlink [mobile station to mobile station (MS–MS)] interferences. Under the assumption of rank-deficient BS–BS MIMO interference channel, we mathematically characterize the achievable degrees-of-freedom of the proposed DIA technique for a given number of antennas at BS/MS. Through extensive computer simulations, it is shown that the proposed DIA technique outperforms the conventional techniques in terms of sum-rate at the cost of signaling overheads for the BS–BS and MS–MS channel overhearing.

A Novel Cell Reconfiguration Technique for Dynamic TDD Wireless Networks

In dynamic time division duplexing (DTDD) systems, the uplink (UL) and downlink (DL) resources can be configured to adapt to changing traffic conditions. Therefore, DTDD systems are advantageously deployed in scenarios in which the UL and DL traffic demands are asymmetric and time-varying. Unfortunately, multicell DTDD systems give rise to base station-to-base station and user equipment-to-user equipment interference, that can severely degrade the system performance. Previous works on DTDD either assumed that the UL/DL configurations are given, or they did not take into account the negative impact of multicell DTDD interference. In this letter, we propose a novel cell reconfiguration technique that considers both the prevailing traffic conditions and multicell interference levels. The proposed technique is based on an efficient solution of a mixed integer linear program, whose objective is to maximize the overall system throughput taking into account users’ traffic preferences. Realistic system level simulations indicate that the proposed scheme outperforms not only the static TDD system but also other reference schemes, that disregard the DTDD specific interference effects.

Spectral Efficiency Analysis for Bidirectional Dynamic Network With Massive MIMO Under Imperfect CSI

To cope with the exploding demand for higher data rate and the heavier asymmetry of downlink (DL) and uplink (UL) traffic, dynamic time-division duplex (DTDD) has been proposed, where the time resources can be adjusted dynamically between DL and UL transmission. However, the strict synchronization requirements in DTDD bring extra overhead. In this paper, a bidirectional dynamic network (BDN) with massive multiple input multiple output is studied, in which DL and UL transmission can occur simultaneously. Under imperfect channel-state information, the closed-form expressions for the UL achievable rates with maximum ratio combination (MRC) and zero-forcing (ZF) receivers as well as the DL achievable rates with maximum ratio transmission (MRT) and ZF beamforming are derived in both BDN and DTDD systems. Based on these expressions, we compare the spectral efficiency of BDN and DTDD systems. Numerical results show that the simulation results match well with the closed-form expressions in both BDN and DTDD systems. Furthermore, BDN is more spectral efficient than DTDD. ZF achieves better performance in spectral efficiency than MRC and MRT.

Pricing-Based Distributed Beamforming for Dynamic Time Division Duplexing Systems

Multicell dynamic time division duplexing (TDD) systems make it possible to adapt the number of uplink and downlink time slots in each cell to the prevailing cell-wide traffic demand. Although dynamic TDD systems can be advantageously deployed in scenarios in which the uplink and downlink traffic demands are asymmetric and time varying, dynamic TDD systems give rise to base station to base station (BS-to-BS) interference and user equipment to user equipment (UE-to-UE) interference that negatively impact the system performance. In this paper, we propose employing a distributed beamforming scheme to mitigate the BS-to-BS interference and thereby to improve the uplink performance. Specifically, the proposed scheme uses interference pricing to find the appropriate precoder vectors at the BSs, which also improves the signal-to-interference-plus-noise ratio (SINR) performance in the downlink. We compare the performance of the pricing-based (PB) beamforming scheme with that of zero-forcing beamforming in an outdoor picocell environment specified by the 3rd Generation Partnership Project using a realistic system simulator. We find that the proposed PB scheme boosts the SINR in the uplink at the expense of a small degradation of the downlink SINR compared with the zero-forcing scheme. On the other hand, the PB beamforming approach can significantly reduce the downlink transmit power levels and thereby improve the overall energy efficiency of the system.

Distributed Beamforming in Dynamic TDD MIMO Networks With BS to BS Interference Constraints

This letter proposes distributed beamforming as a means of reducing interference in dynamic time division duplexing multiple input multiple output networks. Specifically, we formulate an optimization task, whose objective is to minimize the base station (BS) transmit power, while satisfying a predefined signal-to-interference-plus-noise ratio threshold for each downlink user equipment and keeping the BS to BS interference power below a tolerable level. An iterative decentralized solution requiring reduced signaling load based on primal decomposition is proposed. This decentralized algorithm is shown to iterate towards the centralized solution, while feasible but suboptimal solutions can be obtained at any iteration.

