User association and channel assignment in downlink multi-cell NOMA networks: A matching-theoretic approach

Abstract

This paper studies the problem of stable user association and channel assignment in downlink multi-cell non-orthogonal multiple-access (NOMA) networks. To be specific, the goal is to assign network users to the channels at each base station, while accounting for inter-user interference and maintaining quality of service (QoS) per user. To that end, a low-complexity iterative solution procedure is devised to obtain the optimal power allocation for proportional fairness signal-to-interference-plus-noise ratio (SINR)-based maximization, which is then utilized to determine the preferences of network users over the channels available at each base station and the preferences of base stations over the network users. In turn, a many-to-one matching-theoretic model based on the student-project allocation problem is applied. Particularly, two polynomial-time complexity stable matching algorithms are proposed to associate users with base stations and perform channel assignment, such that no user or base station would deviate and change its association or channel assignment unilaterally. To validate the efficacy of the proposed solution procedure and stable matching algorithms, extensive simulation results are presented to compare them to a centralized joint user association, channel assignment, and power allocation (C-J-UA-CA-PA) scheme. It is demonstrated that the proposed algorithms efficiently associate users with base stations and assign them to channels as well as efficiently yielding comparable SINR per user to the C-J-UA-CA-PA scheme, while maximizing proportional fairness and satisfying QoS constraints.

An Algorithm for Strong Stability in the Student-Project Allocation Problem with Ties

“…Further, s 8 becomes provisionally assigned to p 1 . It can be verified that all the edges in G (3) r are bound edges. Moreover, the reduced assignment graph G…”

“…2 An edge (si, pj) ∈ E can change state from bound to unbound, but not vice versa. 3 We note that we only remove this edge to form Gr, we do not delete the edge from G. 4 If si is bound to more than one projects offered by l k , for all the bound edges involving si and these projects that we remove from Gr, we only reduce l k 's quota in Gr by one. 5 An intuition as to why we add dummy students to Gr is as follows.…”

“…At the beginning of this iteration, the only student that is unassigned and has a non-empty list is s 4 ; thus we add edges (s 4 , p 5 ) and (s 4 , p 6 ) to the provisional assignment graph obtained at the termination of iteration (2) to form G (3) r . The provisional assignment of s 4 to p 5 led to p 5 becoming oversubscribed; thus (s 8 , p 5 ) is deleted (since s 8 is dominated on L 5…”

“…These include SPA with lecturer preferences over (i) students [15], (ii) projects [13,22,23], and (iii) (student, project) pairs [2]. Outwith assigning students to projects, applications of each of these three variants can be seen in multi-cell networks where the goal is to find a stable assignment of users to channels at base-stations [3,4,5].…”

A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G

“…Power domain Resource allocation [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] Energy harvesting and transfer [36][37][38][39][40][41][42][43][44][45][46][47][48][49] Interference cancellation techniques [50][51][52][53][54][55] Sleep/wake modes [56][57][58][59][60] Code domain Sparse code multiple access (SCMA) [61,62] Space time block coding (STBC) [56,63] Polar codes [64][...…”