43,143 research outputs found
Optimal Joint Power and Subcarrier Allocation for MC-NOMA Systems
In this paper, we investigate the resource allocation algorithm design for
multicarrier non-orthogonal multiple access (MC-NOMA) systems. The proposed
algorithm is obtained from the solution of a non-convex optimization problem
for the maximization of the weighted system throughput. We employ monotonic
optimization to develop the optimal joint power and subcarrier allocation
policy. The optimal resource allocation policy serves as a performance
benchmark due to its high complexity. Furthermore, to strike a balance between
computational complexity and optimality, a suboptimal scheme with low
computational complexity is proposed. Our simulation results reveal that the
suboptimal algorithm achieves a close-to-optimal performance and MC-NOMA
employing the proposed resource allocation algorithm provides a substantial
system throughput improvement compared to conventional multicarrier orthogonal
multiple access (MC-OMA).Comment: Submitted to Globecom 201
Joint Subcarrier and Power Allocation in NOMA: Optimal and Approximate Algorithms
Non-orthogonal multiple access (NOMA) is a promising technology to increase
the spectral efficiency and enable massive connectivity in 5G and future
wireless networks. In contrast to orthogonal schemes, such as OFDMA, NOMA
multiplexes several users on the same frequency and time resource. Joint
subcarrier and power allocation problems (JSPA) in NOMA are NP-hard to solve in
general. In this family of problems, we consider the weighted sum-rate (WSR)
objective function as it can achieve various tradeoffs between sum-rate
performance and user fairness. Because of JSPA's intractability, a common
approach in the literature is to solve separately the power control and
subcarrier allocation (also known as user selection) problems, therefore
achieving sub-optimal result. In this work, we first improve the computational
complexity of existing single-carrier power control and user selection schemes.
These improved procedures are then used as basic building blocks to design new
algorithms, namely Opt-JSPA, -JSPA and Grad-JSPA. Opt-JSPA
computes an optimal solution with lower complexity than current optimal schemes
in the literature. It can be used as a benchmark for optimal WSR performance in
simulations. However, its pseudo-polynomial time complexity remains impractical
for real-world systems with low latency requirements. To further reduce the
complexity, we propose a fully polynomial-time approximation scheme called
-JSPA. Since, no approximation has been studied in the literature,
-JSPA stands out by allowing to control a tight trade-off between
performance guarantee and complexity. Finally, Grad-JSPA is a heuristic based
on gradient descent. Numerical results show that it achieves near-optimal WSR
with much lower complexity than existing optimal methods
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