Flexible duplex in FDD spectrum

Balachandran, K.; Kang, Joseph H.; Karakayali, Kemal; Rege, Kiran M.

doi:10.1109/iccw.2017.7962673

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…However, due to the deployment of NB-IoT within the LTE system, these techniques might not be a suitable option as they will affect the whole network architecture. One of the possible solutions is to use flexible FDD/TDD duplexing within the NB-IoT frame as it will not hamper the LTE transmission [10]. More details on flexible duplexing is presented in Section III-C.…”

Section: Half-duplex Frequency Division Duplexing (Hd-fdd) Supportmentioning

confidence: 99%

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Radio Resource Management in NB-IoT Systems: Empowered by Interference Prediction and Flexible Duplexing

et al. 2020

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Narrowband Internet of Things (NB-IoT) is a promising cellular technology for enabling low cost, low power, long-range connectivity to the IoT devices. With the bandwidth requirement of 180 kHz, it provides the flexibility to deploy within the existing Long Term Evolution (LTE) band. However, this raises serious concerns about the performance of the technology due to severe interference from multi-tier 5G heterogeneous networks (HetNets). Furthermore, as NB-IoT is based on halfduplex frequency division duplexing (HD-FDD), the symmetric allocation of spectrum band between downlink and uplink results in underutilization of resource, particularly in case of asymmetric traffic distribution. Therefore, an innovative radio resource management (RRM) strategy needs to be devised to improve the spectrum efficiency and device connectivity. This article presents the detailed design challenges that need to be addressed for the RRM of NB-IoT and proposes a novel framework to devise an efficient resource allocation scheme by exploiting cooperative interference prediction and flexible duplexing techniques.

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Section: Half-duplex Frequency Division Duplexing (Hd-fdd) Supportmentioning

confidence: 99%

“…Flexible duplexing (flexible FDD/TDD) is one of the promising technique for optimising the radio resource and coping with the channel and traffic asymmetry in wireless networks [10], [14]. The key idea is to perform adaptive resource allocation in the joint time-frequency domain, without static choices in TDD or FDD.…”

Section: Flexible Fdd/tdd Duplexingmentioning

confidence: 99%

Radio Resource Management in NB-IoT Systems: Empowered by Interference Prediction and Flexible Duplexing

et al. 2020

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“…Here, we assume that UL and DL occur at the same time but over different SCs [15]- [17], i.e., the flexible frequency division duplex (FDD) mode [19], [20]. To avoid inter-link interference, each SC can be assigned to either UL or DL of a user.…”

Section: A System Modelmentioning

confidence: 99%

“…Note that OFDMA-based multiuser transmission is more preferable for high-speed demanding applications. More importantly, in OFDMA systems, the parallel transmission structure of OFDMA channels allows UL and DL traffic to be scheduled across different SCs [15]- [20], which opens a new dimension for flexible UL/DL resource allocation. The flexible DL and UL resource allocation has been investigated in traditional cellular networks [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Joint Uplink and Downlink Transmissions in User-Centric OFDMA Cloud-RAN

Lin

Liu

2019

IEEE Trans. Veh. Technol.

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This paper studies joint uplink (UL) and downlink (DL) resource allocation in user-centric orthogonal frequency division multiple access (OFDMA) cloud radio access network (CRAN), where the users select the distributed remote radio heads (RRHs) to cooperatively serve their UL and DL transmissions over different subcarriers (SCs). The goal of this paper is to maximize the system throughput through jointly optimizing UL/DL scheduling, SC assignment, RRH grouping/clustering and power allocation under the maximum power and fronthaul capacity constraints. The problem is formulated as a mixed integer programming problem which is non-convex and NP-hard. We propose an efficient algorithm based on the Lagrange duality method to obtain an asymptotically optimal solution for this problem. A heuristic algorithm is further proposed to reduce the complexity. Simulation results illustrate that the proposed heuristic algorithm also has a close-to-optimal performance, and the proposed algorithms can considerably improve the system throughput compared to other benchmark schemes.Index Terms-Cloud radio access network (CRAN), resource allocation, orthogonal frequency division multiple access (OFDMA).

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“…This, however, can result in closely-spaced frequency channels being assigned to transmit and receive at the same time, which generates system-level interference [2]. This interference has previously been managed through the use of power control algorithms [3] and device muting [4], both of which are suboptimal approaches.…”

Section: Introductionmentioning

confidence: 99%

RF Canceller Tuning Acceleration Using Neural Network Machine Learning for In-Band Full-Duplex Systems

Kolodziej¹,

Cookson²,

Perry³

2021

IEEE Open J. Commun. Soc.

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The fifth-generation wireless system framework provides the option to evaluate the performance of in-band full-duplex (IBFD) operation through flexible duplexing. The resulting self-interference, however, must be mitigated within a fraction of a symbol duration for successful communication. This paper introduces the use of neural network machine learning to accelerate the tuning of multi-tap adaptive RF cancellers. Additionally, the optimal network configurations, input data structures and training dataset densities that optimize the performance of this technique are presented. The tuning results of a prototype system using a two-tap canceller were measured over 20 and 100 MHz bandwidths centered at 2.5 GHz, and demonstrated averages of 40 dB cancellation and 6 tuning iterations. These results are compared to a survey of previously-reported adaptive cancellers, and illustrate that this novel application of machine learning to RF canceller tuning provides the fastest convergence speed to date, which can enable IBFD operation in dynamic interference environments.

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Flexible duplex in FDD spectrum

Cited by 9 publications

References 6 publications

Radio Resource Management in NB-IoT Systems: Empowered by Interference Prediction and Flexible Duplexing

Radio Resource Management in NB-IoT Systems: Empowered by Interference Prediction and Flexible Duplexing

Joint Uplink and Downlink Transmissions in User-Centric OFDMA Cloud-RAN

RF Canceller Tuning Acceleration Using Neural Network Machine Learning for In-Band Full-Duplex Systems

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