Adaptive feedback bits and power allocation for dynamic TDD systems

Lee, Deokhui; So, Jaewoo

doi:10.1109/jcn.2019.000009

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Assume that the number of subchannels is not less than the number of UEs served by any BS, i.e., N ≥ |U b |, ∀b. Let φ n b,u (T ) ∈ {0, 1} denote whether or not subchannel n is allocated to UE u inside cell b for DL transmissions, where φ n b,u (T ) = 1 denotes the subchannel n is allocated to UE u for f b (T ) number The channel fading gain of g n tx,rx (T ) includes both large-scale and small-scale fading [29]. To compute the large-scale fading, the distance from transmitter tx to receiver rx is needed.…”

Section: System Modelmentioning

confidence: 99%

Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control

Yin

Wang

et al. 2023

IEEE J. Sel. Top. Signal Process.

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The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink time-frequency resources of base stations (BSs) to meet the asymmetric and heterogeneous traffic demands while alleviating the inter-cell interference. We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) that maximizes the long-term expected sum rate under the users' packet dropping ratio constraints. In order to jointly optimize the global resources in a decentralized manner, we propose a federated reinforcement learning (RL) algorithm named federated Wolpertinger deep deterministic policy gradient (FWDDPG) algorithm. The BSs decide their local time-frequency configurations through RL algorithms and achieve global training via exchanging local RL models with their neighbors under a decentralized federated learning framework. Specifically, to deal with the large-scale discrete action space of each BS, we adopt a DDPG-based algorithm to generate actions in a continuous space, and then utilize Wolpertinger policy to reduce the mapping errors from continuous action space back to discrete action space. Simulation results demonstrate the superiority of our proposed algorithm to benchmark algorithms with respect to system sum rate.

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Section: System Modelmentioning

confidence: 99%

Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control

Yin

Wang

et al. 2023

IEEE J. Sel. Top. Signal Process.

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“…Lee et al [86] have addressed the problem of the interference signals generated by the coexistence of uplink and downlink transmission, which can be helpful for the UDN scenario. They have developed the Zero Forcing Beamforming (ZFBF) for uplink and downlink transmission to minimize the mean rate loss between perfect channel knowledge and limited feedback.…”

Section: Figure 8 Femtocell and Macrocell Deploy Can Causes Severe Interferencementioning

confidence: 99%

5G New Radio: Dynamic Time Division Duplex Radio Resource Management Approaches

et al. 2021

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The Internet of Everything is currently in demand and has burdened the network tremendously. Accommodating this exponential increase in demand will require improved Radio Resource Management technology. This problem can be curbed with higher spectrum bands, reevaluation of Time Division Duplex, deployment of Software Defined Network, and Network Function Virtualization into 5G New Radio (NR) network. Therefore, this work aims to provide an in-depth survey on the recent resource management schemes that can be proposed for 5G NR enhancement by exploiting both rulebased algorithms and machine learning methods. Radio resource management consists of managing user allocation, the antenna transmission power, bandwidth, and modulation scheme. Therefore, in this paper, three categories of radio resource management technologies are introduced: resource allocation, energy efficiency, and interference management. The discussion revolves around their potentials and contributions as well as challenges faced to produce efficient 5G resource management schemes.

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“…A joint design of the channel estimation and quantization is necessary for identifying the optimal limited feedback systems, since the CSI estimation error is closely related to the codebook design. Nevertheless, due to non-convex and combinatorial nature of problem formulations, most works on the limited feedback systems [1], [2], [8] have studied the CSI estimation procedure and the quantization separately by assuming an ideal scenario where perfect CSI is available at the receiver. Another problem is a complexity issue on the channel estimation.…”

Section: Introductionmentioning

confidence: 99%

“…It is obvious that regardless of the value of z, the output of the stochastic binarization b(z) becomes either 1 or −1. The setup in(8) leads to the zero-mean property for the quantization noise E e [e] = 0, making b(z) an unbiased estimator for the soft value z, i.e., E e [b(z)|z] = z.Due to the probabilistic operation, the forward propagation (7) of the stochastic binarization layer has no closed-form gradient expression which is not applicable to the GD-based2 Tensorflow does not support complex number calculations.…”

mentioning

confidence: 99%

Deep Learning-Based Limited Feedback Designs for MIMO Systems

Jang

Lee

Hwang

et al. 2020

IEEE Wireless Commun. Lett.

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We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel codebook design, and beamforming vector selection. The DNNs are trained to yield binary feedback information as well as an efficient beamforming vector which maximizes the effective channel gain. Compared to conventional limited feedback schemes, the proposed DL method shows an 1 dB symbol error rate (SER) gain with reduced computational complexity.

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Adaptive feedback bits and power allocation for dynamic TDD systems

Cited by 5 publications

References 23 publications

Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control

Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control

5G New Radio: Dynamic Time Division Duplex Radio Resource Management Approaches

Deep Learning-Based Limited Feedback Designs for MIMO Systems

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