Double iterative waterfilling for sum rate maximization in multicarrier NOMA systems

Abstract

This paper investigates the subcarrier and power allocation for the downlink of a multicarrier non-orthogonal multiple access (MC-NOMA) system. A three-step algorithm is proposed to deal with the sum rate maximization problem. In Step 1, we assume that each user can use all the subcarriers simultaneously and apply the synchronous iterative waterfilling algorithm (SIWA) to obtain a power vector for each user. In Step 2, subcarriers are assigned to users by a heuristic greedy method based on the achieved power allocation result of Step 1. In Step 3, SIWA is used once again to further improve the system performance with the obtained subcarrier assignment result of Step 2. The convergence of SIWA in Step 3 is proved when the number of multiplexed users is no more than two. Since SIWA is applied twice, we call our three-step method Double Iterative Waterfilling Algorithm (DIWA). Numerical results show that the proposed DIWA achieves comparable performance to an existing near-optimal solution but with much lower time complexity.

Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks

“…4 where a single high-power RRS (HP-RRS), i.e., MBS, is located in the center of a circular area with radius 500 m and 4 low-power RRSs in km [5], [36], [43]. The small-scale fading of the wireless channel is modeled as independent and identically distributed Rayleigh fading with variance 1 [36]. The CDI is averaged over 1000 CSI samples for a fixed channel power loss, since the location of users and RRSs are fixed in our numerical examples.…”

“…Step 2: Here, we jointly find the binary variables Υ, ρ, θ, and τ for the given p, φ, r FH , r BH by solving the following subproblem: repeat 2: Update the convex approximated forms of (29b), (36e)-(36i) and substitute them into (36).…”

“…THE PROPOSED SCA ALGORITHM FOR SOLVING (36) To tackle the non-convexity of both (36a) and (29b) and all constraints in (36e)-(36i), the access data rate functionr Ac b,u should be transformed into concave and convex forms, respectively. To approximate the access data rate functionr Ac b,u in (36a) and (29b) to a concave format at each iteration κ 2 of the SCA algorithm, we first define…”

“…which means the optimal solution generated for the convex approximated problem of (36) at each iteration κ 2 is feasible with respect to the nonconvex problem (36). Moreover, based on (54), we have By using the fact that the DCP approximated problem of (36) can be efficiently solved at each iteration κ 2 , where the globally optimal solution is in the feasible region of (36), it can be derived m∈M u∈Um b∈B n∈N…”

Optimal Joint Subcarrier and Power Allocation in NOMA Is Strongly NP-Hard

“…This algorithm is mostly used as a benchmark due to its high computational complexity. A more efficient heuristic based on iterative waterfilling method is introduced in [15].…”