Performance of strong interference cancellation in flexible UL/DL TDD systems using coordinated muting, scheduling and rate allocation

Lukowa, Anna; Venkatasubramanian, Venkatkumar

doi:10.1109/wcnc.2016.7564832

Cited by 14 publications

(15 citation statements)

References 16 publications

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“…CLI mitigation and coordination schemes have therefore been widely investigated over recent prior art. In [10][11][12], coordinated cross-cell beam-forming, UL power control and cell muting are proposed to limit the residual network CLI, especially towards the more CLI-sensitive cell-edge UEs. Joint UL transceiver design [13][14][15], based on inter-cell signaling of the UEs' spatial signatures, is also introduced in order to isolate the BS-BS CLI spatial subspace from that is of the desired UL transmission.…”

Section: A State Of the Art Dynamic Tdd Studiesmentioning

confidence: 99%

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

2020

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The fifth generation (5G) radio access technology is designed to support highly delaysensitive 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.

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Section: A State Of the Art Dynamic Tdd Studiesmentioning

confidence: 99%

“…• Unlike the state-of-the-art relevant TDD solutions [7][8][9][10][11][12][13][14][15][16][17][18][19], the proposed solution considers the joint capacity and latency statistics to optimize the URLLC outage latency performance. It is fully compliant with the current 3GPP 5G-NR standard specifications for dynamic-TDD deployments.…”

Section: Paper Contributionmentioning

confidence: 99%

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

2020

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“…Non-coordinated TDD (NC-TDD): no RFC coordination is assumed. Cells independently and dynamically in time pick the RFCs from the code-book which most meet their individual traffic demand, as in (6). Hence, maximum TDD RFC flexibility is achieved with no coordination overhead; however, associated with potentially a large slot misalignment and severe average CLI levels accordingly.…”

Section: Comparison To the State-of-the-art Tdd Studiesmentioning

confidence: 99%

Quasi-Dynamic Frame Coordination for Ultra-Reliability and Low-Latency in 5G TDD Systems

Esswie

Pedersen

Mogensen

2019

2019 IEEE International Conference on Communications Workshops (ICC Workshops)

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The fifth generation (5G) mobile technology features the ultra-reliable and low-latency communications (URLLC) as a major service class. URLLC applications demand a tight radio latency with extreme link reliability. In 5G dynamic time division duplexing (TDD) systems, URLLC requirements become further challenging to achieve due to the severe and fastvarying cross link interference (CLI) and the switching time of the radio frame configurations (RFCs). In this work, we propose a quasi-dynamic inter-cell frame coordination algorithm using hybrid frame design and a cyclic-offset-based RFC code-book. The proposed solution adaptively updates the RFCs in time such that both the average CLI and the user-centric radio latency are minimized. Compared to state-of-the-art dynamic TDD studies, the proposed scheme shows a significant improvement in the URLLC outage latency, i.e., ∼ 92% reduction gain, while boosting the cell-edge capacity by ∼ 189% and with a greatly reduced coordination overhead space, limited to B-bit.

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“…Commonly used clustering methods include threshold method [16][17][18] and heuristic algorithm [19]. Without losing generality, we use a threshold method that based on link coupling loss [17].…”

Section: System Modelmentioning

confidence: 99%

“…Step 2: If the corresponding interference is small for l i , i.e., it meets the condition of formula (8), then the shifting strategy which coincides with the W-TDD method [19] is adopted, and if the corresponding interference for l is too big, the C-TDD method is adopted. In all other cases, shifting-based dynamic TDD is used.…”

Section: Shifting Flowmentioning

confidence: 99%

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

Gao¹,

Binyong²,

Chuai³

2017

J Wireless Com Network

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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.

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Performance of strong interference cancellation in flexible UL/DL TDD systems using coordinated muting, scheduling and rate allocation

Cited by 14 publications

References 16 publications

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

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

Quasi-Dynamic Frame Coordination for Ultra-Reliability and Low-Latency in 5G TDD Systems

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

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