55 research outputs found
Sub-channel Assignment, Power Allocation and User Scheduling for Non-Orthogonal Multiple Access Networks
In this paper, we study the resource allocation and user scheduling problem
for a downlink nonorthogonal multiple access network where the base station
allocates spectrum and power resources to a set of users. We aim to jointly
optimize the sub-channel assignment and power allocation to maximize the
weighted total sum-rate while taking into account user fairness. We formulate
the sub-channel allocation problem as equivalent to a many-to-many two-sided
user-subchannel matching game in which the set of users and sub-channels are
considered as two sets of players pursuing their own interests. We then propose
a matching algorithm which converges to a two-side exchange stable matching
after a limited number of iterations. A joint solution is thus provided to
solve the sub-channel assignment and power allocation problems iteratively.
Simulation results show that the proposed algorithm greatly outperforms the
orthogonal multiple access scheme and a previous non-orthogonal multiple access
scheme.Comment: Accepted as a regular paper by IEEE Transactions on Wireless
Communication
Resource Management Optimally in Non-Orthogonal Multiple Access Networks for Fifth-Generation by using Game-Theoretic
In this paper, several number of users were optimized in resource allocation management by applying the game theory User-sub-channel-Soap Matching Algorithm (USMA) in Non-Orthogonal Multiple Access (NOMA) for fifth-generation wireless networks. multiple access users can be increased up to 63 users in NOMA. This method reduces interference between users, which include costs, and resource to access for other users
V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks
Benefited from the widely deployed infrastructure, the LTE network has
recently been considered as a promising candidate to support the
vehicle-to-everything (V2X) services. However, with a massive number of devices
accessing the V2X network in the future, the conventional OFDM-based LTE
network faces the congestion issues due to its low efficiency of orthogonal
access, resulting in significant access delay and posing a great challenge
especially to safety-critical applications. The non-orthogonal multiple access
(NOMA) technique has been well recognized as an effective solution for the
future 5G cellular networks to provide broadband communications and massive
connectivity. In this article, we investigate the applicability of NOMA in
supporting cellular V2X services to achieve low latency and high reliability.
Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed
to tackle the technical hurdles in designing high spectral efficient scheduling
and resource allocation schemes in the ultra dense topology. We then extend it
to a more general V2X broadcasting system. Other NOMA-based extended V2X
applications and some open issues are also discussed.Comment: Accepted by IEEE Wireless Communications Magazin
Channel Assignment in Uplink Wireless Communication using Machine Learning Approach
This letter investigates a channel assignment problem in uplink wireless
communication systems. Our goal is to maximize the sum rate of all users
subject to integer channel assignment constraints. A convex optimization based
algorithm is provided to obtain the optimal channel assignment, where the
closed-form solution is obtained in each step. Due to high computational
complexity in the convex optimization based algorithm, machine learning
approaches are employed to obtain computational efficient solutions. More
specifically, the data are generated by using convex optimization based
algorithm and the original problem is converted to a regression problem which
is addressed by the integration of convolutional neural networks (CNNs),
feed-forward neural networks (FNNs), random forest and gated recurrent unit
networks (GRUs). The results demonstrate that the machine learning method
largely reduces the computation time with slightly compromising of prediction
accuracy
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