## 5 Conclusions  
In this paper, we developed a D-TDD framework for 5G NR that allows each BS dynamic adjust duplex pattern to adapt services buffer. In order to reduce signaling overhead and control delay, we designed a distribute MARL based decentralized D-TDD configuration solution. Further, our proposed LC-MARL based D-TDD configuration method uses leniency control to enable BSs cooperate configure duplex pattern based on local observations. LC-MARL is a learning cooperation method to improve MARL performance. Different from learning communication method, learning cooperation means agent maximizes their shared reward without communication. This is the reason why LC-MARL can reduce signaling overhead and control delay. In order to verify the performance of our proposed method, we perform our proposed method performance simulations in different scenarios and compare them with another learning cooperation MARL method. Simulation showed that the proposed MARL converged stably in various environments. Compared with another learning cooperation method (DDRL), our solution provides data rate gains. For our future work, we will continue to consider other available duplex method in B5G technology.  
Conceptualization, X.C.; Data curation, X.C.; Formal analysis, X.C.; Methodology, X.C.; Resources, X.C. and G.C.; Supervision, G.C.; Validation, X.C. and W.G.; Writing--original draft, X.C.; Writing--review & editing, G.C. and W.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Project of 403 China under Grant 2020 YFB1806703.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
The following abbreviations are used in this paper:  
3 GPP 3 rd generation partnership project  
5G 5 th generation mobile communication technology  
NR  New radio  
B5G  Beyond 5G  
URLLC  Ultra-reliable and low latency communications  
IoT  Internet of things  
IoV  Internet of vehicle  
D-TDD  Dynamic time division duplex  
S-TDD  static time division duplex  
LTE  Long-term evolution  
TD-LTE  Time division long-term evaluation  
MARL  Multi-agent deep reinforcement learning  
BS  Base station  
UE  User  
LSTM  Long short-term memory  
BLSTM  Binary LSTM  
MEC  Mobile edge computing  
UL  Uplink  
DL  Downlink  
LC-MARL  Leniency control based MARL  
DDRL  Decentralized deep reinforcement learning  
OFDM  Orthogonal frequency-division multiplexing  
NN  Neural network  
\(U\)  The set of UE  
\(B\)  The set of BS  
\(C\)  The set of reused orthogonal sub-channels of each BS  
\(\mu\)  Numerology parameter  
\(\delta\)  UL/DL transmission periodicity  
\(\eta_{u,b}\)  Association indicator of UE \(u\) between BS \(b\)  
\(\tau_{b}(t)\)  Transmission direction of BS \(b\) at time \(t\)  
\(\kappa^{c}_{u,b}\)  Association indicator of UE \(u\) between BS \(b\) on  
sub-channel \(c\)  
\(S^{t}_{b}\)  Time series state of BS \(b\) at time \(t\)  
\(a_{b}(t)\)  Action of BS \(b\) at time \(t\)  
\(R_{b}(t)\)  Reward of BS \(b\) fed by environment at time \(t\)  
\(\gamma\)  Discount factor