Abstract
With ever-growing machine type users in the 6G
wireless ecosystems, uncontrolled multiple access control (MAC)
is vital to alleviate random collision and fading in their transmission.
In this paper, 2-steps random access method is applied for
a learning-aided non-orthogonal random access (NORA) system.
Specifically, each user independently selects a slot and a power
level for uplink packet transmission without any information
about other users’ selection and channel state information
(CSI); and the base station (BS) performs successive interference
cancellation (SIC) to decode packets from multiple users with
the use of power differences on the same slot. To design a
model-free multiple access under growing complexity and CSI
uncertainty, the joint slot and power level selecting problem is
modelled as a Markov decision process (MDP) where actions
are slot-power pairs. Multi-state Q-Learning algorithms and a
confidence-aided Q-Learning method are tailored for the NORA
system to solve the MDP under heterogeneous environments.
Simulation results show that the three proposed algorithms
help the distributed users to find their strategies for slot and
power level selections, improving system throughput and fairness
simultaneously. The proposed algorithms are particularly shown
to make superior performance compared to the benchmarks in
high congestion traffics scenarios. This is crucial for achieving
massive connectivity in 6G ecosystems, which requires intelligent
random access designs to accommodate the growing number of
machine type users in diverse conditions.
wireless ecosystems, uncontrolled multiple access control (MAC)
is vital to alleviate random collision and fading in their transmission.
In this paper, 2-steps random access method is applied for
a learning-aided non-orthogonal random access (NORA) system.
Specifically, each user independently selects a slot and a power
level for uplink packet transmission without any information
about other users’ selection and channel state information
(CSI); and the base station (BS) performs successive interference
cancellation (SIC) to decode packets from multiple users with
the use of power differences on the same slot. To design a
model-free multiple access under growing complexity and CSI
uncertainty, the joint slot and power level selecting problem is
modelled as a Markov decision process (MDP) where actions
are slot-power pairs. Multi-state Q-Learning algorithms and a
confidence-aided Q-Learning method are tailored for the NORA
system to solve the MDP under heterogeneous environments.
Simulation results show that the three proposed algorithms
help the distributed users to find their strategies for slot and
power level selections, improving system throughput and fairness
simultaneously. The proposed algorithms are particularly shown
to make superior performance compared to the benchmarks in
high congestion traffics scenarios. This is crucial for achieving
massive connectivity in 6G ecosystems, which requires intelligent
random access designs to accommodate the growing number of
machine type users in diverse conditions.
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Cognitive Communications and Networking |
| Publication status | Accepted/In press - 29 Sept 2024 |
Bibliographical note
This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.Keywords
- Reinforcement learning
- distributed learning
- NORA
- Q-Learning
- multiple access control