LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

User Pairing for Downlink Non-Orthogonal Multiple Access Networks Using Matching Algorithm

Photo by dylan_nolte from unsplash

In this paper, we study the user pairing in a downlink non-orthogonal multiple access (NOMA) network, where the base station allocates the power to the pairwise users within the cluster.… Click to show full abstract

In this paper, we study the user pairing in a downlink non-orthogonal multiple access (NOMA) network, where the base station allocates the power to the pairwise users within the cluster. In the considered NOMA network, a user with poor channel condition is paired with a user with good channel condition, when both their rate requirements are satisfied. Specifically, the quality of service for weak users can be guaranteed, since the transmit power allocated to strong users is constrained following the concept of cognitive radio. A distributed matching algorithm is proposed in the downlink NOMA network, aiming to optimize the user pairing and power allocation between weak users and strong users, subject to the users’ targeted rate requirements. Our results show that the proposed algorithm outperforms the conventional orthogonal multiple access scheme and approaches the performance of the centralized algorithm, despite its low complexity. In order to improve the system’s throughput, we design a practical adaptive turbo trellis coded modulation scheme for the considered network, which adaptively adjusts the code rate and the modulation mode based on the instantaneous channel conditions. The joint design work leads to significant mutual benefits for all the users as well as the improved system throughput.

Keywords: multiple access; pairing downlink; orthogonal multiple; user pairing

Journal Title: IEEE Transactions on Communications
Year Published: 2017

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.