Packet Throughput Analysis of Static and Dynamic TDD in Small Cell Networks

We develop an analytical framework for the performance comparison of small cell networks operating under static time division duplexing (S-TDD) and dynamic TDD (D-TDD). By leveraging stochastic geometry and queuing theory, we derive closed-form expressions for the uplink (UL) and downlink (DL) packet throughput, also capturing the impact of random traffic arrivals and packet retransmissions. Through our analysis, we confirm that: 1) the number of scheduled user equipment may strongly affect the network throughput and 2) D-TDD outperforms S-TDD in DL, with the vice versa occurring in UL, since asymmetric transmissions reduce DL interference at the expense of an increased UL interference. We also find that in asymmetric scenarios, where most of the traffic is in DL, D-TDD provides a DL packet throughput gain by better controlling the queuing delay, and that such gain vanishes in the light-traffic regime.

The Value of Full-Duplex for Cellular Networks: A Hybrid Duplex-Based Study

Recent work has demonstrated the gain of full-duplex (FD) network over half-duplex (HD) network in bidirectional sum throughput under the assumption of symmetric uplink-downlink traffic demands and perfect self-interference suppression (SIS). In this paper, we study the performance gain of FD network over HD network under asymmetric bidirectional traffic demands and non-ideal SIS. To this end, we investigate the traditional static time division duplex (TDD) transmission mode and the advanced dynamic TDD transmission mode to obtain the performance of HD network, and investigate the pure FD transmission mode and a flexible HD-FD hybrid transmission mode, namely, XD mode, to obtain the performance of FD network. We use the number of users supported by a network as performance metric, which is defined as the minimum of the weighted numbers of users supported in uplink and downlink given random traffic demands of users. To maximize the number of users, we optimize the bidirectional transmit power for pure FD mode, bidirectional time slot configuration for dynamic TDD mode, and both for XD mode. Numerical results show an evident gain of pure FD mode and XD mode over static TDD mode for different levels of traffic asymmetry, but the gain over dynamic TDD mode is marginal, which cannot justify the application of FD technology in cellular systems without advanced interference control mechanisms.

Performance of Dynamic and Static TDD in Self-Backhauled Millimeter Wave Cellular Networks

Initial deployments of millimeter wave (mmWave) cellular networks are likely to be enabled with self-backhauling. In this work, we propose a random spatial model to analyze uplink (UL) and downlink (DL) SINR distribution and mean rates corresponding to different access-backhaul and UL-DL resource allocation schemes in a self-backhauled mmWave cellular network with Poisson point process (PPP) deployment of users and base stations. In particular, we focus on heuristic implementations of static and dynamic time division duplexing (TDD) for access links with synchronized or unsynchronized access-backhaul (SAB or UAB) time splits. We propose PPP approximations to characterize the distribution of the new types of interference encountered with dynamic TDD and UAB. These schemes offer better resource utilization than static TDD and SAB, however potentially higher interference makes their choice non-trivial and the offered gains sensitive to different network parameters, including UL/DL traffic asymmetry, user load per BS or number of slave BSs per master BS. One can harness notable gains from UAB and/or dynamic TDD only if backhaul links are designed to have much larger throughput than the access links.

Aligned Reverse Frame Structure for Interference Mitigation in Dynamic TDD Systems

The dynamic time division duplex (TDD) system has been proposed as a way to meet today's asymmetrically and dynamically changing traffic demand. However, this approach causes cross-link interference, because neighboring base stations and user elements transmit in opposite directions. In this paper, we investigate and analyze the characteristics of cross-link interference in dynamic TDD systems. Based on this observation, we propose an aligned reverse frame structure to utilize and cancel the cross-link interference. Mathematical analysis and numerical results verify that the proposed scheme achieves performance enhancement in terms of capacity compared with conventional dynamic TDD systems.

Service- and interference-aware dynamic TDD design in 5G ultra-dense network scenario

The 5G wireless communication system supports varied applications, making the uplink/downlink traffic asymmetry more and more serious. Dynamic time division duplex (TDD) technique has become a key technology of 5G networks due to its flexibility to support asymmetric services. In this paper, we study dynamic TDD sub-frame reconfiguration algorithm based on shifting. Firstly, we define cell shifting priority considering both traffic and interference. Then, we perform cell shifting-based TDD sub-frame reconfiguration following cell shifting priority. Simulation results show that the proposed dynamic TDD algorithm can guarantee high data rate and low interference, thus effectively increases the network throughput.