146 research outputs found
Competitive MA-DRL for Transmit Power Pool Design in Semi-Grant-Free NOMA Systems
In this paper, we exploit the capability of multi-agent deep reinforcement
learning (MA-DRL) technique to generate a transmit power pool (PP) for Internet
of things (IoT) networks with semi-grant-free non-orthogonal multiple access
(SGF-NOMA). The PP is mapped with each resource block (RB) to achieve
distributed transmit power control (DPC). We first formulate the resource
(sub-channel and transmit power) selection problem as stochastic Markov game,
and then solve it using two competitive MA-DRL algorithms, namely double deep Q
network (DDQN) and Dueling DDQN. Each GF user as an agent tries to find out the
optimal transmit power level and RB to form the desired PP. With the aid of
dueling processes, the learning process can be enhanced by evaluating the
valuable state without considering the effect of each action at each state.
Therefore, DDQN is designed for communication scenarios with a small-size
action-state space, while Dueling DDQN is for a large-size case. Our results
show that the proposed MA-Dueling DDQN based SGF-NOMA with DPC outperforms the
SGF-NOMA system with the fixed-power-control mechanism and networks with pure
GF protocols with 17.5% and 22.2% gain in terms of the system throughput,
respectively. Moreover, to decrease the training time, we eliminate invalid
actions (high transmit power levels) to reduce the action space. We show that
our proposed algorithm is computationally scalable to massive IoT networks.
Finally, to control the interference and guarantee the quality-of-service
requirements of grant-based users, we find the optimal number of GF users for
each sub-channel
On Secure NOMA-Aided Semi-Grant-Free Systems
Semi-grant-free (SGF) transmission scheme enables grant-free (GF) users to
utilize resource blocks allocated for grant-based (GB) users while maintaining
the quality of service of GB users. This work investigates the secrecy
performance of non-orthogonal multiple access (NOMA)-aided SGF systems. First,
analytical expressions for the exact and asymptotic secrecy outage probability
(SOP) of NOMA-aided SGF systems with a single GF user are derived. Then, the
SGF systems with multiple GF users and a best-user scheduling scheme is
considered. By utilizing order statistics theory, closed-form expressions for
the exact and asymptotic SOP are derived. Monte Carlo simulation results
demonstrate the effects of system parameters on the SOP of the considered
system and verify the accuracy of the developed analytical results. The results
indicate that both the outage target rate for GB and the secure target rate for
GF are the main factors of the secrecy performance of SGF systems
Application of NOMA in 6G Networks: Future Vision and Research Opportunities for Next Generation Multiple Access
As a prominent member of the next generation multiple access (NGMA) family,
non-orthogonal multiple access (NOMA) has been recognized as a promising
multiple access candidate for the sixth-generation (6G) networks. This article
focuses on applying NOMA in 6G networks, with an emphasis on proposing the
so-called "One Basic Principle plus Four New" concept. Starting with the basic
NOMA principle, the importance of successive interference cancellation (SIC)
becomes evident. In particular, the advantages and drawbacks of both the
channel state information based SIC and quality-of-service based SIC are
discussed. Then, the application of NOMA to meet the new 6G performance
requirements, especially for massive connectivity, is explored. Furthermore,
the integration of NOMA with new physical layer techniques is considered,
followed by introducing new application scenarios for NOMA towards 6G. Finally,
the application of machine learning in NOMA networks is investigated, ushering
in the machine learning empowered NGMA era.Comment: 14 pages, 5 figures, 1 tabl
Hybrid Successive Interference Cancellation and Power Adaptation: a Win-Win Strategy for Robust Uplink NOMA Transmission
The aim of this paper is to reveal the importance of hybrid successive
interference cancellation (SIC) and power adaptation (PA) for improving
transmission robustness of uplink non-orthogonal multiple access (NOMA).
Particularly, a cognitive radio inspired uplink NOMA communication scenario is
considered, where one primary user is allocated one dedicated resource block,
while M secondary users compete with each other to be opportunistically served
by using the same resource block of the primary user. Two novel schemes are
proposed for the considered scenario, namely hybrid SIC with PA (HSIC-PA)
scheme and fixed SIC with PA (FSIC-PA) scheme. Both schemes can ensure that the
secondary users are served without degrading the transmission reliability of
the primary user compared to conventional orthogonal multiple access (OMA)
based schemes. Rigorous analytical results are presented to evaluate the
performance of the proposed two schemes. It is shown that both schemes can
avoid outage probability error floors without any constraints on users' target
rates in the high SNR regime. Furthermore, it is shown that the diversity gain
achieved by the HSIC-PA scheme is M, while that of the FISC-PA scheme is only
1. Numerical results are provided to verify the developed analytical results
and also demonstrate the superior performance achieved by the proposed schemes
by comparing with the existing HSIC without PA (HSIC-NPA) scheme. The presented
simulation results also show that HSIC-PA scheme performs the best among the
three schemes, which indicates the importance of the combination of HSIC and PA
for improving transmission robustness.Comment: arXiv admin note: substantial text overlap with arXiv:2307.0151
Toward Autonomous Power Control in Semi-Grant-Free NOMA Systems: A Power Pool-Based Approach
In this paper, we design a resource block (RB) oriented power pool (PP) for semi-grant-free non-orthogonal multiple access (SGF-NOMA) in the presence of residual errors resulting from imperfect successive interference cancellation (SIC). In the proposed method, the BS allocates one orthogonal RB to each grant-based (GB) user, and determines the acceptable received power from grant-free (GF) users and calculates a threshold against this RB for broadcasting. Each GF user as an agent, tries to find the optimal transmit power and RB without affecting the quality-of-service (QoS) and ongoing transmission of the GB user. To this end, we formulate the transmit power and RB allocation problem as a stochastic Markov game to design the desired PPs and maximize the long-term system throughput. The problem is then solved using multi-agent (MA) deep reinforcement learning algorithms, such as double deep Q networks (DDQN) and Dueling DDQN due to their enhanced capabilities in value estimation and policy learning, with the latter performing optimally in environments characterized by extensive states and action spaces. The agents (GF users) undertake actions, specifically adjusting power levels and selecting RBs, in pursuit of maximizing cumulative rewards (throughput). Simulation results indicate computational scalability and minimal signaling overhead of the proposed algorithm with notable gains in system throughput compared to existing SGF-NOMA systems. We examine the effect of SIC error levels on sum rate and user transmit power, revealing a decrease in sum rate and an increase in user transmit power as QoS requirements and error variance escalate. We demonstrate that PPs can benefit new (untrained) users joining the network and outperform conventional SGF-NOMA without PPs in spectral efficiency
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